Subsurface Rock Research Articles

A pore-scale study of fracture sealing through enzymatically-induced carbonate precipitation (EICP) method demonstrates its potential for CO2 storage management

Geological fractures are mechanical breaks in subsurface rock volumes that provide important subsurface flow pathways. However, the presence of fractures can cause unwanted challenges, such as gas leakage through fractured caprocks, which must be addressed. In this study, the dynamics of enzymatically induced carbonate precipitation in rock fractures and their subsequent influence on CO2 leakage were investigated from a pore-scale perspective for the first time. This was achieved through real-time monitoring of the injection of the solution into a rock-microfluidic flow cell using optical and scanning electron microscopy. It was revealed that the main growth dynamics occur during the first three injection cycles, with growth continuing until the fracture aperture is fully closed in the 6th cycle. Based on the flow simulation, a significant reduction of up to 25% in the CO2 conductivity of the original fracture is expected even after the first treatment cycle. Future studies are suggested to explore different resolutions, testing conditions, and to conduct 3-dimensional investigations of the growth dynamics.

Identification of Aquifer Layer Using Geoelectric Method in Oil Palm Plantation Area of PT Unggul Widya Teknologi Lestari Pasangkayu Regency

Introduction: Aquifer layers have been identified in the oil palm plantation area of PT Unggul Widya Teknologi Lestari Pasangkayu Regency. This study aims to determine the lithology of subsurface rocks and the depth of the aquifer layer as a source of clean water in the study area. Method: This study uses the geoelectric method of type resistance with Wenner configuration, with the number of electrodes 24 rods and a spacing of 10 meters. 2D cross-sectional model using Res2Dinv software program. Results and Discussion: Based on the value of the type resistance of the subsurface constituent rock lithology at the research site is interpreted to consist of three layers, namely clay with a specific resistance value < 29.15 Ωm, sand and sandy clay with a specific resistance value of 29.15 Ωm to 72.89 Ωm, and conglomerate with a specific resistance value > 72.89 Ωm. The aquifer layer is interpreted with a specific resistance value of 29.15 Ωm to 72.89 Ωm. The aquifer layer is detected to spread from the northeast to the southwest of the research site with a depth of ±25-39 m. Conclusion: In the oil palm plantation of PT Unggul Widya Teknologi Lestari, the subsurface layer consists of clay (1.98 Ωm - 29.15 Ωm), sand and passive clay (29.15 Ωm - 72.89 Ωm), and conglomerate (72.89 Ωm - 1120.5 Ωm). Thin aquifers, with a thickness of 5-10 m and depth of 25-39 m, are detected in the sand and sandy clay layers.

Identification Of Liquefaction Slide Planes Using The Geoelectric Resistance Type Method In Balaroa Village

Introduction: Research has been conducted with the title "Identification of Liquefaction Sliding Fields Using the Type Resistance Geoelectric Method in Balaroa Village". This study aims to determine the subsurface constituent rock layers and the position of the sliding plane based on the type resistance data. Method: This study consists of 6 measurement tracks with a track length of 150 meters using the Vertical Electrical Sounding (VES) method with the Schlumberger configuration. Data processing uses the software program Progress version 3.0 and ipi2win. Results and Discussion: The results obtained indicate the presence of subsurface constituent layers, namely: Passive clay, clay sand, and gravel, and the presence of an inclined plane of 25° to 55.5% with a steep slope. Conclusion: Based on data analysis and interpretation, it can be concluded that the rock lithology at the study site consists of passive clay, clay sand, and gravel, based on the value of specific resistance. The slope of the sliding plane layers that have the potential to trigger landslides and liquefaction at the site tends to be west-east with a slope angle of 25°, or 55.6%, which is included in the steep slope category

CO2-Rock-Brine Interactions in Feldspar-Rich Sandstones That Underwent Intense Heating.

The success of any carbon capture and storage method largely depends on, among other factors, its safety, reliability, and thorough understanding of the interactions among CO2, underground geological formation, and resident brine. Upon injection into the subsurface rock formation, CO2 interacts with the host geological formation and brine, initiating complex geochemical reactions that are often poorly understood and could potentially affect the overall stability and storage capacity of the geological formation, particularly those in close proximity to an intense heat source. For instance, the impact of intense and prolonged heat due to, say, magmatic intrusion on sandstones' framework, authigenic mineralogies, and CO2-storage potentials is still poorly understood. Consequently, in this study, we have investigated the impact of firing on CO2-rock-brine reactions in the Bandera Gray (BG) sandstone. Prior to the CO2 injection using 60 000 ppm brine at 75 °C and 28.7 MPa for 30 days, two samples of the BG sandstone were fired for 6 h in a muffle furnace at 700 and 1100 °C each. The BG samples were then studied for XRD, SEM, and ICP-OES analyses before and after the CO2 injection, mainly to investigate any changes in mineralogical compositions and fluid chemistry. To determine the impact of the CO2-rock-brine interactions on the authigenic and framework mineralogies of the BG sandstones under low pH (∼3) conditions, powdered samples of the pre- and postfired BG sandstones were treated with nitric acid. The findings of the study indicate that there were no observable reactions involving rock-forming minerals and carbonate cement in the unfired and fired (at 700 °C) sandstones after the CO2 injection. However, pervasive feldspar-dissolution porosity was formed in the postfired BG sandstone (1100 °C) after CO2 injection. This was mainly because albite was partly to pervasively transformed into anorthite during firing at 1100 °C, making the feldspar highly susceptible to dissolution under CO2 conditions. This implies that the conversion of albite into chemically unstable anorthite in natural sandstones that underwent intense and prolonged heating could develop significant amounts of secondary dissolution porosity due to CO2 injection, thereby impacting their storage capacities and overall petrophysical properties. This dissolution was separately corroborated using a nitric acid treatment. The findings of the study will provide a better understanding of the CO2-rock-brine reactions involving sandstones that experienced intense heat due to, for instance, magmatic activity over a long geologic time scale, which has largely transformed the chemistry of their feldspars, particularly plagioclase.

Incision rate of the Manas River, northern Tian Shan: Insight from luminescence dating of terrace cobbles

Fluvial deposits in piedmont environments are usually enriched by cobbles, which can be used for luminescence dating, as an alternative to the conventionally employed sand-sized minerals. In this study, a modified multi-elevated-temperature post-infrared infrared stimulated luminescence (MET-pIRIR) procedure was employed to establish luminescence-depth profiles of upper and lower sides of fluvial cobbles and obtain the burial ages of cobbles from high terrace of the Manas River in northern Chinese Tian Shan. The age-stimulation temperature (A-T) plateau was combined with the conventional age-depth (A-D) plateau in luminescence-depth profile to evaluate the reliability of burial ages of cobble luminescence dating from the aspects of fading and bleaching of signals. When the signal was only bleached to a limited depth given rise to no A-D plateau, the A-T plateau could serve as an internal-check to evaluate the degree of bleaching of the luminescence signal just through a single rock slice. Our results showed that self-evidenced and reliable buried ages of fluvial cobbles could be determined by only using first sub-surface rock slice in the presence of A-T plateau. Thereafter, the deposition age of cobble from top of T6 terrace and bottom of T5 terrace were ∼23.6–24.0 ka and ∼22.7 ka, respectively, inferring a very fast incision rate of ∼20 mm/a. The observed variability in bleaching depth between upper and lower sides across different cobble samples suggested complex transport dynamics.

Effect of fluid patch clustering on the P-wave velocity-saturation relation: a critical saturation model

Quantitative analysis of the relationship between seismic wave velocities and fluid saturation in porous media is of great significance for any fluid injection and extraction operation in subsurface rock formations. However, seismic velocities are not only dependent on the amount of saturation, but also on the distribution of fluid patches and their size. The patch size variation during changes in saturation is oftentimes ignored in modeling studies, even though it is natural to assume that with increasing saturation, fluid patches will form larger and, at some critical saturation, percolating clusters. To capture the evolution of patch size with saturation implied in the velocity-saturation relations, we are inspired by percolation theory. By incorporating the connectivity of water-filled patches in the continuous random medium model, we develop a critical saturation model. We apply this critical saturation model to examine recently reported experimental measurements, specifically analyzing the patch size changes. For measurements of drainage or imbibition processes in four sandstone samples, we indeed find a clear indication of growing patch size with water saturation. The predictions of the critical saturation model are in reasonable agreement with observations. Our approach improves the accuracy of the interpretation of the velocity-saturation relations in partially saturated rocks and forms a basis for exploring its underlying mechanisms.

Quartz Dissolution Effects on Flow Channelization and Transport Behavior in Three‐Dimensional Fracture Networks

AbstractWe perform a set of reactive transport simulations in three‐dimensional fracture networks to characterize the impact of geochemical reactions on flow channelization. Flow channelization, a frequently observed phenomenon in porous and fractured subsurface rock formations, results from the spatially variable hydraulic resistance offered by a geological structure. In addition to geo‐structural features such as network connectivity, geometry, and hydraulic resistance, geochemical reactions, for example, dissolution and precipitation, can dynamically inhibit or enhance flow channelization. These geochemical processes can change the fracture permeability leading to increased flow channelization, which are localized connected regions of high volumetric flow rates that are seemingly ubiquitous in the subsurface. In our simulations, fractures partially filled with quartz are gradually dissolved until quasi‐steady state conditions are obtained. We compare the flow field's initial unreacted and final dissolved states in terms of flow and transport observations. We observe that the dissolved fracture networks provide less resistance to flow and exhibit increased flow channelization when compared to their unreacted counterparts. However, there is substantial variability in the magnitude of these changes which implies that the channelization strongly depends on the network structure. In turn, we identify the interplay between the particular network structure and the impact of geochemical dissolution on flow channelization. The presented results indicate that geological systems that have been weathering or reactive for longer times in older landscapes are likely to have increased flow channelization compared to their equivalent but younger counterparts, which implies a time dependence on flow channelization in fractured media.

Electrical conductivity model for transversely isotropic rocks with interconnected cracks

The electrical conductivity of subsurface rocks is generally anisotropic. The anisotropy of the subsurface electrical conductivity provides important information on the stress-strain state and geodynamics. To quantitatively interpret anisotropic conductivity structures revealed by electromagnetic surveys, it is essential to use a mixing model considering the anisotropy. Although there exists a mixing model for transversely isotropic rocks with crack-shaped pores, the previous model seems inappropriate in interpreting conductive anomalies revealed by electromagnetic exploration because cracks are assumed to be isolated in the model. Therefore, this study develops a theoretical mixing model for transversely isotropic rocks with mutually interconnected cracks by a statistical approach. The derived mixing model considers the macroscopic tortuosity of a collection of cracks as well as the tortuosity of each crack. The derived model can represent general transverse isotropy and includes the isotropic and parallel models as special cases. I compare the developed model to previously proposed mixing models, showing that the developed model can reproduce a much wider range of anisotropy than the already-existing anisotropic mixing model. By applying the developed model to an example of the anisotropic conductivity in the oceanic upper crust inferred by electromagnetic exploration, I demonstrate that the developed mixing model enables us to quantitatively infer the crack orientation and fluid volume fraction that reproduce significant anisotropic conductivity found by field observations. Furthermore, I compare the developed model to the anisotropic seismic velocity model for fluid-filled cracks.

Exploring the astrobiological potential of rock varnish from a mars analogue field site of Ladakh, India

Rock varnish, a dark-coloured natural feature rich in manganese (Mn), iron (Fe), and clay minerals that forms on rock surfaces and subsurface rock fractures in extremely dry and cold environments, is believed to provide nutritional support to microbiota. Because varnish supports an extensive microbial community, this rock coating is considered a substrate for potential microbial life to thrive in extreme environments on Earth. Although research in the past decades have advanced understanding of the varnish microbiome, little is known about this microbial community in settings that are high altitude (lower oxygen), dry, and cold. We present here new morphological, chemical, and rock magnetic results of rock varnish from this environmental setting, the Ladakh, a potential analogue site for life in extreme environments. Our results include the presence of putative magnetofossils-in the form of nanochains present in the rock varnish layer. Further, the higher concentrations of oxidised Mn4+ and carboxylic acid functionality on the varnish surface revealed organic signatures. These collective results point towards the enriched concentration of magnetic minerals on the varnish layer that are possibly sourced through biotic forms. Consequently, the rock varnish can serve as an archive of ancient environmental records, as well as a potential geomaterial for astrobiological studies from the Martian analogue field location of Ladakh, which needs to be explored further for extensive biogeochemical studies.

Innovative lithology identification enhancement via the recurrent transformer model with well logging data

In the field of oil and gas exploration and development, accurately predicting lithology is crucial for strategic decision-making. Identifying subsurface rock types helps determine the distribution, quantity, and recoverability of oil and gas reserves, guiding exploratory efforts and increasing the likelihood of successful resource extraction. Our research focuses on enhancing lithology prediction accuracy through the development of the Recurrent Transformer model. This innovative model combines the Transformer architecture with recurrent elements, optimizing the processing of well logging data, inherently comprising time-series information. The recurrent structure effectively learns local contextual information, improving responsiveness to geological variations impacting lithology. Additionally, we integrated the Recurrent Scale-wise Attention (RSA) mechanism into our model, uniquely suited to well logging data. RSA captures and interprets multi-scale information by implementing attention mechanisms at varying scales, enhancing the model's understanding of sequence data. The recursive element within RSA further strengthens the model's ability to process contextual nuances. Key well log curves, including Density (DEN), Acoustic (AC), Gamma-ray (GR), and Compensated Neutron Log (CNL), serve as primary data sources for extracting geological features. These features are input into the Recurrent Transformer, establishing correlations between lithological characteristics and well logging parameters. Comparative analysis with leading-edge models using an experimental dataset revealed the Recurrent Transformer's high prediction accuracy and remarkable generalization capabilities across diverse lithological prediction tasks and geological conditions. This model represents a significant advancement in machine learning for well logging lithology prediction, providing geologists and engineers with a precise and efficient tool, enhancing resource exploration and development quality and reliability. The Recurrent Transformer model showcases the potential of integrating advanced machine learning in geosciences, opening new horizons in oil and gas resource exploration and utilization.

Graph convolutional network for lithological classification and mapping using stream sediment geochemical data and geophysical data

Lithological mapping plays a vital role in acquiring a comprehensive understanding of subsurface rocks and geological structures. Traditional lithological mapping relies heavily on manual field surveys, with the accuracy of the mapping greatly constrained by the expertise of mappers, and thus this process is commonly slow and costly. In the present study, the graph convolutional network (GCN) method was proposed for efficient and automatic lithological mapping, using various geological survey data (e.g. geochemical and geophysical data). The GCN is not limited by the spatial distribution of sampling data, and hence it can be directly applied to the integrated resampling data that do not adhere to the regular spatial distribution patterns of Euclidean space. To evaluate the potential application of the GCN method in lithological classification and mapping, a case study was conducted in the Maqin region (China). Geochemical and geophysical data were utilized as the basis for lithological classification, as they can provide valuable information on geochemical compositions and physical properties of geological bodies, respectively. The study process was primarily divided into three parts. First, basic pre-processing was conducted on the geochemical data and the geophysical data, including centred log-ratio transformation and resampling. Subsequently, the resampled points were divided into training and testing sets in the proportion of 5.5:1. In the training set, 20% of the points are randomly selected as the validation set. Ultimately, using each resampled point as a node and the integrated geochemical and geophysical data as features, a GCN model and an undirected graph were constructed based on an adjacency distance of 1 km. The graph was employed by the constructed GCN model for lithological classification. The results reveal that the majority of lithological units can be accurately classified with an overall accuracy of 86%, indicating that the GCN model exhibits good applicability in lithological classification.

Analysis of Reservoir Properties and Quantification of Hydrocarbon Volumes Within LK Field, Niger Delta, Nigeria

This work analyzes and estimates the qualities and volumes of hydrocarbons in the identified reservoirs (Thin Sand and Sand B) of the LK field in the Niger Delta, Nigeria. The LK field is a promising hydrocarbon reservoir located at the border of the Greater Ughelli Depobelt in the Niger Delta Basin. The data utilized encompassed a 3D seismic survey, which was analyzed to delineate faults, identify stratigraphic horizons, and generate maps illustrating the time and depth structures within the subsurface. Well log data, including gamma ray, resistivity, and density/neutron measurements, were analyzed to evaluate and describe the petrophysical characteristics of the subsurface rock formations. The Thin Sand reservoir unit contains natural gas, while the Sand B reservoir is divided into two separate zones: the first zone (Z1) contains natural gas, and the second zone (Z2) contains oil. For Sand B, Z1, and Z2, the gross thicknesses are 13.16m and 57.46m, net thicknesses are 11.32m and 45.39m, NTGs are 0.86 and 0.79, shale volumes (Vsh) are 0.13 and 0.21, effective porosities (?e) are 0.28 and 0.21, water saturations (Sw) are 0.11 and 0.39, and hydrocarbon saturations (Sh) are 0.89 and 0.61, respectively. For the Thin Sand reservoir, the gross thickness is 6.00m, net thickness is 4.74m, NTG is 0.79, Vsh is 0.21, ?e is 0.23, Sw is 0.12, and Sh is 0.88. The stock tank oil initially in place (STOIIP) in the Sand B, Z2 reservoir is estimated to be 46.8 million barrels, with a recoverable oil volume of 9.4 million barrels, assuming a 20% recovery factor. The stock tank gas initially in place (STGIIP) is estimated at 10.4 billion cubic feet (Bcf) in the Thin Sand reservoir and 30.6 Bcf in the Sand B, Z1 reservoir, with recoverable gas volumes projected at 8.3 Bcf and 24.5 Bcf, respectively, assuming 80% recovery factors. After estimating the volume of hydrocarbons in the known reservoirs, significant and economic amounts of gas and oil for commercial purposes were discovered. However, this research study faces challenges such as uncertainty, unreliability, and varying scales due to different origins within the field. Further research using unconventional computational tools together with high-resolution simulating software is needed to produce more detailed and accurate results.

Ensemble Smoother with Fully Convolutional VAE for seismic facies inversion

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ES-MDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for uncertainty quantification. However, there are significant challenges related to the computational cost, Gaussian assumptions, and its application for categorical model variables. Recent developments have proposed to integrate ES-MDA with Variational Autoencoder (VAE) to reparametrize facies into a continuous feature space and reduce computational costs. Despite promising results, it has introduced new limitations concerning network convergence, spatial correlation loss, and manipulation of the latent space. In this paper, a Fully Convolutional Variational Autoencoder (FCVAE) was proposed to outperform the VAE in terms of preserving spatial information, requiring fewer network parameters, presenting higher dimension reduction and yielding better facies estimation. The objective of the application example is to draw sound conclusions based on a 2D synthetic application, where the results of both methods can be compared with reference facies for quality evaluation.

RESEARCH INNOVATIONS IN DIRECTIONAL AND HORIZONTAL DRILLING AND SIDETRACKING TECHNIQUES

Directional and horizontal drilling, and sidetracking techniques have become integral components of the oil and gas industry, which allows access to hydrocarbon reserves in challenging environments. This paper provides an overview of these drilling methods and explores recent trends in the development of technical equipment for horizontal drilling and sidetracking. Directional drilling allows for drilling at various angles, including horizontal and multilateral, providing access to reservoirs beneath the Earth's surface. This technique involves specialized equipment and precise control of the drill bit's direction and deviation, resulting in better reservoir management, increased productivity and reduced environmental effect. Horizontal drilling, on the other hand, involves drilling laterally through subsurface rock formations, particularly beneficial for accessing unconventional reservoirs (shale formations). Horizontal drilling allows operators to access more of the formation, resulting in increased productivity and reduced environmental footprint. Sidetracking techniques are employed to redirect the path of a wellbore, necessary for avoiding geological hazards, accessing untapped reservoirs, or bypassing damaged sections of the well. This process requires careful planning, specialized equipment, and skilled personnel to ensure successful execution while minimizing risks. The paper also discusses modern trends in the development of technical equipment for horizontal drilling and sidetracking technology. These include automation and robotics to improve drilling efficiency and safety, advancements in drilling fluids and measurement-while-drilling (MWD) technologies for better wellbore stability and real-time data acquisition, and innovations in extended reach drilling (ERD) tools and casing and cementing solutions. Additionally, there is ongoing development in sidetracking techniques to improve efficiency and reliability, such as whipstock and casing exit technologies. The Ultrashort-Radius Radial System (URRS) is highlighted as an advanced drilling technology, enabling access to reservoirs with restricted space or challenging geometries. Although challenges such as cost and adaptation to downhole conditions remain, ongoing research and development is aimed at overcoming these barriers and demonstrating the feasibility of innovative drilling technologies. Ultimately, these advances contribute to the industry's goal of maximizing hydrocarbon production while ensuring operational efficiency and environmental sustainability. Keywords: directional drilling, horizontal drilling, sidetracking techniques, technical equipment development, ultrashort-radius radial system, laser drilling.

Identification of Ground Vulnerability Based on Kg, PGA, and GSS Assessment by Single Station Microtremor in Bendosari Hamlet, Kalasan Village, Yogyakarta

Bendosari Hamlet, Kalasan, Yogyakarta is a research area that is one of the areas affected by the earthquake that occurred on May 27, 2006. The earthquake with a magnitude of 6.4 caused quite severe damage to buildings around the research area. Based on the phenomenon of earthquakes that have occurred, there needs to be further study related to vulnerable areas in the event of another earthquake. Research data is primary data taken using a single statiun microtremor tool with an overall acquisition point of 50 measurement points. This study aims to find out the value of seismic vulnerability index (Kg), Peak Ground Acceleration (PGA), and Ground Shear Strain (GSS). Using the Horizontal to Vertical Spectral Ratio (HVSR) method, the dominant frequency result (f0) and amplification factor value (A0) from data that has been recorded through the microtremor tool. Based on research that has been done, the results are obtained in the f0rm of seismic vulnerability index values that are in the range of 0.49 - 30.18, the value of the acceleration of ground movement is in the range of 0.21 - 0.61 Gal, and the ground shear strain value is in the range of 4 x 10-6 to 38 x 10-5. Point K26 which is the original documentation of buildings affected by the earthquake, analyzed that the value of Vs of 858.112 m / s on the vs ground profile indicates that there is hard soil at a depth after 27.2 m. This shows that the thickness of subsurface sedimentary rocks at point K26 is quite thick. Keywords: Bendosari Hamlet; Mikrotremor; GSS; HVSR; PGA; Seismic Vulnerability

Groundwater potential identification using electrical resistivity tomography method in Sendangrejo Village, Minggir District, Sleman Regency, Special Region of Yogyakarta

Groundwater is vital element for living. One of methods to identify groundwater potential is Electricity Resistivity Tomography (ERT). This study aims to identify the groundwater potential occurrence in Sleman, Yogyakarta through non-destructive geoelectrical surveys. Based on the ERT models and geological information, supported by hydrogeological data in the research area, drilling a well at the planned location was deemed necessary to reach a depth of at least 100 to 250 meters in order to tap into the maximum groundwater potential. Groundwater is predicted to flow and accumulate in the pores and fractures of rocks. Well drilling in the northern part of section line 1 was more favourable and recommended due to the lower elevation compared to the area of section line 2, where the drill location was originally planned at a higher elevation uphill. Layers of hard rock, presumably lava and breccia, have already been found at a depth of 5 to 15 meters based on historical open well excavations around the area. Identifying the characteristics of subsurface volcanic rocks during drilling is suggested to evaluate drilling and well planning. Well logging should be conducted to collect lithostratigraphy and rock mass information. Further tests of hydraulic conductivity for each potential layer.

How do Porosity, Size and Interconnectivity of Pores Influence The Values of the Archie Equation Exponents in Carbonate and Sandy Rocks?

Objective: The aim of this study is to investigate the exponents of the Archie equation in order to support the evaluation of hydrocarbon or groundwater reserves through electrical resistivity measurements of subsurface rocks. Theoretical Framework: The Archie equation describes the relationship between the electrical resistivity of a rock and its water saturation and depends on the cementation (m) and saturation (n) exponents. Several authors state that electrical resistivity and the exponents m and n depend on the size, geometry, and connectivity of the pores. Method: This article applies the finite element method to simulate the propagation of the electric field in digital rock models and thus evaluate the Archie exponents. Results and Discussion: Our results indicate that the relationship between these exponents and pore attributes is different for values below or above a threshold. The variables with the greatest influence on m are the porosity of macropores and their interconnectivity, as well as the volumetric fraction of the microporous phase. As for the saturation exponent, the variables with the greatest influence are the porosity of macropores and the interconnectivity of both potentially conductive domains. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of underground natural resource assessment. These implications may cover the sectors of hydrocarbon production, groundwater, geo-environmental investigations, and geotechnical studies. Originality/Value: This study contributes to the literature by presenting an innovative method for determining the exponents of the Archie equation. The relevance and value of this research are evidenced by its high economic impact on the aforementioned sectors and by its applicability anywhere.

Fracture network characterisation of the Balmuccia peridotite using drone-based photogrammetry, implications for active-seismic site survey for scientific drilling

The presence of discontinuities (e.g. faults, fractures, veins, layering) in crystalline rocks can be challenging for seismic interpretations because the wide range of their size, orientation, and intensity, which controls the mechanical properties of the rock and elastic wave propagation, resulting in equally varying seismic responses at different scales. The geometrical characterisation of adjacent outcrop discontinuity networks allows a better understanding of the nature of the subsurface rocks and aids seismic interpretation. In this study, we characterise the discontinuity network of the Balmuccia peridotite (BP) in the Ivrea–Verbano Zone (IVZ), northwestern Italy. This geological body is the focus of the Drilling the Ivrea–Verbano zonE (DIVE), an international continental scientific drilling project, and two active seismic surveys, SEismic imaging of the Ivrea ZonE (SEIZE) and high-resolution SEIZE (Hi-SEIZE), which aim to resolve the subsurface structure of the DIVE drilling target through high-resolution seismic imaging. For fracture characterisation, we developed two drone-based digital outcrop models (DOMs) at two different resolutions (10−3–10 m and 10−1–103 m), which allowed us to quantitatively characterise the orientation, size, and intensity of the main rock discontinuities. These properties affect the seismic velocity and consequently the interpretation of the seismic data. We found that (i) the outcropping BP discontinuity network is represented by three more sets of fractures with respect to those reported in the literature; (ii) the discontinuity sizes follow a power-law distribution, indicating similarity across scales, and (iii) discontinuity intensity is not uniformly distributed along the outcrop. Our results help to explain the seismic behaviour of the BP detected by the SEIZE survey, suggesting that the low P-wave velocities observed can be related to the discontinuity network, and provide the basic topological parameters (orientation, density, distribution, and aperture) of the fracture network unique to the BP. These, in turn, can be used for interpretation of the Hi-SEIZE seismic survey and forward modelling of the seismic response.

Groundwater Study Using Vertical Electrical Sounding (VES) Data Based on Resistivity and Porosity of Rocks in Kampung Melayu, Bengkulu City

This research aims to identify groundwater based on the value of resistivity and porosity of rocks, which are important parameters in the management of groundwater resources and Development Planning in the region. This groundwater research using Vertical Electrical Sounding (VES) data based on the value of resistivity and porosity of rocks was carried out by measuring resistivity using the (VES) method at 20 sounding points located in Kampung Melayu District, Bengkulu City. The VES method is one of the approaches in the geoelectric method to determine the lithological characteristics of subsurface rocks. Resistivity Data obtained were then inverted and analyzed and interpreted to obtain lithology of subsurface rocks and groundwater level distribution in Kampung Melayu, Bengkulu City. The results showed the presence of groundwater based on low resistivity anomaly values with a range of values from 5 to 30 meters in the West and 10 to 50 meters in the East, with resistivity values ranging between 5 Ω.m up to 45 Ω.m. This research is expected to make a significant contribution to the management of groundwater resources in the District of Kampung Melayu, Bengkulu City and can be the basis for development planning in the area. In addition, it is hoped that the results of this research can be a reference for further research in the field of hydrogeology and groundwater modeling. Keywords: Aquifer; geoelectricity; groundwater; porosity; resistivity

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.