Dongxu Zhang

Dry-out and salt precipitation (DSP) near the wellbore is a frequently observed phenomenon in underground gas storage during injection–withdrawal cycles. It causes pore-space contraction and localized pore–throat clogging, thereby reducing reservoir porosity and permeability. However, existing clogging relations often fail to explicitly represent how clogging material occupies and evolves within the pore space, and they also struggle to capture the strongly nonlinear permeability response in a unified manner. To address these limitations, we conducted long-term gas-flooding experiments and measured key petrophysical properties and clogging-state variables before and after DSP. Mercury intrusion porosimetry (MIP) combined with fractal analysis was further used to characterize the evolution of salt-precipitation–induced microscale heterogeneity. Building on two classical conceptual frameworks—capillary bundles (CB) and equal-diameter sphere packings (EDSP)—we propose a clogging state-variable–driven strategy to construct porosity–permeability relations for occupancy-type clogging. By incorporating dual-fractal theory, we further establish a generalized porosity–permeability relation that simultaneously accounts for cumulative pore cross-sectional-area scaling (described by Ds) and volumetric space filling of the connected pore network (captured by the pore-volume fractal dimension Dv), together with a parameter-reduced form for data-limited applications. The CB and EDSP relations emerge as assumption-dependent, geometry-specific special cases of the proposed dual-fractal framework. Experiments show that DSP decreases both porosity and permeability, with permeability being markedly more sensitive. Permeability exhibits pronounced nonlinear decay with increasing solid-salt saturation and clogging factor. The MIP-derived Dv exhibits physically interpretable associations with petrophysical properties and clogging-state variables, whereas the corresponding Ds, inferred from Dv through the stated additive approximation, provides a model-consistent representation of pore-surface scaling rather than independent experimental evidence. Taken together, these results indicate that salt clogging is manifested not only by pore-volume reduction but also by changes in the effective scaling characteristics and flow-relevant heterogeneity of the connected pore system. Evaluations across relations and published DSP and other occupancy-type clogging datasets confirm that the generalized dual-fractal relation provides the best overall in-sample fit among the four proposed forms, while the CB/EDSP and simplified-fractal forms provide comparable descriptions of the dominant trend and remain useful lower-complexity alternatives in data-limited settings. This work provides a mechanism-informed and implementable framework for interpreting and predicting DSP-induced permeability impairment, and it supports embedding clogging relations into numerical simulators for performance assessment in occupancy-dominated clogging scenarios.

Deep marine shale reservoirs are controlled by multi-factor coupling effects, and the genetic mechanism of “sweet spots” exhibits strong complexity, leading to prominent difficulties in quantitative prediction and precise evaluation of sweet spots. Aiming at the problems of an unclear lithofacies-controlled sweet spot evolution law and insufficient accuracy of multi-parameter quantitative evaluation in traditional evaluation methods, this paper takes the Wufeng Formation and Long1 member of the Longmaxi Formation in the LZ block, Southern Sichuan, as the research object. Innovatively integrating machine learning (ML), grey correlation analysis (GRA), and three-dimensiona (3D) geological modeling technologies, a refined prediction model for reservoir sweet spot evaluation indicators under lithofacies constraint conditions is established, and a multi-parameter fusion quantitative evaluation method for deep marine shale gas sweet spots with high prediction accuracy is proposed. The results demonstrate that the LightGBM-based prediction model for sweet spot evaluation indicators achieved excellent performance. Based on a total of 380 preprocessed samples divided into training and test sets in a 7:3 ratio, the coefficient of determination (R²) of the model exceeded 0.9 in both the test and validation datasets. The “sweetness index”, a comprehensive evaluation index of reservoir sweet spots constructed via GRA-based multi-factor fusion, shows a correlation coefficient of 0.91 with respect to actual gas well production, presenting a high fitting degree. The 3D sweet spot geological model reveals that Class I sweet spots are mainly developed in the 1st to 3rd sub-layers of the Long1 member, while Class II sweet spots are distributed in the 5th and 6th sub-layers, which is highly consistent with the actual development law of the gas field. This study breaks through the limitations of single evaluation methods and weak lithofacies control consideration in traditional sweet spot evaluation and forms a set of innovative technical process integrating “precision prediction—multi-factor fusion—3D characterization”. It provides a new technical approach for efficient and accurate evaluation of deep marine shale reservoir sweet spots and has important guiding significance for the efficient development of shale gas.

The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we develop an interpretable framework by combining grey relational analysis (GRA) with three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVR), and eXtreme Gradient Boosting (XGBoost). Utilizing small-sample data from 87 shale gas wells in the study area, eight key controlling factors were identified, namely, total fracturing fluid volume, proppant intensity, average tubing head pressure, pipeline transfer pressure, casing head pressure, ceramic proppant fraction, fluid placement intensity, and flowback recovery ratio. These factors were used to train, optimize, and validate a productivity prediction model tailored for deep shale gas horizontal wells. The results demonstrate that XGBoost delivers the highest predictive accuracy and generalization capability, achieving an R² of 0.907 for productivity prediction—surpassing RF and SVR by 12.11% and 131.38%, respectively. Integrating SHapley Additive exPlanations (SHAP) interpretability analysis further enabled immediate post-fracturing productivity assessment and engineering parameter optimization. This research provides a reliable, data-driven strategy for predicting productivity and optimizing operations within the studied block, offering a valuable template for development in geologically similar areas.

Based on dual-fractal theory, this paper proposes a novel methodology to transform nuclear magnetic resonance (NMR) measurements into relative permeability model, thereby realized the challenge of accurately quantifying multiphase fluid flow in dual-fractal porous media. The novel relative permeability model integrates a multitude of parameters associated with the microscopic pore architecture of porous media. It can be verified through case studies that the newly constructed relative permeability model demonstrates excellent applicability. Furthermore, we have discovered a correlation between the segmentation characteristics of the relationship between the pore radius (r) and transverse relaxation time (T2) in low-permeability sandstone reservoirs and the number of peaks in the NMR curve. Specifically, the single-peak type can usually be divided into two segments, while the multi-peak type can generally be divided into three segments. The comprehensive fractal dimension (Dc) derived from the weighting method effectively captures the holistic core heterogeneity. The characteristics of small diameter pores are particularly sensitive to variations in the pore tortuosity fractal dimension (Dτ), the pore structure fractal dimension (Df), Dc, and ε (represents the pore size ratios). Given that the wetting phase preferentially occupies small pores, its relative permeability is significantly affected by these parameter changes. Conversely, the non-wetting phase mainly flows through large pores and is thus less influenced by changes in these parameters.

In this study, the Darcy–Brinkman–Stokes (DBS) model based on the theory of microscopic continuous media is used to model the pore-scale reaction transport in rocks. This reaction transport is solved discretely using a coupled Multiphysics-field simulation software to investigate the dissolution reactions and the evolution of porous media during carbon sequestration. The DBS model elucidates the inherent nonlinear coupling of mineral-dissolution processes, offering insights into the intricate relationship among the seepage, solute transport, and reaction-fluid chemistry. The findings indicate the progressive dissolution of soluble minerals with time to form new seepage channels, subsequently reducing the dissolution rate within the original seepage channels. Additionally, the increased salinity of the formation water decreases the solution pH, affecting the chemistry of the reservoir minerals. Conversely, increased reservoir temperature, pressure, and injection rate promote calcite dissolution. Furthermore, the augmented pressure differential promotes the expansion of the reaction zone toward the midstream, accelerating the dissolution and reaction processes of the calcite nodes. Overall, these findings offer a theoretical foundation for future carbon capture and storage technologies.

Field observations frequently demonstrate stress fluctuations resulting from the reservoir depletion. The development of reservoirs, particularly the completion of infill wells and refracturing, can be significantly impacted by stress changes in and around drainage areas. Previous studies mainly focus on plane fractures and few studies consider the influence of complex transport and storage mechanism and irregular fracture geometry on stress evolution in shale gas reservoirs. Based on the embedded discrete fracture model (EDFM) and finite-volume method (FVM), a coupled geomechanics/fluid model has been successfully developed considering the adsorption, desorption, diffusion and slippage of shale gas. This model achieves coupling simulation of natural fractures, hydraulic fractures with complex geometry, storage and transport mechanism, reservoir stress, and pore-elastic effect. The open-source software OpenFOAM is used as the main solver for this model. The stress calculation and productivity simulation of the model are verified by the classical poroelasticity problem and the simulation results of published research and commercial simulator with EDFM respectively. The simulation results indicate that σ_xx, σ_yy, σ_xy and Δσ changes with time and space due to the time effect and anisotropy of formation pressure depletion; Due to the influence of different mechanisms on shale gas storage and transport, the reservoir pressure and stress distribution under different mechanisms are different; Among them, the stress with full mechanisms differs the most compared to the stress without any mechanism. The reservoir with stronger stress sensitivity (smaller Biot coefficient) is less sensitive to formation pressure depletion, and the stress variation range is smaller. For reservoirs with weak stress sensitivity, formation pressure depletion is more likely to lead to stress reversal. Under the influence of fracture geometry, the pressure depletion regions caused by the three types of fracture geometry are approximately rectangular, parallelogram and square, respectively. The corresponding σ_xx, σ_yy and Δσ also have great differences in spatial distribution and values. Therefore, the time effect, shale gas storage and transport mechanism and the influence of complex fracture geometry should be considered when predicting pressure depletion induced stress under the condition of simultaneous production. This study is of great significance for understanding the evolution law of stress induced by pressure consumption, as well as the design of infill wells and repeated fracturing.

In the process of acidizing carbonate reservoirs, dissolution is employed for reservoir modification to enhance recovery rates. This study establishes a numerical model at the pore scale for acid-rock reaction flow based on a microscopic continuum medium model, integrating phase-field theory and component transport models. Subsequently, the results of the Darcy-Brinkman-Stokes model are compared to those of the arbitrary Lagrange-Euler method to validate the accuracy of the model. Finally, the flow behavior of the acid solution at the pore scale and the complex dissolution mechanisms in carbonate reservoirs are analyzed. The research indicates that the microscopic pore-scale dissolution in carbonate reservoirs mainly manifests as five dissolution modes: uniform dissolution, compact dissolution, conical wormholes, dominant wormhole, and ramified wormholes. Different distributions of microfractures will alter the flow state of the acid solution and the rock-acid reaction process within the pores. Once the wormhole breakthrough occurs, there is an increased probability of acid flow through the wormhole to the outlet, leading to a decrease in the effectiveness of the acidizing carbonate reservoirs. A proper understanding of pore-scale acid-rock reaction laws is of great significance for the development of carbonate oil and gas reservoirs.