Driven by the dual objectives of the global energy industry-substantially enhancing hydrocarbon recovery and achieving long-term CO₂ geological sequestration-this paper systematically reviews the development history and research status of integrated CO₂ fracturing-enhanced recovery-storage technology, with a focus on the experimental investigations of CO₂-water-rock interactions and the associated numerical simulations. Dynamic and static experiments collectively reveal the coupled dissolution-precipitation effects of CO₂-water-rock reactions on reservoir properties under different temperature, pressure and time scales. Nevertheless, several limitations persist, including a scarcity of dynamic reaction equipment and corresponding data, insufficient investigation into micromechanical behaviors, and significant scale-dependent variations in mineral reaction rates. These limitations hinder the accurate prediction of porosity and permeability evolution over geological timescales. Regarding numerical simulation, existing studies have preliminarily modeled CO₂ fracture propagation, multiphase flow, and storage behavior, with increasing use of thermo-hydro-mechanical (THM) coupling models and microscale approaches such as molecular dynamics. Nevertheless, current models exhibit notable shortcomings, particularly in coupling chemical mechanisms, characterizing microscale transport-reaction processes, and simulating the integrated fracturing-enhanced recovery-storage process. These shortcomings limit the ability to accurately predict how these reactions influence fracture growth and storage efficiency. Finally, this paper identifies persistent challenges, including the complex coupling of multiple physicochemical processes and the difficulty associated with achieving integrated full-process simulation. Future research should strengthen the integration of experimental and simulation studies, develop full-process, multi-field coupled numerical models, and optimize collaborative design and real-time monitoring systems. These advancements are essential to propel this technology toward large-scale industrial application.

Helium is a critical strategic resource, with industrial helium primarily derived from helium-rich natural gas fields. Numerous large-scale natural gas fields with variable helium concentrations have been identified in the Ordos Basin through geological exploration. To elucidate the mechanisms underlying the differential helium enrichment in the southwestern Ordos Basin, this study conducted a systematic investigation into the geological settings, hydrocarbon charging histories, and geochemical signatures of three representative areas(C-Area, Q-Area, and L-Area) in this region. The results show that helium concentrations in the C-Area are significantly higher than those in the Q-Area and L-Area. Inorganic geochemical tracers suggest that the C-Area underwent a more pronounced influx of paleo-fluids. Integrated with carbon isotope data, our analysis reveals that natural gases in C-Area and Q-Area are mixtures of coal-derived gas and oil-derived gas with varying proportions, whereas those in L-Area are mixtures of coal-derived gas from different geological periods. Owing to differences in fault systems and hydrocarbon charging histories, compared with Q-Area, the O₂mj in C-Area experienced intense early-stage hydrocarbon charging. This charging process, accompanied by helium-rich paleofluids migrating upward via basin-scale faults, facilitated the migration of deep helium into the reservoirs. Conversely, the L-Area lacks the requisite hydrocarbon-driven transport effect for deep helium. These differences in hydrocarbon charging and paleofluid migration collectively led to the differential helium enrichment in the southwestern Ordos Basin.

Borehole wall instability in unconsolidated formations is a critical challenge in drilling engineering and requires urgent attention. Microbially induced calcium carbonate precipitation (MICP) is a promising solution for reinforcing unstable formations. However, the microscopic mechanisms responsible for borehole wall re-failure after MICP treatment remain unclear. The discrete element method (DEM) model provides an effective means for investigating such processes. In this study, a DEM model was created using vaterite, a form of calcium carbonate, to simulate the compressive failure process under unconfined conditions. During the simulations, the stress, strain, discrete fracture networks (DFN), fragment count, coordination number, contact number, average normal contact force, and bond breakage of the specimens were recorded to understand the microscopic behavior of core failure after MICP enhancement by microbial drilling fluids. The simulation results showed that the proposed DEM model can effectively simulate the failure behavior of cores post-MICP enhancement using microbial drilling fluids. As the calcium carbonate content increased, the peak strength occurred at a larger strain. Furthermore, the peak strength itself increased, and the brittleness of the specimens became more pronounced. The reduction in specimen strength was primarily attributed to the development of DFN and breakage of vaterite–sand particle bonds. Moreover, more concentrated and frequent DFN corresponded to a more rapid decline in the strength of the specimen. The tensile stress at the vaterite–sand and vaterite–vaterite contacts played a crucial role in maintaining the strength of the core specimen, and specimens with a greater number of uniformly distributed contacts showed improved core strength. This study provides new insights into the failure mechanisms of microbial drilling of fluid-enhanced sand in unconsolidated formations.

Due to the presence of highly discrete rock blocks and extensively developed natural fractures in fractured formations, frequent losses of drilling fluid and wellbore instability incidents occur during the drilling process. Meanwhile, for some extremely fractured formations, even after increasing the density of the drilling fluid, the wellbore instability situation shows no obvious improvement, and even the wellbore collapse becomes more severe. Regarding the highly discrete rock blocks in fractured formations, current research mainly focuses on using the discrete element method (DEM) to reveal the mechanism of wellbore instability in fractured formations. However, the DEM emphasizes the representation of the contact behavior of rock blocks and has difficulty effectively representing the multi-field coupling behavior during the actual drilling process in fractured formations. Therefore, in this paper, a combined modeling method of discrete element method (DEM) + finite element method (FEM) is adopted, comprehensively considering the dual-medium seepage effect of fractures and matrix pores, to conduct numerical simulation studies on the wellbore instability after drilling formations with different degrees of fragmentation. The research indicates that as the degree of fragmentation of formation increases, the situation of wellbore instability becomes more severe. Meanwhile, when the degree of fragmentation of formation is low and the occurrence of natural fractures is within the risk range of wellbore instability, due to the large size of discrete rock blocks in the formation, the wellbore instability mainly stems from the secondary fracturing within the discrete rock blocks; while when the degree of fragmentation of formation is high, the initiation of fractures around the wellbore mainly depends on the opening of natural fractures, thereby causing a large amount of shedding of discrete rock blocks and subsequently leading to wellbore instability. Increasing the bottomhole pressure can effectively inhibit the secondary fracturing of discrete rock blocks. For formations with a low degree of fragmentation, the wellbore instability is mainly caused by the secondary fracturing of discrete rock blocks. Thus, increasing the bottomhole pressure can effectively reduce the risk of wellbore instability; while for highly fractured formations, the wellbore instability is mainly caused by the shedding of discrete rock blocks triggered by the opening of natural fractures in the formation. Therefore, increasing the bottomhole pressure cannot effectively reduce the risk of wellbore instability. In terms of drilling fluid loss, the opening of natural fractures is more conducive to the expansion of the range of drilling fluid loss. Therefore, for highly fractured formations, increasing the bottomhole pressure not only fails to effectively reduce the risk of wellbore instability but also exacerbates the situation of drilling fluid loss. By analyzing the influence of the ratio of wellbore size to the size of fractured blocks on wellbore instability, it is concluded that for fractured formations, when the wellbore trajectory cannot avoid it, a small wellbore size should be adopted as much as possible.

Accurately predicting multiphase fluid flow in oil and gas reservoirs is crucial to optimizing production and minimizing costs. However, traditional numerical reservoir simulations are computationally expensive, while innovative data-driven models may not adhere to physical laws. Building on established physics-informed machine learning (PIML) formulations, we present an integrated PIML–reinforcement learning (RL) workflow that embeds the governing fluid-flow equations and uses RL to solve the inverse problem of estimating relative-permeability model parameters from average water-saturation measurements. Using the inferred parameters, the forward physics-consistent model predicts saturation dynamics and enables inference of capillary-pressure trends during unsteady-state waterflooding. The proposed model accurately predicts the average water saturation over time and estimates trends in capillary pressure across three laboratory experiments. Additionally, a sensitivity analysis is performed to understand the impact of the estimated parameters on the model predictions.

Conventional acidification for coalbed methane (CBM) reservoir stimulation is challenged by equipment corrosion, secondary reservoir damage, and environmental pollution. To address these limitations, this study proposes and rigorously evaluates an environmentally friendly alternative to reservoir acidification using low-corrosion, biodegradable chelating agents. Laboratory experiments included static dissolution tests on coal fines, analysis of variations in soluble metal ions, core flooding under simulated reservoir conditions, and porosity measurements. The results indicate that GLDA (L-glutamic acid, N,N-diacetic acid) at 10 wt% and pH = 2 achieved a dissolution rate of 6.55%, with Ca and Fe concentrations in the filtrate reaching 19.66 mg/L and 22.2 mg/L, respectively. Dynamic core flooding tests showed that 10 wt% GLDA (pH = 2) at an injection rate of 0.5 mL/min achieved the maximum permeability enhancement, increasing by 42.08% from 9.03 × 10⁻² mD to 1.283 × 10⁻¹ mD, while simultaneously increasing coal porosity by 13.87%. The effectiveness of GLDA is mainly attributed to the formation of stable chelates with Ca and Fe that remain in the aqueous phase, thereby avoiding secondary reservoir damage associated with precipitation in conventional acid systems. The findings of this study may provide an effective reference guide for CBM acidification.

This paper investigates how the efficiency of the chemical carbonated water EOR technique is optimized using the nonionic surfactant 2-ethoxyethanol in CO₂-rich water solutions. Tests were performed at pressure values of 500–1500 psi, temperature of 25 and 75 °C, and solvent concentration from 2 to 15 vol%. It is shown that 2-ethoxyethanol decreases the interfacial tension (IFT) and increases the oil swelling factor. For instance, when the pressure was 1500 psi, and the volume fraction of solvent in the solution was 15%, the IFT was reduced to 1.495 mN/m, and oil swelling became equal to about 33%. This is evidence of the synergism of interactions between CO₂ and the solvent. The contact angle was altered in favor of wettability alteration due to the oil-to-water contact. The influence of salinity (0–33,000 parts per million) was tested under optimized conditions of 1500 psi, 75 °C, and 15%. The minimum values of interfacial tension (0.84–0.93 mN/m) and contact angle (49°–55°) were obtained when moderate salinity levels were maintained (10,000–15,000 parts per million). Increased salinity levels decreased efficiency due to the ion effect. Overall, the addition of 2-ethoxyethanol enhances CO₂ solubility, promotes hydrophilic wettability, and improves oil mobility, highlighting its potential for efficient carbonated water flooding in reservoir conditions.

Offshore thin interbedded reservoirs are characterized by frequent alternation of sandstone and mudstone, strong vertical heterogeneity, and complex fracture cross-layer propagation mechanisms, which constitute a key challenge constraining efficient hydraulic fracturing stimulation. This study investigates a typical thin interbedded sandstone reservoir, quantitatively classifying the reservoir into three representative models based on adjacent layer thickness ratio: homogeneous-dominated, sand-dominated, and mudstone-dominated types. Using the Planar Three-Dimensional (PL3D) fracture propagation model, 235 simulation scenarios were systematically analyzed to reveal the controlling effects of interlayer stress difference and pumping rate on fracture cross-layer propagation capability. Results indicate that interlayer stress difference is the dominant controlling factor for fracture cross-layer propagation, with 8 MPa identified as the critical threshold for thin interbedded cross-layer propagation. When the stress contrast exceeds this value, fractures transition from three-dimensional penetration to single-layer horizontal propagation. Increasing pumping rate primarily drives horizontal fracture extension, constrained by the diminishing marginal effect of net pressure, and an optimal pumping rate range exists for fracture conductivity. Under high stress contrast conditions, simply increasing pumping rate cannot overcome the stress barrier. A quantitative mapping relationship between thin interbedded reservoir models and fracture geometries was systematically established, where fracture geometry evolution controls the spatial distribution characteristics of conductivity and constrains long-term productivity. A multi-dimensional evaluation chart integrating vertical fracture connectivity and stimulated reservoir area efficiency was constructed, delineating the quantitative boundaries of fracture cross-layer propagation capability under different geological and engineering conditions. Long-term development simulation demonstrates that three-dimensional cross-layer stimulation increases cumulative oil production by 2.7 times compared to single-layer stimulation. The research findings provide a theoretical basis and technical support for hydraulic fracturing parameter optimization and three-dimensional stimulation design in offshore thin interbedded reservoirs.

The oil and gas industry increasingly employs advanced engineering solutions to optimize enhanced oil recovery (EOR). A systematic and effective screening process, supported by multi-criteria decision-making (MCDM) techniques, is essential for selecting appropriate reservoirs and EOR strategies toward production optimization. This study introduces a screening framework designed to identify the most suitable EOR alternative. The proposed approach integrates a coupled objective-subjective weighting method to assign criteria weights, followed by a refined, data-driven, non-linear scoring procedure and an improved approach for prioritizing EOR alternatives. A distance-based scoring method is developed to evaluate alternatives against a desired screening interval, utilizing the full consistency method (FUCOM) and simultaneous evaluation of criteria and alternatives (SECA) model for weighting criteria and subsequently integrating them into a unified assessment. The ranking of alternatives is performed using a modified version of the approach introduced by Dickson et al. (2010). The applicability of the proposed framework is demonstrated through two CO₂-EOR case studies, while its reliability is assessed through technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The Spearman's rank correlation coefficient for three ranking methods was over 0.982 for Iran's CO₂-EOR screening case and above 0.943 for Canada's, showing the robustness and consistency of the ranking results. The findings confirm the effectiveness of the developed hybrid decision-support framework in optimizing EOR strategy selection, thereby contributing to more efficient hydrocarbon recovery in line with carbon capture and storage objectives.

Heavy crude oils present major challenges in transportation and front-end processing due to their extremely high viscosity and low API gravity. This work introduces an integrated experimental–statistical–mechanistic framework to evaluate the effect of four low–molecular-weight solvents-toluene, xylene, light naphtha, and n-hexane-on the rheology and density of Missan heavy crude oil using a high-speed baffled extraction unit designed to ensure uniform mixing. Bench-scale experiments were performed at solvent loadings of 4–12 wt% and temperatures of 15–45℃, with viscosity and API gravity measured before and after dilution. Aromatic solvents (toluene) demonstrated superior upgrading performance, achieving viscosity reductions of 63.2% at 25 ℃ and 63.4% at 35 ℃, and API increasing of 5.3°API at 12 wt% loading and all operating temperatures. One-way ANOVA confirmed that solvent type had a highly significant influence on both viscosity and API gravity across all temperatures (p < 0.0001). Predictive viscosity–temperature correlations of Arrhenius type (μ =Ae^−BT,R² > 0.996) and linear dose–response models (VR =a +bC) captured the observed rheological behavior with high fidelity. Mechanistic analysis attributes the superior efficacy of aromatic solvents to strong π–π interactions enabling asphaltene peptization, while aliphatic solvents act primarily through dilution. The results define solvent-selection and dosage windows relevant to pipeline hydraulics and field operations, and demonstrate that optimized aromatic–aliphatic co-blends provide a tunable, low-complexity strategy for improving the flow ability of Iraqi heavy crude oils.

Lost circulation (LC), as one of the high-risk accidents in drilling, usually occurs when the wellbore pressure is greater than the formation pressure, causing a large amount of drilling fluid to seep into the formation and resulting in a significant decrease in the flow rate at the wellhead. Currently, existing LC prediction studies mainly rely on logging parameters for time series prediction, ignoring the problem of the inherent imbalance in the ratio of positive and negative samples in the LC dataset and geological information. To address these issues, this study proposes a clustering-guided feature enhancement and Bayesian optimization Long Short-Term Memory (BO-LSTM) method for LC time series anomaly detection. This method, based on the mud logging and geological structural information of faults and lithology risks, leverages clustering algorithms to uncover the latent structural information within samples to construct enhanced features, which combine with the selected key features, forming a joint representation that is fed into the LSTM model. At the same time, the BO algorithm is introduced to adaptively optimize the LSTM hyperparameters, and finally outputs the LC occurrence's probability. The results show that the proposed method performs well in LC prediction, with an average false negative rate of 0.825%, an average false positive rate of 1.525%, and an average lead time of 4.825 min for effective early warnings, significantly improving the accuracy and timeliness of early LC identification.

This study introduces an advanced machine learning (ML) framework to predict interfacial tension (IFT) in CO₂-brine systems, a key factor in optimizing carbon capture and storage (CCS) processes. Accurate IFT predictions are critical for enhancing CO₂ trapping mechanisms, including structural, residual, solubility, and mineral trapping. Using a dataset of 1255 experimental IFT measurements, comprehensive preprocessing steps-such as outlier detection and feature standardization-were applied to improve data quality. Six ML models, including gradient boosting, extra trees, categorical boosting (CatBoost), random forest, extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), were developed and rigorously evaluated. Among these, CatBoost demonstrated superior performance with an R² of 0.986 and a mean absolute percentage error (MAPE) of 2.349%. A stacking ensemble methodology was employed to enhance predictive accuracy further, integrating base models using Lasso regression. This approach achieved performance metrics:R² = 0.988, RMSE = 1.158 mN/m, and MAPE = 2.202%. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified density difference (DD), pressure (P), and temperature (T) as the most influential features governing IFT. An analytical expression derived from the stacking model provides interpretable insights into the nonlinear relationships between features and IFT. This robust framework minimizes reliance on time-consuming experimental measurements and accelerates CCS project workflows by delivering reliable IFT predictions under diverse conditions. These findings underscore the framework’s potential to advance CCS optimization and contribute to climate change mitigation efforts.