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Hydraulic fracturing (HF) has been used for years to enhance oil and gas production from conventional and unconventional reservoirs. HF in enhanced geothermal systems (EGS) has become increasingly common in recent years. In EGS, hydraulic fracturing creates a geothermal collector in impermeable or low-permeable hot dry rocks. Artificial fracture networks in the collector allow for a continuous flow of fluid in a loop connecting at least two wells (injector and producer). However, it is challenging to assess the fracability of geothermal reservoirs for EGS. Consequently, it is necessary to design a method that considers multiple parameters when evaluating the potential of geothermal development. This study proposes an improved fracability index model (FI) based on the influences of fracability-related geomechanical and petrophysical properties. These include brittle minerals composition, fracture toughness, minimum horizontal in-situ stress, a brittleness index model, and temperature effect to quantify the rock's fracability. The hierarchical analytic framework was designed based on the correlation between the influencing factors and rock fracability. The results of the qualitative and quantitative approaches were integrated into a mathematical evaluation model. The improved fracability index model's reliability was evaluated using well logs and 3D seismic data on low-permeable carbonate geothermal reservoirs and shale gas horizontal wells. The results reveal that the improved FI model effectively demonstrates brittle regions in the low-permeable carbonate geothermal reservoir and long horizontal section of shale reservoir. We divide the rock fracability into three levels: FI > 0.59 (the rock fracability is good); 0.59 > FI > 0.32 (the rock fracability is medium); and FI < 0.32, (the rock fracability is poor). The improved FI model can assist in resolving the uncertainties associated with fracability interpretation in determining the optimum location of perforation clusters for hydraulic fracture initiation and propagation in enhanced geothermal systems. [ABSTRACT FROM AUTHOR]

Discoveries of heavy crude oil in the Neoproterozoic rocks (Infracambrian rock sequence) from the Bikaner-Nagaur Basin of India emphasize the significance of studying and exploring the Neoproterozoic source rock potential in the southeastern part of Pakistan. This study evaluates the potential of the source rock in the Infracambrian rock sequence (Salt Range Formation) based on surface geochemical surveys, Rock–Eval pyrolysis, source biomarkers, geophysical characterization, and seismic inversion using machine learning for maturity index estimation. Core samples of Infracambrian rock were extracted for Rock–Eval pyrolysis and biomarker characterization. Additionally, 81 geomicrobial soil and gas samples were collected from the surface to explore the petroleum system and potential source rocks in the subsurface. Advanced interpretation techniques were used to investigate the origin and concentration of hydrocarbon gases at the surface, including Rock–Eval pyrolysis, thermal maturity, source biomarkers, and the environment of deposition of organic matter. The results show that the investigated samples are characterized by restricted marine clay devoid of sedimentary carbonate facies with thermal maturity in the early stage of the oil generation window. The seismic inverted maturity index profile demonstrates a reasonable correlation of thermal maturity with the biomarkers and Rock–Eval pyrolysis. Further scrutiny of the surface geochemical samples confirms the presence of higher concentrations of thermogenic C2–C4 hydrocarbons in the vicinity of anticlinal structures, suggesting the existence of an effective migration path along deep-seated faults to the surface. This study concludes that the Infracambrian rocks on the eastern flank of Pakistan are thicker, thermally mature, and have deep-seated structural closures, indicating a greater probability of heavy and light oil in this area than in the Bikaner–Nagaur Basin, India. [ABSTRACT FROM AUTHOR]

The pore structure is an important factor in determining the productivity of shale, which can help us make better operational decisions. Shale formations have an extremely complex pore structure with a wide range of pore sizes, shapes, orientations, and connectivity. Advanced imaging techniques, such as focused ion beam scanning electron microscopes (FIB-SEM), contribute significantly to the analysis and understanding of complex pore networks in shale, but they are time-consuming. Developing new methods and techniques to capture the spatial distribution and connectivity of pores at various scales is essential to understanding pore structure and its impact on fluid flow. This study aims to analyze the pore characteristics of shale reservoirs by using artificial intelligence (AI) and FIB-SEM. We applied a novel attention gate (AG) model to FIB-SEM images that automatically learns to focus on target structures of varying pores' shape and size. AG can be fully incorporated into the U-Net model with minimal computational impact. The AG improved prediction accuracy by making the U-Net model more sensitive to essential features in the input images and achieved a precision of 97%, recall of 71%, and F1 score of 81% at a confidence threshold of 1. Experimental results show that Attention U-Net outperformed in predicting the pore structures in shale samples from Longmaxi Formation, Qingshankou Formation, Cambrian Niutitang Formation, Yanchang shale oil, and Bakken shale while obtaining optimal performance without requiring multiple convolutional neural network (CNN) models. Additionally, Attention U-Net addresses some of the limitations of the Edge-Threshold Automatic Processing (ETAP) method, including the limited ability to obtain a complete description of the pore space as well as the low segmentation accuracy and sensitivity. The proposed workflow offers a promising solution for optimizing the automatic processing of microscopic images for pores and microfractures identification. Researchers and industry professionals can improve their understanding of shale properties and applications in various fields by maximizing the benefits of our workflow. [ABSTRACT FROM AUTHOR]

• Volumetric seismic attributes are quite effective for detecting seismic discontinuities but are sensitive to noise. • Integrating the curvelets transform and the structure-oriented second-order total-variation regularization improves the signal-to-noise ratio. • Volumetric seismic attribute along with structure-oriented filtering can be used to explore deeply buried geothermal reservoirs. • Validated the fracture distribution by overlying the extracted attributes with seismic fault networks and fracture density. In geothermal systems, the geologic structure is a decisive factor in controlling fluid flow. Several geothermal systems around the world are located in complex geological structures, such as closely spaced and intersecting faults and fractures, folds, and dykes. Seismic data provides high-resolution images of complex structures that can be used to explore geothermal reservoirs. In fact, faults and fractures have subtle geologic edges that produce significant lateral variations in seismic data. Volumetric seismic attributes are quite effective for detecting these discontinuities in seismic data but are sensitive to noise and velocity accuracy. In North China, a deeply buried carbonate structure presents a geologically complex system with fault-controlled geothermal circulation. However, due to the deep burial depth and structural complexity, seismic reflection beneath the buried-hill has a low-quality, making it difficult to detect the characteristics of the fault edges. In this study, we first develop a structural architecture of the buried-hill carbonate reservoirs to show the development characteristics in vertical and plane directions. Next, we propose an intelligent workflow based on structure-oriented filtering and sensitive seismic attributes to investigate structural complexity (faults and fractures) in the basin for geothermal reservoir exploration. The results show that by combining the proposed structure-oriented filtering and sensitive volumetric seismic attributes, we can generate high-resolution seismic images to characterize fault edges, geometries, and fracture anomalies in structurally complex settings in order to identify upflow locations along the faults and fractures in geothermal systems. We further validated the fracture networks distribution by superimposing the extracted attributes with seismic fault networks and fracture density map using multilayer feedforward neural network. This workflow provides a reliable basis for the characterization of geothermal reservoirs in deep-buried and structurally controlled geothermal fields. [ABSTRACT FROM AUTHOR]

The economic success of shale gas plays depends expansively on the brittle-ductile behavior of shale rock for effective hydraulic fracturing. Successful hydraulic fracturing requires targeting the most brittle rocks. Therefore it is worthwhile to classify the shale regarding brittle and ductile zones. To study the impact of brittleness indices in the brittle-ductile transitional zone, we have estimated the mineralogy-based brittleness index, TOC (total organic carbon), pore pressure and geomechanical properties from well logs and core description laboratory measurements. The petrophysical model of Sembar shale from Indus basin Pakistan was compared to brittleness index, organic contents, and pore pressure to differentiate the transitional zones in shale gas reservoirs. The result shows that constant change in rock minerals distribution and brittleness index follow the trend in TOC content, in brittle- ductile and transitional zone. Also, the data was plotted in λ-μ lithology templates and plots of Young's modulus and Poisson's ratio, shale with high quartz and clay contents trap in less ductile to less brittle zone while shale with abundant quartz and low clay contents give rise in the brittle zone. The observations of this study support the previous research idea by suggesting the zones of brittle and transition controlled by TOC to design the hydraulic fracture more efficient. [ABSTRACT FROM AUTHOR]

Petrophysical properties of rocks can be better understood by core and well log analysis. Exact porosity estimation in the Potwar sub-basin of Pakistan is one of the main issues because of complex tectonic settings. Core and wireline log data of two wells: Injra-1 and Nuryal-2 drilled in the study area are used for the porosity evaluation in the Jurassic reservoirs. The log interpretation and laboratory analysis show that the Datta Formation of the early Jurassic is mainly sandstone with minor shaly beds. A workflow is developed to correlate the calculated porosity from logs with the measured porosity from core plug samples. The results show that log porosity and core porosity of Datta sandstone are comparable. Porosity in the younger formations is higher than in the older formations, which is an indication of presence of secondary porosity and structurally deformed fractures in sandstone. The Datta sandstone encountered in Nuryal-2 is about 20-30 % less porous than in Injra-1 well. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine Learning provide a promising solution to predict carbon dioxide trapping in saline aquifers for geo-sequestration. • The global field data was collected for developing the fast prediction model. • Extreme Gradient Boost could provide the most accuracy prediction among three machine learning models. • This study provides a template for prediction carbon dioxide storage performance based on real field data. Carbon dioxide storage in underground saline aquifers is considered a promising technique for decreasing atmospheric CO 2 emissions. The CO 2 residual and solubility in deep saline aquifers are crucial processes for improving CO 2 storage security. In this study, the trapping efficiency of CO 2 sequestration in saline formations was predicted by developing three supervised machine learning (ML)-based models: random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR). A diverse field-scale simulation database of 1509 samples were collected from the literature, and the proposed models were examined for training and testing. To verify the prediction accuracy of the three ML models, the prediction results were analysed and compared using graphical and statistical indicators. From the prediction results, the proposed ML models were ranked based on their accuracy: XGBoost > RF > SVR. The XGBoost-based predictive model achieved an extremely low root mean square error (RMSE = 0.0041) and high correlation factor (R2 = 0.9993) for both residual and solubility trapping efficiency. However, RF and SVR exhibited RMSEs of 0.0243 and 0.074 and R2 values of 0.9781 and 0.9284, respectively. Furthermore, the applicability of the XGBoost model was validated and only 15 suspected data points were detected across the entire database. Therefore, the proposed model can be a valuable and viable template for predicting the CO 2 trapping index in other saline formations worldwide. Utimately, the XGBoost model has been tested against reservoir simulation models in comprehensive blind testing and may be used as a robust screening and process planning tool for the uncertainty assessment of carbon storage projects. [ABSTRACT FROM AUTHOR]

Deeply buried fractured reservoirs have evolved into significant oil and gas potential in many basins of the world. However, fracture prediction in deeply buried carbonate reservoirs has always been challenging. Fracture prediction in the deep-buried carbonate structure of North China is problematic because of multiphase tectonic movements, variable sediment lithology, and complex diagenesis. Because of deep burial depth and complex heterogeneity, the resolution of seismic reflection data beneath the buried-structure is poor, making it challenging to identify the fault reflection characteristics. This paper proposes a novel idea to identify natural fractures in carbonate reservoirs using conventional logs with seismic reflection data. The proposed model can also predict the fracture aperture and fracture density, a distinctive feature. Another novel hybrid model based on deep-learning neural network (DNN) and cluster analysis is proposed to predict further the spatial variations of lithology, porosity, and fracture parameters from seismic inversion. The proposed models provide valuable insights that help determine fracture parameters in the Paleozoic strata and associated reservoirs through quantitative analysis using petrophysics, rock physics, seismic inversion, and seismic attributes. The overlapping of seismic interpreted fault networks and spatial variations of the inverted fracture parameters indicate a high correlation of fracture development zones. The methodology proposed in this study presents a valuable template valid for the characterization of fractured reservoirs in deeply-buried carbonate reservoirs throughout the world. • Fracture prediction in the deeply buried carbonate structure of North China is challenging and fraught with difficulties. • Seismic reflection beneath the buried-hill has a low resolution, making it difficult to identify fault reflection patterns. • To predict fracture characteristics, a hybrid model based on deep neural networks and cluster analysis is proposed. • The methodology proposed here serves as a useful template for characterizing fractured reservoirs in deeply buried rock. [ABSTRACT FROM AUTHOR]

Black shales of the Niutitang Formation are widespread throughout western Hunan (China). Despite the promising potential of these black shales as unconventional reservoirs, a comprehensive commercial viability analysis has not been previously conducted. The geological characteristics and petroleum potential of these shale reservoirs require further investigation. A multidisciplinary approach is applied to evaluate the shale gas potential of the Niutitang Formation by incorporating geochemical, biomarker, mineralogical, organic petrographic, and carbon isotope analyses. The results revealed that the Niutitang Formation contains more organic carbon (5.80 wt %) than exploited global shale reservoirs. Its organic matter predominately originated from the sapropelic maceral group, displaying a lower carbon isotopic composition (δ13C org < −30 ‰), indicative of type-I kerogen (oil-prone). The organic material is derived from lower aquatic organisms in Lower Cambrian ecosystems. The abundance of aromatized dinosteroids suggests that dinoflagellates or their predecessors were prolific in the Lower Cambrian period. Stable molecular fossils confirm that the deposition of these shale deposits occurred under anoxic marine conditions, and multiple saturated and aromatic parameters establish that the organic matter has reached a high (post-mature) thermal maturation level. Maturation has resulted in the liquid hydrocarbons being thermally cracked into gaseous hydrocarbons and resulted in numerous pores formed within the organic matter. However, the low S 1 + S 2 pyrolysis yields and high T max values suggest that these outcrop samples are post-mature and most likely have expelled most of their generated gas. The pore-scale distribution analysis (2D and 3D) indicated that the pore sizes are primarily between 10 and 80 nm, which is conducive for storing dry gases. Additionally, these shale deposits contain a significant proportion of brittle minerals, characterized by a brittleness index (BI) exceeding 0.48. These results confirm the Niutitang shales possess commercially attractive characteristics and are worthy of further appraisal to seek buried locations with more retained gas. • Black shales are evaluated for their geological characteristics using an integrated approach. • Geochemical features suggest a high concentration of organic matter and is highly mature. • Molecular fossils indicate anoxic conditions and thriving dinoflagellates in Cambrian ecosystems. • Various shale plays have been compared globally and identified as potential targets for shale gas exploration. [ABSTRACT FROM AUTHOR]

Breakthroughs in shale gas exploration and production technology in China point to a possible solution to Pakistan's current energy crises. In this study, we evaluate the shale gas prospects in the Kohat-Potwar Plateau of Pakistan by establishing an integrated approach involving the analysis of fundamental elastic and petrophysical properties, Rock-Eval pyrolysis, and the sealing mechanism of shale. Detailed geochemical and petrophysical evaluation of the Patala Formation in the Kohat-Potwar Plateau indicates the good potential for shale gas with the following characteristics similar to the Longmaxi shale of Sichuan Basin China, i.e., (i) complex structural types sandwiched by tight limestone strata with low porosity (less than 3%), ultra-low permeability, high density, and large thickness which provides strong sealing capacity for gas preservation and enrichment; (ii) multiphase tectonic evolution of Patala Formation allowed various slippage processes to develop fractures and enhance the porosity and permeability; (iii) high organic matter content and thermal maturation stage (TOC > 2%, 416–445 °C); (iv) high brittle minerals content (e.g., calcite and dolomite > 40%); and (v) large formation thickness (>30 m) at shallow burial depth < 2500 m. This study emphasizes the critical role of the sealing mechanism in shale gas reservoirs. Furthermore, the results suggest that the evaluation and development of shale gas potential using petrophysical and geochemical analysis may be inadequate. Further scrutiny of the roof and floor of reservoirs' efficient sealing capacities is essential to understand the extent of shale gas preservation and enrichment. The study results provide valuable insights for identifying the sweet spots and preservation condition of shale gas in the Kohat-Potwar Plateau of Pakistan. • Evaluation of shale gas reservoirs in complex structural enclosures based on integrated study. • Patala Formation has good potential of shale gas with similar characteristics to the Longmaxi shale, Sichuan Basin China. • This study emphasizes the critical role of the sealing mechanism in shale gas reservoirs. • Proposed workflow provide guidance for identifying the preservation condition of shale gas in complex structural enclosure. [ABSTRACT FROM AUTHOR]

Porosity is one of the key factors of a reservoir system that is typically distributed in a spatially non-uniform and non-linear manner. Nevertheless, spatial distribution of porosity in stratigraphic traps (channel sand bodies) is challenging due to the variations in depositional environments, shale intercalation, and repetition of textural changes in the pore sizes. This paper presents a strategy of joint inversion that combines support vector machine (SVM) and particle swarm optimization (PSO) algorithms to predict the spatial distribution of porosity using well logs and seismic data. In addition, Gaussian simulation algorithms (sequential Gaussian and Gaussian indicator simulations) were used to predict lithology and porosity distribution using wireline logs and core data. Rock physics modeling was performed to bridge the gap between the elastic and petrophysical properties for seismic-inversion based reservoir characterization. The results show that the joint inversion strategy leads to the most stable prediction of AI and porosity distribution in the lower Goru heterogeneous reservoir of Sawan Gas Field, Pakistan. The calibration of the individual spatial distribution of lithology and porosity from wireline logs data using Gaussian simulation algorithms and post-stack seismic inversion using SVM and PSO; revealed favorable matching. In fact, the spatial pattern of low AI corresponds with the high porosity and sandstone lithofacies. The study results provide valuable insights for identifying the sweet spots in the heterogeneous lower Goru clastic packages of Sawan Gas Field through quantitative analysis of rock physics, petrophysics, seismic inversion, and fuzzy optimization. The proposed joint inversion strategy may be considered useful for the prediction of porosity in similar context of extremely heterogeneous stratigraphic traps in other basins of the world. • Porosity is one of the key factors of reservoir system that is typically distributed in a random manner. • A joint inversion strategy is applied to predict the spatial variation of AI and porosity. • Rock physics model is developed to bridge the gap between the elastic and petrophysical properties. • The proposed strategy leads to the most stable prediction of AI and porosity distribution in heterogeneous reservoir. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings. [ABSTRACT FROM AUTHOR]

A shear wave is a critical property that helps to constrain rock properties such as lithology, pore fluid, and pore pressure. Nevertheless, valid and reliable estimation of shear wave velocity in highly heterogeneous reservoirs is still considered a challenge to be faced. In this paper, we propose a strategy for improving shear wave velocity estimation based on the combination of clustering and classification plus regression using conventional logs. The approach includes two steps. In step one, data clustering algorithms are applied to build a classifier model for identifying the distinct electrofacies (EF) based on similarity of well log responses. In step two, a distinct and specialized regression model is selected from more sophisticated methods (empirical models, statistical regression, virtual intelligence, and rock physics modeling) for determining the final shear wave velocity profile. The effectiveness of this proposed strategy is validated over a dataset from offset wells and their respective measured shear wave velocity data. The final results revealed that the proposed strategy that combines data mining task of clustering and classification plus regression, led to more uniform and better predictive performance of shear wave velocity (R2 = 0.97) in comparison with the use of stand-alone rock physics model (R2 = 0.70), virtual intelligence (R2 = 0.82), and statistical regression (R2 = 0.91). The silent features of this proposed strategy is that field-specific regression models are built based on log derived EF and geological information. • Combining clustering and classification plus regression for improving shear wave velocity estimation. • The experiment involved three standard shear velocity models, one regression technique, and two data clustering algorithms. • The proposed strategy provides excellent results compared with the traditional methods. [ABSTRACT FROM AUTHOR]

Abstract The Sawan Gas Field is one of the most promising gas fields of Middle Indus Basin in Pakistan with a cumulative production of 850 BCF. In the reservoir interval, the thin shale sequence is interbedded and dispersed resulting in extreme heterogeneity. Consequently, conventional seismic amplitude interpretation fails to delineate the sand-shale facies distribution. In this case study, the depositional facies of the reservoir C-sand interval with the integration of electrofacies analyses, historical production data, and seismic attribute analyses using 3D seismic and well log data were analyzed for prospect evaluation and field development plan to achieving maximized production. The electrofacies analyses show that the reservoir C-sand interval falls in funnel-shaped, bell-shaped, and cylindrical-shaped trends. The obtained results suggest deposition of C-sand in proximal delta front to pro-delta settings with medium to fine grain pore size distribution. Conversely fine grained lime mud, silt, and shale with poor-sorting fall in the irregular-shaped trend which suggests deposition in pro-delta settings. C-sand interval was evaluated using multiple seismic attributes including relative acoustic impedance, root mean square amplitude, envelope, sweetness, instantaneous frequency, structural smoothing, and phase shift. The results of all the seismic attributes demarcate the transitional boundary between the delta front sand facies and pro-delta sandy-shale facies. The calibration of electrofacies analysis with 3D seismic attributes indicates an appropriate agreement between them. This study effectively predicted the spatial distribution of sweet spots in the heterogeneous reservoir using 3D seismic attributes analysis, which can provide valuable guidance for the development of the area. Highlights • Depositional facies for the C-sand of the Lower Goru Member is studied in detail for the very first time. • C-sand was deposited in fluvial delta-distributaries to delta-front while lime mud/silt and shale in pro-delta settings. • Depositional environment of the C-sand interval lies on the transitional zone between proximal delta front to pro-delta. • Seismic attributes revealed significant stratigraphic traps within the C-sand which can be exploited for future drilling. • The current research has disclosed new natural gas prospect zones for future exploitation of the Sawan gas field. [ABSTRACT FROM AUTHOR]

A comprehensive characterisation of the pore structure in shale oil reservoirs is essential for forecasting oil production and exploration risks. This study forecasted these risks in the oil-rich Songliao Basin using combination of high-resolution field emission scanning electron microscopy and quantitative X-ray diffraction to analyze the pore genesis and evolution mode within the first member of the Cretaceous Qingshankou Formation (K2qn1). The results showed the dominance of inorganic pores over organic pores, wherein diagenetic processes, such as compaction, pressure solution, and cementation, were responsible for the destruction of pore structure in the formation. Notably, the pores formed by dissolution and shrinkage cracks resulting from clay mineral transformation improved the oil storage space. Furthermore, according to the geochemical data and clay composition, the K2qn1 shale is in the middle diagenetic stage A, which can be further subdivided into A1 and A2 stages from top to bottom. The porosity slowly decreased in both sub-stages A1 and A2, wherein the decrease was stable in the latter. The diagenetic observations in this study are significant for the exploration of unconventional shale oil in petroliferous basins worldwide. [ABSTRACT FROM AUTHOR]

Conventional neural networks (NNs) have been extensively used to model the spatial heterogeneity of rock properties from seismic inversion. Nevertheless, these generic NNs have a single network structure, which leads to overfitting and convergence difficulties. Furthermore, for stable predictions, conventional NNs highly depend on the initial weights and bias values. This research focuses on resolving the key problems of the existing NNs. In this paper, we propose and apply a novel neural network based on a multilayer linear calculator (MLLC) to estimate seismic anisotropy and fracture porosity in structurally complex and deeply buried carbonate reservoirs. This method, unlike conventional NNs, develops a nonlinear projection relationship between seismic and well log parameters to predict the spatial variation of seismic anisotropy and fracture porosity. We evaluate inversion effectiveness further by optimizing the MLLC with the particle swarm optimization (PSO) algorithm. We evaluate this new kind of MLLC neural network using computer-based simulations of complex models. After verifying the model′s reliability, we used it to estimate anisotropy and fracture porosity in two case studies from separate regions of China. Focused on anisotropy and fracture porosity estimations, the MLLC neural network outperformed the conventional NNs using backpropagation (BP) neural networks in simulation and field studies. The results indicate that the proposed methodology is considered valid for the anisotropic and porosity prediction of fractured reservoirs in other basins in China with similar geological settings and analogous basins anywhere in the world. [ABSTRACT FROM AUTHOR]

The Punjab Platform is a gently dipping monocline that has been drilled since the 1960s with a low success ratio. Most of the drilling targets in the past were based on the structural interpretation. Furthermore, the exploration focus remained in Paleozoic sequences, and little attention has been paid to the Cretaceous systems. In this paper, a prograding Cretaceous (Sembar-Goru) mega-sequence is subdivided into three sequences using a semi-automated and integrated workflow. A data-driven chronostratigraphic chart is prepared, which revealed several regressive stages in the study area. The sands developed during these stages are laterally sealed by shales and claystones that form a stratigraphic play. The stratigraphic play area lies in a shoreface environment where shoreface sands are expected to be charged by underlying Lower Cretaceous black shales. A prominent gas cap above the proposed stratigraphic play further increases the confidence on the presence of a reservoir. This paper is the first attempt to study the Cretaceous deltaic sequences in the study area, which has remained unexplored for the last six decades. [ABSTRACT FROM AUTHOR]

This study is aimed at imaging the deltaic depositional sequences in the Sukkur Rift Zone (SRZ) of the Lower Indus Basin, Pakistan. The seismic sequence, back stripping, and thin-bed wedge modeling tools were applied to the gas field of the SRZ to develop the foundations for tectonics and sedimentary processes in the distribution of potential hydrocarbon reservoirs. The conventional seismic method does not have the capability to predict the quantitative aspects of deltaic systems. Five passive seismic sequences were utilized for mapping the sediments of the foreland margin within the Indian Plate. The subsidence curves showed that the tectonic activity had occurred between the Lower Cretaceous and Eocene depositional sequences. The maximum tectonic activity was observed during 50–70 Ma within the SRZ. The upper shoreface depositional system was modeled by a thin-bed wedge model at enhanced thicknesses of 52 m, 55 m, 56 m, 57 m, 58 m, and 61 m. Thin-bed wedge modeling has provided solid evidence and implications for the development of stratigraphic traps within the upper shoreface depositional system. The thin bed wedge model remained an accurate tool for the prediction of direct hydrocarbon indicator (DHI) within the tectonically-influenced depositional systems. Hence, the presented research work can be used as an analogue for the development of tectonically and stratigraphically stable fairways within the Asian Basins and similar geological settings. [Display omitted] • Five seismic sequences were mapped within passive and foreland margin sediments of the Indian Plate. • Onlapping of Cretaceous sedimentary rocks onto the Jurassic rocks images regional unconformity. • Thin-bed wedge model expects 52 m, 55 m, 56 m, 57 m, 58 m and 61 m thick facies in upper shoreface system. • Thin-bed wedge model authenticated the tectonically diversified and fluctuating sea-level systems. [ABSTRACT FROM AUTHOR]

Shale diagenesis controls the generation, migration and accumulation of hydrocarbons through time and has become a frontier area of study in the fields of sedimentology and petroleum geology. There have been only a few studies on the generation of organic pores in lacustrine shale oil reservoirs during the oil generation thermal evolution stage. In this study, lacustrine organic-rich shale samples at various stages of maturity, from the first member of the Songliao Basin Qingshankou Formation were investigated, and a thermal transformation law of macerals was established, based on organic petrology. The diagenetic alteration styles and sequences were investigated using a high-resolution field emission scanning electron microscope, and cathode luminescence. Furthermore, automated pore extraction technology enabled the examination of the characteristics and genetic process of organic and inorganic pores under the influence of thermal maturation. The results indicate that a small quantity of pre-oil bitumen is generated during the early oil mature stage (R o : 0.7% - 0.9%), and that the generation of a large quantity of post-oil bitumen that occurs following the peak oil generation stage (i.e., late oil mature stage, R o : 1.1% - 1.4%), is controlled by the unimodal distribution of the activation energy of Type I kerogen. Compaction intensity certainly influences pore volume reduction. Siliceous cementation primarily occurs during peak oil generation maturity and the subsequent thermal evolution stage. The pores formed in the oil generation window primarily arise from mineral dissolution and organic matter polycondensation. Following peak oil generation, the pores undergo iron dolomitization, smectite illitization, and bitumen cracking. The degree of development of inorganic pores, the most common pore type in shale oil reservoirs, appears to have a significant impact on the total porosity of a reservoir shale. During thermal evolution, the inorganic pore volumes show a decreasing trend. There is a secondary pore development stage, reached when R o exceeds 1.2%, closely related to the transformation of clay minerals. According to the hydrocarbon generation kinetics and compaction model, organic pore volumes increase gradually at 0.7%R o , reach their peak at 1.1%R o , and then steadily decrease due to compaction. The organic pores are relatively well developed when R o is >0.85% (at the start of the oil window). The shale oil reservoir's most favorable thermal evolution stage is late oil maturity stage that follows the oil generation peak. • Lacustrine shale samples with various thermal maturity were investigated. • Post-oil bitumen appears together with a large number of organic pores after the oil generation peak. • Organic pores begin to develop after entering the oil generation peak but are occupied by an oil residue. • The late maturity stage is characterized by well-developed organic and inorganic pores. [ABSTRACT FROM AUTHOR]

[Display omitted] • Various robust AI methods were implemented for predicting IFT of CO 2 -brine system. • A comprehensive database including more than 2184 experiment samples was employed for implementing the AI study. • The newly proposed random forest model for predicting IF outperforms the other AI models. Given the current global climate change, renewable energy sources, carbon capture, utilization, and storage (CCUS) are being considered as a potential solutions to this critical global issue. Structural and capillary processes can be used to store carbon dioxide (CO 2) in deep saline aquifers in a way that is safe and does not harm the environment. Due to this fact, the interfacial tension (IFT) of the CO 2 -brine system is an important factor influencing the capacity of storage formations to sequester CO 2. As a result, IFT is essential for conducting a thorough and accurate evaluation in order to determine the optimal strategy for CO 2 storage projects. This paper used intelligent models such as Gaussian Process Regression (GPR), Radial Basis Function (RBF), and Random Forest (RF) to forecast IFT in the CO 2 -brine system with high precision and substantial time saving. The results reveal that the constructed RF model could deliver excellent performance in predicting IFT with the lowest average absolute percent relative error (AAPRE = 1.9156 percent), highest coefficient of determination (R2 = 0.9907), and lowest root mean squared error (RMSE = 0.7279). Furthermore, a sensitivity analysis was performed to ascertain the most critical parameters in the RF model to be considered. The parametric analysis found that both pure and non-pure CO 2 systems had a significant impact on IFT prediction. Also, the RF model was used to assess the structural trapping capacity of a storage location in the Cuu Long Basin. The estimation results obtained in this study agreed perfectly with the previous ones. The findings of this study can aid in a better understanding of how machine learning models can be applied to predict IFT values for the evaluation of the structural CO 2 storage capacity. [ABSTRACT FROM AUTHOR]

The presence of clays in hydrocarbon reservoirs challenges the producible amount of oil and gas significantly. Therefore, this study reports a detailed quantitative characterization of clays' specific properties from two fundamental aspects which include clays' type and amount, and their impact on reservoir's fluid flow. We used Scanning Electron Microscopy (SEM) images and respectively adopted deep learning for typing and quantifying clays, and the Lattice-Boltzmann Method (LBM) for flow simulations with and without the presence of clays. The trained deep learning model of the present study was translated into a MATLAB application that is a convenient tool for clay characterization by the future user. This model was trained using 2160 images of different clay minerals based on transfer learning using AlexNet and resulted in more than 95.4% accuracy while applied on the unforeseen images. Moreover, we established the technique of depth-slicing of 2D SEM images, which provides the possibility of 3D processing of the routine SEM images. The results from this technique proved that clays could reduce reservoir porosity and permeability by more than 30% and 400 mD, respectively. The introduced approach of the present study provides new insights into the detailed impacts of clay minerals on the reservoir's quality. [Display omitted] • Developing an App for deep learning-based quantification of clay minerals. • Developing the depth-slicing technique to address the issue of the "depth of field". • Deep learning could satisfyingly type and quantify clay textures in SEM images. • 3D processing of SEM images could better reveal impacts of clays on reservoir rock. • 3D quantitative analysis of various clays' impact on the pore space remains unknown. [ABSTRACT FROM AUTHOR]

A decline in conventional petroleum reserves has resulted in the study of unconventional reservoirs. Techniques, such as hydraulic fracturing, permit new types of reservoirs to be developed. Organically rich shales are the prime horizons for the exploration and development of unconventional oil/gas. The Talhar Shale is a unit of the Lower Goru Formation (Cretaceous), mainly composed of dark grey-black shale with minor siltstone and sandstone. In the study area, the Talhar Shale is 70–90 m thick. This research primarily focuses on the geochemical evaluation of the Talhar Shale to evaluate its shale oil and gas potential by using total organic carbon, Rock-Eval pyrolysis, gas chromatography-mass spectrometry (GC-MS), X-ray diffraction (XRD), vitrinite reflectance, and maceral analysis. Talhar Shale has moderate to good hydrocarbon potential (0.43–10.73 mg HC/g rock). The laboratory results of the TOC reveals a good match with the Δlog R technique relative to the CARBOLOG technique. Biomarker data of the studied samples exhibit the dominance of C 19 tricyclic terpanes, C 30 pentacyclic triterpanes, and C 27 steranes. Maturity parameters (T max , and vitrinite reflectance) suggest that the Talhar Shale is thermally peak-late mature, consistent with biomarker maturity parameters (steranes and hopanes). Oxic environmental conditions prevailed during the deposition of these Cretaceous sediments, and the organic matter was mainly mixed (primarily derived from terrigenous sources). This interpretation is based on various biomarker criteria, e.g., elevated Pr/Ph values, a large amount of C 24 TeT comparative to C 23 TT, the lower amounts of C 29 /C 30 17α(H)-hopane, etc. Organic petrographic analysis exhibits vitrinite as the dominant maceral group. However, solid bitumen, inertinite, and liptinite macerals were also observed. The van Krevelen diagram and the HI vs. T max indicate the presence of mixed type II/III kerogen. Various geochemical parameters, e.g., T max , n -alkanes carbon preference index (CPI), sterane parameters (20S/20S + 20R, ββ/ββ+αα), and the hopane Ts/(Ts + Tm) and C 31 22S/(22S + 22R) suggest the orgaic matter (OM) in Talhar Shale is thermally mature. Based on XRD, the Cretaceous Talhar Shale contains a high percentage of quartz. Additionally, the crossplot between TOC and quartz content displays a positive association that suggests the quartz content in Talhar Shale might be from the biogenic origin, which supports a brittle nature and could be favorable for hydraulic fracturing. Moreover, it has a lower value of Poisson's ratio and moderates to high Young's modulus, indicating favourability for hydraulic fracking. Cretaceous sediments from the Lower Indus Basin of Pakistan are chiefly considered as oil and gas-prone horizons. Geochemical properties of the Talhar Shale compared with other major unconventional shale reservoirs around the globe (United States, Germany, Canada, and China) exhibits suitable average organic matter abundance (2.19 wt %), mixed type II/III kerogen, an adequate level of thermal maturity, massive shale thickness, burial depth, and high porosity. These characteristics of the Cretaceous Talhar Shale in the Lower Indus Basin compared to other global unconventional reservoirs may indicate its possibility as a favorable target for unconventional energy exploration. • The Talhar Shale is a dark grey-black, thick (70–90m) member of Lower Goru Formation. • It has good genetic potential. T max , R o , PI, and biomarker parameters consistent with the generation of hydrocarbons. • Vitrinite is the leading maceral group, and the OM was deposited under oxic settings. • Higher quartz content and elastic properties designate brittle nature suitable for hydraulic fracturing. • It could be a favorable unconventional target compared with other global reservoirs. [ABSTRACT FROM AUTHOR]