Subsurface Reservoirs Research Articles

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

A computational model that can accurately describe the thermodynamics of a hydrocarbon system and its properties under various conditions is a prerequisite for running reservoir and pipeline simulations. Cubic Equations of State (EoS) are mathematical tools used to model the phase and volumetric behavior of reservoir fluids when compositional effects need to be considered. To anticipate uncertainty and enhance the quality of their predictions, EoS models must be adjusted to adequately match the available lab-measured PVT values. This task is challenging given that there are many potential tuning parameters, thus leading to various tuning results of questionable validity. In this paper, we present an automated EoS tuning workflow that employs a Generalized Pattern Search (GPS) optimizer for efficient tuning of a cubic EoS model. Specifically, we focus on the Peng–Robinson (PR) model, which is the oil and gas industry standard, to accurately capture the behavior of diverse multicomponent, complex hydrocarbon mixtures encountered in subsurface reservoirs. This approach surpasses the limitations of conventional gradient-based (GB) methods, which are susceptible to getting trapped in local optima. The proposed technique also allows physical constraints to be imposed on the optimization procedure. A gas condensate and an H2S-rich oil were used to demonstrate the effectiveness of the GPS algorithm in finding an optimized solution for high-dimensional search spaces, and its superiority over conventional gradient-based optimization was confirmed by automatically tracking globally optimal and physically sound solutions.

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times.

Assessing the geological storage potential of CO2 in Baltic Basin: A case study of Lithuanian hydrocarbon and deep saline reservoirs

The need to reduce carbon emissions, via exploring the CO2 storage potential in the depleted subsurface hydrocarbon and the deep saline aquifers in the Baltic Basin region of Lithuania, is discussed. The paper presents a unique methodology, using multi-deterministic numerical models, for evaluating the CO2 storage potential of the deep saline aquifers and the depleted hydrocarbon fields of Lithuania. Such an approach has never been presented before for evaluation of Lithuanian subsurface reservoirs for CO2 storage potential. The paper also explains the limitations of previous estimates of CO2 storage potential computed for deep saline aquifers in Lithuania and introduces a new study that uses uncertainty quantification principle and numerical modelling to estimate the maximum CO2 injection and storage potential in the region. Three deep saline aquifers D11 (offshore), Syderiai and Vaskai (on shore) are evaluated along with depleted hydrocarbon reservoirs of Gargzdai residual oil zone. Evaluation estimates presented in this paper suggest a maximum theoretical storage potential in range of 96 –407 Mt. of CO2 for the three deep saline aquifers and in range of 64–267 Mt. of CO2 for Gargzdai residual oil zone. Although the primary focus of this paper is on the storage capacity estimation, a high-level analysis of the risks associated with CO2 injection and storage are also presented.

Introduction to this special section: Imaging faults and fractures

The exploration and management of subsurface reservoirs relies heavily on unraveling the intricate complexities of faults and fractures. In this special section, we present a collection of papers that, in the absence of world-class outcrop exposures such as that shown on this issue's cover, utilize various data sets and geophysical processing techniques to illuminate fault and fracture networks. The collected papers address a spectrum of fault- and fracture-related topics pertinent to scientists across disciplines, spanning hydrocarbon plays to geothermal systems and carbon storage sites.

On‐Demand Magnetically‐Activated Drug Delivery from Additively Manufactured Porous Bone Implants to Tackle Antibiotic‐Resistant Infections

AbstractThis study proposes a new concept for an on‐demand drug releasing device intended for integration into additively manufactured (i.e., 3D printed) orthopedic implants. The system comprises a surface with conduits connected to a subsurface reservoir used for storage and on‐demand release of antimicrobial agents, covered with a cap that prevents the antibacterial agents from being released until alternating magnetic field (AMF) raises the temperature of the cap, thus, releasing the stored drug. To demonstrate this concept, Ti6Al4V specimens are directly 3D printed using selective laser melting and their surface, reservoirs, and drug releasing properties are characterized. A new synthetic antimicrobial peptide, SAAP‐148, is thereafter tested for its cytotoxic, osteogenic, and immunomodulatory effects at concentrations relevant for its minimal bactericidal concentration (MBC) and is compared with its natural analogue, LL‐37. The results showed that AMF successfully activated the release from the 3D printed loaded samples. Both peptides demonstrated to be non‐cytotoxic within the MBC levels for macrophages and preosteoblasts and did not influence their osteoimmunomodulatory behavior. The findings of this study indicate that the proposed concept is technically feasible and has the potential to be used for the development of bone implants with on‐demand delivery systems to fight IAI without systemic or continuous local release of antibiotics.

A Method for Enhancing the Simulation Continuity of the Snesim Algorithm in 2D Using Multiple Search Trees

Multiple-point geostatistics (MPS) has more advantages than two-point geostatistics in reproducing the continuity of geobodies in subsurface reservoir modeling. For fluvial reservoir modeling, the more continuous a channel, the more consistent it is with geological knowledge in general, and fluvial continuity is also of paramount importance when simulating fluid flow. Based on the pixel-based MPS algorithm Snesim, this study proposes a method that utilizes multiple search trees (MSTs) to enhance simulation continuity in 2D fluvial reservoir modeling. The objective of the MST method is to capture complete data events from a training image (TI), which aims to achieve enhanced continuity in fluvial reservoir sublayer modeling. By resorting to search neighborhoods based on their proximity to the central node of the data template, multiple data templates that correspond to the MSTs will be generated. Here, four data templates were generated by arranging the relative search neighborhood coordinates in ascending and descending order with respect to the central node. Parallel computing was tried for the construction of the search trees. This work calculated the conditional probability distribution function (CPDF) of the simulating nodes by averaging the CPDFs derived from the MSTs, and double retrieval was employed to filter out the search trees that possessed an inaccurate local CPDF for the simulating nodes. In addition, the connected component labeling (CCL) method was introduced to evaluate the simulation continuity in MPS. The results indicated that the MST method can enhance the simulation continuity of the Snesim algorithm by reproducing the fine connectivity of channel facies in 2D fluvial reservoir modeling.

Technology Focus: Formation Evaluation (February 2024)

The scientific research process begins as one tries to find explanations for a phenomenon. We make observations, define the problem statement, and review the existing domains of research that could be used. Another approach is to explore theoretical problems, those that are purely conceptual at present but provide a solution when a related observation is made in the future. Though these approaches sound isolated, both are part of characterizing uncertainty, and uncertainty comes in all scales and dimensions. This challenges us to learn at all scales possible, from the fume hoods in the laboratory to magnificently exposed outcrops and through deep narrow boreholes that drill through subsurface reservoirs. The combined efforts often convert learnings to actionable intelligence. At a smaller scale, porosity and permeability are probably the two most-studied rock properties among those that have meaningful implications for hydrocarbon reservoirs. Paper SPE 216856 considers machine-learning (ML) methods for classifying reservoir texture at a microscale. Borehole-image logs long have been used to obtain a picture of subsurface reservoirs. Unfortunately, a majority of the observations are qualitative. Quantifying these features faces the challenge of continuity, upscaling, and regional correlation. As we explore the latitude of ML-based applications, the use of these techniques for quantifying image logs becomes very relevant. The authors of that paper contribute to quantifying textural features at a “fume-hood scale” and develop a work flow with the potential for estimating properties such as porosity and permeability from a different domain of reservoir characterization. I often wonder how much the domain on formation evaluation encompasses. While geoscience-driven reservoir characterization is a big part of it, how reservoirs change over time also is a complementary observation. Paper URTeC 3864861 discusses various aspects of geomechanical changes that a hydraulically fractured reservoir goes through during its life cycle. The authors here study the relationship between measured strain from the fiber-optic sensors and wellhead pressure. Research like this could be extended to predicting production profiles and estimating recovery factors, which are important considerations in designing a stimulation program for sustaining production, maximizing recovery, and improving financial matrices for the capital program. I believe information could be categorized as learning, knowledge, and intelligence. Any scientific process starts with set of careful observations bound by an envelope of hypotheses. This is learning. Learning, which could be verified by predictable and repeatable outcomes from carefully designed experiments of complementing domains, becomes knowledge. Actionable knowledge, which then could be used to alter an outcome or a process, becomes intelligence. Paper URTeC 3871303 discusses a development strategy in a restrictive development unit with an existing parent well. Here, considerations are heavily weighted toward optimizing both interwell spacing and capital efficiency. The search for answers to a problem like this must seek guidance from a variable-scale experiment. The study here establishes the big picture with the structural elements of the basin that could restrict both the continuity of the reservoir and the nature of the producible fluid. With this framework, the model is then set to iterate from several different perspectives. Potential interwell communications are explored by measuring fracture-driven interactions (FDIs) and quantifying stimulated reservoir volume. What is impressive here is the different domains from which the authors seek answers. Direct observations from acoustic-fiber measurements for FDI and the geochemistry of produced fluids for identifying unique signatures from vertically separated formations are individual domains that seek the same answers in various scales. The study recommends the optimal spacing between wells and a stimulation design that minimizes well interference, reduces competition for resources between wells, and avoids overcapitalizing the program. This is how knowledge transforms into intelligence. I hope the readers appreciate the scales of characterization in these three papers. As a student of geology, I have always been fascinated by the concept of scale and its relation to the domains of science that we deal with. Unlike general relativity and quantum mechanics, most geologic phenomena are observed in all scales. It is just the uncertainty that needs to be quantified.

P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments

Abstract. The porous microenvironment of soil offers various environmental functions which are governed by physical and reactive processes. Understanding reactive transport processes in porous media is essential for many natural systems (soils, aquifers, aquatic sediments or subsurface reservoirs) or technological processes (water treatment or ceramic and fuel cell technologies). In particular, in the vadose zone of the terrestrial subsurface the spatially and temporally varying saturation of the aqueous and the gas phase leads to systems that involve complex flow and transport processes as well as reactive transformations of chemical compounds in the porous material. To describe these interacting processes and their dynamics at the pore scale requires a well-suited modelling framework accounting for the proper description of all relevant processes at a high spatial resolution. Here we present P3D-BRNS as a new open-source modelling toolbox harnessing the core libraries of OpenFOAM and coupled externally to the Biogeochemical Reaction Network Simulator (BRNS). The native OpenFOAM volume-of-fluid solver is extended to have an improved representation of the fluid–fluid interface. The solvers are further developed to couple the reaction module which can be tailored for a specific reactive transport simulation. P3D-RBNS is benchmarked against three different flow and reactive transport processes: (1) fluid–fluid configuration in a capillary corner, (2) mass transfer across the fluid–fluid interface and (3) microbial growth with a high degree of accuracy. Our model allows for simulation of the spatio-temporal distribution of all biochemical species in the porous structure (obtained from μ-CT images), for conditions that are commonly found in the laboratory and environmental systems. With our coupled computational model, we provide a reliable and efficient tool for simulating multiphase, reactive transport in porous media.

On the Derivation of Closed‐Form Expressions for Displacements, Strains, and Stresses Inside Poroelastic Reservoirs

AbstractWe critically review the derivation of closed‐form analytical expressions for elastic displacements, strains, and stresses inside a subsurface reservoir undergoing pore pressure changes using inclusion theory. Although developed decades ago, inclusion theory has been used recently by various authors to obtain fast estimates of depletion‐induced and injection‐induced fault stresses in relation to induced seismicity. We therefore briefly address the current geomechanical relevance of this method, and provide a numerical example to demonstrate its use to compute induced fault stresses. However, the main goal of our paper is to correct some erroneous assumptions that were made in earlier publications. While the final expressions for the poroelastic stresses in these publications were correct, their derivation contained conceptual mistakes due to the mathematical subtleties that arise because of singularities in the Green's functions. The aim of our paper is therefore to present the correct derivation of expressions for the strains and stresses inside an inclusion and to clarify some of the results of the aforementioned studies. Furthermore, we present two conditions that the strain field must satisfy, which can be used to verify the analytical expressions.

Spatio-temporal sequence prediction of CO2 flooding and sequestration potential under geological and engineering uncertainties

CO2 injection for subsurface hydrocarbon development not only enhances oil and gas recovery but also enables CO2 sequestration in the subsurface. It is essential to develop effective methods for evaluating the potential of CO2 flooding and sequestration. Despite the existence of methods for predicting the effectiveness of hydrocarbon development using historical production data, insufficient emphasis has been placed on adequately incorporating geological and engineering uncertainty information to enhance prediction accuracy. To address this issue, a novel spatial-temporal ResNet (ST-ResNet) model is proposed for predicting hydrocarbon production, CO2 sequestration volume and CO2 diffusion pattern in the subsurface, which represent the CO2 flooding and sequestration potential. First, high-dimensional reservoir property fields are parameterized using the combined method of principal component analysis and discrete cosine transform (PCA-DCT). Second, the spatial sequence information of various reservoir property fields is extracted with features based on residual neural network (ResNet). Then, the time series information such as dynamic well control parameters is encoded with stacked BiLSTM (SBiLSTM). Specifically, the ST-ResNet model incorporates the above modules to overcome the limitations of collaborative consideration of temporal and spatial information involved in subsurface hydrocarbon development. Comparison between simulation and prediction results on the 2D/3D reservoir model reveals a significant achievement in prediction accuracy by the ST-ResNet model (with R2 and SSIM scores of 0.947, 0.911 and 0.937, 0.922, respectively). In comparison to CNN, LSTM and their combined approach CNN-LSTM, the ST-ResNet model demonstrates an improvement of 3% to 5% in R2, along with reductions of 20% to 30% in MAE and 15% to 25% in RMSE, respectively. These results highlight the superior stability and generalization of the ST-ResNet model. The contribution of this work is to provide a more accurate and efficient prediction tool to guide the integrated development of CO2 flooding and sequestration in subsurface hydrocarbon reservoirs, which facilitates decision-making processes for engineers.

Three-dimensional surface deformation from multi-track InSAR and oil reservoir characterization: A case study in the Liaohe Oilfield, northeast China

Oil productions can result in pore pressure drop in the reservoir, generating an increase in effective stress and leading to reservoir compaction. The compaction in the subsurface reservoir translates to the earth's surface, which is manifested as a loss of elevation (land subsidence), causing damages to oil production facilities and surface infrastructures. The Liaohe oilfield, located in Liaohe River Delta (LRD), northeast of China, is one of the most significant subsidence areas in China as a direct consequence of oil extraction from the reservoir. Previous studies carried out in this area assumed the oil production-induced displacement retrieved from Interferometric Synthetic Aperture Radar (InSAR) corresponds only to vertical deformation. In this work, for the first time, we proposed a method to retrieve the full three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west displacement components by combining the multiple-geometry InSAR line-of-sight (LOS) observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement. Two ascending and two descending datasets from Sentinel-1 satellite covering the area were processed by an InSAR time series analysis over the 2017 to 2021 period, providing consistent displacement rate maps and displacement time series in the LOS direction. Spatial local-scale land subsidence was found in several producing fields over the deltaic region, including Shuguang, Huanxiling, and Jinzhou oilfields. The 3D displacement decomposition was then conducted in Shuguang oilfield. The derived 3D displacement field exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50–60 mm/year moving towards the subsidence center. The retrieved 3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir.

A Critical Geological Evaluation of the Hydrogen Storage Potential in the Cousland Gas Field, Midland Valley of Scotland

Hydrogen is envisaged to be an important element in the drive to replace hydrocarbons in the energy mix and its geological storage in man-made salt caverns or porous subsurface reservoirs onshore in the United Kingdom is being actively investigated. It has recently been suggested that porous Carboniferous sandstone reservoirs of the partially depleted and abandoned Cousland Field, located c.15 km SE of Edinburgh, could be used for the storage of hydrogen as part of efforts to decarbonise the industrialised and heavily populated central belt of Scotland. The gas field occurs in an onshore surface anticline situated in a culmination along a topographic ridge on the eastern flank of the Midlothian Coalfield. Mapping of outcropping beds led to a well, Cousland-1, being drilled in 1937-39 as part of a national campaign of oil exploration, which discovered natural gas in two thin sands. Despite the initial success, all the subsequent five appraisal wells, drilled between 1939 and 1960, were devoid of commercial hydrocarbons. Despite the disappointing drilling campaign, the field was still developed based on the original Cousland-1 well result and some 0.25 billion standard cubic feet (“scf”) (7.0 million m3) of gas were produced between 1939 and 1965 before the well was plugged and abandoned. This paper draws upon and integrates hitherto unpublished historical records, obtained from the archives of the UK Onshore Geophysical Library, to critically examine the possibility that the Cousland gas field might be re-purposed for hydrogen storage. These studies conclude that it is currently not possible to demonstrate the subsurface configuration, host reservoir distribution or sealing mechanism of the proposed storage container because the information required is not available and may be impossible to obtain. Rather than supporting the case for hydrogen storage, our new evaluation demonstrates that the Cousland field is a poor site that fails to meet the criteria for safe subsurface storage and should not be used for this purpose until and unless the significant challenges can be addressed.

Anaerobic hydrocarbon biodegradation by alkylotrophic methanogens in deep oil reservoirs.

In subsurface biodegraded oil reservoirs, methanogenic biodegradation of crude oil is a common process. This process was previously assigned to the syntrophy of hydrocarbon-degrading bacteria and methanogenic archaea. Recent studies showed that archaea of the Candidatus Methanoliparum named as alkylotrophic methanogens couple hydrocarbon degradation and methane production in a single archaeon. To assess the geochemical role of Ca. Methanoliparum, we analyzed the chemical and microbial composition and metabolites of 209 samples from 15 subsurface oil reservoirs across China. Gas chromatography-mass spectrometry analysis revealed that 92% of the tested samples were substantially degraded. Molecular analysis showed that 85% of the tested samples contained Ca. Methanoliparum, and 52% of the tested samples harbored multiple alkyl-coenzyme M derivatives, the intercellular metabolites of alkylotrophic archaea. According to metagenomic and metatranscriptomic analyses, Ca. Methanoliparum dominates hydrocarbon degradation in biodegraded samples from the Changqing, Jiangsu, and Shengli (SL) oilfields, and it is persistently present as shown in a 15-year-long sampling effort at the Shengli oilfield. Together, these findings demonstrate that Ca. Methanoliparum is a widely distributed oil degrader in reservoirs of China, suggesting that alkylotrophic methanogenesis by archaea plays a key role in the alteration of oil reservoirs, thereby expanding our understanding of biogeochemical process in the deep biosphere.

A data-driven hyperspectral method for sampling of diagenetic carbonate fabrics – A case study using an outcrop analogue of Jurassic Arab-D reservoirs, Saudi Arabia

Diagenetic alteration commonly overprints depositional fabrics in carbonate-dominated sediments and impact reservoir quality. Dolomitization is a prevalent diagenetic process observed in subsurface reservoirs that profoundly alters the depositional precursor's pore network, thereby influencing subsurface storage capacity and fluid flow behavior. Typical workflows to characterize the dolomitized sequences, textures, degree and extent of dolomitization rely on mapping, spatial sampling, and time-consuming geochemical, petrographic and petrophysical analysis. In this study, we propose a hyperspectral data-driven workflow for identifying dolomitized horizons and extracting sample sets optimized to characterize textural and chemical variations. Hyperspectral imaging (HSI) data was acquired with 1.5 mm spatial sampling along a 50 m long core drilled behind an outcrop of the Late Jurassic Jubaila-Arab sequence in Wadi Daqlah, Saudi Arabia. Spectral features in the visible (VNIR), shortwave (SWIR), mid-wave (MWIR), and long-wave (LWIR) infrared regions were then used to classify carbonate mineralogy, allowing for the rapid identification of dolomitized zones, and k-means clustering applied exclusively to the dolomitized areas used to identify intra-dolomite variations and suggest representative sample locations. Petrographic and geochemical analyses were carried out on these samples, revealing that clusters identified with the hyperspectral data represent four distinct diagenetic fabrics. These results demonstrate the value of HSI for objective and data-driven sampling, reducing the number of samples required for petrographic, geochemical and geophysical analysis and hence time and costs required to spatially characterize diagenetic alteration.

Trapping, hysteresis and Ostwald ripening in hydrogen storage: A pore-scale imaging study

Green hydrogen, produced from surplus electricity during peak production, can be injected into subsurface reservoirs and retrieved during high-demand periods. In this study, X-ray tomography was employed to examine hysteresis resulting from repeated hydrogen injection and withdrawal. An unsteady state experiment was performed to evaluate the distribution of hydrogen and brine after drainage and imbibition cycles: images of the pore-space configuration of fluids were taken immediately once injection had stopped and after waiting for a period of 16 h with no flow. A Bentheimer sandstone sample with a length of 60 mm and diameter of 12.8 mm was used, and hydrogen was injected at ambient temperature and a pore pressure of 1 MPa. The gas flow rate was decreased from 2 ml/min to 0.08 ml/min over three cycles of gas injection followed by water flooding, while the brine injection rate was kept constant. The results showed the presence of capillary pressure hysteresis and hydrogen migration through Ostwald ripening due to the diffusion of gas dissolved in the brine. These phenomena were characterized through analysis of interfacial curvature, area, connectivity and pore occupancy. The hydrogen tended to reside in the larger pore spaces, consistent with water-wet conditions. 16 h after flow had stopped, the hydrogen aggregated into larger ganglia with a single large connected ganglion dominating the volume. Moreover, the Euler characteristic decreased after 16 h, indicating an improvement in connectivity. The work implies that Ostwald ripening – mass transport of dissolved gas – leads to less hysteresis and better connectivity than would be assumed ignoring this effect, as done in assessments of hydrocarbon flow and trapping.

Investigating the behaviour of NaCl brines and hydrocarbons in porous alumina using low-field NMR relaxation and diffusion methods.

The behaviour of multiple fluid phases within a porous medium is hard to predict. NMR measurements offer an excellent tool to probe such systems in a fast and non-invasive way. Such systems can be relevant to hydrocarbon recovery, catalysis, and CO2 and H2 geo-storage, among others. Since electrolyte solutions are always present in subsurface reservoirs, understanding their behaviour within porous media is highly important. In this study, we use NMR relaxation and diffusion methods to investigate the diffusion coefficients and strength of interactions between alumina surfaces and brines at various NaCl concentrations, focusing on the effect of salt concentration on transport and interactions within the porous structure. Furthermore, we study the spontaneous displacement of dodecane, a model hydrocarbon, from the same alumina pellets using the same brine solutions. Results show that brines of lower salinity consistently displace more dodecane in total, after soaking dodecane-saturated pellets in a brine solution for several days. This indicates that increased salt concentrations can reduce wettability towards the aqueous phase in simple metal oxide surfaces and highlights the capabilities of NMR to efficiently study such systems.

Geothermal resource estimation of Uttarakhand region, India

With many countries dependent on imported fuels, governments and industries are diversifying energy sources, including geothermal energy. Geothermal energy, derived from the Earth's internal heat, holds significant potential as a renewable energy source. The present work focuses on the geothermal resource estimation of the Uttarakhand region in India. Critical subsurface reservoir parameters for geothermal extraction and estimates of potential resources are reviewed through an analysis of geological formations, temperature gradients, and rock permeability. Various estimation methods, including surface heat flux, volume methods, and probabilistic methods, are discussed, with a particular emphasis on the Monte Carlo method. Assessment of the resource potential of the Uttarakhand geothermal system using available data estimates the resource potential of the Uttarakhand prospect, revealing an energy estimate of 1.26*10^15 J/kg (P50 case). Correspondingly, for the P90 and P10 scenarios, the estimated heat stands at 2.30*10^15 J/kg and 2.6*10^14 J/kg, respectively. However, successful exploitation requires a thorough understanding of subsurface reservoir parameters and careful resource estimation. This comprehensive analysis offers valuable insights for policymakers, researchers, and industry stakeholders interested in harnessing geothermal energy for sustainable development.

Reservoir Development Geologic Modeling and Residual Oil Prediction Research

The aim of this thesis is to investigate the geological modeling methods in reservoir development as well as residual oil prediction techniques. Geological modeling is a crucial step in oilfield development, which involves the three-dimensional description of subsurface reservoirs and helps to better understand reservoir properties. Residual oil prediction, on the other hand, is an estimation of the future production capacity of the reservoir and is crucial for decision making and resource management. This study explores different geological modeling methods, including seismic inversion, core analysis, and wellbore data, and introduces a variety of residual oil prediction techniques, including numerical simulation and statistical methods. Through example analyses and case studies, this paper demonstrates the application of these methods in practical reservoir development and their potential to improve reservoir development efficiency and residual oil prediction accuracy.

Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes of samples from analogous formations. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage.

STRUCTURAL STYLE AND TIMING OF NW‐SE TRENDING ZAGROS FOLDS IN SW IRAN: INTERACTION WITH NORTH‐SOUTH TRENDING ARABIAN FOLDS AND IMPLICATIONS FOR PETROLEUM GEOLOGY

The Zagros foldbelt – foreland system in SW Iran is a prolific hydrocarbon province with known reserves of more than 90 billion brl of oil and 800 TCF of natural gas. Establishing the structural style of folding in the Zagros area presents a major challenge due both to the geographical extent of the foldbelt, which is some 1600 km long in total, and the presence of marked lateral variations in fold style related to the complex regional tectonic history. In addition, while numerous high‐quality structural studies of the Zagros have been completed over the last 20 years, they support a variety of different interpretations and are therefore diffcult to synthesize. In this paper, we review the general structural style of the Zagros fold‐and‐thrust belt in SW Iran, and in particular the style of folding in the Lurestan arc, Dezful embayment, Izeh Zone and Fars arc. We summarise relationships between folding in these areas and fracture development, and investigate the timing of folding and the interaction between NW‐SE oriented “Zagros” folds and north‐south oriented “Arabian” folds. Finally, we briefly assess the implications of fold style for petroleum systems in the Zagros area. Although no new data are presented in this paper, a series of unpublished maps are used to illustrate the main results and include: a map showing the extent of the main detachment levels across the Lurestan, Dezful and Fars structural domains; two palaeotectonic maps (for Late Cretaceous – Paleocene and Miocene – Pliocene times, respectively), showing the position of the deformation fronts of the Zagros and the North Oman thrust systems and their potential spatial and temporal relationship with folding; and a set of four maps showing the distribution of reservoir rocks which are grouped by age into the Permian – Triassic Dehram Group, the Late Jurassic – Early Cretaceous Khami Group, the Late Cretaceous Bangestan Group, and the Oligocene – Miocene Asmari Formation. In addition, for the Lurestan, Dezful and Fars structural domains, a series of regional cross‐sections at the same scale are presented and discussed.Most of the data in this review paper were acquired in order to gain an improved understanding of the petroleum systems in the Zagros area; however the data are used here to investigate a range of interacting processes including tectonics, sediment deposition and subsurface fluid flow in the development of the fold‐and‐thrust belt and its associated foreland basins. The resulting synthesis is intended to provice a starting point for future tectonostratigraphic and hydrocarbon‐related studies which will make use of both existing and new multidisciplinary techniques to constrain the results. The knowledge acquired and the techniques used will be of benefit in future challenges including the identification of subsurface reservoirs suitable for the permanent storage of CO2 to mitigate the effects of climate change.