Carbon Capture and Storage (CCS) has emerged as a critical strategy for mitigating carbon dioxide (CO2) emissions; however, spatially explicit and integrated assessments of coal seam suitability remain limited in developing regions such as Nigeria. This study evaluates the CO2 sequestration and enhanced coalbed methane (ECBM) potential of the Anambra Basin by integrating laboratory-based geochemical characterization with GIS-based Analytic Hierarchy Process (AHP) modelling. Physicochemical properties, initial gas-in-place (IGIP), producible gas-in-place (PGIP), and effective CO2 storage capacity were determined for three representative coal seams (Seams I–III) and incorporated into a multi-criteria spatial suitability framework. Results indicate significant variability in coal properties across the seams. Seam III recorded the highest preliminary effective CO2 storage capacity of approximately 4.95 × 109 tonnes CO2, together with the greatest seam thickness, estimated reserves, IGIP and PGIP, and the lowest moisture content. However, its high ash content of 79.00% and relatively shallow depth represent important technical limitations. Seam II ranked second, with an effective CO2 storage capacity of tonnes CO2, whereas Seam I recorded the lowest storage capacity of tonnes CO2. The resulting storage-capacity ranking was therefore Seam III > Seam II > Seam I. Correlation analysis revealed a strong positive relationship between seam thickness and calculated storage capacity (Pearson correlation coefficient: r = 0.94) and a positive relationship between PGIP and IGIP (r = 0.81). However, these descriptive correlations were based on only three observations and partly reflected the use of overlapping volumetric inputs. GIS-based spatial modelling provided a preliminary representation of variations among the investigated seam locations. This study presents an integrated geochemical–geospatial framework for the preliminary screening and comparison of coal seams for CO2 storage and ECBM recovery. The findings provide preliminary insight into Nigeria’s coal-seam storage potential and identify Seam III as a priority for further characterization. Additional sampling, adsorption testing, permeability and injectivity measurements, caprock assessment and reservoir simulation are required before pilot-scale CCS or ECBM deployment can be considered.
To improve the efficiency of joint porosity and oil-saturation interpretation, enhance the use of parameter coupling, and improve model generalization under complex reservoir conditions, this study proposes an MAE-MoE joint prediction-inversion model. The method is developed using logging data from the L-well series in Block N of the Daqing Oilfield. Multi-source logging curves, including acoustic transit time, compensated neutron, compensated density, gamma ray, deep and shallow resistivity, and spontaneous potential, are used as inputs. By introducing shale-correction terms and deep-shallow resistivity separation features, a joint inversion framework for continuous depth-series modeling is established. In the proposed framework, a Masked Autoencoder (MAE) is first employed for self-supervised pretraining, where random masking and reconstruction are used to learn intrinsic cross-channel relationships and depth continuity from multi-channel log responses. Then, a Mixture of Experts (MoE) module dynamically routes shared representations to expert-specific sub-networks, enabling collaborative prediction of porosity and oil saturation. The results show that the proposed method outperforms comparative models in prediction accuracy, cross-well adaptability, and robustness to disturbance. In particular, using engineering-tolerance-based accuracies, the proposed model achieves 91.2% accuracy for porosity and 91.0% for oil saturation, where the tolerance thresholds are defined as an absolute porosity error within 2 percentage points and an absolute oil-saturation error within 0.10. The corresponding RMSE values are provided for standard regression-based evaluation. The main contribution of this study does not lie in treating MAE or MoE as new algorithms individually, but in adapting and integrating masked sequence reconstruction, expert-specialized routing, uncertainty-weighted dual-task learning, and petrophysical feature construction into a unified framework for continuous well-log-based joint inversion. This work offers a practical reference for intelligent well-log interpretation and fine reservoir evaluation in sandstone–mudstone reservoirs with similar geological and logging-response conditions.
The Heyuan Basin in eastern Guangdong is a significant Late Mesozoic–Cenozoic continental sedimentary basin along the southeastern coast of China. Based on integrated data from field geological surveys, geophysical reflection profiles, and a scientific borhole approximately 2 km deep, this study investigates its deep geological structure and geothermal genetic mechanism. The results indicate that the Heyuan Basin is a fault-controlled Mesozoic–Cenozoic rift basin, bounded by the Heyuan Fault to the north and the Zijin–Boluo Fault to the south. The basin exhibits a clear two-layer architecture: an upper sedimentary cover of Late Cretaceous to Neogene red beds and volcanic rocks underlain by an Early Paleozoic basement. Geophysical data reveal the presence of a concealed Yanshanian granitic sill exceeding 3000 m thick beneath the basin, which significantly influences the thermal structure and subsequent deformation. The study area is characterized by elevated surface heat flow (70–85 mW/m2), with 35 hot springs distributed along the Heyuan Fault Zone, indicating pronounced geothermal anomalies. Heat source analysis demonstrates that the regional high heat flow background is primarily attributed to radiogenic heat production from granitic batholiths enriched in radioactive elements (heat production rate: 4.5–6.8 μW/m3), while the localized thermal anomalies along the fault zone are inferred to be mainly caused by convective heat transfer from deeply-circulating groundwater. Fault intersections appear to act as preferential conduits for hydrothermal upwelling. A preliminary 3D structural model and geothermal resource assessment suggest substantial geothermal potential within the Heyuan Fault Zone (∼8.2 × 1019 J), which merits further exploration to assess its potential for high-temperature geothermal power generation, though this potential remains to be confirmed through deep drilling. However, the reservoir permeability is highly heterogeneous, necessitating future development efforts to focus on detailed characterization of the fracture network and coupled thermal-hydrological-mechanical-chemical (THMC) modeling.
The Brunauer–Emmett–Teller (BET) surface area is an essential indicator of the adsorption capacity and suitability of materials for CO2 capture. However, accurately predicting BET is challenging due to the nonlinear and interconnected effects of synthesis parameters such as precursor type. BET experiments are time-consuming, costly, and limited in their ability to capture complex parameter interactions. To overcome these challenges, data-driven approaches have emerged as promising alternatives to experimental research. In the literature, some studies used temperature, ratio, and contact time as inputs to predict BET. In addition, they used only one method, which is response surface methodology, without the model’s interpretability and had less accuracy with an R of -0.46. To address these limitations, this study proposes a comprehensive, data-driven framework that employs 30 machine learning techniques to predict BET. This study considers temperature, ratio, KOH activator, precursor type, heating rate, and activation time as predictors of BET. The performance of these models is assessed through different methods, such as trend analysis and statistical error analysis. Among all models tested, the LMB-based artificial neural network demonstrates the highest predictive accuracy with an R of 0.92. The SHAP analysis was used to demonstrate the interpretability of the proposed LMB ANN model. The SHAP study proves that temperature is the most influential factor affecting BET. The precursor concentration and KOH activator content are also important in determining the BET. Time has the lowest contribution among the parameters. Ultimately, this study delivers an easy-to-use predictive tool enabling rapid and precise BET estimation, supporting the optimization and design of high-performance porous materials for CO2 capture applications.
The Middle Silurian Hanjiadian Formation is a promising exploration target for hydrocarbon resources in the Sichuan Basin. Historically, research on this formation remains limited, leading to a poor understanding of its sequence stratigraphic architecture and internal sedimentary evolution, which in turn constrains hydrocarbon exploration. Using integrated outcrop, drilling, logging and seismic data, this study establishes a high-resolution sequence stratigraphic framework and a sedimentary filling evolution model for the Hanjiadian Formation guided by sedimentological and sequence stratigraphic principles. Three sequence boundaries are identified within the Hanjiadian Formation, and used to divide the formation into two third-order sequences (SQ1 and SQ2), within which four distinct systems tracts are recognized. A delta-shelf depositional system predominated during the deposition of the Hanjiadian Formation. Controlled by erosional processes and sequence boundaries, three subfacies are recognized in the study area: delta front (inner and outer fronts), prodelta, and argillaceous shelf. Longitudinally, the regressive systems tract of SQ1 (RST1) features the shallowest water depth and the most extensive delta-front sandbodies, creating a favorable interval for reservoir development. Taking systems tracts as fundamental units, the study analyzes sedimentary environmental evolution and sea-level changes via sequence stratigraphic correlation, and compares these results with coeval global sea-level fluctuations. Relative sea level in the Sichuan Basin during the Hanjiadian period underwent four cycles: deepening, shallowing, re-deepening, and re-shallowing. Finally, a sedimentary evolution model of the Hanjiadian Formation is proposed. This study provides important insights for hydrocarbon exploration and reservoir prediction of the Hanjiadian Formation in the Sichuan Basin.
The Changchang Sag, located in the Qiongdongnan Basin in the South China Sea, is a particularly promising area for deep-water hydrocarbon exploration. The spatial distributions and geochemical features of the source rocks in the sag remain unconfirmed, owing to the limited availability of rock samples and corresponding geochemical data resulting from the sparsity of boreholes. This study combines geochemical data with seismic attributes to preliminarily reveal the thickness and distribution of the mudstones of the 2nd Member of the Oligocene Lingshui Formation (LS2) and the 2nd Member of the Oligocene Yacheng Formation (YC2). Analysis of 47 source rock samples from the LS2 and YC2 members reveals the Changchang Sag mudstones as potential effective source rocks, with average total organic carbon (TOC) contents of 0.5 wt.% and 0.6 wt.%, respectively. The thermal maturity of the source rocks in the Changchang Sag was calculated using the vitrinite reflectance (Ro)-porosity model and representative thermal history curves of individual wells, with Ro values all exceeding 1.2%. The areal distribution of the effective source rocks was therefore determined by combining the measured geochemical data with the thickness, thermal maturity distribution, and organic facies of the mudstones. The most favorable source rocks in the LS2 and YC2 members occur in the eastern and central parts of the sag, with maximum thicknesses of up to 800 m and 500 m, respectively. The method provides a practical approach to typical source rock prediction for petroliferous basins with limited drilling and geochemical data.
Geothermal energy presents a unique opportunity to address the global energy trilemma of security, sustainability, and affordability while supporting resilient infrastructure and urban development. Offering reliable, dispatchable heat and power, geothermal systems expand the role of renewables beyond electricity generation to include district heating and cooling, industrial processes, critical infrastructure, and the food-water-energy nexus. This paper synthesizes recent developments in three areas essential to geothermal deployment at scale: Enhanced Geothermal Systems (EGS), closed-loop geothermal technologies, and digital twin-enabled thermal networks, alongside resource classification frameworks. Case studies from China, Europe, the Americas, and Africa demonstrate the increasing alignment between geothermal deployment and national decarbonization strategies, climate resilience planning, and regional development priorities. The analysis highlights that while institutional and financial barriers remain critical, technical challenges related to drilling cost, reservoir performance, and long-term system behavior continue to influence the pace and scale of deployment. Geothermal energy, if integrated within long-term planning frameworks, has the potential to evolve into a strategic component of global clean energy portfolios and climate-resilient infrastructure systems.
Iron oxide nanoparticles are widely recognized for their magnetic properties; however, their dispersion stability in subsurface-relevant nanofluid systems can be affected by aggregation under high-ionic-strength reservoir conditions. Ensuring colloidal stability is therefore critical, yet selecting a suitable dispersant and formulation conditions remains challenging. This study establishes a dual-screening framework for identifying Fe2O3 nanofluid systems with improved dispersion stability under controlled laboratory conditions. Four dispersants, including SDS, CTAB, Tween-80, and PVP, were comparatively evaluated by varying both dispersant chemistry and concentration. Stability was assessed using absorbance-based extractability index, sedimentation testing, Zeta potential measurement, Turbiscan analysis at elevated temperatures, and rheological characterization. Among the dispersants tested, PVP showed the most favorable overall stability, achieving the highest extractability index and dispersion stability index. During the 14-day static evaluation in saline media, minor to moderate visible sedimentation was observed, while macroscopic phase separation was not detected. During the 24-hour Turbiscan assessment at 80 °C, the optimized PVP-Fe2O3 nanofluid showed Turbiscan stability index (TSI) of 19.59 in NaCl brine and 21.80 in synthetic reservoir brine, compared with 10.18 in deionized water. These values indicate a stronger but gradual destabilization response in saline media, but no abrupt aggregation or macroscopic phase separation during the test. Zeta potential analysis indicated that electrostatic repulsion alone could not account for the stability trend, particularly for PVP, whose improved stability was likely attributable to polymer-related steric effects. Rheological analysis showed Newtonian behavior at 30, 60, and 90 °C, with viscosity decreasing with increasing temperature. The results highlight the importance of considering both dispersant chemistry and concentration when screening Fe2O3 nanofluid formulations, and serve as a foundation for further evaluation of porous-medium flow under representative geological storage conditions.
Geological storage of CO2 in deep saline aquifers has been widely proposed as a long-term mitigation option. However, the influence of vertical reservoir heterogeneity on CO2 trapping mechanisms remains insufficiently quantified, representing a critical gap. This study uses high-resolution 2D compositional reservoir simulations to systematically evaluate how vertical permeability trends control CO2 trapping in deep saline aquifers. A vertical cross-section model with 5 years of CO2 injection followed by 195 years of monitoring was developed, covering heterogeneity levels from homogeneous to highly heterogeneous (VDP = 0.11–0.93), and explicitly comparing upward-decreasing (UD) and upward-increasing (UI) permeability architectures. The results show that permeability architecture fundamentally controls trapping behavior. At low heterogeneity, both trends exhibit similar performance, with dissolution and residual trapping differing by less than ∼7% and ∼4%, respectively. At moderate heterogeneity (VDP ⁓ 0.5), UI enhances vertical connectivity, resulting in ∼25–30% higher dissolution after 200 years, while UD promotes earlier plume segmentation and residual trapping. At high heterogeneity (VDP ≥ 0.85), UD profiles create effective vertical barriers that increase plume compartmentalization and maximize long-term immobilization, whereas UI cases maintain a large, connected gas cap and reduced trapping efficiency. Sensitivity analysis shows that injection-layer permeability variations (110–350 mD) affect trapping by less than ±5%, confirming that vertical permeability structure is the dominant control. Permeability trends also influence injectivity: low-permeability layers near the well increase bottom-hole pressure, while high-permeability bases maintain lower injection pressure. These findings provide practical guidance for site selection: in moderately heterogeneous sandstones (VDP ⁓ 0.3–0.5), UI trends favor efficient injection and enhanced early-time dissolution (∼50 years), whereas in highly heterogeneous reservoirs (VDP > 0.5), UD trends support long-term (century-scale) CO2 trapping, albeit with higher injection pressure.
Quantitative analysis of accumulation and spatial distribution patterns of tight sandstone oil is critical for improving hydrocarbon accumulation theory. In this study, based on the evolution of accumulation conditions in the Fuyang Oil Layer (FYOL) of the Sanzhao area, Songliao Basin, we analyzed the reservoir distribution, quantitatively clarified the accumulation mechanism, and revealed the distribution patterns using seismic, drilling, logging, production test, and laboratory analysis data. The lenticular, faulted reservoirs are laterally connected, vertically stacked, and regionally continuous. During the Late Nenjiang stage, the paleoburial depth was 900–1800 m; the porosity was 9%–16%; the vitrinite reflectance (Ro) was 0.5%–0.9%; and the hydrocarbon-generation-induced differential pressure between source rocks and the reservoirs was 16 MPa. The conventional oil accumulations occurred in the central area of the Sanzhao Sag. During the Mingshui Period, the paleoburial depth was 1000–2100 m, and the corresponding porosity, Ro and differential pressure were 7%–15%, 0.5%–1.1% and 20 MPa, respectively, resulting in the formation of widespread tight oil accumulations and local conventional oil accumulations in the Sanzhao area. Four hydrocarbon accumulation models were identified across the central depression, slope, placanticline and terrace zones based on tight oil accumulation conditions. The areas of oil enrichment and high productivity are close to the effective hydrocarbon generation window of source rocks, with good reservoir petrophysical properties, well-developed fault systems, and high structural position. The findings of this study are expected to provide a reference for understanding hydrocarbon accumulation mechanisms and distribution patterns in analogous settings.
To clarify the pore-throat structure characteristics, their correlation with reservoir physical properties, and the oil charging thresholds of continental shale oil reservoirs, this study focuses on the Permian Lucaogou Formation in the Jimsar Sag of Junggar Basin, a typical continental saline lacustrine shale oil play in China. Integrated experimental techniques, including microscopic observation of cast thin sections, high-pressure mercury intrusion, nuclear magnetic resonance, and N2 adsorption experiments before and after oil washing, were employed to systematically analyze the reservoir pore system, classify pore-throats, analyze property correlation, and clarify oil charging mechanisms. The results indicate that the pore-throat structure can be categorized into 5 major types and 9 sub-types, showing progressive degradation from Type I (high quality reservoirs) to Type V (non-effective reservoirs), which is closely controlled by lithological assemblages and diagenetic alterations. Reservoir permeability is mainly governed by the macropore (>200 nm) content and median pore-throat radius (R50), while porosity is jointly influenced by pore-throat radius, pore type, and diagenesis, with a segmented correlation between porosity and R50. The oil charging pore-throat diameter threshold is determined to be 30 nm via theoretical calculations of hydrocarbon generation pressurization and capillary pressure, as well as experimental verification by oil washing contrast tests. This study refines the understanding of pore-throat evolution and oil accumulation mechanisms in continental shale oil reservoirs, providing critical theoretical and technical support for reservoir classification and evaluation, sweet spot delineation, and efficient development of the Lucaogou Formation shale oil and similar continental shale oil resources.
MALIK et al. (2026) investigated hydrogen-induced pyrite reduction and associated H2S generation under hydrothermal conditions relevant to underground hydrogen storage (UHS). They report measurable H2S formation in batch experiments containing pyrite, NaCl brine, hydrogen (500 psi), and variable calcite additions at 120–250 °C. While the topic is timely, several aspects of the geochemical interpretation require clarification. The proposed reaction mechanisms, analytical interpretations, and mass balance constraints are critically evaluated. Inconsistencies in redox logic, sulphur cycling, and mineralogical evidence preclude the proposed direct reduction pathway and the inferred catalytic role of calcite. Instead, mechanistic constraints imposed by pH buffering, hydrogen fugacity, and Fe–S equilibria indicate that interpretations must be grounded in coupled dissolution–precipitation processes and quantitative mass balance.
Machine learning (ML) has advanced reservoir engineering by enabling data-driven optimization for improved injection performance and cost efficiency. This study develops three supervised ML regression models trained on high-fidelity numerical data from an industry-standard black oil simulator CMG-IMEX to predict key parameters governing injectivity and fracture propagation in horizontal water injectors under constant-pressure lateral boundary conditions: bottom-hole pressure (BHP), fracture-tip pressure, and the percentage of injection rate entering the fracture. The pressure quantities are represented as pressure drops relative to the constant-pressure reservoir boundary, denoted by delpres (well-to-boundary pressure drop) and delptip (fracture-tip-to-boundary pressure drop), from which absolute pressures can be readily recovered.Conventional analytical models rely on oversimplified assumptions, while fully coupled numerical simulations incur prohibitive computational cost. A dataset comprising 30 000 high-fidelity numerical simulations was generated, capturing near-wellbore and fracture-tip dynamics across diverse reservoir, fracture, and operational conditions. Input features include reservoir properties, fluid properties, well geometry, fracture characteristics, and near-wellbore damage parameters. Eleven ML algorithms—CatBoost, LightGBM, XGBoost, Random Forest, Gradient Boosting, Support Vector Regression, Linear, Ridge, Lasso, Elastic Net, and K-Nearest Neighbours—were rigorously trained and evaluated. CatBoost delivered the best overall performance, achieving a mean absolute error of approximately 30 psi and an R² of 98%–99% across the prediction targets. Although trained on static configurations under constant-pressure lateral boundaries and no-flow top and bottom boundaries, these surrogates enable rapid evaluation of horizontal well injectivity and fracture propagation potential. The resulting surrogates can be integrated with time-dependent skin evolution and dynamic fracture propagation models to enable rapid simulation of evolving injection performance in field-well investigations.
Shale gas production forecasting remains challenging because flow is governed by strong nonlinearity, matrix–fracture interaction, and regime-dependent transport behaviour, while available production histories are often limited and erratic. Purely data-driven machine learning models can approximate complex input–output mappings but may generalize poorly when physical consistency is not enforced. In this study, we develop a physics-informed deep operator learning framework for single-phase gas flow that couples a spatiotemporal sampling strategy with a forward mapping from key matrix and fracture properties to cumulative production response. Mass conservation and momentum closure are embedded as soft constraints via automatic differentiation, penalizing the residuals of the governing equations in the training objective. We further quantify the impact of non-Darcy fracture flow by incorporating a Forchheimer momentum closure and comparing it against a Darcy-based formulation, showing improved short-term predictive performance when inertial effects are non-negligible. A different field-scale simulation benchmark based on the Marcellus shale model dataset has been used to validate the model. Across multiple forecasting horizons, the proposed approach achieves high agreement with reference cumulative gas production, with R2 values above 0.97 and normalized error metrics (NRMSE, NMAE, and WAPE) on the order of ∼4%, outperforming comparable standalone deep-learning baselines. Overall, the results indicate that embedding single-phase physics constraints improves model robustness and predictive accuracy, particularly when production data is limited.
Deep geothermal resources hold significant potential, and their large-scale development is critical for ensuring national energy security and achieving China's national “dual-carbon” strategic goals (carbon peaking by 2030 and carbon neutrality by 2060). Located in the southeastern Bohai Bay Basin, the Jiyang Depression hosts abundant geothermal resources with great development potential, alongside numerous existing oil and gas wells. Strengthening research on deep geothermal resources in the Jiyang Depression is conducive to promoting the adjustment of the energy structure and advancing the exploration and development of deep geothermal resources. Based on geological, geophysical, and well log data, this study focuses on the systematic thermal reservoir characterization, geothermal field analysis, and geological modeling for the Gudong buried hill in the Tanhai structural belt of the depression, as well as alculations of static and recoverable resources.The results indicate that the Lower Paleozoic carbonate reservoir in the studied Gudong buried hill of the Jiyang Depression possess favorable properties deep geothermal development. After the strategic retrofitting and on-site testing of well GD4, a reservoir temperature of 136.8 °C was measured at a depth of 3110 m. This confirms the strong potential for deep geothermal exploration in the Gudong buried hill and provides a reference for analogous prospects across the Jiyang Depression.
Precise prediction of well performance is vital for making informed project decisions and promoting efficient, cost-effective development of oil and gas fields, notably in unconventional reservoirs. Machine learning (ML) has gained prominence as a robust alternative, leveraging its nonlinear mapping strengths over the traditional reservoir numerical simulation and decline curve analysis methods. However, deploying ML for time series production prediction with only one technology remains challenging owing to the intricate application scenarios and the high standards for prediction accuracy. In this paper, we propose a task-oriented hybrid framework named the MSCNN-AttBiGRU model for time-series production forecasting, which is designed to improve prediction performance by hybridizing the Multi-scale Convolutional Neural Network (MSCNN) and Attention-enhanced Bidirectional Gated Recurrent Unit (AttBiGRU). MSCNN is adopted to capture the multi-scale spatial features, ranging from coarse to fine details. AttBiGRU leverages the attention mechanism to assign varying weights to the hidden layer states of BiGRU at different time step values, thereby extracting key temporal patterns. Eight cases in total are conducted including four synthetic well cases with varying noise level and four actual field well cases with fluctuations. The results indicate that the MSCNN-AttBiGRU model achieves competitive performance among the evaluated methods by considering the critical multi-scale spatiotemporal features of production sequences. The proposed model has the capacity to serve as a useful candidate tool for accurate and robust time series production prediction and stimulation optimization design.
The Ramshir oilfield in southwestern Iran faces casing collapse in intermediate well sections—specifically 13⅜" and 14" casings in 17½" open holes, and 11¾" liners in 14" open hole—driven by salt creep within the Gachsaran Formation's evaporite sequences. This research pioneers a multidisciplinary methodology integrating field failure analysis, advanced laboratory testing, and 3D finite element modeling (FEM) to diagnose failure mechanisms and engineer solutions. Conventional cement formulations exhibit catastrophic deficiencies, with low elasticity (<3.5 GPa) and high creep compliance (>1.0×10-6 Pa-1) failing to resist anisotropic stresses. A proprietary cement blend was developed with 10% silica fume for elasticity, 5% latex polymer for shrinkage inhibition, and 15% lightweight aggregates for density optimization; laboratory validation confirmed 48.2 MPa compressive strength (89% above industry standards) and 40% lower creep compliance. Field implementation in Well Ramshir-A achieved a 70% reduction in collapse incidents through two key innovations: bi-center PDC bit technology enabled 12"-14" hole recovery after milling obstructions, while DV-valve-assisted two-stage cementing secured 9⅝" casing integrity. FEM simulations quantified hazardous directional side forces exceeding 15 MPa at casing bends—a previously unmodeled failure trigger—and lab tests demonstrated the formulation's unique self-healing microcrack resistance under cyclic salt stress. Future directives prioritize real-time fiber-optic monitoring for early collapse detection, AI-driven predictive models correlating salt rheology with load thresholds, and research into nanocomposites for enhanced long-term creep resistance, recognizing that decadal-scale validation requires continued surveillance beyond the 180-day monitoring period reported here. This work establishes a validated framework for well integrity in the Zagros evaporite basins of Iran, with deployable materials and protocols that can be adapted for salt-affected oilfields following site-specific calibration.
Foreland thrust belts are frequently characterized by complex structures, where multi-phase tectonic activity produces intricate deformation features and highly heterogeneous natural fracture systems. In the Kuqa foreland thrust belt of the Tarim Basin, the distribution patterns of fractures in tight sandstone reservoirs vary significantly across fault blocks. This heterogeneity poses challenges for the exploration and development of deep-buried tight sandstone reservoirs, as well as for the study of water control strategies. This study investigates the differential distribution of natural fractures in the Lower Cretaceous tight sandstone reservoirs within the nearby Bozi-Dabei and Keshen-Kela regions, both of which contain trillion cubic meter scale of natural gas resources. According to imaging logging and drill core data, the fracture orientations show obvious differences between the Bozi-Dabei and Keshen-X areas, being predominantly nearly N-S-trending and E-W-trending, respectively. Analysis of structural geological characteristics indicates that the thick overlying salt layer in the Keshen area effectively mitigates vertical compaction pressure and balances lateral compression. In contrast, the presence of a basement paleouplift in the Bozi-Dabei area restricts the space available for large-scale fold deformation, and the absence of an overlying salt layer for stress regulation results in intense tectonic compression. Consequently, the anticline in the Bozi-Dabei area underwent weak deformation due to multi-level fault development and associated stress release. Under this structural regime, the Keshen area develops large-scale anticline structures with a high degree of deformation, as evidenced by maximum structural curvature and interlimb angles. By coupling the structural characteristics with natural fracture features of the two regions, we find that the Bozi-Dabei area is dominated by regional tectonic shear fractures, whereas the Keshen X area primarily consists of tensile fractures related to anticline deformation. Furthermore, using structural deformation curvature and fault-controlled ranges, this study proposes a genetic zoning evaluation approach for natural fractures in this study area. Meanwhile, identification criteria for the developmental characteristics of three genetic types of fractures are established, providing guidance for the analysis and evaluation of natural fractures in structurally complex regions. (c) 2026 Sinopec Petroleum Exploration and Protection Research Institute. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Large quantities of natural gases have recently been discovered in the marine carbonate formations in the Honghuayuan Formation (O1h) and Baota Formation (O2b) in the Weiyuan area, Sichuan Basin, China, which has demonstrated great exploration potential. However, uncertainties about natural gas sources and hydrocarbon accumulation seriously restrict further oil and gas exploration in the carbonate reservoirs of O1h and O2b in the Weiyuan area, so detailed research on oil and gas accumulation is urgently required. To address this issue, 86 source rock and 37 gas samples were collected and subjected to geochemical and geological analyses such as Rock-Eval pyrolysis, carbon isotope analysis, and fluid inclusion studies. Combined with gas-source correlation and basin modeling techniques, the origin and sources of natural gas, as well as the patterns of hydrocarbon accumulation and enrichment in the O1h and O2b formations of the Weiyuan area, have been analyzed and discussed in detail. The main findings of this study are as follows: (a) The Qiongzhusi (Є1q) and Longmaxi Formation (S1l) source rocks demonstrate favorable hydrocarbon generation potential. They are dominated by type I kerogen, and are in a high to overmature thermal maturity stage. (b) The natural gas in the O1h and O2b is classified as overmature oil-type gas of mixed origins. It is mainly sourced from the Є1q source rock. (c) H2S in the O1h and O2b reservoirs is attributed to thermochemical sulfate reduction (TSR). Carbon isotope reversal in the Ordovician natural gases is primarily attributed to the over-mature background, the mixing of natural gases from multiple genetic origins and TSR. (d) Hydrocarbon charging of the Ordovician reservoirs took place in two stages: the Late Permian and Early Cretaceous. A “lower-generation and upper-accumulation” model was established for the Weiyuan area, featuring structure-stratigraphy-lithology composite gas reservoirs controlled by large anticlines and fault systems. This study provides a scientific basis for the geological evaluation and further exploration in the Ordovician carbonate sequences of the Sichuan Basin, China.
Depleted gas reservoirs offer promising options for large-scale CO2 sequestration due to their pre-existing pore networks, low formation pressure at the start of injection, demonstrated caprock integrity, and substantial operational history. However, safe and efficient storage requires a detailed understanding of coupled thermo-hydro-mechanical-chemical (THMC) processes, impurity interactions, and geomechanical responses. This review provides the first integrated synthesis that connects these multiphysics mechanisms in depleted gas reservoirs, addressing a gap not covered in existing CCS reviews. The study consolidates experimental, numerical, and field-scale evidence to explain interactions governing CO2 behavior in carbonate, sandstone, and shale systems. Supercritical CO2 injection maximizes storage efficiency but remains sensitive to phase transitions and hydrate formation, which may impair injectivity and containment. A novel contribution of this review is the combined assessment of impurity effects and depletion history, demonstrating how species such as H2S, SOx, NOx, and N2 modify CO2 properties, drive mineral reactions, and influence plume migration pathways in ways distinct from saline aquifers. Geomechanical risks, including compaction, subsidence, and potential fault reactivation, are linked to pore-pressure evolution and stress changes. Integrated monitoring strategies (such as downhole gauges, fiber-optic sensing, microseismic surveillance, InSAR, and time-lapse seismic) together with advanced THMC modeling enhance anomaly detection and guide adaptive operations. Insights from long-term field projects (K12-B, Rousse, Otway) confirm that with careful pressure management, impurity awareness, and continuous monitoring, depleted gas reservoirs can safely store CO2 for decades. By unifying these mechanisms into a coherent framework, this review provides a novel and practical roadmap for optimizing storage performance and mitigating operational and geomechanical risks in depleted gas reservoirs.
