Author: PhD Marko Sinčić, MScEng.

Figure 1 Examples of sampling strategies:(A) polygon sampling,(B) buffer sampling and (C) smoothed landslide conditioning factor sampling. (Sinčić et al., 2024)

Figure 2 Geographic positions of study areas; in Europe (A), in Croatia (B), in NW Croatia (C). Detailed presentation of the study areas: whole Podsljeme Area in the City of Zagreb (D), selected representative Podsljeme Area (E) and selected representative part of Hrvatsko Zagorje (F) (Sinčić, 2025)

The first major finding was that data quality is absolutely crucial. To create reliable maps, it’s important to have an accurate landslide inventory (a detailed record of past landslides) and high-quality information about the conditions that cause them, such as soil and rock type, terrain characteristics, and vegetation. This can be achieved mainly through remote sensing technologies (Figure 3), especially LiDAR (Light Detection and Ranging) and high-resolution orthophoto imagery, which allow scientists to detect even small changes in the landscape (Sinčić et al., 2022).

Figure 3 An example of how high-resolution digital terrain model (DTM) (acquired by LiDAR) derivatives enable improving the spatial accuracy of geological landslide conditioning factors (Sinčić et al., 2022)

Finally, the research emphasized the importance of not only quantitative (numerical) assessments (Figure 4) but also qualitative evaluations (Figure 5) —expert judgments based on real-world knowledge of the terrain. Combining both methods led to more reliable and meaningful results.

Figure 4 An example of quantitative model assessment, namely Cohen's Kappa and Area Under the Curve (AUC) values (Sinčić et al., 2024)

Figure 5 An example of qualitative model assessment, namely close up view comparisons of classified landslide susceptibility models (Sinčić et al., 2024)

Overall, the doctoral study provided a clear and practical methodology for assessing landslide susceptibility at a large scale. The framework outlines essential steps—from gathering and checking data to applying the most suitable models—and highlights the importance of using accurate input data like landslide inventories and conditioning factors. The findings can support better land use planning, risk reduction, and mitigation strategies.

Reference

PhD Marko Sinčić, MScEng. is a Postdoctoral researcher at the Department of Petroleum and Gas Engineering and Energy on the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. He finished his PhD on 23.4.2025. with a dissertation entitled Statistically-based methodology for large scale landslide susceptibility modelling of small and shallow landslides in the Pannonian Basin.

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Autor: Ana Brcković Gruić, PhD

The main objective of this study was to correlate sandstone deposits, confirmed in existing wells, across areas where no well data are available. This type of correlation between wells represents one of the greatest challenges in geological and geophysical interpretation. How can the spatial distribution of rocks be reconstructed when data are only available locally?

The most direct insight into the subsurface is provided by well data and core samples, which allow for precise determination of lithological composition and physical rock properties. However, these data are always limited to a very narrow spatial extent (Telford et al., 2004). For this reason, seismic data are traditionally used to fill the gaps between wells (Figure 1). Interpretation is based on the principle of superposition and the ability to track horizons laterally. However, the assumption that seismic signals can be uniquely associated with geological boundaries is often not valid in practice due to noise, variable resolution, and geological complexity. As a result, reliably correlating sandstone bodies of channel infills within a progradational delta becomes very difficult (Vukadin and Čikeš, 2017).  

This is where machine learning comes into play, as it has increasingly transformed interpretation workflows across many scientific fields over the past two decades. The goal is not to replace the interpreter, but to accelerate and enhance steps that are still largely performed manually, such as data cleaning, pattern recognition, and prediction of subsurface properties where measurements are missing (Banas et al., 2021; McDonald, 2021).

In this study, several approaches were applied: algorithms for noise removal from well log data (IF, SVM, LOF), models for predicting well log curves at locations without measurements (with the Long Short-Term Memory neural network achieving the best results) (Figure 2), and Self-Organizing Map clustering on seismic data to determine the distribution of rock types without predefined classification (Chaveste et al., 2023; Li and Gao, 2023) (Figure 3). In addition, a semi-automatic approach for seismic horizon extraction was developed using a greedy local optimization algorithm, enabling a more precise definition of reservoir body geometry.

All methods were applied in the Drava Basin, resulting in the first geological model of the spatial distribution of sandstone reservoir bodies in the northwestern part of the Gola field created entirely using Python (Brcković et al., 2026) (Figure 4). The research was conducted using extensive datasets and with expert support from INA d.d..

The study demonstrates that machine learning can be an extremely valuable tool in geophysical interpretation. It enables faster analyses, detection of hidden patterns, and more reliable conclusions while still requiring the constant “watchful eye” of the interpreter, which remains indispensable.

Figure 1. The correlation between clusters and lithological composition is based on:

(a) porosity (POR), water saturation (SW), and shale volume (VSH) derived from well log data, showing the grouping of well data based on these parameters; (b) the different lithological compositions that can be identified from this grouping; (c) following the assumption that identical lithologies should be distinguishable in seismic data, four seismic clusters correspond to the well-based grouping; (d) the definition of the four clusters is based on the distribution of POR, VSH, and SW in the well data.

Figure 2. Comparison of predicted values (red) against measured values (blue) for wells WB, WE, WF, WG, and WH, with the coefficient of determination (R²) and root mean square error (RMSE) used as evaluation metrics (left); and the prediction of sonic (AC), density (DEN), and compensated neutron (CN) logs using an LSTM neural network in well WB (right).

Figure 3. Correlation of sandstone bodies identified through SOM-based interpretation of seismic data, validated using porosity measurements from wells. It can be observed that average porosity values assigned to sandstone deposits are generally higher in vertically and laterally smaller, more confined bodies.

Figure 4. Geological model of the Gola field developed using a Python-based framework, including a digital elevation model (DEM), horizon surfaces (automatically extracted using a greedy algorithm), and 14 geobodies representing potential sandstone reservoirs with varying petrophysical properties (identified using unsupervised learning methods).

References

Telford, W. M., Geldart, L.P.,  Sheriff, R.E. (2004): Applied Geophysics,  Second Edition. Cambridge University Press. Cambridge

Vukadin, D., & Čikeš, K. (2017). Using seismic inversion and seismic stratigraphy as a combined tool for understanding a small scale turbidite systems: Gola field, Pannonian basin, Croatia. https://www.researchgate.net/publication/341569134

Banas, R., McDonald, A., & Perkins, T. (2021, May 17). NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR. SPWLA 62nd Annual Online Symposium Transactions. https://doi.org/10.30632/SPWLA-2021-0070

McDonald, A. (2021). Data Quality Considerations for Petrophysical Machine-Learning Models. In Petrophysics (Vol. 62, Number 6, pp. 585–613). Society of Petroleum Engineers (SPE). https://doi.org/10.30632/PJV62N6-2021a1

Chaveste, Alvaro, Roden, Rocky R., and Smith, Tom. 2023. Machine Learning-Based Method Provides Economic Option To Seismic Inversions Conventional Inversion. The American Oil & Gas Reporter, Volume 66, No. 11. 43-49.

Li, J., & Gao, G. (2023). Digital construction of geophysical well logging curves using the LSTM deep-learning network. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.1041807

Brcković, A., Vukadin, D., Medved, I., & Orešković, J. (2026). Reconstructing the Distribution of Turbiditic Gas Sandstones in the SW Pannonian Basin Using Machine Learning Methods. Geophysical Prospecting, 74(2). https://doi.org/10.1111/1365-2478.70146

Autor: Vjekoslav Herceg, PhD mag. ing. min.

In modern mining, it is increasingly clear that competitiveness is no longer based solely on productivity, but also on energy efficiency and the reduction of CO₂ emissions. Excavators, as key machines in crushed stone quarries, consume significant amounts of diesel, and every liter directly represents both a cost and an emission. For this reason, understanding how different materials influence the energy consumption of excavation has become highly important. Field measurements carried out in an active quarry provided insight into how much energy is actually consumed when excavating five typical material types—overburden, blasted rock material, boulders, crushed stone, and crushed stone with clay.

The measurements for this research were conducted under real operating conditions, on a hydraulic excavator equipped with a precise measurement system containing hydraulic pressure transmitters and draw‑wire displacement sensors mounted on the hydraulic cylinders. Combined with knowledge of the very complex kinematics of the excavator (Figure 1), this enables direct monitoring of forces and bucket trajectory during excavation.

Figure 1 Kinematics of the excavator  (Herceg et.al. 2023)

The system records data every 0.1 seconds, making it possible to calculate the digging energy in great detail, not only as an average value but also as instantaneous changes throughout the digging cycle. The digging mechanism and the trajectory of interaction between the material and the bucket are shown in Figure 2. Since only the portion of the cycle in which the bucket actually digs the material is taken into account, the resulting data reflect the pure bucket–material interaction, without the influence of lifting, swinging, or dumping. Additionally, each material was measured through approximately twenty repetitions to determine repeatability, and the excavator was operated by the same person throughout the entire study, thereby eliminating operator-related variability. After excavation, the mass of material in the bucket was determined from the boom cylinder pressure, enabling the calculation of specific energy (kJ/t), which is the key indicator of true efficiency and fuel consumption. This level of detail provides data reliable enough for use in planning, optimization, and CO₂ emission assessment in everyday quarry operations.

Figure 2  Mechanism of digging  (Herceg et.al.2023)

The results show a very clear and practically important difference between materials. Blasted rock material was found to be the most energy‑demanding: a wide range of fragment sizes, well size distribution and irregular behavior during bucket penetration create high resistance, which means higher energy consumption and higher CO₂ emissions per ton of loaded material. Immediately behind it is overburden, whose main issue is the high plasticity of clay—clay causes adhesion and increases the force required for digging.

Contrary to expectations, boulders require less energy than blasted rock material. Despite their large dimensions, they are uniform, and during loading the bucket typically contacts them at only a few points, effectively rolling them into the bucket. The lowest energy consumption was recorded for crushed stone and crushed stone with 15% clay. For clean crushed material, the reason is straightforward: the fine grain-size distribution and small size range allow smooth bucket penetration. Interestingly, even a relatively small amount of clay (only 15%) increases energy consumption by approximately one-fifth, which over long-term excavator operation can represent a significant difference in fuel usage. A symbolic representation of the results is shown in Figure 3.

Figure  3  Symbolic representation of energy consumption for each material

The practical application of these results is very direct. Knowing that digging blasted rock material consumes the most energy, it becomes possible to better plan the allocation of machinery—for example, assigning equipment with newer engines and lower specific fuel consumption to the most demanding tasks, while less demanding materials can be handled by more economical or older machines. This yields two benefits: overall fuel consumption is reduced and the service life of machines is extended.

This research also reveals another important point: optimizing emissions in mining does not always require expensive machinery upgrades or switching to alternative fuels. Sometimes it is sufficient to understand how certain materials behave during excavation and then intelligently plan work tasks. Likewise, operations can be scheduled during periods with lower soil moisture, which reduces adhesion forces and digging resistance, especially for clay-rich materials.

In a broader sense, this type of analysis contributes to the development of modern, energy‑efficient mining. By monitoring digging energy and understanding how different materials create different energy demands, it becomes possible to produce precise energy‑demand maps and calculate expected fuel costs and CO₂ emissions for each stage of the mining process. Ultimately, such studies confirm that efficiency and ecology in mining are not mutually exclusive but mutually reinforcing: everything that reduces energy consumption also reduces emissions while increasing productivity and long‑term sustainability.

References

Herceg, V.; Klanfar, M.; Herceg, K.; Domitrović, D. (2023): Specific energy consumption of material handling by excavator in the quarrying of crushed stone. Rudarsko-geološko-naftni zbornik, 83–92. https://doi.org/10.17794/rgn.2023.1.8.

Dadhich, S.; Bodin, U.; Andersson, U. (2016): Key challenges in automation of earth-moving machines. Automation in Construction, 68. https://doi.org/10.1016/j.autcon.2016.05.009.

Du, Y.; Dorneich, M. C.; Steward, S. (2016): Virtual operator modeling method for excavator trenching. Automation in Construction, 70, 14–25

Jassim, H.S.H.; Lu, W.; Olofsson, T. (2018): Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations. In: Advanced Computing Strategies for Engineering, LNCS 10863, 431–453.

Kim, B. Y., Ha, J., Kang, H., Kim, P. Y., Park, J., Park, F. C. (2013): Dynamically optimal trajectories for earthmoving excavators. Automation in Construction, 35, 568–578.

Klanfar, M., Herceg, V., Kuhinek, D., & Sekulić, K. (2019). Construction and testing of the measurement system for excavator productivity. Rudarsko-geološko-naftni zbornik, 34(2).

Koivo, A. J. (1994): Kinematics of excavators (backhoes) for transferring surface material. Journal of Aerospace Engineering, 7, 17–32.

Kujundžić, T.; Klanfar, M.; Korman, T.; Briševac, Z. (2021): Influence of Crushed Rock Properties on Productivity of a Hydraulic Excavator. Applied Sciences, 11, 2345.

Lee, S.; Hong, D.; Park, H.; Bae, J. (2008): Optimal path generation for excavator with neural networks based soil models. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 632–637.

Author: Tomislav Baketarić, mag.geol.

In Croatian part of Pannonian Basin System (Mura and Drava depression), Late Miocene-Pliocene post-rift sediments (Pannonian stage) are represented by large complex prograding clinothem sets visible on seismic sections. Using seismic stratigraphy as a method in other parts of the PBS Magyar et al. (2006, 2007), Sztano et al. (2013), Balasz et al. (2017) and Sebe et al. (2020) concluded that the lacustrine infill of Upper Miocene sediments consists of prograding deltaic complexes characterized by clinoforms on seismic reflection data. The reconstruction of the gradual progradation, i.e. the infill of the basin in this part of the PBS in time and space, was done through the analysis of spatial and temporal variations of the clinoform geometry and the trajectories of the slopes. During interpretation of the clinoforms, determined by distinct seismic facies, the upper and lower edges of the slope (rollover boundaries) were mapped (sensu Patruno et al., 2015; Paumard et al., 2018). By applying seismic stratigraphy principles, as well as analyzing the movement of the slope and the rollover boundaries within the clinoforms, an insight into the spatial diachronous closure of Lake Pannon within these depressions was obtained. Throughout the area 15 regional clinothem surfaces were mapped (Pa-1 to Pa-15). Based on stratigraphic data linked with seismic relative chronostratigraphic framework of the Upper Miocene and Pliocene infill was built (Figure 1). Clinothems mapped over the area of two depressions spatially define the closure of Lake Pannon during the Late Miocene and the Early Pliocene through time. Although often aggravated by lack or poor quality of the data, such framework considerably improved reconstruction of paleogeographic evolution and sediment fairways.

Figure 1. Trajectories of the Lake Pannon delta front (upper rollover boundaries) connected by line) shown on present-day topography of the Pannonian Basin. Upper rollover trajectories of Mura and Drava Basins are based on this work, while geographical position of the trajectories in other parts of the PBS are correlated and connected based on Magyar et al.(2013) i Magyar (2021). Dominant progradation direction during Pannonian in Hrvatsko Zagorje basin are based on Kovačić et al. (2004). For reference, international borders are marked in orange, while the largest cities are marked with red dots.

Reference:

Patruno et al., 2015; Paumard et al., 2018

Balázs, A., Magyar, I., Matenco, L., Sztanó, O., Tokes, L., Horváth, F. (2017): Morphology of a large paleo-lake: Analysis of compaction in the Miocene-Quaternary Pannonian Basin. Global and Planetary Change. 171, 134-147, doi:10.1016/j.gloplacha.2017.10.012.

Kovačić, M., Zupanič, J., Babić, Lj, Vrsaljko, D., Miknić, M., Bakrač, K., Hećimović, I., Avanić,R., Brkić,M. (2004): Lacustrine basin todelta evolution in the Zagorje Basin, a Pannonian sub-basin (Late Miocene: Pontian, NW Croatia). Facies 50, 19–33.

Magyar, I., Fogarasi, A., Vakarcs, G., Bukó, L., Tari, G.C., (2006): The largest hydrocarbon field discovered to date in Hungary: Algyő. In: Golonka, J., Picha, F.J. (Eds.) The Carpathians and their foreland: geology and hydrocarbon resources. American Association of Petroleum Geologists, Memoir, 84, pp. 619–632, doi: 10.1306/985734M843142

Magyar, I., Lantos, M., Ujszászi, K., Kordos, L., (2007): Magnetostratigraphic, seismic and biostratigraphic correlations of the Upper Miocene sediments in the northwestern Pannonian Basin System. Geologica Carpathica, 58, 277–290.

Magyar, I., Radivojević, D., Sztanó, O., Synak, R., Ujszászi, K. Pócsik, M. (2013): Progradation of the paleo-Danube shelf margin across the Pannonian Basin during the Late Miocene and Early Pliocene. Global and Planetary Change, 103,168-173, doi:10.1016/j.gloplacha.2012.06.007.

Magyar I. (2021): Chronostratigraphy of clinothem-filled non-marine basins: Dating the Pannonian Stage. Global and Planetary Change, 205, 103609, doi:10.1016/j.gloplacha.2021.103609.

Patruno, S., Hampson, G. J., Jackson, C.A.L. (2015): Quantitative characterisation of deltaic and subaqueous clinoforms. Earth Science Reviews, 142, 79–119, doi:10.1016/j.earscirev.2015.01.004

Paumard, V., Bourget, J., Payenberg, T., Ainsworth, B., George, A.D., Lang, S., Posamentier, H.W., Peyrot, D. (2018): Controls on shelfmargin architecture and sediment partitioning during a syn-rift to post-rift transition: Insights from the Barrow Group (Northern Carnarvon Basin, North West Shelf, Australia). Earth-Science Reviews, 177, 643-677, doi:10.1016/j.earscirev.2017.11.026.

Sebe, K., Kovačić, M., Magyar, I., Krizmanić, K., Špelić, M., Bigunac, D., & Sütőné Szentai, M., Kovács, A., Korecz, A., Bakrač, K., Hajek Tadesse, V., Troskot-Čorbić, T., Sztanó, O. (2020): Correlation of upper Miocene-Pliocene Lake Pannon deposits across the Drava Basin, Croatia and Hungary. Geologia Croatica, 73, 177-195, doi:10.4154/gc.2020.12.

Sztanó O., Szafián P., Magyar I., Horányi A., Bada G., Hughes D.W., Hoyer D.L., Wallis R.J. (2013): Aggradation and progradation controlled clinothems and deep-water sand delivery model in the Neogene lake pannon, Makó Trough, Pannonian Basin, SE Hungary. Global Planet. Change, 103, 149-167, doi:10.1016/j.gloplacha.2012.05.026.

Tomislav Baketarić, mag.geol. is a geology PhD candidate at the Faculty of Mining, Geology and the Petroleum Engineering, University of Zagreb. He currently works at INA-Industrija Nafte d.d. as the Chief Expert for Geology and Geophysics within Exploration.

Author: Tomislav Baketarić, mag.geol.

In Croatian part of Pannonian Basin System (Mura and Drava depression), Late Miocene-Pliocene post-rift sediments (Pannonian stage) are represented by large complex prograding clinothem sets visible on seismic sections. Using seismic stratigraphy as a method in other parts of the PBS Magyar et al. (2006, 2007), Sztano et al. (2013), Balasz et al. (2017) and Sebe et al. (2020) concluded that the lacustrine infill of Upper Miocene sediments consists of prograding deltaic complexes characterized by clinoforms on seismic reflection data. The reconstruction of the gradual progradation, i.e. the infill of the basin in this part of the PBS in time and space, was done through the analysis of spatial and temporal variations of the clinoform geometry and the trajectories of the slopes. During interpretation of the clinoforms, determined by distinct seismic facies, the upper and lower edges of the slope (rollover boundaries) were mapped (sensu Patruno et al., 2015; Paumard et al., 2018). By applying seismic stratigraphy principles, as well as analyzing the movement of the slope and the rollover boundaries within the clinoforms, an insight into the spatial diachronous closure of Lake Pannon within these depressions was obtained. Throughout the area 15 regional clinothem surfaces were mapped (Pa-1 to Pa-15). Based on stratigraphic data linked with seismic relative chronostratigraphic framework of the Upper Miocene and Pliocene infill was built (Figure 1). Clinothems mapped over the area of two depressions spatially define the closure of Lake Pannon during the Late Miocene and the Early Pliocene through time. Although often aggravated by lack or poor quality of the data, such framework considerably improved reconstruction of paleogeographic evolution and sediment fairways.

Figure 1. Trajectories of the Lake Pannon delta front (upper rollover boundaries) connected by line) shown on present-day topography of the Pannonian Basin. Upper rollover trajectories of Mura and Drava Basins are based on this work, while geographical position of the trajectories in other parts of the PBS are correlated and connected based on Magyar et al.(2013) i Magyar (2021). Dominant progradation direction during Pannonian in Hrvatsko Zagorje basin are based on Kovačić et al. (2004). For reference, international borders are marked in orange, while the largest cities are marked with red dots.

Reference:

Patruno et al., 2015; Paumard et al., 2018

Balázs, A., Magyar, I., Matenco, L., Sztanó, O., Tokes, L., Horváth, F. (2017): Morphology of a large paleo-lake: Analysis of compaction in the Miocene-Quaternary Pannonian Basin. Global and Planetary Change. 171, 134-147, doi:10.1016/j.gloplacha.2017.10.012.

Kovačić, M., Zupanič, J., Babić, Lj, Vrsaljko, D., Miknić, M., Bakrač, K., Hećimović, I., Avanić,R., Brkić,M. (2004): Lacustrine basin todelta evolution in the Zagorje Basin, a Pannonian sub-basin (Late Miocene: Pontian, NW Croatia). Facies 50, 19–33.

Magyar, I., Fogarasi, A., Vakarcs, G., Bukó, L., Tari, G.C., (2006): The largest hydrocarbon field discovered to date in Hungary: Algyő. In: Golonka, J., Picha, F.J. (Eds.) The Carpathians and their foreland: geology and hydrocarbon resources. American Association of Petroleum Geologists, Memoir, 84, pp. 619–632, doi: 10.1306/985734M843142

Magyar, I., Lantos, M., Ujszászi, K., Kordos, L., (2007): Magnetostratigraphic, seismic and biostratigraphic correlations of the Upper Miocene sediments in the northwestern Pannonian Basin System. Geologica Carpathica, 58, 277–290.

Magyar, I., Radivojević, D., Sztanó, O., Synak, R., Ujszászi, K. Pócsik, M. (2013): Progradation of the paleo-Danube shelf margin across the Pannonian Basin during the Late Miocene and Early Pliocene. Global and Planetary Change, 103,168-173, doi:10.1016/j.gloplacha.2012.06.007.

Magyar I. (2021): Chronostratigraphy of clinothem-filled non-marine basins: Dating the Pannonian Stage. Global and Planetary Change, 205, 103609, doi:10.1016/j.gloplacha.2021.103609.

Patruno, S., Hampson, G. J., Jackson, C.A.L. (2015): Quantitative characterisation of deltaic and subaqueous clinoforms. Earth Science Reviews, 142, 79–119, doi:10.1016/j.earscirev.2015.01.004

Paumard, V., Bourget, J., Payenberg, T., Ainsworth, B., George, A.D., Lang, S., Posamentier, H.W., Peyrot, D. (2018): Controls on shelfmargin architecture and sediment partitioning during a syn-rift to post-rift transition: Insights from the Barrow Group (Northern Carnarvon Basin, North West Shelf, Australia). Earth-Science Reviews, 177, 643-677, doi:10.1016/j.earscirev.2017.11.026.

Sebe, K., Kovačić, M., Magyar, I., Krizmanić, K., Špelić, M., Bigunac, D., & Sütőné Szentai, M., Kovács, A., Korecz, A., Bakrač, K., Hajek Tadesse, V., Troskot-Čorbić, T., Sztanó, O. (2020): Correlation of upper Miocene-Pliocene Lake Pannon deposits across the Drava Basin, Croatia and Hungary. Geologia Croatica, 73, 177-195, doi:10.4154/gc.2020.12.

Sztanó O., Szafián P., Magyar I., Horányi A., Bada G., Hughes D.W., Hoyer D.L., Wallis R.J. (2013): Aggradation and progradation controlled clinothems and deep-water sand delivery model in the Neogene lake pannon, Makó Trough, Pannonian Basin, SE Hungary. Global Planet. Change, 103, 149-167, doi:10.1016/j.gloplacha.2012.05.026.

Tomislav Baketarić, mag.geol. is a geology PhD candidate at the Faculty of Mining, Geology and the Petroleum Engineering, University of Zagreb. He currently works at INA-Industrija Nafte d.d. as the Chief Expert for Geology and Geophysics within Exploration.

Author: Laura Bačani, mag.ing.geol.

Spring Rakovac is located on the Mt Žumberačka gora in the Poklek area, about 1.5 km NW of the village Koretići and about 1 km NW from Eko Selo Žumberak (Figure 1). This research has been carried out in order to determine the possibility of water usage from the spring Rakovac as an additional source of drinking water for the needs of the public water supply of the City of Samobor. The first step was to design and build a rectangular sharp-crested weir situated directly downstream from the spring (Figure 2). A metal pipe with a cover and a padlock was installed next to the weir, in which automatic data loggers were installed. The logers measure the water level (head) over the weir (Figure 3). Water levels are then calculated into discharges using the flow curve. The flow curve of the spring shows the connection between water levels and discharges. Monitoring of the spring discharge has begun in 2019 and is still ongoing. The Rakovac spring hydrograph (discharge – time graph) shows that most of the monitored time discharges are low, i.e. less than 10 l/s, whereas the discharges greater than 50 l/s appear in less than 10 % of monitored time. On several occasions the discharge was greater than 300 l/s, but such great discharges are of very short duration. Great differences in minimum and maximum discharges, and rapid changes in discharge from a few l/s to several hundred l/s are typical characteristics of karst springs. This youtube link shows the Rakovac spring discharge at 30 l/s (https://www.youtube.com/watch?v=8T99jAE85Xg).

Figure 1 Location of the spring Rakovac

Figure 2 Rectangular sharp-crested weir at the Rakovac spring

Figure 3 Automatic data loggers installed inside the metal pipe

Laura Bačani, mag.ing.geol. is research asistant at Department of Geology and Geological engineering, Faculty of Mining, Geology and Petroleum Engineering, Zagreb University. She is enrolled in Doctoral study Applied Geosciencies, Mining and Petroleum Engineering. Her PhD topic is modeling the water flow in the unsaturated part of Zagreb aquifer.

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Autor: Ana Kamenski, MSc.

The Aiza research area covers over 650 km² of the northern Adriatic offshore, a common Adriatic foreland of the older Dinarides on the NE, and the younger Apennines on the SW. This paper’s main objective is to reconstruct the evolution of the carbonate platform and the geometry of its margin in the area. It also aims to define the structural-tectonic setting of the research area, with a particular focus on investigating the possible continuation of inferred transverse structures (oriented obliquely or perpendicularly to the platform margin and the orogenic belts).

Figure 1 (a) Location, paleoenvironmental map after Grandić et al. (2010). and tectonic map after Korbar (2009) of the study area, including the position of the Aiza research area, locations of the two exploration wells, Susak more-1 and Alessandra-1, situated offshore Kvarner and southern Istria. Contours delineate the shorelines of the mainland and the Kvarner islands. A dashed-dotted line indicates position of the 2D regional seismic profile CROP-M16. Inset shows map’s location in the Northern Adriatic area and the thrust fronts of the surrounding orogenic belts. (b) The frame of the Aiza 3D Block depicts placement of the seismic sections, including inlines, crosslines, composite lines, and 2D seismic lines. These sections have been selected to provide a clear and representative reflection of the subsurface relationships within the exploration area covered by seismic data.

High-quality 3D reflection seismic data were used to investigate the area’s Mesozoic to Cenozoic tectono-stratigraphic evolution. Four main seismo-stratigraphical horizons were recognized: Base of Carbonate Platform (BCP), Top of Carbonate Platform (TCP), Messinian Erosional Surface (MES), and a Plio-Quaternary horizon (PlQh), as well as the dominant faults. The results depict the geological setting and tectonic evolution of the area.

Figure 2 Stratigraphic correlation of Alessandra-1 and Susak more-1 wells with main prominent horizons, adopted from Špelić et al. (2021). These horizons include BCP (Base Carbonate Platform), TCP (Top Carbonate Platform), MES (Messinian Erosional Surface), and PlQh (Plio-Quaternary horizon). Well positions are depicted in relation to the investigated Aiza Block and the 3D seismic area available for this research.

Figure 3 (a) Location of seismic profiles. (b) Seismic profile IL 9435 with interpreted horizons tentatively calibrated using data from the remote Susak more-1 well. The position of the carbonate platform margin is indicated by the red solid line, extrapolated by the red dashed line. (c) Seismic section XL 2069 illustrates the platform escarpment on both the NW and SE sides. (d) Seismic section IL 10330 reveals the SE slope of the platform, along with the presence of extensional faults (short red solid lines) and indications (red dashed lines) of strike-slip faulting intersecting the profile.

Figure 4 Horizon mapping on seismic section XL 2844: (a) Location of seismic profile; (b) Uninterpreted classic seismic view with highlighted position of four main horizons (yellow arrows); (c) Seismic attribute Cosine of phase view with the interpreted BCP horizon; (d) Seismic attribute 3D edge enhancement view with the interpreted PlQh and TCP horizons.

Figure 5 Structural-stratigraphic interpretation of seismic sections IL 9910 and XL 2844: (a) On seismic section IL 9910, the major structural features include a pronounced platform margin marked by a steep slope (indicated by the thick red line on the left) and a relatively simple intra-platform basin (represented by the two thick red lines on the right). Thin red lines depict extensional faults. Abbreviations used: Adratic Basin (AB), Adriatic Carbonate Platform (ACP), and Tethyan Mega Platform (TMP). (b) Seismic section XL 2844 reveals a more complex intra-platform basin within a releasing bend (listric fault, thick red line on the left) and a restraining bend (strike-slip fault, middle thick red line). Some faults within the hybrid flower structure exhibit signs of reactivation (double arrows). Additionally, preserved anticlinal structures formed as a result of fault reactivation with an opposite character are indicated by black arrows. Multiple reflectors are marked by “m”. (c) Location of the seismic sections is shown for reference.

Figure 6 (a) Uninterpreted and (b) Interpreted XL 1544 seismic section. This section depicts chronostatigraphic Plio-Quaternary sequences of the study area. The grey rectangles represent cropped areas displayed in (c) and the black arrows highlight the presence of gas chimneys. (c) Seismic sequences, denoted as SEQ1 to SEQ7, are described here along with their defining characteristics. (d) Inset displays the location of the section for reference.

Figure 7 Acquired tectonic configuration based on the interpretation of seismic sections. The fault polygons are represented in a 3D view from three different perspectives: (a) From the south; (b) From the southwest; and (c) From the top. Time-slice maps have been selected at six distinct depths: (d) −3000 ms, (e) −2500 ms, (f) −2000 ms, (g) −1500 ms, (h) -1000 ms, and (i) -500 ms. The sharp transitions from high to low amplitudes on these maps represent significant variations in elevation. Red lines delineate the primary faults within the study area, while black lines indicate the platform’s margin and white lines delineate the edges of the intra-platform basin.

Figure 8 Time-structural maps for the: (a) BCP; (b) TCP; (c) MES; and (d) PlQh horizons. Isochron intervals are set at 25 ms TWT for (a), (b), and (c), while (d) has a 10 ms TWT isochron interval. The grey outlines represent all seismic sections referenced in this paper.

Figure 9 The 3D model illustrates the configuration of platform carbonates within the Aiza exploration Block. The upper image of TCP surface depicts the Adriatic carbonate platform margin and the intra-platform basin, while the lower image of the BCP surface depicts the dome-shaped geometry of the basement. To enhance visibility, a vertical exaggeration factor of five has been applied to the Z-scale. The contour interval is 25 ms TWT.

Figure 10 (a) Location of the sections, with highlighted interpretation of the carbonate platform margin (thick black line) and the major Kvarner Fault System marked by red line; (b) Seismic section CROP-M16, with a black rectangle indicating the Aiza area and a yellow rectangle indicating the apparent structure previously recognized by Del Ben (2002); (c) Composite section CL-1; (d) Composite section CL-2. Both sections include interpreted regional horizons and the Kvarner Fault System; the Z-scale in these figures has a vertical exaggeration factor of five.

Figure 11 Schematic cross-section of the Dinaric Foreland Basin system from the late Cretaceous to Palaeogene (modified after Korbar (2009) and DeCelles & Giles (1996)), with the proposed position of the Aiza area (red rectangle). Not to scale.

Figure 12 Geological map of the Istria and Kvarner region (adopted and modified after Špelić et al. (2021) and references therein), integrated with findings from Grandić et al. (2013). This map encompasses the significant structural insights obtained by this research, such as the delineation of the Mesozoic Adriatic carbonate platform margin and the interpretation of major faults, including their extrapolation towards the fault system identified by Špelić et al. (2021).

A long-lasting (Jurassic to Cretaceous) stable NW-SE striking platform margin evolved probably along the inherited Triassic normal fault. The marginal belt of the platform was affected during the Late Cretaceous to Palaeogene by extension and opening of the intra-platform basin, probably on the southern limb of the then developing Dinaric forebulge. The transverse fault system (Kvarner fault) was probably reactivated as a strike-slip zone during the late Miocene tectonic reorganization. The area was tilted to the SW during the Pliocene, in the distal foreland of the progressively northward propagating Northern Apennines. Sub-horizontal late Quaternary cover of Dinaric and Apenninic structures could imply active subsidence of the foreland in between nowadays sub-vertically exhuming neighboring orogenic belts.

References:

DeCelles, G.P.; Giles, A.K. Foreland basin systems. Basin Res. 1996, 8, 105–123.

Del Ben, A. Interpretation of the CROP M-16 seismic section in the Central Adriatic Sea. Mem. Soc. Geol. Ital. 2002, 57, 327–333.

Grandić, S.; Kratković, I.; Rusan, I. Hydrocarbon potential assesment of the slope deposits along the SW Dinarides carbonate platform edge. Nafta 2010, 61, 325–338.

Grandić, S.; Kratković, I.; Balić, D. Peri-Adriatic platforms Proximal Talus reservoir potential (part 1). Nafta 2013, 64, 147–160.

Kamenski, A.; Korbar, T. Platform-to-Basin Evolution of a Tectonically Indistinct Part of a Multiple Foreland—Analysis of a 3D Seismic Block in the Northern Adriatic Sea (Croatian Offshore). Geosciences 2023, 13, 323. https://doi.org/10.3390/geosciences13110323.

Korbar, T. Orogenic evolution of the External Dinarides in the NE Adriatic region: A model constrained by tectonostratigraphy of Upper Cretaceous to Paleogene carbonates. Earth-Sci. Rev. 2009, 96, 296–312.

Špelić, M.; del Ben, A.; Petrinjak, K. Structural setting and geodynamics of the Kvarner area (Northern Adriatic). Mar. Petrol. Geol. 2021, 125, 104857.

Ana Kamenski, MSc., PhD student/scholarship holder of the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, and Senior Expert Associate at the Department of Geology of the Croatian Geological Survey.

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Autor: Natali Neral

After completing my graduate study in geology at the Faculty of Science, University of Zagreb, I am employed as an assistant at the Institute of Archaeology, on the research project of the Croatian Science Foundation (prePOT, UIP-2020-02-3637, http://prepot.iarh.hr/index.php/en/) The project represents the first systematized research of pottery, raw materials, and technological choices in Croatia based on connecting different scientific fields (archaeology and geology). As part of that, under the mentorship of the project manager, Ph.D. Andreja Kudelić (Institute of Archaeology) and associate professor Ph.D. Ana Maričić (Faculty of Mining, Geology and Petroleum Engineering), I enrolled in postgraduate study at the Faculty of Mining, Geology and Petroleum, University of Zagreb. The doctoral research is focused on the pottery raw materials and pottery technology through different periods of the past in Croatia. The research is conducted on archaeological ceramics (pottery) from multi-period archaeological sites (from the Neolithic to the Late Mediaeval period, figure 1) and on pottery raw materials (clay and tempers) collected in the vicinity of the sites (figure 2). The research includes four case studies; Eastern Croatia, Central Croatia, Istria and Northern Adriatic and Central Dalmatia located in different geological settings whereas in the archaeological context, the areas are under different cultural influences. The research aims to determine the type, properties, and provenience of the pottery raw materials and the characteristics of the technological process by applying different analytical methods. These objectives enable an understanding of the variabilities in the choice of the raw materials and pottery technology within communities from different periods of the past.

Figure 1. Pottery sherds from multiperiod site Jagodnjak-Krčevine in Baranja region. Pottery from (a, b) the Neolithic, (c, d) the Bronze Age, (e, f) the Late Iron Age, (g, h) the Roman period and (i, j) the Mediaeval (from Neral et al., 2023).

Figure 2. Clayey material suitable for making pottery from (a) Istria and (b) Baranja. (c) Calcite and (d) different types of fragmented rocks that potters intentionally add to the clay during the paste preparation.

In archaeological science, pottery (ceramic vessels) represents the most numerous sets of findings found on sites during almost all periods of human history. It is the main and often the only remnant of the material culture that constitutes the essential evidence for interpreting ancient human history. The study of pottery in the context of material science, therefore starts from the analysis of the raw materials but also examines each segment of the production process which enables a wide range of scientifically based data that consider the technology of the ancient communities. In such research, it is necessary to apply different analytical methods that enable the determination of the mineralogical-petrographic and geochemical composition of ceramics and pottery raw materials, as well as the determination of the characteristics of technological process.

The basic analytical method used in my research is optical microscopy of thin sections (ceramic petrography, figure 3) which permits collecting data regarding the mineralogical and petrographic composition of ceramics, clay paste preparation (paste recipe), and estimating the firing temperatures (Quinn 2013; Quinn 2022). X-ray powder diffraction provides the mineralogical composition and the estimated firing temperature of ceramics through the mineral phases present in a specific temperature range. Various geochemical methods are used to investigate the provenience of pottery raw materials. Such research is carried out using the comparative method of archaeological material and potential raw materials from the vicinity of the sites. The most frequently used geochemical methods (ICP-ES, -MS, -OES) are destructive methods performed on the homogenized powdered ceramics samples, leading to the loss of data on certain raw materials (clay and tempering materials). For this reason, I supplemented the already used ICP-ES and ICP-MS methods with the application of non-destructive methods (SEM-EDS and p-XRF) which enable precise determination of the geochemical composition of individual pottery raw materials. I applied SEM-EDS and p-XRF on ceramic samples at the University College London as part of the competition of the Croatian Science Foundation "Mobility Program - outgoing mobility of assistants (MOBDOK-2023)". The first part of the scientific research training was related to the application of p-XRF on ceramics, while the second part was devoted to work on SEM-EDS (figure 4). I also gained valuable experience through pleasant cooperation with the host-mentor and expert in the analysis of ceramics, Ph.D. Patrick Quinn.

Figure 3. Thin section microphotographs of ceramic samples (XPL). Ceramic sherds are composed of clayey material and intentionally added tempering materials such as (a) various metamorphic rocks, (b) calcite, (c) mollusc shells and (d) grog (modified after Neral et al., 2024, in press).

Figure 4. SEM-EDS in the Wolfson Archaeological Science Laboratory at the University College London.

Application of the aforementioned analytical methods enables the reconstruction of the technological process of pottery production and an insight into the types and origin of the materials used in the past. My doctoral research thus contributes to the understanding of the variability in the choice of raw materials and pottery technology, as well as to the consideration of dynamics of landscape use through a broad spatiotemporal context, i.e. in different areas and within cultural groups from different periods of the past in Croatia.

References:

Neral, N., Kudelić, A., Maričić, A., Mileusnić, M. (2023). Pottery technology through time: Archaeometry of pottery and clayey raw material from the multi- period site in eastern Croatia. Rudarsko-geološko-naftni zbornik, 38, 63, 1-21. DOI: 10.17794/rgn.2023.2.1

Neral., N., Kudelić, A., Maričić, A., Mileusnić, M. (2024). Tracing the origin of raw materials used for the production of ancient ceramics: Case study of multi-period archaeological sites (Continental Croatia). Geologia Croatica, in press

Quinn P. S. (2013). Ceramic Petrography: The Interpretation of Archaeological Pottery & Related Artefacts in Thin Section. Archaeopress. Oxford. 260 p.

Quinn, P. S. (2022). Thin Section Petrography, Geochemistry and Scanning Electron Microscopy of Archaeological Ceramics. Archeopress. 466 p.

Author: Igor Medved, PhD

In recent period, researchers have started conducting laboratory research to determine whether different types of biodegradable food waste can be used as additives in water-based drilling mud. Correctly designed composition of the mud is extremely important for the successful construction of the wellbore, since mud is complex fluid whose composition and properties affect the efficiency of drilling process (Gaurina-Međimurec et al., 2000). Therefore, any new type of additive must be researched in detail in relation to all the necessary properties that must be obtained during drilling operations. Wasted drilling mud and associated waste generated during drilling operations represents second largest volume of waste generated during exploration and production projects in oil and gas industry, so this kind of research can have considerably positive results for the environment (Haut et al., 2007; Gaurina-Međimurec et al., 2020). Along with environmental protection, the disposal of generated waste presents a significant cost, which leads to a logical conclusion that reducing the amount of hazardous waste to the lowest possible value must be a priority. Since many commercially available water-based mud additives fall into the category of non-degradable and environmentally hazardous materials (Zheng et al., 2020), there is a need to find and create new environmentally friendly additives that ensure the achievement of all the necessary mud properties at the level of current commercial additives, but with a minimal impact on the environment. Therefore, many researchers are actively conducted on the use of food waste (Haider et al., 2019) and other types of biodegradable waste as additives that could be used for this purpose. Among the analyzed types of biodegradable waste, there is some data on the use of mandarin peel for these purposes, and due to the considerable annual production of this type of fruit in Croatia along with contradictory results of previously published results, it was decided that detailed laboratory research will be carried out on this type of biodegradable waste.

By adding dried, grinded and sieved mandarin peel powder, in the water-based drilling mud, it was tested how this additive affects the mud filtration properties, rheological properties and the wellbore stability. Drilling muds that have a certain concentration of mandarin peel powder in their composition (Figure 1) were compared with base mud which consists of water, bentonite and sodium hydroxide (NaOH). In addition to studying the influence of the concentration of mandarin peel powder, the influence of different particle sizes divided into two groups, particles smaller than 0.1 mm and particles between 0.10 mm and 0.16 mm in size, was also researched. The selected concentrations in the mud samples varied from 0.5% to 2% by the volume of water, since it was concluded during preliminary laboratory test phase that a concentration higher than 2% leads to a significant increase in plastic viscosity of the tested muds.

Figure 1 Drilling mud with added mandarin peel powder in its composition

On the basis of the conducted laboratory tests and the analysis of the results, useful insights were obtained on the effect of mandarin peel on the observed mud properties. Results show that mandarin peel has positive effect on mud filtration, since every mud sample with added mandarin peel powder showed decrease of filtrate volume in room conditions. Even better results were recorded in conditions of elevated temperature and differential pressure on PPT device (Figure 2).

Figure 2 Permeability Plugging Tester

An exceptionally positive influence of mandarin peel powder was also observed in data obtained after swelling of pellets that have a clay component in their composition (Figure 3), when pellet is in contact with mud to which this eco-friendly additive is added. Laboratory tests showed significant swelling reduction, and this correlates well with results of filtration measurements which is essential to prove the improvement of the wellbore stability.

Figure 3 Artificial rock samples (pellets) before and after swelling test

The rheological properties of mud samples that are containing mandarin peel were not decreased but also did not significantly increase observed properties. Rheological parameters show increase and remain within acceptable limits up to concentration of 1.5% of mandarin peel powder by volume of water. Because of this data set, the recommended concentration of this additive should be kept in the range of 1.0% to 1.5%, and at those concentrations observed additive has a significant positive effect on the filtration properties and wellbore stability as well as maintaining stable rheological properties at the same time. Considering the particle size used for laboratory research of mentioned mud properties, it can be concluded that slightly better results were obtained with larger particles between 0.10 mm and 0.16 mm but both sizes provide satisfactory water-based drilling mud properties. Also, by conducting laboratory tests of temperature stability, it can be concluded that water-based drilling mud with added mandarin peel powder is stable even after exposing the water-based drilling mud to temperatures up to 133 °C.

Ecotoxicity measurements showed that toxicity of water-based drilling mud that has mandarin peel powder in its composition is reduced, compared to conventionally used commercial additives. This test confirmed the ecological component of using this type of material, and whole research confirmed the applicability of the circular economy concept in drilling technology.

References:

Gaurina-Međimurec N., Simon K., Matanović D. (2000): Drill-In Fluids Design Criteria. Nafta (Spec. Issue), 27–32.

Gaurina-Međimurec N., Pašić B., Mijić P., Medved I. (2020): Deep underground injection of waste from drilling activities—An overview. Minerals, 10, 303.

Haider S., Messaoud-Boureghda M.Z., Aknouche H., Akkouche A., Hammadi L., Safi, B. (2019): An ecological water-based drilling mud (WBM) with low cost: Substitution of polymers by wood wastes. J. Pet. Explor. Prod. Technol., 9, 307–313.

Haut R.C., Rogers J.D., McDole B.W., Burnett D., Olatubi O. (2007): Minimizing Waste during Drilling Operations. In Proceedings of the AADE National Technical Conference and Exhibition, Houston, TX, USA, 10–12.

Zheng Y., Amiri A., Polycarpou, A.A. (2020): Enhancements in the tribological performance of environmentally friendly water-based drilling fluids using additives. Applied Surface Science, 527, 146822.

Igor Medved, PhD is a senior assistant at the Department of Petroleum and Gas Engineering and Energy, Faculty of Mining, Geology and Petroleum Engineering at University of Zagreb. He successfully defended his doctoral dissertation on April 11^th, 2023 entitled Influence of Mandarin Peel on Water-Based Mud Properties and Wellbore Stability.

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Amalia Lekić Brettschneider, mag.ing.petrol., Assoc. Prof. Luka Perković, PhD Sc. ing.

Decarbonization of heating and cooling systems represents a special challenge in densely populated areas due to the increased concentration of heating and cooling energy consumption. One of the possibilities is the use of shallow geothermal systems, and the dimensioning it in the way that should anticipate and maximally avoid the unwanted effect of undercooling and overheating of the reservoir with the aim of meeting energy-efficient heating and cooling needs in the long term. The intensity of unwanted effects depends on numerous physical parameters of the reservoir and the flow of underground water, as well as the dynamics of the demand for heating and cooling of facilities on the surface.

The direct use of geothermal energy most often refers to geothermal heat pumps, whereby the system is used for heating and cooling spaces of various purposes (Lebbihiat et al., 2021). The possibilities of using heat pumps depend on the soil properties (thermal conductivity, thermal gradient, hydraulic conductivity, slope), dimensions and properties of borehole heat exchangers (BHE), as well as external factors such as external temperature and amount of precipitation. The properties of BHE are: length, diameter, wall thickness and cement properties. Vertical systems are more efficient and require a smaller space for their installation (Chen et al., 2022). Vertical closed systems have been shown to be more efficient than open systems, e.g. for heating greenhouses (Benli, 2013). Soil thermal conductivity values are influenced by lithological and physical properties, such as porosity, texture, water saturation, hydraulic and thermal conductivity, etc. (Luo et al., 2016).

A computer simulation of one such system was carried out in the area of the city of Zagreb. The simulation was carried out on a square model with a size of 100x100 m and a depth of 120 m. The borehole heat exchangers are located at a depth of 100 m. 3 cases of different lithology were considered: case A (clay, gravel, sand), case B (clay and gravel) and case C (clay and sand), shown in Figure 1.

Figure 1. Lithology of the 3 considered cases

Each individual case was tested with 4 types of borehole heat exchanger configurations (16 BHE, 10 BHE, 6 BHE and 3 BHE) arranged in the rectangular grid shown in Figure 2. This resulted in 12 considered scenarios.

Figure 2. Configurations of borehole heat exchangers

The reservoir model (Figure 3) contains initial and boundary conditions. The boundary conditions are hydraulic head on the north and south side, fixed temperature profile on the north side, fixed temperature on the top and bottom side, and zero-gradient boundary condition on the lateral east and west sides for both fluid flow and heat transport. Initial conditions are set for temperature and hydraulic gradient. For the initial conditions and the northern side of the border, a linear temperature increase with depth is applied.

Figure 3. Schematic view of boundary and initial conditions (a) and mesh around BHE (b)

The simulation of each of the 12 scenarios is carried out on an annual basis with hourly values and, considering the amount of data, the results of the C-03-BHE scenario will be presented (Figure 4). The balance for the whole year and two selected weeks, the 1st and 26th week of the year, are shown, representing heating demand in the winter and cooling demand in the summer.

Figure 4. Power and heat balance for scenario C-03-BHE for all year and two selected weeks: week #01 (winter) and week #26 (summer)

Temperature distribution show that scenario with high permeability, B-16-BHE results in lower temperature changes in the reservoir, while scenario C-03-BHE affects temperature field significantly, especially in the vicinity of BHE. Overheating and subcooling of the reservoir is negligible for B-16-BHE due to convective heat transfer but are significant for C-03-BHE. More precisely, for C-03-BHE, in the vicinity of the BHE the reservoir temperature can go up to 40 °C during the cooling and drop to 4 °C during the heating season. Overheating and subcooling of the reservoir reduces the coefficient of performance of the heat pump.

Figure 5. Temperature distribution between B-16-BHE (high hydraulic permeability) and C-03-BHE (low hydraulic permeability)

During the working life of the heat pump, the reservoir goes through phases of undercooling and overheating. Subcooling occurs in winter, when the heat accumulated in the reservoir is used to heat the space, and the temperature of the reservoir decreases. Overheating occurs in the summer. By cooling the space, the accumulated heat is pushed back into the reservoir. In this way, the reservoir is partially regenerated, which extends the working life of the heat pump.

Figure 6. Average reservoir temperatures during one year period for all 12 scenarios

Figure 6 shows the average reservoir temperatures within one year for all 12 scenarios. Lithological properties of case C shows the greatest oscillations of the reservoir temperature, while lithological properties of case B is the most constant.

During the heating season, the main indicator of efficiency of heat pump is coefficient of performance (COP). COP is a measure used to evaluate the efficiency of a heat pump system. It represents the ratio of the desired output (cooling or heating) to the required input (usually in the form of electrical energy). The larger the COP, the better the efficiency of the heat pump. In Figure 12 are presented relations between COP and probability density function (PDF) for all 12 scenarios during heating season. For all three lithological cases, the COP is oriented to the right, and it moves to the left (decreases) by decreasing the number of BHE’s due to the need for more energy to obtain the desired amount of heat from a smaller number of BHE. Lithological properties of case B proved to be the best.

Figure 7. COP histogram during heating season for all 12 scenarios

Results show that lithological conditions have a substantial impact on heat pump performance and can be a decisive factor in determining the structure of the surface equipment, mainly number of BHE and required capacity of heat pump and auxiliary heating device. Heat transfer between reservoir and BHE is substantially influenced by the convection of groundwater and this convection depends on the hydraulic conductivity of underlying layers. The movement of the average reservoir temperature depends on the lithology and the number of borehole heat exchangers. The COP decreases with decreasing the number of BHE’s.

NOMENCLATURE

e                      energy                                    kWh

P                     power                                     kW

ϕ                     heat flow rate                         kW

SUBSCRIPTS

B         battery

BHE    borehole heat exchanger

curt     curtailment

dem     demand

exp      export

HE       heater

HP       heat pump

imp     import

PV       photovolatics

REFERENCES

1. Lebbihiat, N., Atia, A., Arıcı, M., Meneceur, N., Geothermal energy use in Algeria: A review on the current status compared to the worldwide, utilization opportunities and countermeasures, Journal of Cleaner Production, 302, 126950, 2021.
2. Chen, K., Zheng, J., Li, J., Shao, J., Zhang, Q., 2022. Numerical study on the heat performance of enhanced coaxial borehole heat exchanger and double U borehole heat exchanger, Applied Thermal Engineering, 203, 117916.
3. Benli, H., 2013. A performance comparison between a horizontal source and a vertical source heat pump systems for a greenhouse heating in the mild climate Elaziğ, Turkey, Applied Thermal Engineering, 50, 197-206.
4. Luo, J., Rohn, J., Xiang, W., Bertermann, D., Blum, P., 2016. A review of ground investigations for ground source heat pump (GSHP) systems, Energy & Buildings, 117, 160-175.

Amalia Lekić Brettschneider, mag. ing. petrol. is an assistant at the Department of Petroleum and Gas Engineering and Energy at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb.

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Luka Perković,  PhD Sc. ing. is an associate professor at the Department of Petroleum and Gas Engineering and Energy at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb.

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Author: Ana Kamenski, MSc.

Proper assessment of the distribution of lithological composition in the subsurface is one of the key elements when evaluating the hydrocarbon potential of an area, as well as geothermal potential and possibility for the CO₂ geological storage. Spatial definition of the distribution of lithological composition is only one of the steps in the characterization of the subsurface.

The data obtained from the exploration of surface outcrops (hard data) and the subsurface characterization (very little hard data is available, e.g. core material) are conditionally compatible. The lithological composition in the inter-well area is conventionally evaluated on the basis of data obtained from the surrounding wells (cuttings, cores, logs) using either the conventional lithofacies mapping approach [1] where interpretation depends solely on the experience of the interpreter, or by making use of mathematical algorithms [2]. Such procedures have high dose of uncertainty in regional surveys where the wells are very distant from each other and irregularly distributed, and comparatively smaller uncertainty in areas with hydrocarbon reservoirs where there is a large number of relatively closely spaced wells. Following the trend of technological development, it is needed to turn to mathematical and statistical tools to eliminate subjectivity when interpreting lithology, although general understanding of the geology is always invaluable [3]. In every subsurface exploration, one of the most important assignments are determining key factor—age, structural settings and lithology [4]. These have a very large influence on scientific results, as well as economic implications if the results are applied to any type of resource estimates.

The purpose of this paper [5] was to analyse the data using both geostatistics and geological knowledge as objectively and realistically as possible. For this purpose, the area of the depleted oil field (Figure 1) was selected for the process, which is located within the Drava Depression, and belongs to the Croatian part of the Pannonian Basin (northern Croatia).

Figure 1 Pannonian Basin System and surrounding tectonic and geographic units with outline of the North Croatian Basin and study area with well locations [6, 7, 8]

This object was chosen due to the available data for lithology interpretation in the wells and 3D seismic coverage needed to define the lithological composition throughout the seismic volume. Clastic Pannonian interval (CPI) was selected for the analysis (Figure 2, 3) as the lithology of this unit can be generalized to three classes—sandstones and marls that occur through the whole interval and coals that are most often found in the top of the interval.

Figure 2 Schematic representation of stratigraphy, lithology and major tectonic events in the surroundings of the exploration area [9]

Figure 3 Mapping model boundaries on classic seismic profiles [5]

The lithological composition of the subsurface (Figure 4) is simplified in accordance with the general geological composition of the Pannonian age sediments in the research area.

Figure 4 Interpreted and upscaled lithology classes within four wells [5]

For the purpose of lithology modeling, selected seismic volume was analysed by using artificial neural networks. Two approaches to artificial neural networks (ANN) were used to observe the influence on result prediction of changing the type of the approach. First approach (DAANN) used a large number of different architecture networks, regarding different number of neurons in the hidden layer and different activation functions. Second approach (SAANN) employed the same architecture network but with different distribution of cases within the training, test and selection datasets, and with a different starting point (case) for the analysis. Out of a 1000 total cases, 100 realizations of each approach were singled out upon which the data points with probability of 50%, 75% and 90% of occurrence of certain lithology category were upscaled in the model. Six models were generated by indicator kriging (Figure 5).

Figure 5 Lithology models performed by indicator kriging on upscaled lithology cells [5]

Although in theory, the higher accuracy data should provide a more accurate result, the geologically most sound results were obtained by 50% accuracy data. In higher accuracy results, sandstone lithology was unrealistically over emphasized as a result of the upscaling process, variography and statistical analysis. Considering that majority of hydrocarbon reservoirs discovered so far are in clastic sediments, the methodology presented in this paper represents one of the possible ways of determining subsurface lithology, that can lead to new discoveries not only in the study area, but also in other sedimentary basins. Presented research can be used in all geoenergy-related subsurface explorations, including hydrocarbon and geothermal explorations, and subsurface characterization for CO₂ storage potential and underground energy storage potential as well.

Reference:

[1]. Forgotson, J.M.: Review and classification of quantitative mapping techniques. Am. Assoc. Pet. Geol. Bull. 44, 83–100 (1960).

[2]. Feng, R., Luthi, S.M., Gisolf, D., Angerer, E.: Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: applying prior geological information. Mar. Pet. Geol. 93, 218–229 (2018). https ://doi.org/10.1016/j.marpe tgeo.2018.03.004.

[3]. Hohn, M.E.: Geostatistics and Petroleum Geology. Springer, Dordrecht (1999).

[4]. Selley, R.C., Sonnenberg, S.A.: Methods of Exploration. Elements of Petroleum Geology, pp. 41–152. Elsevier, New York (2015).

[5]. Kamenski, A.; Cvetković, M.; Kolenković Močilac, I.; Saftić, B. (2020): Lithology prediction in the subsurface by artifcial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method // GEM - International journal on geomathematics, 11, 8; 1-24 doi:10.1007/s13137-020-0145-3.

[6]. Cvetković, M., Matoš, B., Rukavina, D., Kolenković Močilac, I., Saftić, B., Baketarić, T., Baketarić, M., Vuić, I., Stopar, A., Jarić, A., Paškov, T.: Geoenergy potential of the Croatian part of Pannonian Basin: insights from the reconstruction of the pre-Neogene basement unconformity. J. Maps. 15, 651–661 (2019). doi:10.1080/17445647.2019.1645052

[7]. Dolton, G.L.: Pannonian Basin Province, Central Europe (Province 4808)—Petroleum Geology, Total Petroleum Systems, and Petroleum Resource Assessment. (2006)

[8]. Schmid, S.M., Bernoulli, D., Fügenschuh, B., Matenco, L., Schefer, S., Schuster, R., Tischler, M., Ustaszewski, K.: The Alpine-Carpathian-Dinaridic orogenic system: Correlation and evolution of tectonic units. Swiss J. Geosci. 101, 139–183 (2008). doi:10.1007/s00015-008-1247-3

[9]. Malvić, T., Cvetković, M.: Lithostratigraphic units in the Drava Depression (Croatian and Hungarian parts) – a correlation. Nafta. 63, 27–33 (2013)

Ana Kamenski, MSc., PhD student/scholarship holder of the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, and expert associate at the Department of Geology of the Croatian Geological Survey. In December 2018, she enrolled in the doctoral study of Applied Geosciences, Mining and Petroleum Engineering with the topic of her doctoral thesis: Improvement of the deep-geological characterization in the eastern area of the Drava Depression – spatial prediction of lithological properties based on seismic and well data.

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Igor Karlović, mag. ing. geol.

Over the last decades, high nitrate concentrations in Varaždin alluvial aquifer raised public concern regarding groundwater quality. The aquifer is the main source of drinking water for the local population in the Varaždin County in NW Croatia. Moreover, according to its hydrogeological characteristics, it represents one of the strategic groundwater resources in Croatia. For better understanding of nitrate distribution in groundwater and formulating appropriate management strategies for groundwater quality protection, it is necessary to investigate the origin, fate, and transport of nitrate within the Varaždin aquifer. Simply put, nitrates are formed by the nitrification process and disappear by the denitrification process. There are other nitrogen transformation processes, but these two are the main and best researched. The research conducted within the TRANITAL project (Origin, fate and TRAnsport modelling of NItrate in the Varaždin ALluvial aquifer) combined hydraulic, hydrochemical, isotope, microbiological, statistical, and modelling techniques which resulted in numerous findings about the alluvial aquifer, its interaction with surface water and precipitation, and nitrate behaviour within the aquifer. The alluvial aquifer is composed of gravel and sand with variable proportions of silt and clay. It consist of three layers: upper aquifer, semipermeable interlayer, and lower aquifer (Figure 1).

Figure 1 Three-dimensional model of the Varaždin aquifer (Karlović et al., 2022a)

Groundwater, surface water and precipitation sampling (Figure 2) was conducted on a monthly basis during four-year period (from June 2017 to June 2021) for hydrochemical and stable water isotope analyses. Hydrochemical analyses of groundwater samples identified main processes that influence the groundwater chemistry: dissolution and precipitation of carbonate minerals, silicate weathering, cation exchange, transformation of organic matter, and anthropogenic influence. Hydrochemical data suggested that nitrate in groundwater could be related to usage of manure and fertilizers in agricultural production and wastewater. The stable water isotopes indicated that groundwater and surface water are recharged by precipitation (Figure 3).

Figure 2 Monitoring network with groundwater, surface water and precipitation sampling points

Figure 3 The relationship between δ²H and δ¹⁸O in groundwater and surface water (modified according to Karlović et al., 2022b)

Analysis of head contour maps shows that aquifer is recharged from the Drava River and accumulation lake Varaždin for all hydrological conditions, keeping the groundwater flow in the quasi-steady state. The general direction of groundwater flow is from NW to SE (Figure 4).

Figure 4 Map of head contour maps (modified according to Karlović et al., 2021)

Nitrate origin was studied using combination of dual isotope approach (δ¹⁵N and δ¹⁸O in nitrate), chemical and bacterial data, and isotope mixing model. The results showed that manure is the main nitrate source in agricultural, wastewater in urban, and soil organic N in natural area. Nitrification was identified as the main nitrogen transformation process, while denitrification can occur locally, but does not have significant impact on regional scale. The calibrated groundwater flow and nitrate transport model was used to simulate nitrate concentrations in groundwater in the next two decades (Figure 5). Model simulations predict continued downward trend of nitrate concentrations in the central part, and steady low nitrate concentrations in the northern part of the model. The modelling results demonstrated that management of agricultural practices is the most important aspect to gradually reduce nitrate contamination in the Varaždin aquifer, but it takes decades for nitrate concentrations in groundwater to respond to changes in nitrogen input from the surface.

Figure 5 Nitrate distribution in year 2020 and simulated nitrate distribution in year 2040

References:

Karlović, I., Marković, T., Vujnović, T., Larva, O. (2021): Development of a Hydrogeological Conceptual Model of the Varaždin Alluvial Aquifer. Hydrology, 8, 19, 13. doi:10.3390/hydrology8010019

Karlović, I., Posavec, K., Larva, O., Marković, T. (2022a): Numerical groundwater flow and nitrate transport assessment in alluvial aquifer of Varaždin region, NW Croatia. Journal of Hydrology: Regional Studies, 41(3):101084. doi: 10.1016/j.ejrh.2022.101084

Karlović, I., Marković, T., Vujnović, T. (2022b): The groundwater recharge estimation using multi component analysis: case study at the NW edge of the Varaždin alluvial aquifer, Croatia. Water. 14, 42. doi: 10.3390/w14010042

Marković, T., Karlović, I., Perčec Tadić, M., Larva, O. (2020): Application of Stable Water Isotopes to Improve Conceptual Model of Alluvial Aquifer in the Varaždin Area. Water, 12, 2; 1-13. doi:10.3390/w12020379

Igor Karlović, mag. ing. geol. is a research assistant at the Department of Hydrogeology and Engineering Geology at the Croatian Geological Survey. He enrolled in the PhD study program of Applied Geosciences, Mining and Petroleum Engineering at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb in 2018 with a topic entitled Origin, fate and transport modelling of nitrate in the Varaždin aquifer.

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Author: Galla Uroić, mag.ing.min.

Disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) is one of the most challenging engineering tasks, as evidenced by the fact that even after seventy years of their production, there is still no active repository in the world where SNF and HLW are permanently disposed of. There are several issues related to the construction of a deep geological repository for SNF and HLW (Uroić et al., 2022), such as:

• The repository must be constructed at a depth of 500 to 1000 meters in low-permeability host rock under reducing conditions.
• The repository is designed for a period of 10,000 years (HLW) to 100,000 years (SNF), while some countries (e.g., the USA) require the construction of a repository that guarantees the safety of the disposed material for a period of 1,000,000 years.
• To satisfy the above condition, it is necessary to find a geological environment that will remain stable for at least twice the duration defined as the repository's lifetime to ensure that radionuclides will not reach the biosphere and pose a threat to it.

The Republic of Croatia has an obligation to dispose of half of the SNF from the Krško Nuclear Power Plant, located in the Republic of Slovenia, but according to intergovernmental agreement, a single repository is planned to be constructed on the territory of one of the countries.

One specific problem related to repository construction is determining the type of material for the construction of engineering barriers - protective materials that will ensure the retention of radionuclides in the repository during the required period. Considering the geology of RS and RC, it is likely that the repository will be constructed in igneous (crystalline) rocks, and the KBS-3V concept (Figure 1) will be applied as the disposal concept. Based on the expected type of host rock and disposal concept, it can be concluded that the primary material for constructing engineering barriers will be bentonite clay.

Figure 1: Swedish spent nuclear fuel disposal concepts: KBS-3V (left) and KBS-3H (right) (Savage, 2012)

The research conducted for the purpose of the dissertation includes determining the characteristics of bentonite clays that will be used in the construction of engineering barriers at the future repository. To determine the behavior of the engineering barriers in the host rock, it is necessary to establish the process of saturation (moistening) of the installed bentonite clay, the intensity of its swelling, and the pressures generated by its swelling, which will be transferred to the rock and the containers holding SNF. Most of the tests will be conducted at the Geomechanics Laboratory at the Faculty of Mining, Geology, and Petroleum Engineering, University of Zagreb. The mentioned research should include: investigating the rate of bentonite saturation depending on the preparation method, testing the development of swelling pressure in bentonite, and its impact on the quality of the sealing layer/barrier. However, before conducting these investigations, it is necessary to determine the basic properties and characteristics of bentonite clay, which include the tests presented in Table 1.

Table 1: Determination of Basic Properties and Characteristics of Bentonite Clay.

The determination of permeability of cohesive soils and bentonite blankets using a triaxial cell is shown in Figure 2.

Figure 2: Determination of permeability of bentonite clay.

Additionally, in order to determine the properties of materials for constructing engineering barriers, tests on bentonite clay are conducted at the Laboratory for Geological Materials at the Faculty of Mining, Geology, and Petroleum Engineering, University of Zagreb. These tests include:

• X-ray diffraction (XRD) on original samples (Figure 3a and 3b)
• High-temperature X-ray diffraction (HT-XRD) - in-situ recording of material reaction to heating
• X-ray fluorescence (XRF) - for determining the chemical composition
• Ion chromatography (IC) - for soluble salts determination
• Cation exchange capacity (CEC) using ammonium acetate for determining released ions
• Fourier-transform infrared spectroscopy (FTIR) for determining the chemistry of the fine fraction
• Determination of surface area using methylene blue

• Particle size analysis using laser diffraction

Other basic material tests will include determination of zeta potential, electron microscopy, and differential thermal analysis.

(a)                                                                                                                           (b)

Figure 3: Preparing a sample for X-ray diffraction (XRD) (a) and XRD measurement device (b)

All the tests and their results will be verified using numerical models and simulations conducted in software packages such as Geostudio and Plaxis, where previous modeling has already been performed (Figure 4). The research results will be compared with studies on natural analogs, such as bentonite in nature and their swelling behavior.

(a)                                                                                                         (b)

Figure 4: Numerical models of stress distribution in the cross-section of the spent nuclear fuel repository created in Plaxis software (a) and Geostudio software (b).

The research will define the rate of bentonite saturation in the protective layers of the deep geological repository for HLW or SNF and determine the potential depth of saturation. The relationship between bentonite saturation and the effectiveness of the bentonite barrier in terms of permeability will be determined. Additionally, the relationship between the swelling pressure of bentonite and the structure of the protective layers made of bentonite clay and the bentonite-rock container system will be defined.

The comparison of research results with studies conducted on natural analogs will contribute to the development of a safety study and facilitate communication with stakeholders.

References:

Savage, D., Arthur, R. 2012. Exchangeability of bentonite buffer and backfill materials. STUK. Helsinki.

Uroić, G., Veinović, Ž. & Alexander, W. R. (2022): KBS-3V And Axial Canister Emplacement Of SNF - Comparison Of Disposal Concepts. Proceedings of 13th International Conference of the Croatian Nuclear Society, Zadar, Hrvatska, 2022. str. 115-1

Veinović, Ž., Vučenović, H., Uroić, G. & Rapić, A (2022): Numerical models of the deep geological repository for the spent nuclear fuel // Mathematical methods and terminology in geology 2022. Malvić, Tomislav ; Ivšinović, Josip (ur.). Zagreb: Rudarsko-geološko-naftni fakultet, 2022. str. 21-33

Galla Uroić, mag. ing. min. is PhD student at the Department of Mining and Geotehnical Engineering, Faculty of Mining, Geology, and Petroleum engineering, University of Zagreb. She enrolled in the doctoral program in Applied Geosciences, Mining, and Petroleum Engineering in 2020.

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Author: Associate professor Martin Krkač

Landslides, generally defined as the movement of a mass of rock, debris, or earth down a slope (Cruden, 1991), play an important role in the evolution of a landscape (Crozier, 2010). Landslides include different materials, various types of movements, states of activity and different velocities (Cruden and Varnes, 1996; Hungr et al., 2014). Knowledge of kinematics helps to reveal temporally and spatially variable stresses acting within landslides, their boundary geometries, mechanical properties of materials, external forcing conditions, and characteristics of future landslide movement (Schulz et al., 2017). Slow-moving landslides sometimes accelerate rapidly and fail catastrophically, causing widespread destruction and casualties (Lacroix et al., 2020), so knowledge about kinematics is important for those responsible for managing the risks posed by landslides (Glastonbury and Fell, 2008). Monitoring is one of the most important tools to understand landslide kinematics and dynamics (Angeli et al., 2000). One of the landslides which is monitored by scientists from the Faculty of Mining, Geology and Petroleum Engineering (RGNF) is the Kostanjek landslide, the biggest landslide in the Republic of Croatia. The Kostanjek landslide area is 1 km², which is approximately 1,000 times bigger than the area of an average-sized landslide (730 m²) in the City of Zagreb (Bernat Gazibara et al., 2019). The displaced mass consists of the topmost part of the Sarmatian deposits and Lower and Upper Pannonian deposits. Generally, dominating rocks of the Sarmatian and Pannonian ages, in the area of landslide, are very weak to weak marls. The landslide was triggered due to the excavation of marl, in the open marl pit, which was used for cement production in the nearby cement factory 'Sloboda' (Stanić and Nonveiller, 1996).

Kostanjek landslide monitoring observatory (Figure 1), one of the laboratories of the RGNF, was established in the frame of the Croatian-Japanese SATREPS project 'Risk identification and land-use planning for disaster mitigation of landslides and floods in Croatia' in the period from 2009 to 2014. The objectives of the monitoring system were civil protection of the residents (approx. 300 single-family houses and infrastructure networks are placed on the moving landslide mass), scientific research and education. Protection of residents from the consequences of sliding is done through continuous monitoring of movement and its causes, and through the development of an early warning system, which includes: 1) definition of empirical thresholds for landslide velocities, precipitations and groundwater levels and definition of different emergency levels; 2) development of statistical models for prediction of landslide velocities. The scientific research activity related to the Observatory is carried out in the form of numerous projects, for example, the ongoing PRI-MJER project (Applied landslide research for development of risk mi') financed by the European Regional Development Fund (KK.05.1.1.02.0020) and co-financed by the Environmental Protection and Energy Efficiency Fund (https://pri-mjer.hr/). Education of students within the Observatory is carried out for graduate students of Mining Engineering (Geotechnical Engineering) and graduate students of Geological Engineering (Hydrogeology and Engineering Geology) from the Faculty of Mining, Geology and Petroleum Engineering.

Figure 1 Central measuring station of the Kostanjek landslide with the GNSS antenna in the foreground.

The monitoring system at the Kostanjek landslide consists of multiple sensor networks for observations of (Krkač et al., 2019): (1) external triggers (rain gauge and meteorological station); (2) hydrological properties (pore pressure gauges and water level sensors); (3) movement/activity. All sensors measure in almost real-time, and the measured data is transmitted via the Internet to the application/data server at the RGN faculty. The movement of the Kostanjek landslide is measured with 15 GNSS (Global Navigation Satellite System) sensors. GNSS is a system of satellites and earth stations used for precise positioning on the Earth. Satellites orbit the Earth twice a day in very precisely defined orbits and continuously transmit signals with information about the time of signal transmission and its position at the time of signal transmission. Earth stations consisting of antennas and receivers receive satellite signals and determine the distance of satellites based on the difference in the time of transmission and reception of signals. From the distances between the antenna and a minimum of four satellites and the position of these satellites, the receivers accurately calculate the position on the Earth. GNSS receivers use GPS and GLONASS satellite signals, and the system operates continuously 24 hours a day, in different weather conditions, and does not require optical visibility between measurement sensors (Ghiliani and Wolf, 2012).

The precision of GNSS measurements at the Kostanjek landslide, calculated as the root mean square error on the 24-h post-processing position (at 2σ, 95 % confidence), is 3.2–4.6 mm in  planimetry and 6.1–10.5 mm in altimetry (Krkač et al., 2017). During the monitoring period (2013-2019) all GNSS sensors showed a significant displacement, i.e. the measured displacements were greater than the measurement errors, except for GNSS 01 which is located outside the landslide (see Figure 3). The largest measured horizontal displacement is 65 cm and the largest vertical displacement is +41 cm. Since 2013., a total of eight periods of faster movement have been measured (Figure 2). During the periods of faster movement more than 90% of the total displacement of the Kostanjek landslide occurred. The maximum observed velocity was 4.5 mm/day and it was measured in the first week of April 2013. The highest velocities were measured in the central part of the landslide (Figure 3).

Figure 2 Cumulative horizontal displacements at the Kostanjek landslide, measured with GNSS sensors during. The grey areas represent periods of faster movements (Krkač et al., 2020a).

Figure 3 Spatial distribution of average yearly velocities at the Kostanjek landslide, and vectors of horizontal displacements measured by GNSS sensors (Krkač et al., 2020a).

All periods of faster movement occurred during the periods of high groundwater levels (Figure 4), which occurred after periods of intensive precipitation and snow melting. The data records from water level sensors in the central part of the landslide show groundwater levels (GWL) oscillations up to 8.5 m, between the depths of 19 and 10.5 m, corresponding to the variation of the pore pressures at the sliding surface from 425 to 510 kPa. The cumulative precipitations that caused GWL rise at the central part of the Kostanjek landslide ranged from 21 mm to 180 mm, depending on initial GWL depth, i.e. depending on a season. During the summer months, due to high evapotranspiration and surface runoff, there was no measured increase in groundwater levels that would affect landslide movement. Compared with the average annual precipitation (889 mm) in City of Zagreb, the meteorological conditions during 2013–2014, when the greatest movement occurred, can be considered very wet. In 2013, the total precipitation (rainfall and snow) was 1092 mm, and in 2014 it was 1234 mm. Also, during that period of two years, several extremes occurred. The highest daily precipitation (55.2 mm) was recorded in February 2013 and the maximal monthly precipitation (208 mm) was recorded in September 2014. One of the most significant triggers of landslide movement is an earthquake. The Zagreb earthquake, magnitude 5.5 (March 22, 2020), and the earthquake near Petrinja, magnitude 6.4 (December 29, 2020), resulted in a Kostanjek landslide displacement of a totally 2 cm.

Figure 4 Cumulative horizontal displacements and groundwater level depths measured at the central monitoring station of the Kostanjek landslide and 3-day antecedent precipitations measured at the Zagreb-Grič meteorological station (Krkač et al., 2020b).

Reference:

Angeli M.-G., Pasuto A., Silvano S. (2000): A critical review of landslide monitoring experiences. Engineering Geology, 55, 3, 133-147. https://doi.org/10.1016/S0013-7952 (99)00122-2.

Bernat Gazibara S., Krkač M., Mihalić Arbanas S. (2019): Verification of historical landslide inventory maps for the Podsljeme area in the City of Zagreb using LiDAR-based landslide inventory. The Mining-Geology-Petroleum Engineering Bulletin, 34, 1, 45-58. DOI: 10.17794/rgn.2019.1.5

Crozier M.J. (2010): Landslide geomorphology: An argument for recognition, with examples from New Zealand. Geomorphology, 120, 3-15. https://doi.org/10.1016/j.geomorph.2009.09.010

Cruden D.M. (1991): A simple definition of a landslide. Bulletin of the International Association of Engineering Geology, 43, 27-29. doi:10.1007/BF02590167

Cruden D.M., Varnes D.J. (1996): Landslide types and processes. In: Turner, A.K., Schuster, R.L. (eds.): Landslides, Investigation and Mitigation. Transportation Research Board, Special Report 247, Washington D.C., USA, 36–75, 673 p.

Ghiliani C.D., Wolf P.R. (2012): Elementary Surveying: An Introduction to Geomatics (Thirteenth Edition). Pearson Education, Inc., New Jersey. 984 p.

Hungr O., Leroueil S., Picarelli L. (2014): The Varnes classification of landslide types, an update. Landslides, 11, 2, 167–194. https://doi.org/10.1007/s10346-013-0436-y

Krkač M., Špoljarić D., Bernat S., Mihalić Arbanas S. (2017): Method for prediction of landslide movements based on random forests. Landslides, 14, 3, 947–960. https://doi.org/10.1007/s10346-016-0761-z

Krkač M., Bernat Gazibara, S., Sečanj, M., Arbanas, Ž., Mihalić Arbanas, S. (2019): Continuous monitoring of the Kostanjek landslide. Proceedings of the 4th Regional Symposium on Landslides in the Adriatic-Balkan Region. Sarajevo: Geotechnical Society of Bosnia and Herzegovina, 43-48

Krkač M., Bernat Gazibara S., Sečanj M., Sinčić M., Mihalić Arbanas S. (2020a): Kinematic model of the slow-moving Kostanjek landslide in Zagreb, Croatia. Rudarsko-geološko-naftni zbornik, 36/2, 59-68. doi:10.17794/rgn.2021.2.6

Krkač M., Bernat Gazibara S., Arbanas Ž., Sečanj M., Mihalić Arbanas S. (2020b): A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides, 2515–2531. https://doi.org/10.1007/ s10346-020-01476-6

Lacroix P., Handwerger A.L., Grégory G. (2020): Life and death of slow-moving landslides. Nature Reviews Earth & Environment, 1, 404–419. https://doi.org/10.1038/s43017-020-0072-8

Schulz W.H., Coe J.A., Ricci P.P., Smoczyk G.M., Shurtleff B.L., Panosky J. (2017): Landslide kinematics and their potential controls from hourly to decadal timescales: Insights from integrating ground-based InSAR measurements with structural maps and long-term monitoring data. Geomorphology, 285, 121-136. https://doi.org/10.1016/j.geomorph.2017.02.011.

Stanić B., Nonveiller E. (1996): The Kostanjek landslide in Zagreb. Engineering Geology, 42, 269-283.

Martin Krkač is Assistant Professor of Engineering Geology at the University of Zagreb, Faculty of Mining, Geology and Petroleum Engineering. His research interests are landslides, especially landslide monitoring and remote sensing. He is a member Croatian Landslide Group, and he is the author of more than 70 scientific works in international journals, books and conference proceedings. He participated in more than 40 engineering geology / geotechnical studies in the field of landslide research and stabilization.

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Autor: Ana Brcković, mag. geol.

Ambiguity of interpretation is a term that is characteristic for most of geophysical data andit showcases the impossibility of defining a single underground model based solely on measured data. This phenomenon is caused by the heterogeneity of the observed surrounding, or the fact that the properties of the rocks change in all directions, although these properties are mostly correlated (Klose, 2006). Ambiguity is greatly reduced by the application of different geophysical research methods, but different methods imply that the data collected are differently structured, very often at different intervals, and depending on the method in different dimensions also. Such examples are seismic and well data. Seismic data are most often collected within a predefined 3D cube (Figure 1) and geophysical parameters of the rocks are obtained using the velocities of waves propagating through the subsurface.

Figure 1 Transverse profiles within the 3D seismic cube (X and Y axes represent geographical coordinates, Z axis shows the amplitude of the seismic wave at twice the travel time)

On the other hand, well data is a set of 1D data where, depending on the probe used, geophysical parameters are observed in relation to depth (Figure 2).

Figure 2 Representation of well data from one well (blue curve shows the values ​​of spontaneous potential on the basis of which permeable and impermeable rocks can be separated; green curve shows the natural radioactivity of drilled deposits, pink shows the propagation of sound waves, and red curve shows densities)

Linking of the data on different scales is achieved by making synthetic seismograms, which are used for modeling the wave through the drilled layers based on accoustic logs and density los.

In practice, not all the parameters mentioned are measured along the entire borehole channel, but only at interesting intervals where the reservoir rocks that may contain hydrocarbons are expected (Figure 2).

The collected measurement data must then be interpreted, the useful information needed to make a model of the surface must be read. The information we would like to get is diverse, ranging from different types of rocks (lithology), fractured zones (faults) and pores (porosity), all the way to different fluids content.

Figure 3 Calculated porosity values (blue) and average values (orange). Low values indicate more compact rocks with fewer pores and cracks, especially visible at the deepest part of the well (right). Higher values indicate rocks with more pore space in which different fluids can be found.

The implementation of machine learning methods can facilitate and improve the interpretation of measured data. With the help of certain algorithms, it is possible to more efficiently detect regularities in seemingly irregular data and group and characterize them based on that.

The self-organizing map (SOM) algorithm is a type of artificial neural network based on unsupervised data learning, which means that it does not use a test set to find similarities in the data, but analyzes the entire set at once (Kohonen, 1981). Different features can be recognized by displaying multidimensional data in the form of the clusters on a 2D map (Figure 4).

Figure 4 SOM structure (the network consists of input data and a layer of neurons with  weighting factors based on whose distances the data are arranged into categories in the plane)

It is necessary to add labels to the organized clusters, and based on the output it is necessary to see whether the parameters within the network can be further adjusted for better resolution (Figure 5).

Figure 5 Seismic data dimensionaly reduced and clustered using SOM on the example of two seismic attributes (light areas show clusters with similar characteristics, and the darkest parts are the boundaries between clusters)

Based on the clustered data that are representing areas with similar characteristics in the subsurface, facies are classified in the researched area and geophysical parameters such as porosity or velocity of seismic waves are obtained at intervals with insufficient or incomplete well data (Junno, 2019).

Figure 6 Well data clustering by SOM (six categories of grouping based on 5 log curves from one well were assumed)

Reference:

Junno, N., Koivisto, E., Kukkonen, I., Malehmir, A., Montonen, M. (2019): Predicting Missing Seismic Velocity Values Using Self-Organizing Maps to Aid the Interpretation of Seismic Reflection Data from the Kevitsa Ni-Cu-PGE Deposit in Northern Finland; Minerals, pp 16

Kohonen, T. (1981): Automatic formation of topological maps of patterns in a self-organizing system. In Proceedings of the Second Scandinavian Conference on Image Analysis, Helsinki, Finland, 15–17; Springer: New York, NY, USA, 214–220.

Klose, C. D. (2006): Self-organizing maps for geoscientific data analysis: geological interpretation of multidimensional geophysical data. Computational Geosciences, 10, 265–277.

Taner, M. T., J. D. Walls, M. Smith, G. Taylor, M. B. Carr, and D. Dumas (2001): Reservoir characterization by calibration of self-organized map clusters. 71st Annual International Meeting, SEG, Expanded Abstracts, 20, 1522–1525.

Ana Brcković, mag. geol. je asistentica na Zavodu za geofizička istraživanja i rudarska mjerenja na Rudarsko-geološko-naftnom fakultetu Sveučilišta u Zagrebu.

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Author: Ivica Pavičić, PhD., Postdoctoral researcher

One of the most common challenges of geological exploration is reconstructing underground geological settings based on surface data. When conducting such research, collecting as much data as possible from the surface is important to reconstruct the subsurface geological settings more realistic and precise. This is often the case when investigating rock discontinuities (bedding, fractures, etc.). The fracture orientation data can be collected in different ways: from the drill hole cores, the optical and acoustic televiewer survey, drill hole geophysics, and drill hole camera surveying. The best way to observe and measure fracture system parameters (orientation, density intensity, cross-cutting relations etc.) is in open pits, outcrops, and road cuts. When it is impossible to obtain the fracture data from the terrain, the only alternative option is to obtain them underground by drilling.

The investigated area was located on the E side of Pelješac Peninsula, not far from Osobjava Village. Since terrain in our research area was impassable and had vegetation cover, and other drill hole analyses (geophysical, optical, and acoustic televiewer logging methods) were relatively expensive for the investor, especially in the initial, “pre-quarry” phase of the research, improvisation was needed to find a relatively affordable way to measure the discontinuities orientation in the drill holes. Given that the discontinuity orientations in the subsurface are one of the basic data, especially in the preliminary exploration for many further analyses, the team of scientists from the RGN faculty (Ph.D. Ivica Pavičić and Prof. Ivo Galić, Ph.D.) and Perica Vukojević, M.Sc., director of Hidro-geo projekt d.o.o. have developed an innovative and relatively affordable way of discontinuity orientation survey in boreholes using a borehole camera. The results of these investigations were published in 2020 in the scientific journal Applied Sciences: Fracture System and Rock-Mass Characterization by Borehole Camera Surveying: Application in Dimension Stone Investigations in Geologically Complex Structures.

Figure 1. Graphical abstract – photos of the borehole and from them derived orientation data for one of the set of discontinuities.

Based on the drill hole position and the drill hole core logs, six drill holes were chosen for survey by the drill hole camera. We used a drill hole camera system with spatial orientation of the bottom and side camera (Figure 1-3, Figure 5). The borehole camera was transported by terrain vehicle to the drill hole location (Figure 5). The camera was then centered above the drill hole by the tripod, and the depth was set to zero m. Camera orientation was checked (on each drill hole) while the camera was still at the surface.

A fracture surface is a three-dimensional plane in space with two main characteristics: dip direction and dip angle. Each fracture is measured manually in four points (Figure 2) by down and side cameras to define fractures in space adequately. First, from the bottom camera we defined the strike and azimuth of the fracture, which is then checked and improved by the side camera (Fig 4A, C, D, E, F). Dip angles were also checked on the drill hole cores for higher measurement precision. The whole surveying was recorded so later interpretation, reinterpretation, and validation could be made.

Figure 2. A) Relationship between the cross-section of the borehole and the rock discontinuity B) Borehole camera; C) Vertical image of the borehole, D, E, F, G)  Side shot of the borehole and crack trace (Pavičić et al., 2020).

In conditions where it is impossible to collect enough data from the surface, borehole camera surveys and derived interpretations and analyses (Figure 4) provide a solid basis for making decisions for further investment plans and the dimension stone exploitation type.

Through the borehole camera survey, it is possible to visualize rock mass in the borehole and measure the characteristics of fractures and fracture systems. The achieved results and conclusions are as follows: (1) Borehole camera survey enables fast, efficient, and relatively affordable geological research of fracture systems and the state of the rock mass; (2) statistical parameters of fracture distribution, fracture orientation, fracture set delineation, P10, aperture, Volumetric Joint Count (Jv) (Figures 3 and 4)...

Table 1. Objectives and possibilities of the method of recording discontinuities with a borehole camera.

Figure 3. Diagram showing the block size distribution in the rock mass and types of blocks in the rock mass (Palmstrom, 2001)

The presented methodology is an innovative, relatively fast, and low-cost method that gives solid input on the state of the investigated rock mass, bedding orientation, degree of jointing, and preliminary block size estimation, which is very important for decision making in the initial phase of quarry investment, since these factors control the potential of the location for dimension stone deposit and type of excavation. The methodology is also applicable in the hydrogeological (fractured aquifers), geotechnical, civil engineering, and engineering geology research (rockfalls, construction of roads, viaducts, railways, bridges, tunnels, etc.), where knowledge about fracture systems in the rock mass is crucial for further works.

Figure 4. A) Sets of discontinuities obtained by borehole camera measurements and correlation with the position within the geological structure to define the parameters of exploitation of a potential quarry on Pelješac (Pavičić et al., 2020)

Figure 5. Field recordings of the conducted measurements.

References:

1. 1. Palmström, A. Measurement and characterizations of rock mass jointing. In In-Situ Characterization of Rocks; CRC Press/Balkema: Leiden, The Netherlands, 2001; pp. 1–40.
   2. Pavičić, Ivica, Ivo Galić, Mišo Kucelj, and Ivan Dragičević. 2021. "Fracture System and Rock-Mass Characterization by Borehole Camera Surveying: Application in Dimension Stone Investigations in Geologically Complex Structures" Applied Sciences 11, no. 2: 764. https://doi.org/10.3390/app11020764

Ivica Pavičić, Ph.D. is a Postdoctoral Researcher at the Department of Geology and Geological Engineering at the Faculty of Mining, Geology and Petroleum, University of Zagreb. He received his Ph.D. on July 12, 2018. with a doctoral thesis entitled: Origin, spatial distribution, and quantification of porosity in Upper Triassic dolomites in Žumberak Mts.

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Author: Katarina Žbulj, PhD

Through the process of oil and gas production, gathering, treatment and transportation, most of the process equipment and pipelines are made of carbon steel. In most cases, depending on the type of the reservoir, the production of hydrocarbons involves the production of a certain amount of brine. In addition to brine, the produced fluid may contain impurities such as sand, additives that were used during the production process (biocides, scale inhibitors, demulsifiers, etc.), but also dissolved gas, carbon dioxide (CO₂) and hydrogen sulfide (H₂S) which, dissolved in water (brine) cause corrosion and damage the equipment. In general, carbon steel is susceptible to corrosion. Sastri (2011) defines corrosion as the destruction of a material caused by the aggressive environment to which the material is exposed. The consequences of corrosion in the petroleum industry could affect the environment (potential cause of fluid spill) but also, due to equipment damage, and in addition to the impact on the environment, they also have a great economic impact. In systems where water is present, electrochemical corrosion will occur. Flowlines are the most sensitive part of an oil and gas transportation system. Fluid, transported in flowlines, is not yet treated, and it contains brine and impurities, such as carbon dioxide. Flowlines, gathering pipelines and waterlines are mostly made of carbon steel, and that makes them susceptible to CO₂ corrosion. Some of corrosion types, that can be found in petroleum industry, can be seen in Figure 1.

Figure 1 Different types of corrosion: (a) uniform corrosion, (b) pitting corrosion, (c) erosion corrosion (Bhardwaj, 2020.)

One of the ways to slow down the corrosion process is the use of corrosion inhibitors, which are mostly organic inhibitors. Since there are some limitations in the usage of those organic conventional corrosion inhibitors due to their toxicity, plant extracts, among other things, have been studied as so-called green corrosion inhibitors. Currently, the most researched green corrosion inhibitors are plant extracts. In order to examine their effectiveness as corrosion inhibitors, extracts of some wild plants that can be found in Croatia were tested. For the preliminary research, ten commercially available plant extracts were selected as potential carbon steel corrosion inhibitors in a simulated brine solution saturated with CO₂. The aim of the conducted laboratory research was to select certain plants based on their corrosion inhibition efficiency and to examine the possibility of their application as green corrosion inhibitors in the petroleum industry. After preliminary research, lady’s mantle extract (Figure 2a) and dandelion root extract (Figure 2b) showed inhibitor efficiency higher than 90%. Therefore, those two extracts were further researched.

(a)                                                                                                         (b)

Figure 2 Examples of (a) lady’s mantle and (b) dandelion root (Plantea, 2021.a, Plantea, 2021.b)

Efficiency of selected plant extracts as corrosion inhibitors was determined throughout electrochemical measurements (polarization measurements with Tafel extrapolation, electrochemical impedance spectroscopy), in brine with saturated CO₂ in static and in flow conditions (Figure 3 and Figure 4).

Figure 3 Comparison of Lady’s mantle extract and dandelion root extract efficiency determined from polarization measurements with Tafel extrapolation (Žbulj, 2021.)

Figure 4 Comparison of Lady’s mantle extract and dandelion root extract efficiency determined from electrochemical impedance spectroscopy (Žbulj, 2021.)

Both plant extracts showed to be effective corrosion inhibitors in both static and flow conditions. In static conditions, in comparison with lady’s mantle extract (IE= 92.68%), dandelion root extract efficiency of 98.37% was achieved. However, under flow conditions, the efficiency of dandelion root extract was not higher than 90%, while the efficiency achieved with lady’s mantle extract was almost equal to the efficiency in static conditions (IE= 91.59%). Both extracts showed to be a mixed type of corrosion inhibitor.

Reference:

Bhardwaj, A., 2020., Fundamentals of Corrosion and Corrosion Control in Oil and Gas Sectors, U: SAJI, V.S., UMOREN, S.A., ur., Corrosion Inhibitors in the Oil and Gas Industry, Weinheim, Germany, Wiley-VCH Verlag GmbH & Co. KGaA, pp 41-76

Plantea, 2021.a, Vrkuta, URL: https://www.plantea.com.hr/vrkuta/ (24.03.2021.)

Plantea, 2021.b, Maslačak, URL: https://www.plantea.com.hr/maslacak/ (24.03.2021.)

Sastri, V. S., 2011., Green Corrosion Inhibitors: Theory and Practice, Hoboken, New Jersey, John Wiley & Sons, Inc.

Žbulj, K., 2021., Extracts of selected plants as steel corrosion inhibitors in hydrocarbon production and transportation systems, doctoral thesis, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Zagreb, 07.12.2021.

Katarina Žbulj, mag. ing. petrol. is a postdoctoral researcher at the Department of Petroleum and gas engineering and Energy, Faculty of Mining, Geology and Petroleum Engineering. She successfully defended her doctoral dissertation on December 7^th, 2021 entitled Extracts of selected plants as steel corrosion inhibitors in hydrocarbon production and transportation systems.

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Author: Ivor Perković, mag. geol.

Cretaceous palaeosols and terra rossa soils from the succession of the western-Istrian anticline formed in contrasting redox environment which makes them ideal for mutual comparison, as the behavior of trace elements and lanthanides is highly dependent of the redox conditions.

Slika 1. 1) geological map of Istria based on the division onto large-scale megasequences; 1 – Bathonian to lower Kimmeridgian 2 – upper Tithonian to early/late Aptian, 3 – upper Albian to upper Cenommanian/upper Santonian, 4 – foraminiferal beds (lower Eocene), 5 – fysch (middle to upper Eocene), 6 – Quaternary, 7 – normal geological boundary, 8 - erosional geological boundary, 9 - normal fault, 10 - reverse fault,11 – Western Istrian anticline, 12 – Savudrija-Buzet anticline, 13 – sampling sites of Cretaceous palaeosols, 14 – sampling sites of terra rossa.; 2) terra rossa profile in Kanfanar quarry, 3) Cretaceous palaeosol profile in Kanfanar quarry

Terra rossa soils from this area formed through pedogenesis of materials such as loess, tephra, insoluble residue, flysch and other non-carbonate material (Durn et al., 2007) in an oxidizing environment, which their red color clearly indicates. The red color in terra rossa is a consequence of rubification, the preferential formation of hematite over goethite (Boero and Schwertmann, 1989).

The Cretaceous palaeosols formed in a reducing marsh environment during the emersion which enveloped the Adriatic carbonate platform in the area od today’s Istria 120 million years ago, and it lasted between 19 to 11 million years. Their currently considered mode of formation is the deposition of volcanic material in shallow water bodies in a marshy environment, at that time covering certain areas of today’s Istria. In those marshes this material was pedogenetically altered into grey paleosols that we can find today in quarries of Istrian yellow and roadcuts in the southwestern Istria.

Five samples of both terra rossas and Cretaceous palaeosols was selected for XRF, XRD, ICP-MS, ICP-OES and Tessier sequential extraction analysis. The obtained results displayed the differences in the behavior in major and trace elements.

Cretaceous palaeosols are clay-rich, contain pyrite and have a complete absence of iron oxides when compared with terra rossa soils, which besides clay minerals also contain other silicate minerals such as quartz and feldspars together with iron oxides. Mineralogical composition is clearly mirrored in the concentrations of major oxides.

Figure 2. 1 – mineralogical composition of terra rossa (TR) and Cretaceous palaeosol (CP), 2) major oxide values (upper graph) and trace element values (lower graph) in terra rossa and Cretaceous palaeosols, 1 – individual terra rossa sample values, 2 – mean values for terra rossa, 3 – individual sample values for Cretaceous palaeosols, 4 – mean values for Cretaceous palaeosols; 3) lanthanide values in terra rossa (left graph) and Cretaceous palaeosols (right graph) in different sequential extraction steps, 1 – adsorbed fraction, 2 – carbonate-bound fraction, 3 – fraction bound to iron oxides, 4 – fraction bound to organic matter or pyrite, 5 – fraction bound in silicates, 6 – total lanthanide concentrations

The most prominent differences were those in the behavior of trace elements and lanthanides. In comparison to terra rossa soils, Cretaceous palaeosols are enriched in uranium, antimony, vanadium and molybdenum. This additionally confirms their formation in reducing redox environment, as those elements are stable in such conditions as insoluble complexes which can be retained and enriched in the sediment.

The Terra rossa has no significant enrichment in trace elements when compared to Cretaceous palaeosols, but in them characteristic trends and enrichments in lanthanides were observed which cleary indicate the formation in an oxidizing environment. Ferromanganese oxides and organic matter are commonly a sink for lanthanides in soils (Davranche et al., 2011; Laveuf et al., 2008, 2012a), which was also true for studied terra rossas. Besides the general lanthanide enrichment the enrichment of middle lanthanides (Sm, Eu and Gd) and the positive cerium anomaly was also observed. This is commonly observed as a consequence of oxidation and absorption processes related to manganese oxides (Coelho and Vidal-Torrado, 2000; Laveuf et al., 2012b; Ohta and Kawabe, 2001) and organic matter (Davranche et al., 2011).

Cretaceous paleosols are completely devoid of organic matter and iron oxides, which is a consequence of their formation in a reducing soil environment, which ultimately resulted in much smaller values of lanthanides in Cretaceous paleosols. The behavior of lanthanides are not only a proxy for redox conditions but also for general environment in which Cretaceous paleosols have formed. The enrichment of heavy lanthanides (Tb, Dy, Ho, Er, Tm, Yb and Lu) can be connected with the formation in a brackish to marine marsh environment. The elevated concentrations of other ionic species in seawater impairs the adsorption of large light lanthanide (La, Ce, Pr and Nd) ions, while the heavy lanthanides are not as affected allowing them to be more easily retained and adsorbed onto clay minerals. This is additionally confirmed with Sr/Ba values higher than 0.2, which indicates a formation in a brackish environment (Wei and Algeo, 2020).

Reference:

Boero, V. and Schwertmann, U. (1989), “Iron oxide mineralogy of terra rossa and its genetic implications”, Geoderma, Vol. 44 No. 4, available at:https://doi.org/10.1016/0016-7061(89)90039-6.

Coelho, M.R. and Vidal-Torrado, P. (2000), “Cerium (Ce) in some nodular ferricretes developed in soils of the adamantina formation”, Scientia Agricola, Vol. 57 No. 2, available at: https://doi.org/10.1590/S0103-90162000000200021.

Davranche, M., Grybos, M., Gruau, G., Pédrot, M., Dia, A. and Marsac, R. (2011), “Rare earth element patterns: A tool for identifying trace metal sources during wetland soil reduction”, Chemical Geology, Vol. 284 No. 1–2, available at:https://doi.org/10.1016/j.chemgeo.2011.02.014.

Durn, G., Aljinović, D., Crnjaković, M. and Lugović, B. (2007), “Heavy and light mineral fractions indicate polygenesis of extensive terra rossa soils in Istria, Croatia”, in Mange, M. and Wright, D. (Eds.), Heavy Minerals in Use. Developments in Sedimentology, Vol. 58, Elsevier, pp. 701–737.

Laveuf, C., Cornu, S., Guilherme, L.R.G., Guerin, A. and Juillot, F. (2012a), “The impact of redox conditions on the rare earth element signature of redoximorphic features in a soil sequence developed from limestone”, Geoderma, Elsevier B.V., Vol. 170, pp. 25–38.

Laveuf, C., Cornu, S., Guilherme, L.R.G., Guerin, A. and Juillot, F. (2012b), “The impact of redox conditions on the rare earth element signature of redoximorphic features in a soil sequence developed from limestone”, Geoderma, Elsevier B.V., Vol. 170, pp. 25–38.

Laveuf, C., Cornu, S. and Juillot, F. (2008), “Rare earth elements as tracers of pedogenetic processes”, Comptes Rendus - Geoscience, Vol. 340 No. 8, pp. 523–532.

Ohta, A. and Kawabe, I. (2001), “REE(III) adsorption onto Mn dioxide (δ-Mn02) and Fe oxyhydroxide: Ce(III) oxidation by δ-MnO2”, Geochimica et Cosmochimica Acta, Vol. 65 No. 5, pp. 695–703.

Wei, W. and Algeo, T.J. (2020), “Elemental proxies for paleosalinity analysis of ancient shales and mudrocks”, Geochimica et Cosmochimica Acta, Elsevier Ltd, Vol. 287, pp. 341–366.

Ivor Perković, mag. ing. geol. je doktorand na Zavodu za mineralogiju, petrologiju i mineralne sirovine na Rudarsko-geološko-naftnom fakultetu Sveučilišta u Zagrebu. Diplomirao je 13.7.2020. godine s diplomskim radom pod nazivom Hidrotermalne alteracije rudnog tijela Vršnik u bakarnom porfirnom ležištu Bučim, Republika Sjeverna Makedonija. Trenutno je zaposlen kao mlađi istraživač na HRZZ projektu WIANLab, u sklopu kojeg proučava boksite i druge terestričke materijale taložene u sklopu donjokimeridžke do gornjetitonske emerzije i gornjecenomanske/gornjesantnoske do donjoeocenske emerzije u sklopu slijeda zapadnoistarske antiklinale.

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Author: Šime Bilić, Ph.D.

In the Banovina area, peridotites and pyroxenites are rocks formed as parts of the former earth's upper mantle, representing material that comes from deep parts of the earth (up to 100 km), which cannot be reached by drilling. Farther more, the appearance of such rocks is evidence of the existence of the former oceanic realm, so their exploration is extremely important for all those involved in petrology, mapping, regional and structural geology. With the help of modern, but also classical analytical and petrological research methods, very interesting information related to the genesis of these rocks can be obtained, such as: how old are these rocks; what temperatures and pressures prevailed during their formation; from what depths do they come; how these rocks came to the surface, etc. Based on the results of detailed field research, petrographic and geochemical analysis of the studied rocks, it was found that in the Banovina area there are structural and chemical characteristics two different types of peridotites that also geographically belong to different localities and can be classified into two belt, northern and southern belt. Within the northern belt (N-belt), serpentinite breccias and serpentinized spinel lherzolites predominate, which are recognizable in the field by the structure characteristic of ophiolite mélange. The geochemical characteristics of N-belt peridotites indicate its origin from the suboceanic mantle that underwent melting processes in the mid-ocean ridge area. The southern belt (S-belt) contains spinel lherzolites, dunites, and pyroxenites that vary within spatially very confined spaces. The geochemical characteristics of S-belt peridotites indicate subcontinental origin and were most likely formed in the initial rift phase during which they ascended to the upper crust as parts of the continental mantle and at the same time underwent a relatively low degree of melting. Pyroxenites located exclusively within the S-belt show completely different petrographic and geochemical characteristics. They were most likely formed separately, by crystallization from a melt of unknown origin that was circulating through the mantle. Petrological characteristics of peridotite and pyroxenite from Banovina show a very good correlation with similar rocks found in Bosnia and Herzegovina, so they can be declared as a part of the Central Dinaric Ophiolite Belt (CDOB; Lugović et al, 1991), and a new division of peridotite from our study can therefore be translated to CDOB. Petrological research in the Banovina has shown that the Peridotites and Pyroxenites from the Banovina record three different phases of ocean evolution: a) the early stage of the initial rift and ocean opening (S-belt peridotites); b) later phase of already developed ocean (N-belt peridotites) and c) phase of ocean closure which is evident from the geologic structures and metamorphic rocks in contact with peridotites.

Figure 1 Outcrops of mantle rocks, Zrinska gora.

Figure 2 Talc minerals (vivid colors) fill cracks in gray serpentinite (seen through a petrographic microscope).

Reference:

Lugović, B., Altherr R., Raczek I., Hofmann A. W., Majer V. (1991): Geochemistry of peridotites and mafic igneous rocks from the Central Dinaric Ophiolite Belt, Yugoslavia, Contributions to Mineralogy and Petrology, 106(2), 201–216

Šime Bilić, dr. sc. is a postdoctoral fellow at the Department of Mineralogy, Petrology and Mineral Resources at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. He successfully defended his doctoral dissertation on 23 July 2021 at 12:00 entitled Petrogenesis of Peridotite and Pyroxenite in the Banovina area, Croatia.

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Author: David Rukavina, PhD mag.geol., mag.ing.min.

The aim of the research is to present an estimate of the geological storage potential of carbon dioxide (CO₂) based on the tectonostratigraphic interpretation of the Lower and Middle Miocene rocks within the eastern part of the Drava Depression. The Lower and Middle Miocene rocks are important regional reservoir rocks, as evidenced by the large number of Croatian oil and gas fields located in them (Velić, 2007). Such reservoir rocks can also be used for the purpose of geological storage of CO₂, one of the most important methods of reducing human greenhouse gas emissions (Figure 1). Nevertheless, previous research of the same rocks in the underground is very limited and their regional correlation and research is lacking (Zečević et al., 2010).

Figure 1. Scheme of the geological storage of carbon dioxide options.

The reason for this is that these rocks are characterized by complex geological setting as a consequence of their formation within the continental rift system and subsequent tectonic processes. The Drava Depression is one of a dozen depressions i.e., basins that make up the Pannonian Basin System (PBS) (Figure 2). The formation of PBS is closely related to the extensional movements that occupied this area in the Miocene (Tari et al., 1992; Fodor et al., 1998; Horváth et al., 2006).

Figure 2. Neogene basins within the Pannonian Basin System (PBS) ((Csontos and Nagymarosy, 1998; Haas, 2012). The Miocene nappe belt delineates the area affected by “slab pull” and/or “subduction rollback” processes, causing extension within the PBS and the formation of Miocene sedimentation basins.

In continental or passive rift systems, extension is caused by the direct action of opposing forces in the lithosphere (Turcotte and Emerman, 1983). Such an extension is well documented in PBS (Figure 2), as a result of subduction processes, i.e., “slab pull” and/or “subduction rollback” (Royden and Burchfiel, 1989; Royden, 1993). Extension in the continental rift system is often associated with thinning of the crust and faulting along low angle detachment faults (Buck, 1988; Hodges et al., 1989; Tari et al., 1992). The tectonic history of continental rift basins is represented by a syn-rift phase, and consists of episodic tectonic movements, lateral rotation of basins around the axis and spatial migration of depocenters, making them more complex than other types of basins formed by stable tectonic subsidence (Wu et al., 2019).

Assessment of the geological storage potential of CO₂ in such conditions involves the use and interpretation of seismic and well data. For this purpose, faults, sequence boundaries, seismofacies distribution, seismic attributes, lithofacies and content of planktonic foraminifera on rock samples were analyzed. Rock formation porosity values were then estimated based on well log measurements, together with temperature, pressure and density in reservoir conditions maps construction in order to calculate the theoretical capacity of geological storage of CO₂. Tectonostratigraphic interpretation enabled the spatial correlation and mapping of Lower and Middle Miocene rocks based on the genetic association of syn-rift infill with fault activity.

The syn-rift phase is characterized by multiphase tectonic activity controlled by large low angle faults i.e., extensional detachment. In their hanging wall syn-rift structures such as half-graben, graben, sag, and supradetachment basin were formed along with a number of normal faults (Figure 3). Extensional movements are predisposed to reactivated inherited structures. Interpreted extensional detachments can be spatially connected into to the system of the main normal fault of the eastern part of the Drava Depression, which bounded the entire depositional area during the syn-rift.

Figure 3. Maps of interpreted tectonic units: a) early rift phase and b) late rift phase.

Based on petrographic-sedimentological analysis, eight lithofacies were defined. These lithofacies are grouped into two associations, one representing deposits associated with predominantly continental environments and the other associated with marine environments. Within these lithofacies associations alluvial fan, fan deltas, transitional, fault aprons and deep-sea environments were interpreted (Figure 4).

Figure 4. Distribution of the depositional environments of interpreted sequences: a) CV-3, M and S; b) D-1 and MP-1; c) D-2 and MP-2.

Based on the interpretation of seismic data, rift tectonostratigraphic sequences were mapped. The 1st order sequence represents the entire syn-rift infill, and it can be divided into higher order sequences associated with the main fault structures. With respect to tectonic and sedimentary conditions, the defined 2nd order sequences can be divided between spatially restricted sequences associated with continental deposition conditions, formed during lower magnitude of extension, and sequences of wider spread associated with marine conditions, formed during higher magnitude of extension (Figure 4). In the tectonostratigraphic sense, the 2nd order sequences formed during the lower magnitude of extension represent the early syn-rift phase, and stratigraphically correspond to the Early Miocene. The 2nd order sequences formed during the higher magnitude of the extension represent the late syn-rift phase, and stratigraphically correspond to the Middle Miocene, more precisely the Badenian (Figure 5). 2nd order sequences can be further divided into 3rd order sequences corresponding to higher order tectonic events and are the result of local rift activity migration.

Figure 5. Syn-rift architecture of basins is illustrated on a generalized profile along the eastern part of the Drava Depression.

For the purpose of applying the interpreted tectonostratigraphic sequences for the geological storage capacity of CO₂ estimation, the following have been constructed:

• Pressure maps at medium depth of interpreted sequences
• Temperature maps at medium depth of interpreted sequences
• CO₂ density maps at medium depth of interpreted sequences
• Facies models of interpreted sequences
• Petrophysical models of porosity distribution of interpreted sequences
• Models of geological storage capacity of CO₂ interpreted sequences.

Storage capacity (Q) was calculated according to the expression (Chadwick et al., 2008) for deep saline aquifers (Figure 1):

 Q (kg)=A*D*∅ * ρCO₂ * h_st

where  A is the area of the aquifer (m²), D is the total thickness of the reservoir rocks (m), ∅ effective porosity, h_st  effective storage coefficient and  pCO₂ density (kgm^-3) of pure CO₂ in reservoir conditions.

In sin-rift infill deposits, reservoir rocks can be defined within the early sin-rift phase and the late sin-rift phase. They are within the early syn-rift phase i.e., the Early Miocene presented with lithofacies within alluvial fan and fan delta sediments in form of channel features and restricted sandy bodies. Prospective Middle Miocene reservoirs in the late syn-rift phase deposits are represented by lithofacies within coastal environments, fan deltas, and debit deposits of fault slopes. Data from wells as well as the assessment of porosity by the multimineral inverse method of logging measurements indicate that these rocks are characterized by low porosity, mostly up to about 10% (Figure 6).

Figure 6. Examples of the projection of three-dimensional facies, porosity and capacity models of the 3rd order sequences CV-2 and CV-3. Compare the distribution of capacity values with the interpreted deposition environments for the sequence CV-3 in Figure 4a.

The research established a methodology for regional assessment of the geological storage potential of CO₂ in syn-rift rocks within the continental (passive) rift system. Due to the complexity of the sedimentary infill, as well as the structural setting of the syn-rift basin, a variety of well data and reliable seismic data are required, as well as knowledge of the characteristics of the lithological composition. The methodological approach includes:

• Interpretation of seismostratigraphic surfaces that represent rift tectonostratigraphic boundaries that can be related to the phases of activity of marginal normal faults.
• Mapping of seismofacies, analysis of lithofacies and analysis of paleostructural relationships within the interpreted sequence enables interpretation of environmental distribution, location of potential reservoir rocks and construction of facies model.
• Estimation of the porosity by the multimineral inverse method on well data, which is required for the construction of a petrophysical model.
• Combining the facies and petrophysical model together with the calculated values of CO₂ density in reservoir conditions, gives results based on which it is possible to map or model the CO₂ specific storage capacity in terms of showing changes in this property in space within each analyzed tectonostratigraphic sequence (Figure 6).

These maps or models can be used to guide exploration, or as a basis for investment decisions in the exploration of this new resource in the deep underground.

Reference:

Buck, W.R., 1988. Flexural Rotation of Normal Faults. Tectonics 7, 959–973. https://doi.org/10.1029/TC007i005p00959

Csontos, L., Nagymarosy, A., 1998. The Mid-Hungarian line: A zone of repeated tectonic inversions. Tectonophysics 297, 51–71. https://doi.org/10.1016/S0040-1951(98)00163-2

Haas, J., 2012. Geology of Hungary. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1016/s1574-8715(07)00020-6

Hodges, K. V., McKenna, L.W., Stock, J., Knapp, J., Page, L., Sternlof, K., Silverberg, D., Wüst, G., Walker, J.D., 1989. Evolution of extensional basins and basin and range topography west of Death Valley, California. Tectonics 8, 453–467. https://doi.org/10.1029/TC008i003p00453

Royden, L., Burchfiel, B.C., 1989. Are systematic variations in thrust belt style related to plate boundary processes? (The western Alps versus the Carpatians) 8, 51–61.

Royden, L.H., 1993. Evolution of retrating subduction boundaries formed during continental collision 12, 629–638.

Tari, G., Horváth, F., Rumpler, J., 1992. Styles of extension in the Pannonian Basin. Tectonophysics 208, 203–219. https://doi.org/10.1016/0040-1951(92)90345-7

Turcotte, D.L., Emerman, S.H., 1983. Mechanisms of active and passive rifting, Developments in Geotectonics. Elsevier B.V. https://doi.org/10.1016/B978-0-444-42198-2.50010-9

Velić, J., 2007. Geologija ležišta nafte i plina. Sveučilište u Zagrebu, Rudarsko-geološko-naftni fakultet.

Wu, Heng, Ji, Y., Wu, C., Duclaux, G., Wu, Hao, Gao, C., Li, L., Chang, L., 2019. Stratigraphic response to spatiotemporally varying tectonic forcing in rifted continental basin: Insight from a coupled tectonic-stratigraphic numerical model. Basin Res. 31, 311–336. https://doi.org/10.1111/bre.12322

Zečević, M., Velić, J., Sremac, J., Troskot-Čorbić, T., Garašić, V., 2010. Significance of the Badenian petroleum source rocks from the Krndija Mt. (Pannonian Basin, Croatia). Geol. Croat. 63, 225–239. https://doi.org/104154/gc.2010.19

PhD David Rukavina, mag. geol., mag. ing. min. is a postdoctoral researches at Department of Geology and Geological Engineering of Faculty of Mining, geology and Petroleum Engineering of University of Zagreb. He received his PhD 21th of July 2021. with thesis: Rift tectonostratigraphic sequences of Lower and Middle Miocene in eastern part of the Drava depression: application of CO2 geological storage assessment

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Marko Sinčić, mag. ing. geol.

Landslide susceptibility is defined as the spatial, time independent probability of landslide occurring in an area depending on the local terrain conditions (Guzzeti et al., 1999). A landslide susceptibility map is a map showing the sub-division of the terrain into zones (classes) that have a different likelihood of a landslide occurring (Corominas et al, 2013).

Landslide susceptibility maps are based on the assumption that landslides are likely to occur under the same conditions as those under which they occurred in the recent past. Due to that reason, the preparation of landslide susceptibility maps requires a landslide inventory map indicating where landslide already occurred used in combination with a series of conditioning factors answering what terrain conditions caused for landslide occurrence. According to Soeters & van Westen, 1996, and Corominas et al., 2013 some of the commonly used conditioning factors are elevation, slope gradient, slope orientation, relief dissection, lithological map, faults, geological contact, drainage network, flow accumulation, topographic wetness, land use, roads, and buildings, some of which are shown in Figure 1.

Figure 1 An overview of landslide conditioning factors used in landslide susceptibility modelling, an example from the modelling of the City of Karlovac (Sinčić et al., 2022)

Landslide susceptibility maps are a result of landslide susceptibility assessments defined as quantitative or qualitative assessments of the classifications and spatial distribution of landslides which exist of potentially may occur in an area. There are several methods of landslide susceptibility assessments, as shown in Figure 2.

Figure 2 Methods for landslide susceptibility assessment (Corominas et al., 2013)

Moreover, for commonly used data-driven statistical methods, according to Reichenbach et al., 2018,  nine steps for preparing a landslide susceptibility assessment and for proper use of associated terrain classifications are: (i) obtain relevant landslide information, (ii) obtain relevant conditioning factor information, (iii) select appropriate mapping unit, (iv) select appropriate statistical model, (v) evaluate the model fitting performance, (vi) evaluate the model predictive performance, (vii) estimate the model uncertainty, (viii) rank the model quality, and (ix) design a landslide protocol. An example of a complex landslide susceptibility modelling testing different combinations of statistical methods, mapping units and types of landslide inventory maps is shown in Figure 3.

Figure 3 An example of complex landslide susceptibility modelling, including testing types of landslide inventory maps, statistical methods and mapping units (Bernat Gazibara et al., 2022)

Landslide susceptibility maps can be created in several scales, depending on the available data and the purpose of the landslide susceptibility assessment. Namely, the scales are site-specific (>1:5 000), local (1:25 000 – 1:5 000), regional (1:250 000 – 1:25 000), and national (<1:250 000) (Corominas et al., 2013). An example of a City District, City, County and national landslide susceptibility map are shown in Figure 4-A, 4-D, 4-C, and 4-B, respectively.

Figure 4 Landslide susceptibility maps: A part of the Podsljeme Zone (City District), B Croatia (national), C Primorsko-Goranska County (County), D the City of Karlovac (City)

Answering “where?” landslides are likely to occur, landslide susceptibility maps make up the input data for landslide hazard maps answering “where and when?”, followed by a landslide risk map answering “where, when, and what?”. In combination, landslide inventory, susceptibility, hazard and risk maps make key tools in landslide management, necessary for landslide mitigation. Identification and map portrayal of areas highly susceptible to landslide occurrence are the first and necessary steps towards loss reduction (Mihalić Arbanas & Arbanas, 2015). Concretely, portraying highly susceptible areas, i.e. classification is defined as the act of dividing land into homogenous areas or domains and ranking them according to degrees of actual or potential landslide susceptibility (Corominas et al., 2013). Landslide susceptibility maps are mostly used by relevant stakeholders in the domain of construction, spatial planning, civil protection and environment protection.

Reference:

Bernat Gazibara, S.; Mihalić Arbanas, S; Sinčić, M.; Krkač, M.; Lukačić, H.; Jagodnik, P.; Arbanas, Ž. (2022): LandSlidePlan - Scientific research project on landslide susceptibility assessment in large scale. In: Peranić, J., Vivoda Prodan, M., Bernat Gazibara, S., Krkač, M., Mihalić Arbanas, S., Arbanas, Ž. Rijeka (eds.): Proceedings of the 5th ReSyLAB 'Landslide Modelling & Applications': Faculty of Civil Engineering, University of Rijeka and Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, 99-106, 257 p.

Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.P., Fotopolou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winger, M.G., Pastor, M., Ferlisi, S., Tofani, V., Hervas, J., Smith, J.T. (2013): Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73, 209–263.

Guzzeti, F., Galli, M., Reichenbach P., Ardizzone, F., Cardinali, M. (1999): Landslide Hazard assessment in the Collazzone area, Umbria, Central Italy. Natural hazards and earth system sciences, 6, 1, 115-131.

Mihalić Arbanas, S., Arbanas, Ž. (2015): Landslides: A Guide to Researching Landslide Phenomena and Processes. In: Gaurina Međimurec, N. (eds.): Handbook of Research on Advancements in Environmental Engineering. IGI Global, 474-510, 660 p.

Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., Guzzetti, F. (2018): A review of statistically-based landslide susceptibility models, Earth-Science Reviews, 180, 60-91.

Sinčić, M.; Bernat Gazibara, S.; Krkač, M.; Mihalić Arbanas, S. (2022): Landslide susceptibility assessment of the City of Karlovac using the bivariate statistical analysis. Rudarsko-geološko-naftni Zbornik, 37(2), 149-170.

Soeters. R., van Westen. C.J. (1996): Slope instability recognition, analysis and zonation. In: Turner, A.K., Schuster, R.L. (eds.): Landslides investigation and mitigation. TRB Special Report 247. National Academy Press, Washington, DC, 129–177, 685 p.

Marko Sinčić, mag. ing. geol. is a Junior Researcher – Assistant at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. He finished Graduate studies in Geological Engineering in 2020 and in 2021 he enrolled in Doctoral studies of Applied Geosciences, Mining and Petroleum Engineering while working on the Croatian Science Foundation LandSlidePlan project.

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Author: Ivan Smajla, mag. ing. petrol.

Although a fossil fuel, natural gas remains one of the most important energy sources in the European Union, having met about a quarter of its energy needs in 2020. With the development of the European Union's green policy, it is evident that natural gas has no prospects as an energy source in the period after 2050, but until then it will have an important transitional role. The gas system in the European Union is the most developed in the world in terms of supply, market organization, distribution, transport, etc., but there is still room for further optimization of the system. The development of gas smart meters has enabled the collection of data on natural gas consumption in real time, which can then be used in a variety of ways to optimize the system. One of the most important ways is certainly to predict the future consumption of natural gas for a particular area because more accurate forecasting of consumption significantly reduces the need to balance the gas system (Smajla et al., 2021). This results in reduced financial costs for suppliers, reduced energy consumption but also fewer working hours. In addition to balancing, the data collected also provide end users with an up-to-date insight into their own consumption, which in most cases results in a reduction in natural gas consumption in order to achieve financial savings (Mogles et al., 2017). This research is based on a pilot project for the installation of gas smart meters conducted in the east of the Republic of Croatia, where several thousand gas smart meters were installed. In addition to these data, publicly available data on similar projects at the European Union level (Af Mercados Emi and ICCS-NTUA, 2015; European Commission, 2019) were used to calculate the financial viability of installing gas smart meters.

A feasibility analysis was conducted as a part of this research that took into account the different energy savings achieved using gas smart meters (savings of 1,83%, 5.,73% and 9,63%). The analysis showed that the installation project is cost-effective if energy savings of 5,73% and 9,63% are taken into account. What further improves the financial viability of such a project are the financial savings that the investor (usually the supplier) will legally achieve because the installation of gas smart meters for the first 5 years of the project is recognized as a measure to achieve energy savings. Figure 1 summarizes the operating and investment costs and benefits of installing gas smart meters.

Figure 1 Costs and achievable savings on the example of installing 100,000 gas smart meters (Smajla et al., 2022)

The research conducted a detailed review of the current literature on the topic of forecasting natural gas consumption. The review showed that modern methods for predicting natural gas consumption most often use complex methods of different variants of machine learning in order to achieve the most accurate results (Smajla et al., 2021). The literature has also shown that for the short-term consumption forecast, the most important input parameters are daily natural gas consumption and outdoor temperature. In accordance with the above and researched in the literature, a flowchart has been proposed for the development of the most accurate method for predicting natural gas consumption (Figure 2). The flowchart indicates that the previously mentioned input parameters need to be filtered and processed in order to form a quality input database. Such a database is then used in a consumption forecasting model whose results must be validated to determine the success of the forecast. If the prediction accuracy is not satisfactory, the model needs to be modified or adjusted for better accuracy.

Figure 2 Proposed flowchart for defining a method for predicting short-term natural gas consumption (Smajla et al., 2021)

A review of the literature also concluded that so far the determination of the most favorable statistical distribution for the distribution of consumers in the analyzed area according to the criterion of daily consumption has not been considered. Further research will be based on determining the most favorable statistical distribution of consumers whose parameters will be correlated with the outside temperature. In this way, it will be possible to use a short-term weather forecast to determine the total natural gas consumption in the analyzed distribution area.

References:

Af Mercados Emi and Institute of Communication & Computer Systems of the National Technical University of Athens ICCS-NTUA, 2015. Study on cost benefit analysis of smart metering systems in EU member states. https://energy.ec.europa.eu/study-cost-benefit-analysis-smart-metering-systems-eu-member-states_en

European Commission (2019): Benchmarking smart metering deployment in the EU-28 - final report. https://op.europa.eu/en/publication-detail/-/publication/b397ef73-698f-11ea-b735-01aa75ed71a1/language-en

Mogles N., Walker I., Ramallo-González A.P., Lee J.H., Natarajan S., Padget J., Gabe-Thomas E., Lovett T., Ren G., Hyniewska S., O’Neill E., Hourizi R., Coley R. (2017): How smart do smart meters need to be? Build. Environ. 12, 439–450. http://dx.doi.org/10.1016/j.buildenv.2017.09.008.

Smajla I., Karasalihović Sedlar D., Vulin D., Jukić L. (2021): Influence of smart meters on the accuracy of methods for forecasting natural gas consumption. Energy Rep. 7, 8287–8297. http://dx.doi.org/10.1016/j.egyr.2021.06.014.

Smajla I., Karasalihović Sedlar D., Jukić L., Vištica N. (2022): Cost-effectiveness of installing modules for remote reading of natural gas consumption based on a pilot project. Energy Rep. 8, 5631-5639. https://doi.org/10.1016/j.egyr.2022.04.019.

Ivan Smajla, mag. ing. petrol. is a doctoral student at the Department of Petroleum and Gas Engineering and Energy at the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb.

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Autor: Hrvoje Lukačić, mag.ing.geol., mag.ing.min.

Successfully implemented interventions in rock masses largely depend on the quality and manner of conducting research work. Mechanical properties of rock mass depend on the properties of intact rock and the properties of discontinuity (ISRM, 1978). For this reason, knowledge of the geometric features of discontinuities and the definition of representative crack systems is of utmost importance for the definition of the engineering geological model and the analysis of possible types of instability.

When defining a discontinuity fracture system, it is necessary to collect a large amount of data on discontinuity orientation. Engineering geologists usually collect this data manually in the field using a geological compass.

Figure 1 Rock mass with different number of discontinuity sets (Pollak, 2007)

This method of data collection results in a limited number of data. The development and implementation of remote sensing methods (laser scanning and photogrammetry) in engineering geology have created the conditions for collecting and analyzing a large amount of structural data, manual and semi-automatic methods, thus defining an objective structural model of rock mass (Riquelme, 2015). The application of remote sensing methods reduces the time required to stay in the field and increases the time available for mapping the rock mass in the cabinet. The provision of additional time for mapping structures increases the amount of data collected and, thus, greater objectivity (Buyer, 2018). To date, many remote methods for data collection have been developed, with the primary goal of obtaining a 3D model of rock mass or 3D point cloud (Figure 2), which serves as a basis for engineering geological mapping of the rock mass.

Figure 2 3D Point Cloud of rock mass obtained by terrestrial laser scanning of a rock slope (Đikić, 2016)

Using the CloudCompare computer software and the Compass tool integrated, it is possible to simulate geological compass measurements and read the orientation of discontinuities on the 3D point cloud.

Figure 3 Measurement of discontinuity orientation on a 3D Point Cloud (Lukačić, 2020)

An identical procedure can be performed using a semi-automatic method for identifying discontinuity orientations from a 3D digital model using open-source software Discontinuity Set Extractor (DSE) developed in the Matlab programming language (Figure 4). The primary purpose of this software is to identify planes and associated sets of discontinuities and determine their orientation from 3D point clouds obtained by laser scanning or digital photogrammetry (Riquelme et al., 2014).

Figure 4 Display of identified discontinuity sets on a 3D Point Cloud of rock mass

The performed analyzes have shown that the application of remote sensing methods in engineering geology enables the collection of a large number of representative data on the geometric features of discontinuities. However, algorithms for semi-automatic discontinuity identification are not at the stage of development to be completely independent, and there is still a need for an engineering geologist to validate the results and assess whether they reflect the actual situation in the field to avoid erroneous conclusions about rock mass condition.

References:

Buyer, A. (2018.): Contributions to Block Failure Analyses using Digital Joint Network Characterization, PhD. Thesis, Institute of Rock Mechanics and Tunnelling, Graz University of Technology, Graz, 123 str.

Đikić, Z. (2016.): Primjena tehnologije oblaka točaka za projektiranje sanacije stijenske kosine Špičunak, diplomski rad, Građevinski fakultet, Rijeka, 105 str.

ISRM (1978.): Commission on standardization of laboratory and field tests Suggested methods for the quantitative description of discontinuities in rock masses. International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts 15: 319-368.

Lukačić, H. (2020.): Inženjerskogeološko kartiranje stijenske mase na zasjeku Špičunak (Gorski kotar) primjenom daljinskih istraživanja, diplomski rad, Rudarsko-geološko-naftni fakultet, Zagreb, 102 str.

Pollak, D., 2007. Utjecaj trošenja karbonatnih stijenskih masa na njihova inženjerskogeološka svojstva, doktorska disertacija, Rudarsko-geološko-naftni fakultet, Zagreb, 299 str.

Riquelme, A. J., Abellán, A., Tomás, R. i Jaboyedoff, M. (2014): A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Computers and Geosciences 68: 38–52.

Hrvoje Lukačić, mag. ing. geol., mag. ing. min. is an assistant at the Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb. He is a member of the Croatian landslide group and the Internationl Society for Rock Mechanics and Rock Engineering.

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Author: Patricia Buškulić, mag.ing.geol.

Isotopes are atoms of the same chemical element that have the same number of protons but different number of neutrons in their nucleus. Take the same position in the table of elements, have the same chemical properties but different mass number. There are stable and unstable, i.e. radioactive isotopes (radionuclides). Oxygen, for example, has three stable isotopes: ¹⁶O, with 8 protons and 8 neutrons; ¹⁷O, with 8 protons and 9 neutrons; and ¹⁸O, with 8 protons and 10 neutrons (Figure 1). In nature, the most abundant oxygen isotope is oxygen-16 (99.76 %), afterwards oxygen-18 (0.2 %) and then oxygen-17 (0.038 %), while the most abundant hydrogen isotope is hydrogen-1 (99.985 %) and then hydrogen-2 (0.015 %). The most relevant isotopes related to hydrology are oxygen-18 and hydrogen-2 (or deuterium, D) ( Mook 2001). Oxygen and hydrogen isotopes form a different types of stable water molecules. For every 10000 water molecules we would find 9977 molecules H₂¹⁶O, 3 molecules H₂¹⁷O and 20 molecules H₂¹⁸O (Figure 2). The rate at which heavy and light stable isotopes react during physical or chemical reactions differs due to mass differences. The heavy isotopes react more slowly than the light isotopes which leads to isotopic separation or fractionation (Clark, 2015).

Figure 1. Oxygen Stable Isotopes (source: Climate Science Investigation. URL: http://www.ces.fau.edu/nasa/module-3/how-is-temperature-measured/isotopes.php)

Figure 2. Stable Water Isotopes (source: Program on Climate Change. URL: https://uwpcc.ocean.washington.edu/file/Water_Isotopes)

Stable isotope concentrations are measured as a ratio of the rare to the abundant isotope. The most commonly used is delta (δ) notation which indicates the difference in ratio between the sample and a known reference expressed in permil units (Clark, 2015). The isotopic composition of water is expressed in comparison with the average isotopic composition of a seawater sample. The standard for water sample is determined by an international agreement and is called Standard Mean Ocean Water (SMOW). SMOW has recently run out and has been replaced with Vienna Standard Mean Ocean Water (VSMOW) developed by the International Atomic Energy Agency (IAEA) from distilled seawater that was modified to have an isotopic composition close to SMOW.

In nature, ratio differences occur in the aqueous medium due to phase transitions. The isotopic separation (fractionation) of stable isotopes of oxygen and hydrogen mostly depends on the phase transition temperature, pressure and initial isotopic composition of water. For example, during evaporation light isotopes evaporate more readily so the water remains enriched with heavy isotopes, while during condensation heavy water isotopes rain and snow more readily (Figure 3). The isotopic composition of precipitation decreases with distance from the coastline, because the more clouds travel to the continent and the more precipitation falls along that path, the precipitation will have more light and less heavy isotopes. Factors influencing the spatial and temporal distribution of water isotopic composition are shown in Figure 4.

Figure 3. Schematic review of isotopic fractionation within the hydrological cycle (Xi, 2014)

Figure 4. Factors influencing the distribution of water isotopic composition (SAHRA, 2005)

Isotopic fractionation leads to differences in stable isotope ratios (²H/¹H and ¹⁸O/¹⁶O) and offers numerous possibilities for research within the hydrogeological cycle, such as defining the recharge areas, tracing the origin of water, determining the age of water, hydrodynamic conditions, rate of water exchange within aquifer and hydraulic connections between layers/aquifers. Since ¹⁸O and ²H are components of the water molecule, they are its ideal tracer.

Stable isotopes concentrations in water samples is usually determined by mass or laser spectrometers. The Laboratory for Spectroscopy, at the Faculty of Mining, Geology and Petroleum Engineering within University of Zagreb, concentrates on stable water isotope analyses. Stable isotopes, δ¹⁸O and δ²H in liquid water samples, are measured by absorption spectroscopy method using a „Laser Water Isotope Analyzer“ (LWIA-45-EP) from the „Los Gatos Research“ (LGR) (Figure 5). At this moment The Faculty of Mining, Geology and Petroleum Engineering participates in few scientific projects within which water stable isotopes are used. The most important are:

• SUPREHILL project (Subsurface preferential transport processes in agricultural hillslope soils) financed by Croatian Science Foundation – within this project the main aim is to quantify soil water subsurface preferential flow in agricultural hillslope soils;
• IAEA TC project CRO7002 (Using Nitrogen and Oxygen Stable Isotopes in the Determination of Nitrate Origin in the Unsaturated and Saturated Zone of the Velika Gorica Wellfield) financed by IAEA – the main goal of this project is to determine nitrate origin in the wider area of the Velika Gorica wellfield;
• IAEA TC project RER7013 (Evaluating Groundwater Resources and Groundwater-Surface-Water Interactions in the Context of Adapting to Climate Change) financed by IAEA – within this project numerous pilot areas have been established. One of the pilot areas is Sava River basin where relationship between precipitation, Sava River and alluvial aquifers will be defined using water stable isotopes;
• NATURAVITA project (Demining, restoration and protection of forest and forestland in protected and Natura 2000 sites in Danube-Drava regions) financed by the European Structural and Investment Funds, which presents a strategic project aimed at demining, reconstruction and protection of forest, forestland and water resources.

Figure 5. Laser spectrometer LWIA-45-EP (photo by Zoran Kovač)

References:

Clark I. (2015): Groundwater Geochemistry and Isotopes. CRC Press, Taylor & Francis Group, 438 p.

Mook, W. M. E. (2001): Environmental Isotopes in the Hydrological Cycle. Principles and Applications. UNESCO/IAEA Series.

SAHRA, Sustainability of semi-Arid Hydrology and Riparian Areas, Isotopes: Oxygen, Arizona, Ariz, USA, 2005

Xi X. (2014): A Review of Water Isotopes in Atmosferic General Circulation Models: Recent Advances and Future Prospects. International Journal of Atmosferic Sciences 2014, 1-16.

Patricia Buškulić, mag.ing.geol., is a Research Assistant at the Faculty of Mining, Geology and Petroleum Engineering within the University of Zagreb on IAEA TC project CRO7002 "Using Nitrogen and Oxygen Stable Isotopes in the Determination of Nitrate Origin in the Unsaturated and Saturated Zone of the Velika Gorica Wellfield".

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Helena Vučenović, PhD mag. ing. min.

The ability of a porous media, and also of soil, to allow the flow of a fluid through it is called permeability. Fluids passing through a porous media can be in gaseous or liquid form. The hydraulic conductivity of soil is a well-known property and it is described by an advective flow regime. However, in nature, fluid in porous media is very commonly found in the gaseous form, and thus there is a need to quantify the gas permeability of certain materials. This property is becoming increasingly interesting lately, mostly related to environmental issues, especially in environmental geotechnics. Particular attention is paid to the huge landfill biogas production, especially in the early stage of a landfill lifetime. As the main components belong to the group of greenhouse gases, it is necessary to prevent their release into the atmosphere.

The emission of landfill gases into the atmosphere, along with other factors, contributes to the greenhouse effect which causes long-term climate change and global warming, and in recent years, this has become a growing problem worldwide. Of all the complex components of landfill gases, methane (CH4) and carbon dioxide (CO2) have the biggest influence on climate change, and they are both products of the anaerobic decomposition of organic waste (Lou & Nair, 2009).

As the main components belong to the group of greenhouse gases, it is necessary to prevent their release into the atmosphere. Geosynthetic Clay Liners - GCLs have recently been installed in the final cover of landfills as a barrier layer (fig 1). GCLs are artificially manufactured products that consist of a thin layer of bentonite between two layers of geotextiles. Bentonite is a sealing component while the layers of the geotextile are the supporting reinforcing components. Earlier research has already shown that GCLs are a very efficient hydraulic barrier, but there is a growing interest in determining GCL efficiency as a gas barrier.

izv. prof. Luka Perković

Amalia Lekić Brettschneider, mag. ing. petrol.

Sustainable development has several key elements: to diagnose the current state of industrial and residential systems, to include appropriate targeting of the needs for heat and power supply and targeting power generation operation. Over two million geothermal heat pumps were installed in Europe as of June 2020 (EGEC, 2020). Sweden, Germany, France and Switzerland are countries with the most installations of shallow geo-thermal heat capture systems, which accounts for 64 % of all installed capacity in Europe (Perez, 2020). Integration of PV and geothermal heat pumps can be used for cost-effective decarbonisation of the energy consumption of the household sector. Integration of heating, cooling and electricity demand with renewable electricity supply can lead to enhanced heat recovery of geothermal reservoir with the low year-to-year thermal degradation of the reservoir.

Marija Pejić, mag.geol.

GEOlogical characterization of the Eastern part of the Drava depression subsurface intended for the evaluation of Energy Potentials (GEODEP) project started in February 2020 and will last until February 2025. It is funded by the Croatian Science Foundation and it involves 10 researchers and two associates. In addition to employees and one student from the Faculty of Mining, Geology and Petroleum Engineering, two researchers are employers of the Croatian Geological Institute. The main aim of the project is to create a geological model of subsurface in the eastern part of Drava Depression. The model will be used for estimation of the hydrocarbon potential and geological storage of CO₂ potential. Within the project, testing of equipment and methods for locating hydrocarbon migration pathways (faults) in the underground was carried out, which took place near city of Kutina, in the area of the Sava Depression.

The method we wanted to test was soil gas monitoring, which has numerous exploration applications including soil contamination by anthropogenic factors (Chylkova et al,2009; Hendel, 2017), health risk from radon concentration in urban planning (Cinelli et al, 2015; Tokonami, 2020), earthquake prediction (Sugisaki et al, 1983) and mineral and hydrocarbon resource exploration (Partington, 1957; Füst& Geiger, 2010). Radon and thoron are interesting to monitor because of their ratio. Mainly two radioisotopes of radon are present in nature, ²²²Rn and ²²⁰Rn (thoron). ²²²Rn has half-life of 3.8 days, while thoron has half-life of 55.6 s (Jönsson, 1995). Radon (²²²Rn) is produced by radioactive decay of radium (²²⁶Ra), as a part of uranium (238U) decay chain, while thoron is produced by radioactive decay of ²²⁴Ra in thorium (²³²Th) decay chain. Due to the above, their ratio may indicate whether the gas being detected arrived by migration from greater depths or is of local origin. Carrier gas transport could be the reason for the non-diffusive radon transport (Kristiansson&Malmqvist, 1982) ,so the non-reactive radon represents a suitable tracer for gas transport from the deeper subsurface to the surface. Carbon dioxide and methane are considered potential carrier gases for radon (Durrance&Gregory, 1990; Etiope&Lombardi, 1995) which is why their concentrations were also measured. The seepage of methane and other hydrocarbons along faults and fractures from hydrocarbon bearing formations is recognized worldwide (Khilyuk et al, 1990; Dyck&Jonasson, 2000). Number of authors stated that it is possible to define active faults by measuring radon concentrations over the study area through anomalies which indicate more emissive zones related either to main faults or secondary fractures (Aubert&Baubron, 1988; Neri et al, 2019; Palacios et al, 2013).

Several soil gas parameters were measured at two different sites. These included the radon (²²²Ra), thoron (²²⁰Ra), CO₂, CH₄, PID (photoionization detector - volatile organic compound), and TP (total petroleum - volatile hydrocarbon component) concentrations. Since radon concentrations are very variable and it is difficult to conclude which parameters affect it, measurements of natural radioactivity were also performed. The purpose of measuring natural radioactivity was to be able to confirm that the increased radon concentrations were not caused by shallow subsurface geological diversity.

Measurements were taken at two sites (Figure 1). The first was selected based on known locations of oil seepage at the surface (site A) and the second because of known hydrocarbon accumulation in the subsurface (site B).

Ivona Ivkić Filipović, M.Geol.

Paleolimnology is a scientific discipline that focuses on past conditions and processes in lake basins (Last & Smol, 2001). Paleolimnologists use physical, chemical and biological indicators in lake sediments to determine the paleoenvironment and evolution of lake systems. Lakes are temporary inland water bodies characterized by sedimentation of autochthonous and allochthonous material derived from the catchment area. The accumulation of material leads to a progressive shallowing of lakes, and the end of their natural cycle is marked with cessation of lake sediment deposition (hence „temporary“). Lake environments are often marked by high rates of sedimentation due to a large catchment:lake area ratio. Furthermore, lakes are especially sensitive to environmental changes governed by climatic variations and human influences. Finally, lake sediments are often well-preserved within the relatively enclosed basins. Such undisturbed, fine-layered lake sediments can provide very valuable and continuous paleoenvironmental and paleoclimatic records (Cohen, 2003) (Figure 1). The most common types of lakes in the world are glacial, tectonic, fluvial, coastal and volcanic crater lakes, while the most common lakes in Croatia are karst lakes.

Maja Arnaut, mag.ing.petrol.

Enhanced Oil Recovery (EOR) methods are used to produce additional oil after the primary production phase (where production is based on natural reservoir energy) or, most often, after waterflooding (secondary phase). Some EOR methods are related to CO₂ injection (CO₂-EOR), which is attractive since part of injected CO₂ retained in the reservoir, enabling a positive effect on storage capacity and cost-effectiveness of CO₂ storage. Therefore, these methods are of particular importance due to the emission reduction obligations under European Union international agreements within the climate change domain (Kyoto protocol from the year 1997 and Paris agreement from the year 2015). Carbon Capture Utilization and Storage (CCUS) comes to focus when possibilities of CO₂ storage and reduction of storage cost are assessed. Although there are other utilization types such as utilization through beverage production or in agriculture, only the CO₂ enhanced oil recovery (CO₂-EOR) is implemented at a commercial level on an industrial scale [2]–[4].

By injecting CO₂ above the miscibility pressure (or minimum miscibility pressure, MMP), microscopic displacement efficiency is improved due to viscosity reduction, oil swelling, lower interfacial tension and change of density of oil and brine [5]. Regardless of the injection conditions, part of the CO₂ is always re-produced so injected CO₂ includes recycled CO₂ and CO₂ that should be brought to complete the total required injected volume (Figure 1)

Author: Barbara Štimac, mag.geol.

One of the main aims of the research, as a part of HRZZ project NEIDEMO, is to develop an improved model of nonideal detonation, based on Wood-Kirkwood’s theory and thermochemical code EXPLO5.

Detonation of explosive charge results in a detonation wave through explosive propagating with velocity up to 10 km/s, pressure up to 40 GPa and temperatures up to 6000 K, all in a timeframe of couple of nanoseconds. Due to this extremely short timeframe and high pressure, energy is transmitted from the detonation products to the unreacted part of explosive by motion. There are two generally accepted theories of detonation based on conservation laws and hydrodynamic theory: Chapman-Jouguet (CJ) theory that assumes instantaneous chemical reaction (meaning there is no chemical reaction zone) and Zeldovich-von Neumann-Doering (ZND) theory that takes into account the existence of a chemical reaction zone of defined width and duration (Figure 1). Explosives that behave in according to CJ theory are called “ideal explosives”.

However, detonation parameters of commercial explosives (so-called “nonideal explosives”) cannot be accurately predicted using CJ theory. Theoretically calculated detonation velocity and detonation pressure of commercial explosives are considerably higher than those experimentally obtained, and detonation velocity shows strong dependence on initial radius of explosive charge and confinement (Esen, 2004; Souers et al, 2004; Minchinton, 2015; Sućeska i ostali, 2019).

Author: Josipa Kapuralić, PhD mag. ing. geol.

Knowledge of the Earth's interior is key to understanding geological structures and their relationships observed on the surface. In the last two decades, many regional studies of the European lithosphere have been carried out, while in the past ten years local geophysical researches have been intensified in the Dinarides. Recent geophysical efforts significantly contributed to the clarification of the crustal and lithospheric geological model in this region. This investigation is a continuation of geophysical studies focused on the Dinarides and its adjacent areas. The study area represents the boundary zone between the African and European plate, i.e. the contact between the Adriatic microplate as part of the African plate and the Pannonian basin as part of the European plate (Figure 1).

Author: Petar Mijić, mag.ing.petrol.

The first scientist who pointed out that devices and materials could one day be produced in a size corresponding to the size of an atom was Richard Feynman in 1959. The term "nanotechnology" was first used in 1974 by scientist Norio Taniguchi. Although nanotechnology penetrates all areas of human activity, from the automotive industry, computers and electronics, robotics, medicine to the textile industry, its application in the oil industry has begun a few years ago. Nanotechnology means the use of materials which have very small dimensions, between 1 and 100 nanometers.

Author: Vjekoslav Herceg, mag. ing. min.

The world mining has been developing rapidly in recent years, following the global trend of economic development. Demand for certain minerals is growing rapidly and natural, technological, law and social conditions can greatly limit capacity. Therefore, one of the biggest challenges of modern mining is to keep up with the times. To achieve this, with quality staff and social support, modern technology is certainly one of the key success factors. Current technological progress in the world is aimed at minimizing the use of fossil fuels with the aim of reducing CO₂ emissions into the atmosphere. In the mining industry, the technology is largely related to mobile machines powered dominantly by internal combustion engines. Therefore, the development of technology is based on the highest possible productivity, which directly affects the reduction of energy consumption and CO₂ emissions. In the processes of obtaining mineral raw materials, various machines are used, the effect of which is investigated separately.

Autor: Laura Bačani, mag. ing. geol.

Groundwater reserves of the Zagreb unconfined aquifer are defined as a strategic resource of groundwater in Croatia within the Croatian Waters’ Water Management Strategy. They present the only source of potable water for the inhabitants of the City of Zagreb and one part of the Zagreb County. In the past decades, groundwater level declines have been identified in the Zagreb aquifer. Consequently, it became important to identify and quantify every single source of its recharge. In order to observe and measure the processes in the unsaturated zone a teaching-research polygon was constructed (Figure 1) in collaboration between Velika Gorica water supply system (VGV) and Faculty of Mining, Geology and Petroleum Engineering (RGNF), University of Zagreb. Polygon is constructed in 2018. and is located in the southern part of the Zagreb aquifer, at the Velika Gorica well field. The research polygon is designed in order to be suitable for scientific research and educational purposes. The construction design of the pedological pit was prepared by the company HIDROPROJEKT-ING d.o.o. from Zagreb in cooperation with Velika Gorica water supply system and the Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb.

Marin Sečanj, mag.ing.geol.

Rock falls are a common phenomenon on the steep slopes and road cuts in the Dinarides. They are the result of unfavourable characteristics of the rock mass, weathering in combination with heavy rainfall and anthropogenic factor. One of the most endangered places by rockfalls is the Town of Omiš, located just at the toe of the steep Omiška Dinara Mt slopes (fig 1). Comprehensive geotechnical investigation financed by the Town of Omiš identified 22 potential rockfall source areas for which was necessary to design remedial measures to ensure protection of buildings, infrastructure, and citizens (Arbanas et al., 2019). Identification of rockfall source areas was performed by conventional field investigations based on visual inspection of the slopes, without rockfall susceptibility analysis, which should be the first step in rockfall hazard and risk reduction.

Author: Marija Macenić, mag.ing.min.

In the Republic of Croatia hydrocarbon production began back in the end of the 19th century. However, modern oil and gas production began in the mid-20th century and is still continuing today. According to data from INA d.d. group, there are around 4500 exploratory, production and development wells in Croatia (INA d.d., 2018). By the end of the 20th century production decline, higher water cut, and pressure decrease in reservoirs were noticed. This results in abandoning or reassigning production wells in exploratory or monitoring wells. It can be assumed that the number of reassigned wells will increase in the future in Croatia and over the world.