The distribution and mineral composition of laminae in shale reservoirs significantly influence hydrocarbon accumulation, rock brittleness, and other key reservoir properties. Accurately determining their spatial patterns within subsurface formations is essential for reservoir evaluation. To meet the logging evaluation demands of sand laminae in lacustrine shale, a quantitative approach was developed for characterizing sand laminae density using borehole electrical imaging. Imaging data were initially projected onto an axial borehole profile based on the borehole's geometric features, and missing image data caused by errors were corrected using inverse distance weighting interpolation. Subsequently, OTSU thresholding was applied to remove anomalous high-resistance or high-conductivity components, such as gravel and voids, and the truncated mean conductivity was calculated. At the same time, grayscale reconstruction was used to extract the rock matrix image, with its average conductivity serving as the baseline for distinguishing sand laminae. Finally, the truncated mean conductivity was overlapped with the average matrix conductivity to identify abrupt changes that match the electrical characteristics of sand laminae, followed by statistical analysis using a dynamic sliding window. Validation using core samples from the Lianggaoshan Formation in the Sichuan Basin, China, demonstrates the method's capability to identify sand laminae within a specific thickness range. The approach also shows potential for core depth correlation. Certain factors overlooked in the current approach and limitations encountered in practical applications are objectively highlighted, with plans for gradual improvements in future work.

Precise identification of water-flooded intervals is critical for optimizing reservoir development, particularly during the mid-to-late stages of water injection in oilfields. This study uses the Guantao Formation in Block X as a case study, where the prevalence of clay-rich lithologies and complex pore structures has led to the widespread occurrence of low-resistivity oil reservoirs. These conditions pose significant challenges to conventional logging interpretation techniques. To address this, the study investigated the underlying mechanisms responsible for low resistivity through integrated rock physics experiments. Building on these insights, an enhanced logging-based evaluation workflow was proposed to more accurately identify water-flooded zones. Firstly, a lithology-based correction was used to the Simandoux equation to improve the estimation of mixed water resistivity under low-resistivity conditions. Secondly, the extreme gradient boosting (XGBoost) machine learning algorithm was employed to predict effective porosity (EPOR), thereby reducing dependence on nuclear magnetic resonance (NMR) logging and improving evaluation efficiency. Finally, a cross-plot technique combining ΔRD and Swz-Swir was introduced to quantitatively classify water-flooded intervals. Validation using production well data from Block X yielded an interpretation accuracy of 90.6%, demonstrating significant improvement in identifying water-flooded zones in low-resistivity formations. Furthermore, successful application in adjacent blocks confirms the method’s transferability. Overall, this research offers a robust and scalable workflow for log-based reservoir evaluation in complex resistivity environments, providing critical insights for remaining oil prediction and improved reservoir management in similar geological settings.

The Archie equation is a fundamental model for describing the relationships between formation resistivity, porosity and water saturation, and plays an important role in log-based saturation evaluation. However, in complex reservoirs, its original structure and constant-parameter assumptions are often insufficient for refined reservoir evaluation because of complex mineral compositions, diverse pore structures and other controlling factors. Meanwhile, numerous Archie-extended equations have been developed, each relying on different applicability conditions, which introduces considerable uncertainty into the selection of the optimal model structure. To address this issue, this study proposes a physics-constrained symbolic-regression method for Archie-extended relationships based on network electrical conduction theory. The method consists of two stages. In the first stage, Archie-extended structures are systematically enumerated under the constraint of a general saturation interpretation model to identify formula structures that better conform to reservoir characteristics. In the second stage, a parallel symbolic regression network (PSRN) is used to construct explicit functional relationships between residuals or structural parameters and reservoir petrophysical properties, including porosity, permeability and shale content, for the formation factor–porosity relationship ( F–φ ) and the resistivity index–water saturation relationship ( I–Sw ), respectively. The proposed method is applied to a complex-lithology carbonate reservoir in the Persian Gulf Basin, yielding a weighted dual-power-law formation-factor model and a variable saturation-exponent resistivity-index model controlled by porosity and permeability. Compared with the classical Archie equation, the coefficients of determination,R2 , of the two models increase from 0.569 to 0.772 and from 0.856 to 0.986, respectively. The log-application results further indicate that the proposed method improves the applicability and accuracy of saturation evaluation in complex reservoirs while preserving the interpretability of Archie-type conduction behavior.

Accurate prediction of reservoir permeability constitutes a central task in hydrocarbon field development, but it remains challenging because core measurements are sparse and heterogeneous reservoirs commonly exhibit nonunique logging responses. Conventional machine learning methods typically treat each core measurement as an independent sample for end-to-end permeability prediction, thereby overlooking the stratigraphic context embedded in depth-ordered core sequences. In this study, we propose a depth-aware Transformer-based hybrid modeling framework, termed DATS, for geological parameter prediction from sparsely sampled core data. DATS constructs sequential samples using sliding windows of core measurements and incorporates depth information and stratigraphic priors through three key components: a continuous Fourier-based depth encoding with learnable frequency parameters, an attention mechanism incorporating depth-distance bias penalties, and a two-stage training strategy that bridges continuous well-logging data and discrete core measurements. Applied as a feature extractor to carbonate reservoirs in the study area, DATS was evaluated by using 2856 core samples. Through comparison among three feature combination strategies and 14 downstream models, the best-performing model, DATS-XGBoost_Combined, achieved an R 2 of 0.9404. Ablation experiments indicate that each constituent module of DATS contributes positively to the overall model performance. SHAP values and attention matrix analyses further suggest that the performance improvement is associated with the ability of DATS to capture depth-dependent stratigraphic patterns consistent with petrophysical principles. These results suggest that DATS can serve as a supplementary tool for permeability prediction in sparsely cored reservoirs and assist parameter evaluation in uncored or undersampled wells.

High-precision quantitative characterisation of reservoir mineral composition is central to hydrocarbon exploration and development. However, integrated mineral–organic evaluation in organic-rich shale reservoirs remains technically challenging, owing to the high cost and limited scalability of elemental logging, and the susceptibility of conventional logs to organic-matter effects. The core innovation of this study is the construction of a comprehensive training dataset that explicitly decouples organic and mineral signals—achieved by integrating elemental logging, conventional logs, and core measurements. This dataset serves as high-fidelity labels, enabling a machine learning model to accurately predict mineral and kerogen contents using only conventional logs. Taking the Chang 7 Member shale-oil reservoir in the Ordos Basin as a case study, the workflow comprises three steps. First, inorganic mineral fractions are accurately inverted from elemental-logging data and used as baseline constraints. Second, a kerogen-content inversion model is calibrated by integrating conventional logs, elemental-logging results, and core measurements, enabling a robust separation of organic matter from the mineral matrix and yielding a complete mineral–organic reservoir-parameter dataset. Third, an improved Random Forest (RF) model optimized using the Sparrow–Bald Eagle Optimisation Algorithm (SBOA) is established, with conventional logs and derived total organic carbon (TOC) curves as input features to simultaneously predict the contents of five mineral groups and kerogen. Application results demonstrate that the SBOA–RF model achieves high predictive accuracy, with a mean relative error (MRE) of 6.71% for clay minerals and an MRE of 0.92, outperforming back-propagation neural networks (BPNN), gradient boosting decision trees (GBDT), and conventional approaches; moreover, SBOA is computationally more efficient than random search for hyperparameter optimisation. Porosity computed with a variable dry-rock skeleton model yields an average MRE of 9.06%, corroborating the reliability of the predicted mineral and organic contents. The model further exhibits strong generalisation in blind wells not used for training, with inversion results in good agreement with elemental-logging outputs and core X-ray diffraction (XRD) data. By reducing reliance on elemental logging, the proposed method provides a robust data foundation for reservoir-parameter evaluation and lithofacies classification in organic-rich shale intervals where elemental logs are unavailable, with substantial engineering relevance.

The content of total organic carbon (TOC) is a key indicator used to evaluate the hydrocarbon-generation capacity of shale formations and to identify potential sweet spots for exploration. To overcome the high cost and limited continuity of conventional core-based analyses, as well as the inadequate accuracy and interpretability of existing logging-based prediction models, this study investigates the Chang7 shale interval in the southwestern Yishan Slope of the Ordos Basin. Using pyrolysis data from 357 cores and five conventional logging suites from 12 wells, we developed an integrated machine learning framework that couples Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and XGBoost. The SHAP framework was incorporated to enhance model interpretability. Through stepwise synergistic optimization using GA and PSO, the method efficiently identified the optimal hyperparameter configurations for XGBoost, thereby markedly improving model convergence. The results show that the GA-PSO-XGBoost model achieves outstanding TOC prediction performance, yielding a cross-validation coefficient of determination (R2) of 0.92 and a validation set R2 of 0.89. The average relative error and root mean square error are 17.83

The genetic mechanism of low-resistivity oil reservoirs in the Guantao Formation of the Bohai Sea is complex, and understanding the role of clay minerals and pore structures in forming low resistivity remains unclear. This study employed multiscale digital core technology to integrate multisource digital core data. Combined with flow characteristic-based upscaling technology, microscale and nanoscale digital cores were reconstructed to calculate parameters such as porosity, permeability, and formation factor. At the same time, mercury intrusion and seepage experiments were simulated. The research reveals two key mechanisms underlying the influence of clay minerals and pore structures on low-resistivity oil reservoirs: first, the additional conductive effect of clay. Among clay minerals, illite-smectite mixed-layer minerals exhibit the strongest conductivity, which is the key factor contributing to reduced resistivity, followed by illite, kaolinite, and chlorite in decreasing order of additional conductive capacity. The second is the regulatory role of pore structures. Kaolinite fills intergranular pores to form a complex micropore system, resulting in high irreducible water saturation in the reservoir, which is the primary cause of low-resistivity oil reservoirs. This study clarifies the influence mechanisms of clay minerals and pore structures on resistivity, provides key technical support for fine geological modeling and optimization of oil and gas reservoir development schemes, and holds significant practical value for reducing exploration risks and improving oil recovery efficiency.

Fracture parameters play a crucial role in productivity prediction, reservoir evaluation and fracturing production of buried-hill reservoirs and carbonate reservoirs. The main method for calculating fracture parameters is electrical logging based on rock-electric experiments. However, due to significant differences in observation systems between rock-electrical experiments and various electrical logging methods, directly calibrating electrical logging data with cores in different fractured formations will lead to large errors. Based on the finite element method calibrated with core samples, we established micro-fractured formation models and conducted fracture parameter simulation experiments for plunger core samples, full-diameter core samples, electrical imaging logging, micro-spherical focusing logging, shallow lateral logging, and deep lateral logging, respectively, aiming at the influence of multiple fracture parameters. A comparison of the measurement results of the six models for the same fractured formation showed that the fracture-induced resistivity reduction rates were ranked in descending order as follows: electrical imaging logging, plunger core testing, micro-spherical focusing logging, full-diameter core testing, shallow lateral logging, and deep lateral logging, with the maximum discrepancy in resistivity reduction rates across these models reaching a factor of 45. Specifically, the resistivity reduction rate of plunger cores was 2.7 times higher than that of full-diameter cores, and the rate of electrical imaging logging was 11.8 times higher than that of micro-spherical focusing logging, whereas the values for shallow lateral logging and deep lateral logging were identical. Finally, this study proposed a correction rate required for fracture core calibration, which could comprehensively optimize the interpretation range of various resistivity logging methods and effectively improve the interpretation accuracy of reservoirs.

Tight sandstone reservoirs are characterized by highly heterogeneous pore structures, in which multiscale pore-throat systems jointly control the shapes of capillary pressure curves and relative permeability, thereby exerting a fundamental influence on water production behavior and the overall development performance of gas reservoirs. The Ordos Basin is generally characterized by the development of tight sandstone. The tight sandstones exhibit porosities of 2-13% and permeabilities of 0.01-10 & times; 10-3 mu m2. To quantitatively elucidate the controlling mechanisms of multiscale pore structure on capillary pressure curve morphology and relative permeability, this study systematically investigates the fractal and multifractal characteristics of pore structures in tight sandstones based on high-pressure mercury intrusion (MICP) and nuclear magnetic resonance (NMR) experimental data, and establishes a quantitative relationship between fractal parameters and the capillary pressure curve shape parameter lambda. First, capillary pressure curves were fitted using the Brooks-Corey model within the effective saturation interval to extract the shape parameter lambda, which characterizes the concentration degree of pore-size distribution and the drainage behavior. Subsequently, based on NMR T2 spectra, the small-pore fractal dimension D1, large-pore fractal dimension D2, and the multifractal singularity spectrum width Delta alpha were extracted to quantitatively describe the geometric complexity of pore structures at different scales. On this basis, the correlations between lambda and D1, D2, and Delta alpha were systematically analyzed, and the predictive performance of lambda under different parameter combinations was compared. The results indicate that: (1) the pore structures of tight sandstones exhibit pronounced fractal and multifractal characteristics at the NMR T2 scale, with significant differences among samples; (2) lambda shows an overall negative correlation with fractal parameters, among which the correlations with the large-pore fractal dimension D2 and the multifractal spectrum width Delta alpha are the most significant; (3) compared with models using a single fractal dimension, the multiparameter model incorporating Delta alpha provides a more comprehensive characterization of multiscale pore heterogeneity, leading to a substantial improvement in the accuracy and stability of lambda prediction; and (4) lambda exerts a clear control on the shape of relative permeability curves, where a larger lambda corresponds to earlier initiation and forward-shifted rising segments of water-phase flow, while a smaller lambda results in overall flatter relative permeability curves. From the perspectives of fractal and multifractal theory, this study establishes an intrinsic linkage among pore structure, capillary pressure curve shape parameters, and relative permeability, providing a novel quantitative framework for constraining relative permeability curve morphology in tight sandstones under conditions where systematic relative permeability experiments are unavailable.

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) technology is a crucial method for obtaining three-dimensional (3D) nanoscale digital rock images. However, due to surface topography or compositional differences in rock samples, “curtain noise” appears in FIB-SEM images, degrading their quality. Traditional image filtering and denoising methods can improve FIB-SEM image quality but are ineffective at fully removing “curtain noise”. To address the issue, this paper proposes two Convolutional Neural Networks (CNNs) for curtain noise denoising: U-Net: Convolutional Networks for Biomedical Image Segmentation (U-Net) and Denoising Convolutional Neural Network (DnCNN). Experiments evaluate the denoising performance of these models using digital rock images obtained from FIB-SEM equipment. The results show that both models significantly enhance image quality. Based on the updated values, the Peak Signal-to-Noise Ratio (PSNR) for the U-Net and DnCNN models are 29.10dB and 27.28dB, respectively. The Structural Similarity Index Measure (SSIM) values are 0.73 and 0.38, and the Learned Perceptual Image Patch Similarity (LPIPS) values are 0.31 and 0.45. After denoising with both models, the estimated porosity of the digital rock images is closer to the true value. For a rock image with a manually segmented porosity of 6.3%, the threshold-segmented porosity decreases from 7.9% to 6.1% and 6.0% following curtain noise removal by U-Net and DnCNN network, respectively. The U-Net model demonstrates strong denoising performance while preserving texture and details, whereas the DnCNN model significantly reduces the curtain effect but introduces noticeable smoothing and blurring. Moreover, the generalization ability of the DnCNN model is relatively weaker than that of the U-Net, making it less effective when applied to unseen FIB-SEM images. This research provides a foundation for the accurate reconstruction of 3D nanoscale digital rock models.

Traditionally, resistivity well logs have been used to determine water saturation (Sw) in oil and gas reservoirs based on Archie's Law. However, the intricate pore structure of tight carbonates poses challenges, as their electric conductivity does not adhere to this law, thereby complicating the assessment of water/gas saturation in gas reservoirs. To address this, the study analyzed SEM images of carbonate samples to understand the pore structures. Utilizing the Slice-Gans model, 3D digital cores were constructed and resistivity was simulated.The findings reveal that as porosity increases, the cementation exponent m in Archie's formula also increases. Theoretical derivations indicate that this is primarily due to changes in the PTRR as porosity varies. Dolomite and calcite are the primary minerals in the carbonates studied, and the PTRR differs between dolomite and calcite pores. Consequently, the m value varies depending on the mineral composition. By applying a parallel conductive model, the m value for cores with different dolomite and calcite volume fractions can be calculated.Additionally, the study analyzed rock resistivity experiment results and found that the saturation exponent n's value also varies with porosity, with a fitted relationship established. This enabled the creation of a final variable m and n model. When applied to practical logging data, the calculated Sw values were found to be consistent with the bounded water saturation obtained from nuclear magnetic resonance (NMR) experiments in gas layers, validating the accuracy of the new model.

The sandy conglomerate reservoir exhibits substantial vertical extent and considerable thickness, which provides significant capacity for CO2 storage. Meanwhile, the shrinkage pores are developed in the tuff-filled material of sand conglomerate reservoir, which is not only the oil/gas storage space, but also the ideal fluid flow channel of CO2. It is particularly important to select a reasonable pore throat cutting factor and accurately describe the structural and physical characteristics of the shrinkage pores for the evaluation of sand conglomerate reservoir. In this paper, the shrinkage pore developed sample is scanned with micro-computed tomography (CT) to obtain the 3D gray image. The shrinkage pore 3D digital rock is segmented, and the corresponding pore network model is extracted. Then, based on the 3D shrinkage pore network model, a different pore-throat cutting factor is selected to construct the shrinkage pore network models with different pore-throat spaces and calculate the physical/structural parameters. It can be found that, with the increase of pore-throat cutting factor, the number of pore-throat and porosity remain unchanged, and the permeability decreases, the pore volume distribution shifts to the left, the throat volume and throat length shifts to the right, and shape factor continuously shifted to the right. The increase of pore-throat cutting factor causes the interface between pore and throat to be more inclined to the pore side; the pore volume thus decreases, and the throat volume increases. Given the close agreement between laboratory-measured permeability (36.3 mD) and Lattice Boltzmann simulation results (38 mD) for the original shrinkage pore digital rock model, a pore-throat truncation factor of 0.3 +/- 0.1 (range: 0.2-0.4) is validated for sandy conglomerate reservoir characterization. When alpha < 0.2, it causes overestimation of pore volumes and underestimation of flow resistance; when alpha > 0.4, it induces excessive throat length and misrepresents real pore-throat morphology. This provides a basic platform for the accurate characterization of the shrinkage pores in sand conglomerate reservoirs.

The prediction of total organic carbon content (TOC) is critical for assessing the organic matter abundance of hydrocarbon source rocks. Advances in machine learning and deep learning have made it possible to predict TOC content using these methods and well-logging data, enabling fast, low-cost assessment of hydrocarbon source rocks. This study compares different models for predicting TOC content using Ordos Basin logging data. Firstly, core TOC data and logging data were collected, and then the core depth was restored by automatic core homogenization to eliminate the error problem caused by the inconsistency of resolution between logging data and core data, and at the same time to improve the correlation between logging data and core data. The automatic core return utilizes a sliding window algorithm to adjust the depth of the TOC core analysis data to be consistent with the logging curve. This process involves depth matching, data extraction, parameter setting (e.g., sampling interval and maximum sliding threshold), and curve selection based on correlation coefficients, with subsequent calculation of Cook’s distance to remove outliers. Different models, including a dual-shale-content prediction model, a ΔlogR model, 14 single machine-learning models, an integrated-learning model, and an improved stacking model, were built. The improved stacking model had a mean absolute error (MAE) of 1.89, root mean square error (RMSE) of 3.14, and coefficient of determination (R2) of 0.66 on the test set, which was superior to the traditional methods (e.g., ΔlogR had an MAE > 5.41, RMSE > 7.08, and R2 < 0.40). Meanwhile, the accuracy and generalization ability of the model on the test set could not be determined using a single evaluation metric; a three-level hierarchical analysis was used, and the qualitative problem of model selection was transformed into a quantitative problem. Predictive models suitable for the region were evaluated. The highest hierarchical weight of the improved stacking model was 0.1303, which confirmed that the accuracy and generalization ability of the improved stacking model in predicting TOC content was better than other models.

Unconventional hydrocarbon reservoirs, characterized by multiscale and complex pore architectures, diverse mineralogical compositions, and pronounced heterogeneity, present significant limitations to conventional saturation estimation and reservoir evaluation methods, with resistivity well logging data based on classic models such as Archie's equations. Digital rock physics technology, integrating multi-scale imaging, three-dimensional reconstruction, and numerical simulation, enables the precise characterization of pore structures and conductive mechanisms, markedly enhancing the accuracy of electrical response simulations and well logging evaluations in complex reservoirs. Through this perspective, this study systematically compares the application limitations and associated impacts of conventional resistivity logging in unconventional reservoirs of various lithologies and evaluates the applicability and merits of distinct rock physics numerical simulation approaches, highlighting existing constraints and challenges. Furthermore, this work outlines future directions for integrating digital rock physics with well logging evaluation.

The buried-hill fractured reservoirs are an important area of offshore exploration and development inChina. The fracture is the key factor affecting seepage and production of buried-hill fractured reservoirs and thefracture width is the core parameter. To solve the problem of logging quantitative calculation of fracture width,based on the previous work, a large-scale granite physical model for the data can be collected by FMI ofSchlumberger's electric imaging logging instrument and ERMI of China Oilfield Services Limited's electricimaging logging instrument is established. Secondly, the fracture widths of the physical model are measuredusing a width gauge with an accuracy of 1 mu m. Finally, FMI and ERMI electric imaging logging data of thephysical model are collected under different mud mineralization conditions. The relationship between theresponse of electric imaging logging of various widths and angles of fractures are analyzed under differentformation contrasts. The research results show that: (1) With the same electric imaging logging instrument, thefracture width shown in FMI and ERMI increases with the increase of formation contrast; (2) Under the sameconditions, the ERMI image shows a slightly wider fracture width than the FMI image; (3) Using accuratecoefficients, the fracture widths calculated by FMI and ERMI have good consistency. The above conclusions andunderstandings will provide experimental bases for the accurate evaluation of fracture widths and other fractureparameters, and technical supports for the efficient exploration and development of offshore buried-hill fracturedreservoirs in China.

The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage and flow. Precisely characterizing the rock internal structures is crucial for reservoir exploration and development. However, it is difficult to accurately characterize the pore structure of rock using traditional imaging methods to meet the simulation requirements. In this context, this study focuses on high-resolution 3D digital core reconstruction using the SliceGAN model. Specifically, the Modular Automated Processing System (MAPS) image and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) image were combined to divide MAPS into three categories: pore, dolomite, and calcite. Then, through the SliceGAN algorithm, the 3D digital core was reconstructed. To evaluate the reconstruction, the auto-correlation function, two-point probability function, porosity, mineral content, and specific surface area were employed. The results show that the SliceGAN can effectively capture the micro-features in the core, and the internal structure of the generated core was consistent with that of the original core. This study provided a new sight for reconstructing cores with complex pore structures and strong heterogeneity and innovatively supports tight carbonate reservoir characterization and evaluation.

Horizontal and directional wells, along with hydraulic fracturing are important techniques in unconventional oil and gas exploration and development. The rock mechanics parameters of formation rocks are crucial for evaluating reservoir engineering quality and well construction design. Currently, the main methods for calculating rock mechanics involve the direct core rock mechanical experiments and indirect well logging data evaluation; however, these methods are both time-consuming and expensive. Digital drilling cuttings (DDC) have become an alternative means for rapid evaluation of rock mechanics parameters at drilling sites. This paper presents a methodology and workflow for computing rock mechanics parameters using drilling cuttings mineral data. Initially, drilling cuttings undergo preprocessing and sample preparation. Subsequently, scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS) is employed to characterize particle morphology, surface porosity, micro-fracture development, and mineral composition. Based on the mineralogical and pore-fracture data, equivalent static rock mechanics parameters are calculated using rock physics modeling. These static parameters are then converted to dynamic parameters through linear regression analysis. Comparative analysis with conventional well-log evaluation results demonstrates that digital drilling cuttings technology can effectively derive key parameters—including elastic moduli, failure index, and brittleness index—providing critical insights for unconventional resource exploration and development. The limitations of the current models are also analyzed and the future research directions are introduced.

The gas-water two-phase seepage capacity of shale gas reservoir has an important influence on its productivity, but there is no method to evaluate the shale gas flow capacity directly by geophysical means such as logging. Three-dimensional digital core models of inorganic pores, organic pores and micro-fractures are established by core focused ion beams-scanning electron microscopy (FIB-SEM). The gas flooding water seepage of shale pores and fractures under different wetting conditions is simulated based on volume of fluid (VOF), and the characteristics of endpoint water saturation are obtained. Based on the understanding of the flow capacity of different pore types obtained from the simulation results, the equivalent flow capacity of each core can be obtained by combining the statistics of different pore types extracted from the large-scale mosaic scanning electron microscopy (Modular Automated Processing System, MAPS). Based on the analysis of petrophysical experiment and logging response, the flow capacity calculation equation and a model for evaluating flow capacity based on logging data are established. The actual logging data processing results show that the evaluation model proposed in this paper has high accuracy, and can obtain continuous shale gas flow profile. It can be used to guide shale gas exploration and development.

Net pay detection is a crucial stage in reservoir characterization, serving various purposes such as reserve estimation, reservoir modeling, simulation, and production planning. Net pay was quantified through the use of petrophysical cut-offs. However, these cut-offs varied according to core and dynamic data, introducing uncertainty into the evolution process. This challenge was particularly pronounced in tight sandstone reservoirs, characterized by low porosity. In the Linxing gas field of the Ordos Basin, the tight sandstone reservoirs of the Shiqianfeng, upper Shihezi, lower Shihezi, Shanxi, and Taiyuan formations exhibited ultra-low porosity and permeability, thereby complicating the determination of net pay cut-offs. This study utilized extensive data from the Linxing gas field, including core data from 50 wells, gas testing data from 217 wells, and comprehensive well logging and gas logging data. An analysis of the study area’s gas-bearing characteristics was presented, accompanied by a straightforward net pay cut-off evaluation workflow. The shale volume was evaluated to identify the net sand, while porosity and permeability evaluations were conducted to identify the net reservoir. Hydrocarbon saturation analysis was employed to establish net pay. Eight methods were employed to determine the net pay cut-offs. These include the particle size analysis for the shale volume cut-off, statistical accumulation frequency, minimum pore throat radius, mercury injection capillary pressure, gas production per meter index, and cross-plot analysis methods—based on fracturing gas test data—for porosity and permeability cut-offs. The bound water saturation and the relative permeability analysis methods were employed to determine hydrocarbon saturation cut-offs. Subsequently, formations were divided into two vertical sections; the upper section (including the fifth layer of the Shiqianfeng and upper Shihezi formations) is the target section in this study, with net pay cut-offs determined as follows: 20% shale volume, 6% porosity, 0.15 mD permeability, and 40% gas saturation. The net pay cut-offs determined in the upper section were validated against actual production data. This study provides a reliable basis for reserve calculation in the Linxing gas field, offering technical support for future development and production.

The characterization of carbonate microstructure is of great significance for the evaluation of carbonate oil and gas resources. However, due to the complexity and heterogeneity of the pore structure of tight carbonate rocks, high-pressure mercury intrusion, nuclear magnetic resonance (NMR) and other methods have different limitations in the characterization. This study takes tight carbonate core samples in the fourth member of the Ordovician Majiagou Formation in the Ordos Basin as the research object, and the rock physics experiments, computed tomography (CT), high resolution large-scale backscatter scanning electron microscopy (MAPS), quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) and focused ion beam-scanning electron microscopy (FIB-SEM) was utilized to characterize the pore structure from micrometer to nanometer, revealing the main mineral composition, and systematically analyzing the relationship between different mineral and pore structures. The results show that the microscopic reservoir space in the study area is mainly composed of inter-crystalline pores, intra-crystalline pores and microfractures; there are obvious differences in the pore structure of different lithologies. The samples with more dolomite have the largest number of pores and throats, the largest coordination number, and the best connectivity; the samples with more calcite have the smallest pore radius. The presence of quartz is conducive to the preservation of pores. This multi-scale characterization method using digital core technology provides us with comprehensive pore characteristic, provides important clues for further understanding the pore structure of tight carbonate reservoirs.