We invite submissions that explore innovative approaches to characterizing the unconventional reservoirs. Topics of interest include, but are not limited to:
Advanced Logging and LWD Technologies: Integration of spectroscopy, nuclear magnetic resonance (NMR), dielectric, and high-resolution imaging logs for enhanced mineralogy, TOC, and porosity assessment.
Multi-Scale Data Integration: Methodologies for reconciling core, log, and seismic data to build accurate and calibrated petrophysical models.
Pore System Characterization: Advances in digital rock physics, mercury injection capillary pressure (MICP), and adsorption isotherms to quantify pore size distribution, surface area, and hydrocarbon-in-place.
Geomechanical Properties: Evaluation of rock brittleness, stress regimes, and natural fractures from logs and core to optimize completion and stimulation design.
Machine Learning and AI Applications: Leveraging data-driven models to predict petrophysical properties, identify sweet spots, and reduce evaluation uncertainty.
Case Studies: Field applications demonstrating the formation evaluation of unconventional reservoirs.
The effective stress coefficient (ESC) is an important parameter in formation pressure prediction and formation stress estimation, which is usually obtained by experiments and by the empirical function formulas of ESC and porosity. However, the calculation accuracy of these empirical formulas is often affected by lithology and critical porosity, and their application in the whole area or multi-lithology formations is limited. In addition, shear wave velocity data is limited by cost and technical conditions in practical logging applications. Therefore, based on the Gassmann equation and the approximation of P-wave modulus and volume modulus, this study realizes a multi-lithology ESC estimation method using P-wave velocity, density, and porosity, and applies it to the logging of the study block. The dynamic ESC along the wellbore direction is obtained, and the logging dynamic ESC estimation model is corrected to verify the reliability of the method. The results show that the logging-derived ESC is mainly distributed in the range of 0.3~0.8, while the average ESC measured in the laboratory is between 0.5~0.6. The ESC of the sandstone layers with high porosity is relatively large, and that of the mudstone layers with low porosity is small. In the absence of shear wave velocity, this method can effectively estimate the ESC and further predict formation pressure, which plays an important role in oil exploration and development.
How to cite: Wang, X.: Research on the Calculation Method of Dynamic Effective Stress Coefficient Based on P-Wave Velocity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2114, https://doi.org/10.5194/egusphere-egu26-2114, 2026.
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.
How to cite: Fang, S., Zhang, Z., Zhang, C., Nie, X., Song, H., and Zhao, B.: Response Mechanism of Multi-scale Electrical Logging in Fractured Formations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6308, https://doi.org/10.5194/egusphere-egu26-6308, 2026.
Fluids in porous rocks can be divided into two categories according to their distribution patterns: irreducible fluids and movable fluids. Due to the influence of various geological processes, porous rocks exhibit a wide range of pore size distributions, leading to complex fluid distributions in pores of different sizes. The microscopic distributions of irreducible and movable fluids, namely the contents of irreducible and movable fluids in rock pores of varying sizes, can directly reflect the petrophysical properties of rocks, such as microscopic pore structure characteristics and seepage capacity. In the exploration and development of oil and gas resources, the quantitative characterization of the distributions of irreducible and movable fluids in reservoirs—especially the characterization of movable fluid distributions—is of great significance for reservoir evaluation, productivity prediction, and efficient reservoir development. However, owing to the limitations of actual core data, traditional modeling methods face bottlenecks at the data-driven level, posing challenges to the establishment of accurate fluid distribution characterization models. In this study, the fluid distribution laws in tight sandstones were first analyzed based on core experimental data. Then, the Generative Adversarial Networks (GAN) were used to expand the core dataset. The results of core data processing indicated that the fluid distribution laws of the generated data were consistent with those of the original data, which verified the effectiveness of the adopted data expansion method. Finally, the fluid distribution prediction model were established based on a Multilayer Perceptron (MLP) and realized the accurate characterization of the distributions of irreducible and movable fluids in tight sandstone reservoirs through core experiments and logging data processing.
How to cite: Jin, G., Qin, S., Ma, Y., and Jin, B.: Research on Fluid Distribution Characterization Method of Tight Sandstone Reservoirs Based on Machine Learning Using Nuclear Magnetic Resonance Logging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6375, https://doi.org/10.5194/egusphere-egu26-6375, 2026.
The total organic carbon content is one of the key controls on hydrocarbon potential in unconventional shale systems. In the Indian Barren Measures Shales, TOC estimation from wireline logs remains challenging due to the strong heterogeneity, variable mineralogy, and limited core-calibrated geochemical measurements of shales. Standard empirical methods, such as the ΔlogR technique, capture first-order trends but often fail to generalise across mineral compositions. Purely data-driven machine learning models improve flexibility but may produce physically inconsistent predictions when sample sizes are small or when petrophysical responses are nonlinear.
This work presents a Physics Informed Convolutional Neural Network designed to estimate TOC from gamma ray, bulk density, and resistivity logs while embedding rock physics behaviour directly into the learning process. The network uses one dimensional convolutional filters to learn depth dependent patterns associated with organic richness, and incorporates the ΔlogR principle as a soft constraint in the loss function. The training workflow includes depth windowing and a hybrid loss function that balances data fidelity with physics consistency, which stabilises learning under limited sample availability.
Using 104 depth-indexed TOC measurements, the model was trained with five-fold cross-validation and a range of physics weighting factors. The final configuration achieved a mean absolute error (MAE) of 0.3, a root mean square error (RMSE) of 0.4, and a Pearson correlation coefficient (r) of 0.9, representing an improvement over both a standard multilayer perceptron (MAE = 0.6, RMSE =1, r =0.6) and the classical ΔlogR approach (MAE = 0.9, RMSE =1.3, r =0.4). These results show that physics informed learning provides a reliable and physically consistent framework for petrophysical characterisation in heterogeneous unconventional reservoirs, offering a generalizable workflow that integrates geological knowledge with machine intelligence to support improved formation evaluation and reduce uncertainty in reservoir assessment.
How to cite: Shaik, N. A. and Vedanti, N.: Physics informed convolutional neural network for TOC estimation in heterogeneous Barren Measures Shales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-232, https://doi.org/10.5194/egusphere-egu26-232, 2026.
Electrical imaging logs are widely used for the quantitative characterization of unconventional reservoirs. However, their applicability is severely limited in pervasively fractured continental shale-oil reservoirs due to strong heterogeneity and complex resistivity responses.
To address this limitation, an electrical statistical framework is developed that integrates variogram-based attributes with smoothness functions to identify fracture occurrence and quantitatively characterize fracture development in shale reservoirs. Resistivity percentile curves derived from electrical imaging logs are analyzed using a moving-window scheme to extract multidimensional parameters, including variance, Shannon entropy, variogram metrics, and smoothness indices. These parameters are jointly used to construct a fracture development index. In addition, fracture-prone intervals are identified using an adaptive thresholding approach constrained by geological rules and resistivity separation characteristics. To suppress the influence of stratigraphic background trends, a local background-resistivity normalization is applied, enabling fracture classification based on resistivity ratios.
The method is validated using shale-oil reservoirs of the Shahejie Formation in the Dongying Sag, eastern China. The results demonstrate that, within a tolerance range of 0.125 m, fracture identification derived from electrical image logs achieves an 85.4% agreement with core descriptions. The identification accuracies for high-conductivity and high-resistivity fractures reach 90.41% and 80.65%, respectively, while approximately 47% of the identified fractures exhibit high conductivity.The proposed approach provides new insights into fracture-filling properties and their vertical distribution, highlighting its applicability to shale-oil reservoir characterization, sweet-spot evaluation, and hydraulic fracturing design.
This research was supported by the National Oil & Gas Major Project (No. 2024ZD1405102).
Fig. 1. Electrical Imaging–Based Statistical Indicators for Fracture Identification in Well FY3
How to cite: Wang, Z., Ge, X., Jiang, L., Sun, H., Li, Z., Zhang, D., and Yang, D.: Electrical Imaging–Based Statistical Indicators for Fracture Identification in Shale-Oil Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3394, https://doi.org/10.5194/egusphere-egu26-3394, 2026.
The precise quantification of mineral composition is a basis for the accurate geomechanical evaluation, brittleness assessment, and completion design of shale reservoirs. Currently, elemental logging has become an indispensable technical method for acquiring key mineral composition information. The standard industry practice involves solving an optimization problem to invert elemental dry weight fractions, measured downhole, into mineral contents. The accuracy of this inversion is fundamentally governed by a predetermined transformation matrix, which ideally requires rigorous calibration against a statistically robust suite of laboratory X-ray diffraction (XRD) analyses from rock samples. This prerequisite poses a significant constraint in poorly cored intervals, leading to substantial uncertainties in the derived mineralogy.
To reduce the core-dependency, the key innovation lies in formulating the element-to-mineral transformation as a joint inversion problem. The proposed algorithm operates by treating the transformation matrix not as a fixed input but as an optimizable variable within the inversion framework. Starting from a geologically reasonable initial model based on regional knowledge, the method employs an iterative optimization loop. In each cycle, it simultaneously adjusts the mineral volumes and refines the transformation matrix to minimize a dual-objective function. The misfit between the log-measured and model-predicted elemental yields, and a regularization term that constrains the matrix adjustments to physically plausible ranges. The iteration converges when the global error is minimized, yielding a formation-specific optimal transformation matrix alongside the final mineralogy.
The efficacy of the method was rigorously tested using data from offshore shale oil wells in China. Comparative analysis demonstrates that the mineralogical profiles produced by the iterative method achieve an excellent correlation with those derived from the XRD-calibrated approach in intervals where core data is available. More importantly, in zones lacking core control, the iterative method provides stable and geochemically consistent results. A detailed comparative analysis indicates that this method significantly enhances the prediction accuracy for critical brittle minerals such as quartz and plagioclase. The reduction in error for these key components directly translates to higher confidence in computed geomechanical properties.
In conclusion, this study presents a robust workflow that significantly enhances the reliability of mineralogical evaluation from elemental logs in core-constrained environments. The iterative inversion method reduces the critical need for extensive, expensive core-based calibration, offering a powerful and practical tool for the accurate and efficient characterization of offshore shale oil reservoirs. This advancement holds substantial value for optimizing drilling, completion, and stimulation strategies, thereby supporting the economical development of complex unconventional resources.
How to cite: Wang, M., Yin, L., and Yan, W.: An Iterative Inversion Method for Mineral Composition Evaluation in Offshore Shale Reservoirs Based on Elemental Logging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1443, https://doi.org/10.5194/egusphere-egu26-1443, 2026.
Metalliferous brine represents a globally significant strategic mineral resource. The western Qaidam Basin in Qinghai, China, harbours immense potential for deep metalliferous brine deposits, yet exploration remains limited due to technical challenges such as the difficulty in identifying deep brine-bearing reservoirs and inaccuracies in delineating their spatial distribution. This paper focuses on the core technological methodology of integrated well and seismic data for deep metalliferous brine detection and intelligent reservoir identification. It comprehensively utilises geological, seismic, and logging data from oil and gas exploration in the western Qaidam Basin to conduct research on multi-source geophysical data fusion and intelligent interpretation. A systematic analysis of brine layer response characteristics in logging (e.g., low resistivity, low natural gamma) and seismic data was conducted. For the first time, seismic forward modelling clearly defined the identification resolution limits of onshore seismic data for halite-bearing sand bodies at primary frequencies of 25–50 Hz: Effective identification requires sandbody interlayers ≥1 metre and single sandbody thickness ≥7–8 metres. Overlapping sandbodies with elevation differences <6 metres are prone to misinterpretation as single layers, while low-velocity interlayers may cause strong reflections to drown out brine-bearing layer signals. This provides crucial theoretical support for practical data interpretation. Building upon this, an innovative approach combining well-logging and seismic data inversion with intelligent recognition based on a deep neural network (UNet++) was proposed. By integrating the high resolution of logging with the lateral continuity advantage of seismic data, automated and high-precision identification of brine layers was achieved. The research successfully established a brine-bearing layer prediction model, enabling quantitative forecasting of the spatial distribution of metalliferous brine layers in the western Qaidam area. This provides scientific justification for subsequent drilling deployment in target zones. It significantly enhances the exploration efficiency and intelligence level for deep metalliferous brine, holding substantial scientific significance and practical value for advancing mineral resource reserves and production.
How to cite: Chen, C., Zhang, H., Fang, X., Zhou, Y., and Xia, Y.: Metalliferous Brine Exploration and Reservoir Intelligent Identification in Qaidam Basin, Northwestern China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2181, https://doi.org/10.5194/egusphere-egu26-2181, 2026.
The Ordos Basin is a core area for deep coalbed methane (CBM) accumulation and production in China. The No. 8 coal seam at the Benxi Formation top is a primary target due to its wide distribution, stable thickness, and good gas-bearing capacity. However, complex roof/floor lithology and coal heterogeneity lead to intricate petrophysical responses of mechanical parameters, affecting fracturing efficiency. Thus, an integrated system incorporating rock properties for mechanical parameter prediction, in-situ stress calculation, and fracturability classification is critical for deep CBM sweet spot identification.
Sixty core samples of major lithologies (limestone, mudstone, sandstone, coal rock) were collected from the central-eastern Ordos Basin. Comprehensive laboratory tests were performed, including X-ray diffraction (XRD), uniaxial/triaxial compression, Brazilian splitting, and basic physical tests (P-/S-wave velocity, density). For coal rock, an extended Gassmann-based petrophysical model was established via Biot’s porous medium theory, incorporating pore-fracture extrusion-spray flow effect to accurately predict cleat density. Pearson correlation analysis identified key factors governing mechanical parameters: acoustic velocity, density, cleat density for coal; acoustic velocity, density, shale content for roof/floor rocks. Multiple nonlinear regression models were built for their mechanical parameters, with R²>0.75 between predicted and measured values, ensuring high accuracy.
Using the predicted rock mechanical parameters, the combined spring model was employed to calculate the in-situ stress of the coal-bearing strata. The prediction results demonstrated excellent consistency with field measured data, with an average relative error of less than 10%. Focusing on CBM reservoirs, relevant parameters were extracted, including rock mechanical properties (USC, E, V……) and in-situ stress components (σH, σh……). The correlation between these parameters and single-well daily gas production was systematically analyzed. The XGBoost ensemble learning algorithm was utilized to screen key influencing parameters from high-dimensional data, identifying four critical factors: minimum horizontal stress difference between reservoir and roof, reservoir horizontal stress difference, reservoir tensile strength, and reservoir elastic modulus. A fracturability evaluation model (FI) was constructed based on these key factors, and clustering analysis was applied to classify reservoir fracturability through iterative updating of cluster centers. The classification results yielded three reservoir grades: Class Ⅰ (FI > 0.32) with excellent fracturability, facilitating the formation of complex fracture networks; Class Ⅱ (0.21 < FI ≤ 0.32) with moderate fracturability, tending to form relatively simple fracture networks; and Class Ⅲ (FI ≤ 0.21) with poor fracturability, for which fracturing stimulation is not recommended.
The results of this study show great potential in evaluating deep CBM in the basin. It significantly improves the accuracy of parameter prediction (R² > 0.75) and in-situ stress calculation (error < 10%). Meanwhile, the combination of the FI model and classification standard effectively enhances evaluation precision and decision-making efficiency, providing strong support for targeted fracturing and sustainable deep CBM development.
How to cite: Luo, J. and Xiong, J.: Prediction of Rock Mechanical Parameters in Deep Coal-Bearing Strata and Fracturability Classification Evaluation of Coalbed Methane Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2550, https://doi.org/10.5194/egusphere-egu26-2550, 2026.
The physical characterization of marine sediment cores is pivotal for the exploration of shallow seafloor resources, such as gas hydrates and shallow gas. While non-intrusive imaging is essential for preserving sample integrity, conventional detection methods face significant limitations. Although X-ray CT offers high-resolution imaging, its application is constrained by bulky instrumentation and time-consuming scanning processes, rendering it impractical for rapid, on-site evaluation of sediment cores. Conversely, Electrical Resistivity Tomography (ERT) offers high sensitivity to electrical properties but relies on contact measurements. This inevitably disturbs the original structure of unconsolidated sediments and suffers from electrode polarization, thereby degrading imaging quality.
To address these challenges, this study investigates a non-intrusive imaging technique based on Magnetic Induction Tomography (MIT) to probe the internal electrical conductivity structure of sediments. In this method, excitation coils surrounding the core are energized with time-harmonic alternating current to generate a primary magnetic field. According to the principle of electromagnetic induction, eddy currents are induced within the conductive sediment, subsequently generating a secondary magnetic field that opposes the primary one. Since the intensity and path of these eddy currents are strictly governed by the spatial distribution of conductivity within the core, the internal structural information can be retrieved by detecting the perturbed total magnetic field via an array of receiver coils.
The feasibility of this proposed method was validated through forward modeling based on the Finite Element Method. To reconstruct the conductivity distribution from the magnetic measurements, the inverse problem was solved using the Gauss-Newton method. Preliminary simulation results demonstrate that the magnetic induction-based approach can effectively recover the internal electrical structure of the target, confirming its potential as a compact and efficient tool for marine sediment characterization.
This work was supported by the National Natural Science Foundation of China, Grant No. 42274232.
How to cite: Yuan, C., Zou, C., and Peng, C.: Research on Non-intrusive Detection of Marine Sediment Cores Using Magnetic Induction Tomography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7276, https://doi.org/10.5194/egusphere-egu26-7276, 2026.
The fractures in the deep shale reservoirs in southern Sichuan are one of the main factors affecting the enrichment of shale gas, fracturing design, and development effectiveness. Therefore, it is necessary to carry out multi-scale joint inversion methods to identify and evaluate fractures at different scales to improve the accuracy of their evaluation and prediction.
To address the above problems, four different scale fracture evaluation methods and their consistency studies have been conducted, including core, logging, remote detection, and seismic. Through the analysis of geological characteristics, core observation, CT scanning and experiments on acoustic anisotropy, the intervals of shale fractures and their anisotropic characteristics are determined. The anisotropy coefficient of shale reservoirs is calculated by dipole cross-wave logging, determining the longitudinal development degree and characteristics of shale reservoir fractures. The acoustic field imaging is used to extract the reflection coefficients of high and low frequency Stoneley wave and equivalent anisotropic coefficients of shear reflection waves, which allowed for the identification and evaluation of the development characteristics and effectiveness of fractures near borehole at a remote acoustic detection scale. Based on high-resolution seismic data with wide azimuth vector offset, the pre-stack seismic anisotropy coefficient is constructed, clarifying the intrinsic relationships between the fracture medium reservoir parameters described at four different scales.
The study shows that the deep shale reservoir fractures of Wufeng-Longmaxi Formation in southern Sichuan are mainly bedding fractures, mostly in a closed state, followed by structural fractures, most of which are filled with calcite. The high-and low-frequency Stoneley wave reflection coefficients in the formations Wufeng-Long11 of the first deep shale gas evaluation well Lu203 in southern Sichuan, show a significant difference, with fracture development and good reservoir connectivity, with an initial production of 1.38×106m3/d. Determining the acoustic anisotropy coefficient is a common parameter for evaluating shale reservoir fractures from core scale to seismic scale, and innovating the acoustic remote detection of shear wave reflection waves equivalent anisotropic coefficients fills the gap in detection range and resolution from logging to seismic scale, achieving high precision prediction of small-scale fractures of 3-20m. The results are generally consistent with core, logging, acoustic remote detection imaging, gas testing results, and production performance, proving the effectiveness of the multi-scale fracture evaluation method of shale reservoirs using cross-scale constraints. Further predictions indicate that the well area Lu 203 is characterized by the development of small fractures/bedding fractures on the basis of relatively stable structures, while the well area Yang101 is the development of structural fractures on the basis of large fault, with multiple calcite fillings, which is one of the main factors contributing to the significant differences in shale gas production between the above two areas. The proposed multi-scale fracture evaluation method based on acoustic anisotropy coefficients provides significant reference value for the comprehensive evaluation of unconventional reservoir fractures.
This research was supported by the National Oil & Gas Major Project (No. 2025ZD1403902) and the CNPC International Science and Technology Cooperation Project (No. 2023DQ0422).
Fig. 1 AVAZ inversion plan along the O3w layer in L203 Block in Southern Sichuan
How to cite: Xiao, Y., Wang, H., Ge, X., Xiao, G., Chen, S., Wei, Z., and Jiang, Z.: Multi-scale Fracture Evaluation Method for Shale Reservoirs in Deep Formation Wufeng-Longmaxi in Southern Sichuan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6994, https://doi.org/10.5194/egusphere-egu26-6994, 2026.
Water-based drilling fluid invasion-induced shale hydration is one of the key mechanisms leading to shale formation instability. Its essence lies in the microstructural damage caused by hydration, which further leads to the deterioration of mechanical and acoustic properties. To thoroughly investigate the intrinsic relationship of the evolutionary characteristics of “hydration–structural damage–mechanical weakening,” shale samples from the Da’anzhai Formation in the Sichuan Basin were selected. A series of comprehensive experiments were designed, covering different fluid systems, immersion pressures , and immersion times. By conducting simultaneous acoustic (velocity and attenuation), rock mechanical (triaxial), and CT scanning tests, the evolution trends of macro- and microstructures, mechanical properties, and acoustic characteristics of shale under water–rock interaction were quantitatively characterized. The response relationship between mechanical and acoustic parameters was clarified, and the influence of different drilling fluid systems on formation collapse pressure was compared, providing a quantitative basis for drilling fluid optimization. The main conclusions are as follows:
(1) Macro- and microstructural damage exhibits time- and pressure-dependent behavior. After immersion in drilling fluid, macroscopic fractures appeared on the shale surface. CT scanning indicated that hydration-induced fractures primarily formed during the initial immersion stage. With prolonged immersion time or increased pressure, existing fractures continued to expand, while the number of new fractures gradually decreased, reflecting irreversible cumulative hydration damage within the shale.
(2) Mechanical and acoustic parameters exhibit a synergistic deterioration trend. As immersion time and pressure increase, shale acoustic wave velocity, peak amplitude, compressive strength, and elastic modulus all decrease, while the acoustic attenuation coefficient increases. Microscopically, this is attributed to hydration-induced microcrack propagation and mineral interface weakening, which complicate wave propagation paths and intensify energy dissipation, leading to a simultaneous reduction in mechanical performance.
(3) Drilling fluid modification effectively inhibits hydration damage. Compared with the original water-based drilling fluid, adding 3% plugging agent increased shale longitudinal wave velocity and compressive strength by 8.2% and 10.2%, respectively. Using 50% organic salt as an inhibitor increased these values by 12.4% and 22.3%, respectively. When both were used in combination, the improvements further increased to 14.7% and 27.2%. This indicates that physical plugging and chemical inhibition can significantly mitigate structural damage and mechanical weakening.
(4) The acoustic attenuation coefficient is a sensitive indicator for evaluating structural damage. After water–rock interaction, the correlation between shale compressive strength, elastic modulus, and acoustic wave velocity is weaker, while the correlation with the acoustic attenuation coefficient is stronger. This suggests that the acoustic attenuation coefficient responds more sensitively to microstructural damage and can serve as a non-destructive evaluation method for optimizing drilling fluid systems.
(5) Drilling fluid optimization significantly reduces collapse pressure risk. As immersion time or pressure increases, the incremental collapse pressure of the shale formation gradually rises, reaching 0.248 g/cm³ and 0.201 g/cm³ under conditions of 15 days and 7 MPa, respectively. After adding 3% plugging agent, 50% organic salt, and their combination, the incremental collapse pressure decreased to 0.167, 0.151, and 0.113 g/cm³, respectively. This confirms that optimizing drilling fluids through physical–chemical synergy can effectively enhance wellbore stability.
How to cite: Xiong, J., Fang, H., Liu, X., Liang, L., and Ding, Y.: Mechanical and Acoustic Dynamic Evolution of Shale under Water-Shale Interaction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2499, https://doi.org/10.5194/egusphere-egu26-2499, 2026.
Shale microstructure is critical for predicting shale-oil “sweet spots” and improving reservoir evaluation. As a typical unconventional reservoir, shale exhibits ultra-low permeability, complex pore systems, strong heterogeneity, and diverse mineral compositions with highly uneven spatial distributions; pore morphology and connectivity, together with mineral assemblages, strongly control fluid migration, mechanical behaviour, and acoustic–electrical responses. However, conventional rock-physics experiments and image-analysis workflows are often time-consuming and insufficiently accurate for such complex materials. Here we develop a multi-scale, multi-modal segmentation workflow based on nnU-Net v2. Using paired 0.3 µm-resolution SEM and QEMSCAN images, we perform multi-class segmentation of pores, organic matter, clays, felsic minerals, carbonates, and heavy metal-bearing minerals, achieving a weighted Dice score of 0.95 and clearly outperforming threshold-based segmentation. The SEM-trained network is then transferred to 0.3 µm CT data to enable cross-modality prediction and reconstruct three-dimensional distributions of the segmented phases. We further extend the model to 4 nm-resolution CT images for cross-scale and cross-modality segmentation; three denoising filters are evaluated to suppress noise and improve nanoscale segmentation accuracy. Finally, we compare 3D digital rock volumes generated from single-direction inference with those obtained by tri-axial inference and fusion, highlighting differences in volumetric consistency and structural representation. This workflow provides a robust basis for future multi-scale digital rock construction and for simulations of porosity, permeability, and saturation, thereby supporting more comprehensive shale-oil reservoir assessment.
How to cite: Huang, X., Guo, Y., Li, X., and Li, Y.: AI-Driven Automatic Segmentation and Quantitative Characterization of Shale Microstructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3653, https://doi.org/10.5194/egusphere-egu26-3653, 2026.
Identification of fluid components and reliable saturation evaluation remain critical challenges in shale exploration and development. Conventional core experimental methods are often limited by high costs and long cycle times, while laboratory nuclear magnetic resonance (NMR) techniques fail to perform rapid and continuous measurements. In this study, we propose a rock-constrained multilevel Gaussian mixture model (GMM) approach for the quantitative evaluation of multiple fluids in shale using wellsite NMR data. The proposed workflow begins with normalization and thresholding of the T₁–T₂ spectra. A rock-physics constraint is then incorporated to partition the relaxation domain and distinguish movable fluids from bound fluids. GMM clustering is first applied independently within each relaxation region to extract representative fluid signatures. These characteristic signatures are subsequently integrated into a second-stage GMM analysis, enabling robust identification and quantitative evaluation of individual fluid components. Application to representative shale NMR datasets, coupled with multi-state core experiments, demonstrates that the proposed method effectively identifies multiple fluid components, including heavy components, bound oil, and movable oil. Furthermore, the saturation of various hydrocarbon components can be quantitatively predicted, demonstrating strong agreement with Rock-Eval measurements. The proposed approach provides a practical and reliable solution for rapid downhole fluid identification and saturation evaluation in shale reservoirs.
How to cite: Shi, P., Wang, L., and Gu, M.: A Rock-Constrained Multilevel GMM Approach for Wellsite NMR Multifluid Quantitative Evaluation in Shale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4645, https://doi.org/10.5194/egusphere-egu26-4645, 2026.
Electrical imaging logging provides rich information on reservoir petrophysical properties and geological features. Fracture identification based on image logs is of great significance for accurate production prediction and reliable estimation of hydrocarbon reserves. However, in electrical imaging log images, fractures typically appear as elongated, low-contrast targets with strong sensitivity to structural continuity. Moreover, variations in formation conditions, imaging parameters, and noise characteristics across different wells pose substantial challenges to existing fracture identification methods, particularly in terms of fine-scale fracture continuity recognition and cross-well generalization. To address these challenges, this study proposes a dual-backbone deep learning framework, termed LGAF-FracNet, for fracture identification in electrical imaging logs. The proposed framework parallelly integrates a convolutional neural network and a Transformer architecture to model local texture features and global semantic relationships, respectively. Considering the circumferential structural characteristics of electrical imaging logs, a liquid ordinary differential equation–based dynamic feature evolution module, an adaptive graph fusion module, and a stripe-aware pooling strategy are incorporated to enhance the representation of elongated and subtle fracture geometries. In addition, a multi-decoder consistency supervision mechanism is introduced to improve cross-well generalization performance. The proposed method is evaluated on a dataset comprising approximately 3,000 electrical imaging log images collected from 21 wells in the Sichuan Basin, covering conductive fractures, resistive fractures, drilling-induced fractures, and bedding structures. A standardized dataset is constructed through manual annotation and data augmentation. Experimental results demonstrate that LGAF-FracNet consistently outperforms mainstream segmentation models in terms of mIoU, F1-score, and pixel accuracy, exhibiting significant advantages in fine-scale fracture continuity, morphological consistency, and cross-well adaptability. These results indicate that the proposed method provides a reliable technical solution for intelligent fracture identification and quantitative characterization in electrical imaging logging.
How to cite: Yang, H., Zhang, C., Xiong, W., and Zhang, J.: LGAF‑FracNet: A Dual‑Backbone Deep Learning Framework for Intelligent Fracture Identification in Electrical Imaging Logging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1966, https://doi.org/10.5194/egusphere-egu26-1966, 2026.
Nuclear magnetic resonance (NMR) T2 spectra provide pore-scale information on fluid occurrence and mobility and are widely used for saturation evaluation. However, in shale and other unconventional reservoirs, strong heterogeneity, multi-fluid signal overlap, and weak/complex relaxation responses often undermine the reliability of conventional cutoff- and template-based saturation methods.
To address this challenge, we propose a data-driven workflow that directly predicts oil saturation from NMR T2 spectra by integrating feature engineering, unsupervised decoupling, and a compact deep-learning regressor. The method first applies robust preprocessing to suppress abnormal values and outliers, followed by dimensionality reduction to extract the most informative latent features. To alleviate multi-component signal superposition, an unsupervised clustering step is introduced to partition spectral patterns into representative groups, providing a more stable feature basis for learning. Finally, a lightweight convolutional neural network (CNN) is employed as the regression model to map processed T2 features to core-calibrated oil saturation, with standard strategies (normalization, dropout/regularization, and learning-rate scheduling) to improve generalization.
The workflow is validated using core-log paired datasets from a shale reservoir, showing that the predicted oil saturation agrees well with laboratory measurements and significantly improves stability compared with conventional interpretation in complex intervals. The proposed approach offers an efficient and scalable route for saturation evaluation in data-limited unconventional plays, supporting sweet-spot identification and development planning.
This research was supported by the National Oil & Gas Major Project (No. 2025ZD1400202) and Natural Science Foundation of Shandong Province, China (No. ZR2023YQ034).
How to cite: Li, Q., Wang, C., Ge, X., Chi, R., Wang, Y., Zhang, W., Miao, Q., and Zhang, J.: Direct Prediction of Oil Saturation from NMR T₂ Spectra Using Unsupervised Feature Decoupling and Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3276, https://doi.org/10.5194/egusphere-egu26-3276, 2026.
Reservoir porosity, permeability, and saturation are regarded as the core parameters in oil and gas exploration. The prediction of reservoir productivity and the analysis of fluid transport behavior in porous media are directly influenced by these parameters. Nuclear magnetic resonance (NMR) is recognized as a non-invasive, non-destructive, and highly quantitative technique. Typically, echo signals are inverted to obtain relaxation spectra, which is then used to calculate rock parameters.
In the existing methods, reservoir petrophysical parameters are usually calculated based on a relaxation spectrum obtained from a single inversion. However, the inversion of NMR relaxation spectra is inherently an ill-posed problem. Its solutions are characterized by non-uniqueness and high sensitivity to noise. In the traditional interpretation workflows, the single relaxation spectrum obtained from inversion is often assumed to be accurate and deterministic. Consequently, the propagation effect of inversion errors in the calculation of petrophysical parameters is ignored. This leads to a lack of credibility assessment for results in evaluations involving low signal-to-noise ratio (SNR) or unconventional reservoirs.
To address this challenge, an improved Bootstrap resampling method is proposed in this study. It aims to achieve uncertainty quantification from NMR relaxation spectra to petrophysical parameters. The traditional approach of seeking a single solution is abandoned. Instead, single-measurement echo data are resampled multiple times to generate statistically significant pseudo-sample sets by fully mining the statistical fluctuation information hidden within the single measurement. Subsequently, each set of samples is inverted independently to construct a distribution set of relaxation spectra.
By performing independent parameter calculations on the relaxation spectrum set, the distribution range, central tendency, and dispersion of each parameter can be obtained. Thus, the impact of data noise and pore size distribution differences on parameter estimation is revealed. On this basis, an error propagation model from the spectral domain to the parameter domain is established. Confidence intervals (CI) and prediction intervals (PI) for porosity, permeability, and saturation are calculated simultaneously. Specifically, model uncertainty caused by the non-uniqueness of the inversion algorithm is primarily quantified by the CI. Meanwhile, data uncertainty resulting from measurement noise is further incorporated into the PI, which provides a broader parameter interval range. A transition from "point estimation" to "interval estimation" for key petrophysical parameters is achieved by this method. Consequently, the robustness and credibility of parameter evaluation under complex reservoir conditions are significantly enhanced.
This work was supported by the National Natural Science Foundation of China (42304118), the Frontier Interdisciplinary Exploration Research Program of China University of Petroleum Beijing (2462024XKQY009), the Young Elite Scientist Sponsorship Program by BAST (BYESS2023027).
How to cite: Zhao, Y., Guo, J., Wan, Q., and Xie, R.: A New Uncertainty Quantification Method for Petrophysical Parameters Based on NMR Relaxation Spectra , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4758, https://doi.org/10.5194/egusphere-egu26-4758, 2026.
Shales exhibit significant seismic anisotropy, which can cause errors in seismic imaging, seismic attribute analyses and reservoir characterization. During deposition and burial, platy clay mineral particles tend to align along bedding planes, creating distinct direction-dependent seismic properties — a primary cause of anisotropy. Therefore, investigating the influence of clay on the evolution of shale anisotropy during compaction is crucial for enhancing seismic interpretation accuracy. However, quartz, serving as the primary rigid grain in shales, complicates this process. It forms a resisting framework that interferes with the preferred orientation of clay platelets. Hence, solely studying pure clay is insufficient, and investigating the compaction behavior of clay-quartz mixtures is of great value to accurately characterize the evolution of seismic anisotropy in realistic shale environments. Given their dominance in shale mineralogy, kaolinite and illite were chosen as the representative clay end-members for our study. We prepared eight clay-quartz mixtures (4 kaolinite-quartz mixtures and 4 illite-quartz mixtures) by mixing different amounts of clay and quartz: 100%, 80%, 60% and 40% of clay by weight. To simulate the natural burial process, these water-saturated loose sediments were subjected to uniaxial mechanical compaction experiments.
Experimental results indicate that for both groups, porosity decreases monotonically while seismic anisotropy increases with increasing compaction stress. However, the influence of quartz content differs significantly between the two mineral systems. In the kaolinite group, Thomsen parameters ε and γ show a negative correlation with quartz content at identical pressure levels. The illite group, conversely, exhibits more complex behavior: while γ remains negatively correlated with quartz fraction, ε displays a non-monotonic trend—initially decreasing, then increasing, and finally decreasing again as quartz content rises. Furthermore, the parameter δ serves as a distinct discriminator: kaolinite mixtures exhibit suppressed δ values (near zero or negative), whereas illite mixtures consistently display positive δ values (> 0.10), indicative of well-stratified compliant bedding.
These findings underscore that the "bridging effect" of quartz and the resulting anisotropy are strictly controlled by clay mineralogy. Specifically, the high positive δ of illite implies that conventional elliptical assumptions may cause significant errors in processing. Therefore, accurate seismic interpretation requires mineral-specific anisotropic models that account for the distinct structural evolution of kaolinite and illite during compaction.
Fig. 1 Thomsen’s anisotropy parameters ε, γ, and δ versus porosity for the K-series and I-series samples.
How to cite: Guo, X. and Han, T.: Evolution of Anisotropic Acoustic Properties in Clay-Quartz Mixtures during Mechanical Compaction: Implications for Shale Burial, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11711, https://doi.org/10.5194/egusphere-egu26-11711, 2026.
Carbonate reservoirs host substantial hydrocarbon resources; however, their characterization remains challenging due to strong heterogeneity and complex pore systems, which often produce asymmetric and highly multimodal NMR T₂ distributions. These characteristics undermine the applicability and robustness of conventional pore-structure interpretation and permeability models. To overcome these limitations, we propose a novel nuclear magnetic resonance (NMR)-based method that employs an Exponentially Modified Gaussian (EMG) model to quantitatively characterize pore structure and improve permeability estimation. First, the EMG function is used to decompose the measured T₂ distribution into multiple components with clear physical implications, enabling separation and quantification of pore contributions across scales. Second, the EMG-derived characteristic parameters are subsequently incorporated to refine the conventional Schlumberger-Doll Research (SDR) permeability model, thereby accounting for the impact of complex pore geometry on flow capacity. Validation using diverse carbonate samples demonstrates that the EMG model provides accurate and stable fitting of T₂ distributions across varying pore complexity, ranging from unimodal to highly multimodal distributions. Moreover, EMG-informed permeability estimation yields significantly improved accuracy and robustness compared with conventional methods. Overall, the proposed NMR EMG-based method offers a reliable solution for pore-structure characterization and permeability evaluation in complex carbonate reservoirs.
How to cite: Li, G., Wang, L., Gu, M., and Ai, Y.: An NMR EMG-Based Method for Pore Structure Characterization and Permeability Prediction in Carbonate Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4329, https://doi.org/10.5194/egusphere-egu26-4329, 2026.
Aiming at the challenge of achieving full-wellbore continuous monitoring and quantitative evaluation of shale gas well production profiles, this study develops an evaluation method based on distributed fiber-optic temperature sensing (DTS). By comprehensively considering the effects of fluid thermal convection in the wellbore, wellbore-formation heat exchange, and the Joule-Thomson effect on wellbore temperature distribution, a forward model of wellbore fluid temperature is established to accurately characterize the depth-dependent temperature distribution of the wellbore. On this basis, with model parameters as inversion variables, a squared error objective function between DTS-measured temperatures and forward-calculated temperatures is constructed, and the Bayesian inversion method is employed to solve the key parameters of the production profile, realizing the quantitative inversion of gas-water two-phase production for each producing layer. Field verification is conducted using an actual shale gas well: results show that the average absolute error between forward-calculated temperatures and DTS-measured temperatures is 0.05°C, indicating a high overall profile agreement; the gas and water two-phase production obtained by inversion is consistent with the on-site production dynamic recognition law, verifying the accuracy and reliability of the proposed method. This research provides effective technical support for the engineering application of DTS technology in production dynamic monitoring and development optimization of shale gas wells.
How to cite: Song, H., Xia, T., Wang, M., Huang, H., Zhang, Z., Fang, S., Nie, X., Zhao, B., and Zhang, C.: esearch on roduction rofile evaluation method for shale gas wells based on distributed Fiber-Optic Temperature Sensing (DTS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15374, https://doi.org/10.5194/egusphere-egu26-15374, 2026.
Look-ahead electromagnetic logging while drilling (EM LWD) technology has been widely used in geostopping due to its capability to characterize resistivity distributions ahead of the bit. However, a complex nonlinear relationship between tool responses and rock properties makes it difficult to image the geological structure. Therefore, inversion is essential to reconstruct resistivity distributions and determine formation boundary positions. Currently, gradient-based and artificial intelligence algorithms are commonly used for look-ahead inversion and have shown considerable potential. However, most related studies have focused on the analysis of tool detection capabilities, while research regarding inversion applicability and thin-layer formations remain insufficiently addressed. Additionally, due to the weak signal contribution from the area ahead of the drill bit, challenges still remain, such as the high tendency of inversion to fall into local optima and strong dependence on tool configuration.
In this paper, a look-ahead inversion framework based on the Levenberg-Marquardt (LM) algorithm is proposed, and its applicability to layered formation inversion is investigated. To prevent the inversion from being trapped in local optima, a continuous random multi-initial-value search strategy is proposed. Specifically, formation resistivity from look-around detection is used as a constraint, and random perturbations are applied to parameters to be inverted to select multiple initial values. Furthermore, the look-ahead signal decay is closely associated with tool configuration. By optimizing the selection of response curves through the adjustment of coil frequencies and transmitter-receiver spacings, the accuracy of the look-ahead inversion is further improved. Results demonstrate that the proposed inversion framework achieves an accuracy of up to 80% in the inversion of the distance to the nearest boundary for layered formations and yields favorable results in thin and multi-layer formations. It also provides algorithmic support for look-ahead EM LWD inversion and lays a theoretical foundation for further research on look-ahead inversion in formations with complex structures.
We are indebted to the financial support from the National Natural Science Foundation of China (42304140).
How to cite: Xiao, H., Cheng, C., and Wu, Z.: An LM Algorithm-Based Inversion and Applicability Analysis for Look-Ahead Electromagnetic Logging While Drilling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15621, https://doi.org/10.5194/egusphere-egu26-15621, 2026.
Electromagnetic logging while drilling (EM LWD) provides unique capabilities for both look-around and look-ahead detection, making it a foundational technology for identifying formation interfaces in complex horizontal and ultra-deep well applications. Over recent decades, its detection depth has advanced from a few meters to a remarkable range of several tens of meters. Nevertheless, directional EM LWD remains the primary geosteering method in horizontal wells, largely due to its sensitivity to formation boundaries and relatively low operational cost. In early directional EM LWD applications, where the maximum detection range was generally below 5 meters, one-dimensional (1D) formation models were widely adopted for forward simulation and inversion. However, current-generation tools can achieve detection ranges approaching ten meters, meaning that formation influences now extend across a significantly larger volume. Continuing to use conventional 1D models under such conditions may introduce substantial errors. While transitioning to two-dimensional (2D) models preserves accuracy, the increased model dimensionality substantially reduces computational speed, hindering real-time inversion.
In this paper, we propose an approximate method for simulating directional EM LWD responses in 2D models, designed to address this inherent trade-off between computational efficiency and accuracy. The core concept involves reducing complex 2D geological models into a series of 1D representations, which are then solved using a pseudo-analytical algorithm. First, the computational domain is divided into sliding windows of varying widths, defined with reference to the tool’s depth of investigation. Within each window, curved formation interfaces are approximated by straight lines to construct a 1D model, from which the tool response is computed. The series of 1D responses is then synthesized to obtain an approximate response for the original 2D model. To ensure both efficiency and reliability, an automated workflow integrating progressive model reduction with real-time quality control is implemented. The process begins with a coarse 1D approximation (typically 3–5 models). A divergence metric quantifies its representativeness; if below a preset threshold, the result is accepted. Otherwise, an iterative refinement phase is activated, dynamically increasing the number of 1D models only where necessary until the synthesized response stabilizes. The algorithm was validated on representative fault and fold models. Numerical results demonstrate that the proposed method successfully avoids the oversimplification inherent in conventional 1D modeling while achieving a computational speedup of more than 10 times compared to conventional full 2D numerical simulations.
We are indebted to the financial support from the National Natural Science Foundation of China (42304140).
How to cite: Wu, Z. and Yue, X.: An Efficient Method for Approximating Directional Electromagnetic LWD Responses in Complex 2D Formation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8555, https://doi.org/10.5194/egusphere-egu26-8555, 2026.
To address the high cost and low efficiency of lithology identification for intrusive rocks in oil and gas exploration, this study proposes a hierarchical identification method based on petrochemical big data and machine learning. By integrating global geochemical databases, a standardized sample set covering ultramafic to felsic intrusive rocks was constructed, and a three-level classification system (“group–subgroup–specific lithology”) was established. Visual discrimination charts and an automatic identification model were developed using Linear Discriminant Analysis and Multilayer Perceptron, respectively. The results show that the method achieves over 90% accuracy in the first- and second-level classifications, effectively identifying major rock types such as gabbro, diorite, and granite. Although the accuracy fluctuates in the third-level classification due to compositional overlap and data quality issues, the method exhibits good interpretability and generalizability. This approach provides a low-cost and efficient technical solution for rapid lithology identification and reservoir evaluation, demonstrating potential application in deep and unconventional hydrocarbon exploration.
How to cite: Quan, Z. and the Quan Zhou: Lithology identification of intrusive rocks based on petrochemical big data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6040, https://doi.org/10.5194/egusphere-egu26-6040, 2026.
Detecting fluid contacts, such as oil-water interfaces, remains a significant challenge for conventional seismic exploration when the acoustic impedance contrast is weak. However, the seismoelectric effect, which depends on electrical conductivity and electrokinetic coupling, provides a promising method for interface detection. In this study, we investigate the radiating seismoelectric electromagnetic (EM) reflection and transmission coefficients generated at an interface between two fluid-saturated porous media under fast P-wave incidence.
We analytically derive the reflection and transmission coefficients for all generated wave modes (fast P, slow P, SV, and EM waves) using Pride’s theory and Helmholtz decomposition. To verify the validity of our derivation, we calculate the energy fluxes of the wavefields using the electrokinetic Poynting vector and confirm that energy conservation is strictly satisfied.
Analyzing the frequency- and angle-dependent behavior of the reflection and transmission coefficients, we show that seismoelectric conversion is directly governed by the contrast in elastic and electric properties of the reservoir and the fluids therein, at the interface. Building on this, we compare the response of an "elastic interface" (strong mechanical contrast) with an "electric interface" (strong conductivity contrast but weak mechanical contrast, representing an oil-water contact). The analysis shows that while the seismic reflection energy at the electric interface is negligible, the reflected seismoelectric EM wave energy is comparable to the seismic signal generated at a strong lithological interface. These findings suggest that reflected seismoelectric waves are a reliable tool for identifying oil-water interfaces that are effectively invisible to conventional seismic surveys.
How to cite: Liu, Y. and Smeulders, D.: Seismoelectric Radiating Reflection and Transmission at Porous–Porous Interfaces: Angle- and Frequency-Dependent Coefficients, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17981, https://doi.org/10.5194/egusphere-egu26-17981, 2026.
Shale reservoirs, characterized by intricate fluid occurrence,elevated clay and organic matter (OM) contents, and diverse pore structures, present complexities that obscure the primary controlling factors of lacustrine shale conductivity and render existing saturation models insufficient in accuracy. Taking the medium-high maturity lacustrine shale of the Qing1 Member in the Changling Sag, Southern Songliao Basin as an example, we use 2D nuclear magnetic resonance and pressure-maintained and sealed coring techniques to obtain the original fluid information of shale reservoirs. By combining geochemical, mineralogical, and geophysical properties, we investigate the impact of mineral components, physical properties, and fluid occurrence on the electrical conductivity properties of shale reservoirs. The clay and carbonate minerals play a primary role in influencing the conductivity of shale, whereas effective porosity plays a secondary role. Conductivity variations within single lithologies are affected by total organic carbon and fluid types. A novel resistivity-saturation interpretation model, which is a function of saturation, for mature lacustrine shale (MLS) is developed based on lithologic distinctions and the influence of OM. This model identifies two primary conductive pathways: free water conduction in matrix pores and additional conduction from clay-bound water. The bound water cementation indexmwb and conductive bound water fraction Swb are introducedto reflect the impact of OM on clay additional conductivity. Compared with the Archie model developed for clay-free rockand the Indonesian model used for shales, the MLS model offers a more accurate calculation of oil saturation in MLS. Our approach makes a step ahead toward reducing uncertainty in the evaluation of MLSs as potentially economic oil reservoirs.
How to cite: Li, Z.: Conducting mechanism and saturation model of mature lacustrine shales: A case study of the first member of the Qingshankou Formation in the Changling Sag, Southern Songliao Basin , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1767, https://doi.org/10.5194/egusphere-egu26-1767, 2026.
Recent shale gas drilling wells in southern China confirms significant resource potential within the Lower Carboniferous marine shale of the Yaziluo Rift Trough, yet exploration efficiency is hindered by poorly understood sedimentological heterogeneity and gas accumulation mechanisms. This study integrates major and trace elements, high-resolution field emission scanning electron microscopy, and adsorption analyses (low-pressure N₂/CO₂, high-pressure CH₄) to systematically characterize sedimentary facies, reservoir properties, and gas enrichment patterns. The trough exhibits an asymmetric "platform-basin" model with three distinct sedimentary facies. Basin facies comprise siliceous shale formed in deep-water anoxic settings, yielding the highest total organic carbon (TOC) content (average 3.63%) controlled by redox conditions and paleoproductivity. Lower slope facies consist of mixed shale in dysoxic environments, with moderate TOC (average 1.80%), dominated by redox conditions. Upper slope facies are calcareous shale in shallow, weakly reducing settings, showing the lowest TOC (average 0.99%), influenced by clay-mediated organic preservation. Reservoir analysis reveals that basin facies are dominated by organic pore, whereas lower slope facies display reduced organic pores but increased inorganic pores and micro-fractures, and upper slope facies shift predominantly to inorganic pores and microfractures. Moving from basinward to upper slope, increasing carbonate content expands dissolution pore networks, yet declining TOC diminishes organic pore development, promoting organic-clay complexes and weakening pore structure. Additionally, three shale associations classified by shale-to-argillaceous limestone ratios correspond to specific sedimentary facies. The lower slope shale association demonstrates optimal gas preservation due to high TOC, argillaceous limestone interlayers acting as direct caps, and fracture-enhanced porosity facilitating gas migration. The upper slope association shows promise for self-sealing bodies via acid fracturing despite lower TOC. In contrast, the basin facies shale association exhibits constrained gas retention capacity owing to clay-dominated mineralogy and absence of argillaceous limestone interlayers. This study emphasizes the critical role of lithofacies heterogeneity and integrated "source-reservoir-seal" configurations in evaluating of shale gas accumulation under "slope-basin" depositional architectures, providing a theoretical basis for reservoir development in analogous geological settings.
How to cite: Chen, X.: Sedimentology Dominated Accumulation Mechanism of Marine Shale Gas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2639, https://doi.org/10.5194/egusphere-egu26-2639, 2026.
With the continuous advancement of oil and gas exploration and development, unconventional resources have become a crucial component of national energy strategies. The extraction of resources such as shale oil and gas relies on technologies like horizontal drilling and multi-stage fracturing, where accurate geomechanical parameters are essential for engineering design. Conventional core-based experiments and well-log inversion methods, though reliable, are often costly, time-consuming, and limited in representativeness. Recent progress in digital cutting analysis—using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS)—offers a fast, economical, and practical alternative. This study presents a workflow for predicting rock mechanical parameters from the mineral composition and pore structure of cuttings. Site-collected cuttings were characterized via SEM-EDS to analyze morphology, mineralogy, pore networks, and microfractures. Given the high heterogeneity and anisotropy of shale, a composite modeling approach integrating heterogeneous structure theory and equivalent medium models was applied. This included the Reuss and Voigt bounds, Voigt–Reuss–Hill average, Hashin–Shtrikman bounds, Kuster–Toksöz theory, and Gassmann fluid substitution to estimate equivalent static elastic parameters. These were then converted to dynamic parameters using linear regression to ensure consistency with logging data. Results show strong agreement between cutting-derived parameters and well-log inversions. Young’s modulus and Poisson’s ratio errors range from –12.62% to 4.03% and –10.18% to 10.47%, respectively, within acceptable limits. Although minor uncertainties arise from mineral identification and image segmentation, overall trends match well-log data closely. The introduction of a Weakness Index effectively highlights reservoir heterogeneity and correlates well with measured fracture pressures. A strong linear relationship between static and dynamic parameters, particularly Young’s modulus, supports the reliability of the regression-based conversion. This study confirms the feasibility and applicability of digital cuttings for building rock-physics models and predicting mechanical properties in unconventional reservoirs. The method not only aligns with well-log results but also better captures formation heterogeneity. More importantly, it enables real-time, cost-effective mechanical characterization from wellsite cuttings, offering a practical alternative to core- or log-dependent methods. This is particularly valuable in complex wells such as horizontals, where rapid formation evaluation and fracture design are critical.
How to cite: Nie, X., Hou, Y., and Zhang, Z.: Rock Mechanical Parameters Prediction Based on Digital Drilling Cuttings Mineral Logging Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2877, https://doi.org/10.5194/egusphere-egu26-2877, 2026.
Marine shales of X-block are featured with poor organic matter, over maturity and complex mineralogical assemblages, which collectively result in weak organic-related logging responses. As a consequence, conventional total organic carbon (TOC) evaluation methods exhibit substantial uncertainties associated with baseline calibration and parameter generalization, thereby limiting prediction accuracy and robustness.
To overcome these limitations, this study develops a physics-constrained deep learning framework for TOC prediction that integrates multi-source logging data using a hybrid DNN–LSTM–GPC architecture. High-resolution nuclear magnetic resonance (NMR) and electrical imaging logs are incorporated as primary data sources to extract multi-scale, organic-matter-sensitive features. These features are further integrated with conventional well logs to construct a comprehensive feature space that captures organic matter distribution, pore structure characteristics, and lithological variability. In addition, an improved ΔLogR model and region-specific rock-physics constraints are embedded within the deep learning framework to ensure physical consistency and geological interpretability.
Application results demonstrate that the proposed method achieves superior prediction performance in low–organic-matter marine shales, yielding a root mean square error of 0.08% and a coefficient of determination (R²) of 0.95. The model consistently outperforms multivariate regression, uranium-based approaches, and porosity-difference methods, while maintaining stable predictive capability in intervals exhibiting pronounced TOC heterogeneity. These results indicate that physics-constrained deep learning integrated with multi-source logging data provides a reliable and effective approach for micro-scale TOC evaluation and favorable reservoir identification in low–organic-matter marine shale systems.
This research was supported by the Natural Science Foundation of Shandong Province of China (ZR2023YQ034) and Shida Jingwei Industry-Education Integration Research Institute Project (15572259-25-ZC0607-0011).
Fig. 1. Comparison of total organic carbon (TOC) prediction results for marine shale in Well JY6, X Block, obtained from different methods.
How to cite: Ge, X., Wang, Z., He, C., Ge, X., Fan, J., Wu, X., Yang, D., and Zhai, C.: A DNN–LSTM–GPC framework for TOC prediction in marine shales of the X Block using multi-source logging data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8697, https://doi.org/10.5194/egusphere-egu26-8697, 2026.
Due to the mutual flow between drilling mud and formation fluid, the pore pressure in the surrounding rock of the wellbore directly affects the rock’s mechanical properties. Since the properties and density of drilling mud are adjusted in real time during drilling operations, wellbore stability is inherently a dynamic process. This study aims to enhance wellbore integrity and reduce drilling accidents by investigating the dynamic characteristics of wellbore stability.
Rock mechanics experiments were conducted to simulate the mechanical properties of rocks under varying pore pressure conditions, yielding the quantitative relationship between pore pressure and rock mechanical behaviors. In conjunction with the radial seepage characteristics around the wellbore at different drilling stages, dynamic fluid-solid coupling simulations of the wellbore were performed based on geomechanical principles, enabling the development of a dynamic wellbore stability prediction method. This method further facilitates the prediction of key parameters such as wellbore collapse period and radial collapse depth.
This dynamic wellbore stability prediction technology was applied to three wells drilled in sandstone formations. It successfully predicted the collapse depth and period under different drilling mud densities and properties, significantly improving drilling efficiency while ensuring wellbore integrity. Compared with static wellbore stability prediction techniques, this technology provides drilling engineers with a richer set of drilling parameters and defines a clear wellbore collapse period, thereby effectively preventing stuck pipe accidents. Comparative drilling tests in the same block and formation layer showed that drilling efficiency increased by 12%, and the stuck pipe accident rate decreased by 27.3%.
This exploratory research demonstrates that wellbore stability in permeable formations during drilling is indeed a dynamic equilibrium process. As drilling mud properties, time, and stress conditions change, wellbore stability evolves accordingly. Predicting dynamic parameters can provide valuable references for drilling design and optimization, thereby enhancing wellbore integrity and minimizing drilling accidents.
How to cite: Liu, X. and Xiong, J.: Research on Dynamic Wellbore Stability Based on Wellbore Seepage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4382, https://doi.org/10.5194/egusphere-egu26-4382, 2026.
As the fundamental constituent units of hybrid shale oil reservoirs, laminae exhibit complex structures and diverse combination types, leading to strong reservoir heterogeneity. Therefore, revealing the structural characteristics of laminae and their controlling mechanisms is crucial for understanding the storage and occurrence properties of such reservoirs. This study focuses on the shale reservoirs of the Fengcheng Formation in Mabei, Xinjiang, integrating methods such as cast thin sections, field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), and nitrogen adsorption to characterize the storage properties of laminae. On this basis, laser scanning confocal microscopy was used to accurately analyze the oil-bearing properties and occurrence differences within individual laminae. Furthermore, nuclear magnetic resonance (NMR) technology was applied to elucidate the controlling effects of complex laminae combinations on storage and occurrence. The results indicate that: (1) The Fengcheng Formation contains diverse laminae types, including felsic clastic laminae, tuff laminae, dolomite laminae, limestone laminae, siliceous laminae, borax laminae, and organic matter laminae. (2) The primary storage spaces consist of dissolution pores in felsic clastic/tuff laminae, intragranular and intercrystalline pores in dolomite laminae, and intercrystalline pores in limestone/siliceous/borax laminae. Strong dissolution, conducive to the development of high-quality reservoirs, occurs when the proportion of felsic clastic laminae is 50%-60% or dolomite laminae is 20%-40%. (3) Confocal microscopy characterization reveals that dolomite, micritic siliceous, and felsic clastic laminae exhibit superior oil-bearing properties. The differences in oil-bearing properties are mainly controlled by two key factors: ① The interbedded distribution of source and reservoir rocks without barriers; the closer a lamina is to the source, the better its oil-bearing properties. ② A coordinated source-to-reservoir ratio; the optimal oil-bearing properties occur when the ratio of source laminae to reservoir laminae is 1:3 to 2:1, with the poorest properties when the ratio exceeds 6:1. NMR core-scale testing further validates these controlling patterns. The conclusion is that the key to efficient storage and occurrence of shale oil lies in the optimal spatial configuration and coordinated proportion between high-quality source rocks and effective reservoirs.
How to cite: Wang, H.: Research on the Control Mechanism of Mixed-Type Shale Laminar Structure on Shale Oil Reservoir and Storage Properties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8636, https://doi.org/10.5194/egusphere-egu26-8636, 2026.
Due to its favorable pore-permeability characteristics, sandstone serves a crucial role in applications such as oil and gas production, freshwater extraction, and underground CO₂ storage. As the primary reservoir space and migration pathway for hydrocarbons, accurate porosity data are essential for seismic exploration and reservoir development. In unconventional reservoirs, establishing an appropriate tight sandstone rock physics model is key to understanding how petrophysical parameters influence porosity and permeability. However, conventional models often fail to dequately represent both the three-dimensional irregularity of pore geometries and the spatial distribution of clay minerals.
To address these limitations, this study develops a novel rock physics model that incorporates superspherical pores and clay distribution to characterize argillaceous sandstone. Clay is categorized into structural clay (within the matrix) and dispersed clay (within pores). Following a sequential approach that reflects natural sandstone grain stacking, the proposed model is constructed by employing Voigt-Reuss-Hill averaging methods to incorporate structural clay, coupling the superspherical pores model, and applying solid substitution equations to determine the saturated rock modulus, which contains dispersed clay. This framework allows quantitative analysis of how pore geometry and clay occurrence states affect rock elastic properties.
However, in practical settings, the high cost of well logging often prevents direct measurement of these parameters. Therefore, a simulated annealing algorithm is employed to inversely determine pore characteristics and clay content in different occurrence states at each sampling point along the wellbore.
The validity and practical applicability of the proposed model are demonstrated through comprehensive sensitivity analyses and real-data applications.
How to cite: Wang, B., Yin, X., Ma, Z., and Li, K.: Development and Application of a Rock Physics Model to Infer Pore-Geometry and Clay-Distribution in Argillaceous Sandstone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16220, https://doi.org/10.5194/egusphere-egu26-16220, 2026.
The effective development of unconventional shale oil reservoirs depends on the accurate prediction of seepage pathways. In the lacustrine shales of the upper submember of the fourth member of the Shahejie Formation (Es4U) in the Dongying Sag, bedding fractures serve as both primary storage spaces and critical fluid conduits. However, intense reservoir heterogeneity makes it challenging to reliably identify fracture-prone intervals using well-log responses alone.
Based on systematic core descriptions and fracture statistics from research wells, this study integrates thin-section petrography, X-ray diffraction (XRD), total organic carbon (TOC) analysis, and logging data to perform a multi-scale correlation of bedding fracture occurrences.
The results demonstrate that: (1) bedding fractures are diagenetic products triggered by clay mineral dehydration/transformation and hydrocarbon-induced overpressure; (2) the development of these fractures is synergistically governed by carbonate and clay mineral contents, organic matter abundance, and lamina density, with laminated clay-rich lithofacies identified as the dominant zones for fracture occurrence.Building upon these geological insights, a vertical development model for bedding fractures was established to provide quantitative geological constraints for logging evaluation. By utilizing conventional logging suites (e.g., integrating AC, GR, and DEN curves), intervals characterized by carbonate content of 20%–60%, clay content of 20%–35%, TOC > 2%, and well-developed laminated structures are identified as potential fracture-bearing zones.
This model effectively bridges geological genesis with logging attributes, providing a direct geological foundation for the prediction of favorable seepage pathways in shale reservoirs using borehole geophysical data.
How to cite: Liu, C., Wang, G., and Yin, Z.: Geological Patterns and Conventional Well-log Prediction of Bedding Fractures in Lacustrine Shales: A Case Study from the Upper Submember of the 4th Member of the Shahejie Formation, Dongying Sag, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10363, https://doi.org/10.5194/egusphere-egu26-10363, 2026.
In deep coal seams, the primary storage space for adsorbed methane resides in the micropores and associated adsorption surfaces of the coal matrix, whereas the fracture network provides the dominant pathway for gas–water transport. Fractures promote the migration and local enrichment of free gas, with their connectivity exerting a fundamental control on methane desorption efficiency and overall seepage capacity. Therefore, elucidating the pore‑scale mechanisms of gas‑driven water displacement is critical for understanding gas–water occurrence and flow dynamics, as well as for assessing the reservoir‑controlling role of different microstructures.In light of the pronounced heterogeneity of coal, this study constructs two pore‑scale digital core models—a matrix‑pore model and a pore–fracture model—based on micro‑CT imaging of low‑rank coal. Utilizing the Volume of Fluid (VOF) method within an immiscible two‑phase displacement framework, we simulate methane‑driven water displacement under reservoir‑formation conditions. The simulation results are used to systematically analyze how the contact angle (θ) and capillary number (Ca) govern displacement morphology, phase connectivity, and residual phase distribution. Furthermore, a capillary‑number–contact‑angle (Ca–θ) phase diagram characterizing the displacement process is established.
Key findings are summarized as follows: (1) Under gas–water viscosity ratios representative of the reservoir‑formation stage, pore‑scale gas‑driven water displacement exhibits three distinct regimes: capillary fingering, viscous fingering, and a transitional capillary–viscous fingering regime. In the matrix‑pore model, displacement is dominated by capillary fingering due to pore‑throat constrictions and microstructural bottlenecks, resulting in a dispersed and fragmented displacement front. In contrast, displacement in the pore–fracture model is governed by an interconnected fracture network, where capillary forces are substantially weakened, leading to displacement patterns characteristic of viscous fingering. (2) At low Ca, displacement follows the capillary‑fingering regime, and the gas phase predominantly forms a connected flow network after displacement. At high Ca, viscous fingering dominates, generating numerous isolated gas bubbles and yielding poor gas‑phase connectivity. At intermediate Ca, a transitional regime emerges, combining features of both capillary and viscous fingering. (3) The influences of wettability, capillary number, and pore‑structure type on final gas saturation and gas‑phase connectivity differ markedly between the two models. Under identical displacement conditions, the pore–fracture model attains significantly higher gas saturation and superior gas‑phase connectivity compared to the matrix‑pore model. These insights advance the understanding of methane occurrence and migration in deep coal seams and provide a basis for optimizing coal reservoir development strategies.
Keywords：Deep coal;Gas-water two-phase flow;Displacement pattern;Wettability, Capillary number
How to cite: Jin, X. and Chen, Z.: Pore‑Scale Simulation of Methane‑Water Two‑Phase Flow in Deep Coal Seams Using the Volume of Fluid Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2800, https://doi.org/10.5194/egusphere-egu26-2800, 2026.
Mean grain size (Mz) is a key indicator of depositional processes and reservoir quality, yet its continuous characterization is commonly limited by the availability of core or cuttings data. This study presents a new method for predicting Mz from conventional well logs by integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Principal Component Analysis (PCA). A density–neutron separation parameter is first constructed to capture lithological and grain-framework variations. Multi-scale components sensitive to grain-size changes are then extracted from this parameter using CEEMDAN, and the dominant controlling features are identified through PCA. The resulting model enables continuous Mz prediction along the wellbore. Comparisons with measured data demonstrate that the proposed approach reliably captures vertical grain-size variations, providing a practical and robust solution for quantitative grain-size characterization and supporting detailed reservoir analysis and geological modeling.
How to cite: Shen, B., Wang, C., Ma, X., and Sun, K.: Quantitative Inversion of Mean Grain Size from Conventional Well Logs Using Complete Ensemble Empirical Mode Decomposition （CEEMDAN）and Principal Component Analysis（PCA）, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3232, https://doi.org/10.5194/egusphere-egu26-3232, 2026.
The accurate quantification of karst volume is essential for evaluating storage capacity and fluid flow behavior in carbonate reservoirs, which are often highly heterogeneous due to complex diagenetic and karstification processes. However, conventional approaches to karst characterization frequently rely on qualitative descriptions or isolated datasets, lacking an integrated framework that effectively combines petrophysical properties with lithological controls. To address this gap, this study proposes a novel and practical workflow that systematically integrates routine well logs and core data to quantitatively estimate karst-dominated porosity in carbonate sequences. The methodology is designed to be both log-based and scalable, making it suitable for field-wide application even in data-constrained environments.
The proposed method is structured into four sequential and interpretative steps: (1) Data Integration and Quality Control: Multi-source data—including conventional well logs, core measurements, geological models, and lithology logs—are carefully depth-matched and subjected to rigorous quality checks to ensure consistency and reliability. Special attention is paid to correcting for borehole environmental effects and log normalization across multiple wells. (2) Definition of Karst Porosity Threshold: Based on systematic analysis of core-derived porosity-permeability cross-plots, a porosity value greater than 15% is established as a robust indicator for significant karst development. This threshold effectively distinguishes karst-related pore space from matrix porosity and is validated through thin-section and CT-scan observations. (3) Identification of Host Lithology: Lithofacies modeling and petrographic analysis are employed to confirm that karst features are predominantly hosted within dolomite intervals, highlighting the lithological control on karst distribution. Log-based facies classification is calibrated with core data to ensure accurate lithology discrimination in non-cored sections. (4) Calculation of Karst Volume Percentage: The proportion of karst volume is quantitatively computed as the ratio of high-porosity (>15%) dolomite volume to the total pore volume, which includes matrix pores, vugs, and fractures. This volumetric approach enables a more realistic representation of karst contribution to total porosity and reservoir performance.
This workflow was applied to a carbonate reservoir in the Pre-Caspian Basin, where it yielded a karst volume proportion of 11.1%. This result aligns closely with independent core statistics and regional geological understanding, validating the method’s accuracy and applicability. Sensitivity analyses were conducted on the porosity threshold, confirming that the selected 15% cutoff optimally balances discrimination capability and geological plausibility. The integrated approach not only enhances the reliability of karst assessment but also offers a scalable and reproducible tool for reservoir characterization. It supports improved geological modeling, reservoir performance prediction, and development planning in complex carbonate settings, ultimately contributing to more efficient hydrocarbon recovery. Future work will focus on extending the workflow to incorporate seismic attributes and dynamic production data for enhanced 3D karst modeling.
Keywords: Karst Volume; Carbonate; Well Logging; Porosity; Integrated Workflow
How to cite: Hou, J. and Yang, Y.: An Integrated Method for Quantifying Karst Volume in Carbonate Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3739, https://doi.org/10.5194/egusphere-egu26-3739, 2026.
Pyrite is widely developed in shale formations with diverse occurrence morphologies and relatively low contents. However, it exerts a significant impact on the electrical properties of reservoirs and severely restricts the accuracy of reservoir evaluation. Based on computed tomography (CT) scanning technology, combined with mathematical morphology methods and finite element simulation techniques, this study focuses on the coupling relationship between pyrite occurrence states and reservoir resistivity, along with the quantitative calculation method of pyrite content. The results indicate that: (1) The influence of pyrite occurrence morphologies on the electrical conductivity of shale varies remarkably. Dispersed pyrite particles exert a weak interference on reservoir resistivity, and the rock-electrical relationship conforms to Archie's law. In contrast, massive and banded pyrite have a more prominent impact on resistivity, their conductive contribution exceeds that of pore water. This renders the traditional Archie's law inapplicable. Furthermore, under the condition of the same content, banded pyrite exerts the most significant influence on reservoir resistivity. (2) Three parameters, namely the Pyrite Resistivity Ratio (PRR), the Conductive Path Influence Characterization (VPYRZ), and the Pyrite Occurrence Index (PYI), are constructed to quantitatively describe the nonlinear influence of pyrite with different occurrence morphologies on reservoir resistivity. Combined with scanning electron microscopy (SEM) data, the PYI thresholds for different occurrence morphologies are determined as follows: dispersed pyrite (< 7.8623), massive pyrite (7.8632–16.986), and banded pyrite (>16.986). On this basis, a quantitative calculation model for pyrite content is established. (3) The verification results of actual well logs show that the calculated results of the proposed model are in high agreement with the formation element logging data, and the accuracy meets the practical requirements of reservoir evaluation.
How to cite: Liu, Y.: A Study on the Coupling Relationship Between Pyrite Occurrence Morphologies in Shale and Resistivity, and the Quantitative Calculation Method of Its Content Based on Numerical Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2946, https://doi.org/10.5194/egusphere-egu26-2946, 2026.
Abstract: Buried hill reservoirs in the eastern South China Sea exhibit highly heterogeneous mechanical properties and complex fracture networks that exert significant control on reservoir productivity, fluid pathways, and wellbore stability. Their tectonic evolution, multistage deformation, and lithological diversity make traditional fracture prediction methods insufficient for supporting safe and efficient offshore drilling operations. To address these challenges, this study develops a new integrated framework that combines geomechanical analysis, finite-element numerical simulation, and multi-source geological and geophysical data to characterize fracture attributes and predict fracture behavior under present-day stress conditions. The workflow incorporates acoustic and imaging logging data, high-resolution seismic attributes, lithology-based mechanical property modeling, and 3D Mohr circle stress analysis. These datasets are used to construct a heterogeneous geomechanical model that captures the spatial variability of elastic and strength parameters across the buried hill. Numerical simulations are performed to evaluate the distribution of stress perturbations associated with structural relief and lithological layering, and to assess the likelihood of fracture initiation, propagation, and reactivation under different stress regimes. The results demonstrate pronounced variations in fracture intensity and failure potential both laterally and vertically. Zones near faulted structural highs exhibit locally elevated differential stress and shear strain concentration, leading to enhanced fracture connectivity and increased reactivation probability. In contrast, massive crystalline units and mechanically strong lithologies show limited deformation and lower fracture susceptibility. By integrating the simulated stress field with observed fracture indicators, the study identifies high-risk intervals with elevated risks of borehole collapse, drilling fluid loss, or induced fracturing, as well as fracture-favorable sweet spots that may enhance reservoir penetration and productivity. Furthermore, the framework provides quantitative guidance for optimizing well trajectories, selecting safe drilling windows, and improving well placement strategies in offshore buried hill settings. The results highlight the importance of incorporating geomechanical constraints into fracture characterization workflows to reduce drilling uncertainty and improve the reliability of fracture prediction models. This integrated analytical–numerical approach offers a robust and transferable methodology for evaluating fracture development in complex tectonic reservoirs. It provides practical insights into wellbore stability management, reservoir development optimization, and risk mitigation in the challenging geological environment of the eastern South China Sea. The proposed workflow can also be adapted to similar buried hill or basement reservoirs worldwide.
Keywords: fracture characterization; geomechanical modeling; finite-element simulation; buried hill reservoirs; in-situ stress analysis; south China Sea
How to cite: Deng, C., Tan, J., and Yin, L.: An integrated geomechanical–numerical simulation framework for fracture characterization and prediction in buried hills of the eastern south China Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2650, https://doi.org/10.5194/egusphere-egu26-2650, 2026.
The fluid inclusion homogenization temperature method is the most widely used approach for reconstructing hydrocarbon accumulation history in deep to ultra-deep carbonate rocks. However, there are three major limitations: (1) The difficulty in restoring the eroded thickness of target strata, leading to uncertainty in the reconstruction of tectonic and burial history. (2) It is common to use the brine inclusion homogenization temperature as a substitute for hydrocarbon inclusion trapping temperature. However, it is often challenging to find coexisting hydrocarbon and brine inclusions within the same host mineral. (3) It is difficult to determine the trapping time based on the trapping temperature. As in multi-cycle superimposed basins, the same trapping temperature may correspond to multiple ages on the tectonic-burial history curve, resulting in non-unique solutions.
The application of laser U-Pb isotopic dating of carbonate minerals and clumped isotope thermometry has addressed these limitations and led to the development of a new method for reconstructing hydrocarbon accumulation history in deep to ultra-deep carbonate reservoirs. (1) Restoration of the eroded thickness of strata is achieved through multi-phase diagenetic mineral age-temperature constraints, effectively resolving the uncertainty in the tectonic-burial history reconstruction. (2) Direct measurement of hydrocarbon inclusion trapping temperature, overcoming the challenge of determining trapping temperature when no coexisting brine inclusions are present in the host mineral. (3) Direct measurement of hydrocarbon inclusion trapping time, addressing the issue of non-uniqueness in accumulation ages for multi-cycle superimposed basins.
This method has been applied to reconstruct the accumulation history of natural gas reservoirs in the Dengying Formation of the Sichuan Basin. It has significantly higher accuracy and success rates compared to the fluid inclusion homogenization temperature method.
How to cite: Anping, H. and Anjiang, S.: A New Method for Reconstructing Hydrocarbon Accumulation History of Deep to Ultra-Deep Carbonate Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8804, https://doi.org/10.5194/egusphere-egu26-8804, 2026.
Unconventional reservoirs are confronted with numerous challenges due to their complex pore structures and diverse fluid occurrence states; therefore, the accurate acquisition of pore structure characteristics and fluid property parameters has become the key factor restricting development efficiency. 2D nuclear magnetic resonance (NMR) logging obtains more observation information by introducing longitudinal relaxation time (T1), and it exhibits good application effects in wettability evaluation, reservoir parameter calculation and fluid property identification .The factors affecting NMR relaxation signals are multivariate, mainly including pore fluid properties (viscosity, density, etc.), pore structure characteristics (pore size and distribution, pore connectivity, etc.), magnetic susceptibility differences between rocks and fluids, and the content and distribution of paramagnetic minerals. The coupling of these factors complicates the mapping relationship between relaxation signals and reservoir properties, which urgently requires in-depth analysis.In the field of shale oil exploration and development, there are various types of 2D NMR instruments, and significant differences exist in their observation methods, measurement sequences and acquisition parameters, which further pose challenges to the cross-scale characterization and comprehensive interpretation of reservoir parameters. Therefore, this study analyzes the influencing factors of acquisition parameters on NMR from both experimental and numerical simulation scales.The influence of instrument frequency on 2D NMR is investigated through experiments, and the effects of echo spacing, waiting time and number of echoes on NMR measurement and inversion results are studied via numerical simulation. This research further clarifies the influence of acquisition parameters on fluid distribution characterized by 2D NMR, and verifies the reliability of NMR logging interpretation data in shale reservoirs.
How to cite: Zhao, J., Xie, B., Wang, Y., and Bai, L.: Research on the Influence of NMR Acquisition Parameters on Fluid Distribution in Shale Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15411, https://doi.org/10.5194/egusphere-egu26-15411, 2026.
Tight sandstone reservoirs are generally characterized by low porosity, low permeability, and complex pore structures, which pose significant challenges to reservoir evaluation. Nuclear Magnetic Resonance (NMR) T2 spectra can characterize pore structures and fluid states in porous media. Therefore, gas-displacing-water petrophysical experiments were carried out to investigate the variations of T2 spectra under different saturation states.The study finds that the T2 spectrum in the original state presents a multi-peak distribution dominated by long relaxation components. With the increase of gas saturation, the macropore peak value decreases and the porosity tends to decrease. Meanwhile, compared with the gas-bearing state, the macropore peak widens when the core is water-saturated. Analysis shows that this phenomenon is caused by the low hydrogen index of gas.Thus, a spectrum correction model for gas-bearing sandstone reservoirs was established based on the Gaussian distribution, which corrects the T2 spectrum morphology and porosity components to their original states. After gas saturation correction, the T2 spectrum mainly presents a bimodal distribution dominated by short relaxation components. The irreducible water saturation calculated from the corrected T2 spectrum is more consistent with the core measurement results.Combined with NMR experiments, this study clarifies the NMR response mechanism of reservoirs under different saturation states, establishes a Gaussian distribution model for T2 spectrum correction of gas-bearing sandstone, and ultimately achieves accurate characterization of pore structures.
How to cite: Xie, B., zhang, B., tang, Q., zhu, X., zhao, J., wang, Y., bai, L., and lai, Q.: A Method for Characterizing Pore Structure of Gas-Bearing Sandstone Based on Nuclear Magnetic Resonance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15708, https://doi.org/10.5194/egusphere-egu26-15708, 2026.
Extra-deep electromagnetic (EM) logging-while-drilling (LWD), with its deep investigation and sensitivity to resistivity distribution, is widely used in high-angle and horizontal wells. Due to the complex tool responses of extra-deep EM LWD in heterogeneous formations, eyeball evaluation is often insufficient for accurate bed boundary identification. Therefore, quantitative formation parameter estimation necessitates inversion, with multi-boundary inversion based on 1D models being the most prevalent approach. The inherent limitation of 1D-based inversion is its inability to accurately resolve structurally complex formations, frequently producing oversimplified results. 2D inversion methods alleviate this oversimplification, but their high computational cost limits applicability in real-time geosteering.
In paper, we propose a 1D-2D adaptive parametric inversion framework to combine the efficiency of 1D inversion with the accuracy of 2D inversion. First, we investigate the tool responses and parameter sensitivities in 2D formations using a 2.5D finite-difference algorithm. Then, a scenario-dependent adaptive parametric inversion strategy is developed for specific models based on the sensitivity analysis. For instance, in a fold model, we use a 1D horizontally layered inversion for gently dipping limb regions and a three-point parameterization scheme for the fold core. To improve global optimization, a probabilistic inversion method is established based on the PT-MCMC algorithm, incorporating multiple prior distributions and multiple joint constraints. Finally, the method is applied to the inversion of fold and fault models. Numerical experiments demonstrate that the proposed method accurately reconstructs fold cores and fault structures. Specifically, the relative errors of fault dip angle and fault throw are less than 10%, while the relative error of the fold core location is less than 8%.
Generally, the proposed 1D-2D adaptive parametric inversion framework provides an efficient and robust strategy for real-time geosteering in horizontal and highly deviated wells within complex reservoirs, showing potential for refined reservoir characterization.
We are indebted to the financial support from the National Natural Science Foundation of China (42304140).
How to cite: Chang, J. and Wu, Z.: An Efficient 1D-2D Adaptive Parametric Inversion Method for Extra-Deep Electromagnetic LWD in Complex Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3483, https://doi.org/10.5194/egusphere-egu26-3483, 2026.
Acoustic Remote Detection Logging can extend the investigation range of conventional borehole logging from the immediate vicinity of the borehole to several meters and even tens of meters, providing complementary information for formation evaluation in reservoirs with heterogeneity such as fracture–cavity systems and thin interbeds. Such complex anomalous bodies may reduce the effectiveness of conventional rock-physics log interpretation. Overall, the current work still faces two main issues: the lack of geology-constrained 3D models and the lack of more efficient and transferable interpretation tools, which makes it difficult to establish a stable relationship between complicated reflected wavefields and subsurface heterogeneity.
This study focuses on Acoustic Remote Detection Logging and develops geology-constrained 3D heterogeneous modeling and response analysis. First, a 3D stochastic medium is generated using the spectral synthesis method to produce correlated random fields, which are then mapped to elastic-parameter perturbation fields. Then, a bisection-based thresholding scheme is applied to satisfy a target volume fraction (or porosity), and local-maximum detection is used to determine seed points and spatial distributions for cavities, enabling 3D construction of fracture–cavity heterogeneity. Cavity geometry and scale parameters are constrained by statistical characteristics derived from existing geological and logging data. Finally, CUDA-based parallelization is introduced to improve the efficiency of full 3D model generation and support rapid construction of high-resolution reservoir models.
Based on forward simulations of Acoustic Remote Detection Logging responses excited by dipole shear-wave sources, we design parameter combinations covering formation porosity and fracture–cavity geometric parameters, and summarize the results into response charts/templates for interpretation. These charts provide quantitative relationships between fracture parameters and key waveform features, supporting comparative identification and interpretation of fracture development, orientation, and spatial position under different geological scenarios. In addition, we explore using a model for feature discrimination under chart-based constraints, thereby providing auxiliary support for comparative evaluation and interpretation of fracture parameters.
How to cite: Wang, H., Guo, Y., Zhang, Z., Ye, Z., and Sun, H.: Acoustic Remote Detection Logging for Heterogeneous Reservoirs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4177, https://doi.org/10.5194/egusphere-egu26-4177, 2026.
Accurate estimation of hydrocarbon content is a critical component of shale reservoir evaluation. Although nuclear magnetic resonance (NMR) T1-T2 measurements are highly sensitive to fluid properties, conventional assessments of hydrocarbon content typically rely on empirical interpretation charts or supplementary experiments, which limit their quantitative reliability and practical applicability. In this study, we propose a novel fractal characterization method based on NMR T1-T2 measurements for quantitative evaluation of hydrocarbon content in shale reservoirs. To mitigate the uncertainty introduced by T1-T2 spectral inversion, fractal parameters are directly extracted from the original NMR echo train data, bypassing the inversion process entirely. Numerical simulations demonstrate that the echo-based fractal parameters exhibit significantly enhanced sensitivity and discrimination capability with respect to hydrocarbon content when compared with fractal parameters derived from inverted T1-T2 spectra. Core-scale experiments further validate that the proposed fractal dimension effectively differentiates movable hydrocarbons from pyrolytic hydrocarbons in shale formations. The proposed method provides a robust, efficient, and inversion-independent approach for shale hydrocarbon content evaluation, offering strong potential for both laboratory studies and field-scale NMR applications.
How to cite: Gu, M., Wang, L., Shi, P., Li, G., and Ai, Y.: Direct Fractal Characterization of Shale Hydrocarbon Content from NMR T1-T2 Echo Train Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4225, https://doi.org/10.5194/egusphere-egu26-4225, 2026.
Carbonate reservoirs possess substantial hydrocarbon resource potential and have become a major focus of exploration in recent years. However, the complex geological settings, pronounced lithological heterogeneity, and strong vertical variability of carbonate reservoirs pose significant challenges to conventional lithofacies identification methods. Such approaches are often time-consuming, highly subjective, and limited in their ability to accurately discriminate complex lithofacies assemblages, thereby constraining the efficient development of carbonate reservoirs. Geophysical well logging offers advantages such as low acquisition cost, continuous coverage, and high vertical resolution, making it a fundamental dataset for continuous lithofacies characterization. In this study, integrated geological information, including whole-rock analysis, petrographic thin sections, and scanning electron microscopy, is employed to systematically investigate the mineral compositions and pore structure characteristics of different lithofacies, and the corresponding logging response mechanisms are quantitatively investigated. Five conventional logging curves—gamma ray, acoustic, neutron porosity, bulk density, and resistivity—are selected to construct a multidimensional feature parameter set. Considering the differences in numerical ranges among logging curves and the imbalance in lithofacies sample distributions, data normalization and class imbalance correction are performed prior to model training. Subsequently, a high-precision lithofacies identification model based on conventional well logs is developed by integrating Bayesian optimization with the Extreme Gradient Boosting (XGBoost) algorithm. The results indicate that the predicted lithofacies are highly consistent with geological interpretations, core observations, and thin-section identifications, demonstrating that the Bayesian-optimized XGBoost model exhibits robust classification performance and effectively captures the complex nonlinear relationships between lithofacies and logging responses. Compared with traditional machine learning methods, the proposed approach achieves significantly improved identification accuracy, providing robust technical support and a reliable theoretical foundation for carbonate reservoir evaluation and hydrocarbon exploration.
How to cite: Yang, J., Wang, L., Gu, M., Ai, Y., and Li, G.: Bayesian-Optimized XGBoost for High-Precision Lithofacies Identification in Carbonate Reservoirs Using Geophysical Well Logs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11545, https://doi.org/10.5194/egusphere-egu26-11545, 2026.
Tight sandstone reservoirs are generally characterized by low porosity and permeability, complex pore structures, and ambiguous electrical responses, which significantly limit the applicability of conventional water saturation evaluation models. To address these challenges, this study proposes a physically constrained reinforcement learning–based symbolic regression framework that integrates nuclear magnetic resonance (NMR)–derived pore size distribution information to automatically derive a dynamic Archie water saturation model with explicit physical interpretability.In the proposed approach, pore size distribution features are embedded into the formation factor formulation, enabling a dynamic correction of the classical Archie equation. A policy neural network combined with reinforcement learning is employed to jointly optimize the model structure and parameters, while explicitly enforcing physical constraints such as monotonicity and nonlinear electrical response behavior.Experimental results demonstrate that, compared with the conventional Archie model, the proposed dynamic model achieves a significant improvement in water saturation prediction accuracy for tight sandstone reservoirs, reducing the mean absolute error by approximately 11%. Moreover, the model more effectively captures the influence of pore structure heterogeneity on the relationship between electrical resistivity and water saturation.This study provides a novel water saturation evaluation methodology that combines physical interpretability with data-driven adaptability for tight sandstone reservoirs and offers valuable insights into the intelligent construction of logging interpretation models for complex reservoirs.
How to cite: Liao, W. and Zhao, B.: A Physically Constrained Dynamically Corrected Archie Saturation Model Based on Pore Size Distribution and Its Application to Tight Sandstone Reservoir Evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4245, https://doi.org/10.5194/egusphere-egu26-4245, 2026.
Shale oil and gas, as typical unconventional resources, have become crucial for stabilizing oil and gas production in China. The calculation of shale reservoir fracturability index is a core step in evaluating engineering sweet spots. However, acoustic logging data of shale formations are susceptible to geological interfaces, borehole deviation, and particularly the bedded structure-induced anisotropy. Such anisotropy causes the measured acoustic interval transit time to deviate from the true formation value, leading to inaccurate in-situ stress calculation and thus compromising the reliability of fracturability evaluation. Accurate acquisition of formation AIT is essential for optimizing fracturing schemes and analyzing borehole stability.
To address this issue, a correction method for acoustic anisotropy of bedded shale in horizontal wells was established. Based on the bedded shale medium model assumption, the formation stiffness coefficients and elastic wave velocities were derived using the equivalent medium theory and elastic wave equation. Shale models with varying bedding angles were constructed to analyze the functional relationships between acoustic velocity, acoustic anisotropy coefficient, and bedding angle.
Numerical simulation results demonstrate that the acoustic interval transit time of bedded shale exhibits significant anisotropic characteristics: both acoustic interval transit time and acoustic anisotropy coefficient increase with the increase of bedding angle, and the acoustic anisotropy coefficient has a good power exponential relationship with the cosine of the bedding angle. A correction model for acoustic anisotropy of bedded shale was thus developed. Field application in horizontal wells of Block L showed that after correction, the peak of the normal distribution curve of acoustic interval transit time in the target reservoir of horizontal wells was highly consistent with that of pilot wells. The corrected acoustic interval transit time was used to calculate Poisson's ratio, Young's modulus, horizontal principal stress difference, and fracture pressure, further deriving the fracturability index. Comparison with the fracturability index calculated from core experimental data indicated that the average relative error of the results was reduced by 9.28%.
This study provides a reliable acoustic anisotropy correction method for bedded shale, which is of great significance for improving the accuracy of shale reservoir fracturability evaluation and guiding the identification of engineering sweet spots.
How to cite: Wu, B. and Zhao, J.: Correction of Acoustic Anisotropy of Bedded Shale and Its Application in Fracturability Evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9050, https://doi.org/10.5194/egusphere-egu26-9050, 2026.
Continental shale oil reservoirs in China are abundant in Solid Organic Matter (SOM), which represents a significant potential hydrocarbon resource. Nuclear Magnetic Resonance (NMR) is an effective tool for evaluating shale oil reservoirs. However, restricted by instrument dead time, it is difficult for the conventional CPMG acquisition mode to capture signals from ultra-short relaxation components such as SOM.To address this, this work introduces the Free Induction Decay (FID) acquisition mode, proposing two novel quantification workflows based on FID signal integration and a direct T1-T2* spectrum, respectively. These approaches are designed to effectively capture the ultra-short components that are typically missed by standard CPMG sequences.The proposed methodology was validated against comprehensive geochemical benchmarks. The results demonstrate that the FID-driven approach yields a superior correlation with geochemical data compared to conventional methods. It exhibits high statistical consistency with Step-by-Step (SBS) Rock-Eval pyrolysis data, proving its capability to accurately quantify total solid hydrogen content, including solid petroleum hydrocarbons, bitumen, and kerogen. This work provides a non-destructive and high-precision means for evaluating the resource potential of shale oil reservoirs.
How to cite: Xu, C., Xie, R., Guo, J., Wang, X., and Zhang, G.: A novel method for quantitative characterization of organic matter based on NMR FID acquisition mode, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21370, https://doi.org/10.5194/egusphere-egu26-21370, 2026.
Micro-resistivity imaging logging is one of the primary techniques for subsurface fracture identification. However, conventional manual interpretation is time-consuming and highly subjective, while existing deep learning-based methods generally require large-scale, well-annotated datasets, resulting in substantial labeling costs and limited applicability in data-scarce scenarios. To address these challenges, this study proposes a fracture identification method for electrical image logs under limited-sample conditions by incorporating prior knowledge derived from outcrop fractures. Leveraging the morphological and statistical similarities between surface outcrop fractures and subsurface electrical image responses, a two-stage training strategy based on a Multi-scale Attention Network (MANet) backbone is developed. In the first stage, optical images of outcrop fractures are preprocessed through grayscale transformation and noise injection to approximate the feature distribution of electrical image logs, enabling the network to learn generalizable edge and texture features. In the second stage, outcrop–electrical image pairs with similar fracture morphology and texture characteristics are generated through similarity matching, and the model is fine-tuned using a composite loss function incorporating Correlation Alignment (CORAL), thereby accelerating domain adaptation to subsurface logging environments. Experimental results from basement reservoirs in the Dongping area of the Qaidam Basin demonstrate that the proposed method significantly improves fracture identification performance under limited-sample conditions. Compared with baseline models, the proposed approach achieves improvements of 13.05% in accuracy and 12.88% in Intersection over Union (IoU), reaching 81.13% and 75.74%, respectively. These results indicate that the proposed method effectively alleviates data scarcity issues in electrical image log interpretation and provides robust technical support for fracture characterization and hydrocarbon resource evaluation.
How to cite: Ai, Y., Wang, L., Gu, M., Gang, L., Shi, P., and Zhao, Z.: Fracture Identification in Electrical Image Logs with Limited Samples by Incorporating Outcrop Priors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8628, https://doi.org/10.5194/egusphere-egu26-8628, 2026.
The construction of three-dimensional multi-mineral digital rock cores is essential for the fine characterization of reservoir pore–throat structures and for the quantitative evaluation of reservoir electrical, acoustic, and mechanical properties.Existing digital rock core modeling approaches can be broadly classified into physical experimental methods and numerical reconstruction methods.Physical experimental methods can produce relatively realistic three-dimensional digital rock cores; however, they are costly and struggle to achieve fine mineral discrimination.Numerical reconstruction methods offer advantages such as low cost and high efficiency; however, most high-fidelity approaches remain limited to single-mineral digital rock cores, whereas multi-mineral modeling methods often rely on idealized assumptions.To address these limitations, this study proposes a 2D–3DGAN-based deep learning algorithm capable of generating three-dimensional multi-mineral digital rock cores from a single AMICS image.Using the “AMICS + 2D–3DGAN” modeling framework, three-dimensional multi-mineral digital rock cores are constructed with high accuracy, efficiency, and realistic multi-mineral representation.The accuracy of the generated results is systematically evaluated by analyzing diagenetic characteristics and by comparing the generated cores with training images in terms of mineral content, pore size distribution, and two-point correlation functions.The results demonstrate that the proposed method significantly enhances reconstruction accuracy and generation efficiency while maintaining economic feasibility, thereby providing a solid foundation for subsequent simulations of multi-mineral diagenetic evolution and reservoir property analysis.Previous studies on diagenesis have largely relied on qualitative approaches, such as identifying diagenetic evolution sequences and constructing two-dimensional schematic representations.Based on the generated three-dimensional multi-mineral digital rock cores, this study proposes a “nucleation–growth” algorithm for numerically simulating diagenetic processes, enabling quantitative modeling of diverse cementation morphologies that closely reflect geological conditions.Meanwhile, the four-parameter structure method is improved to quantitatively simulate dissolution and replacement processes with varying morphologies and to control the precipitation location of dissolution by-products, either inside or outside dissolution pores.Ultimately, a three-dimensional digital rock core diagenetic evolution model based on diagenetic sequences is established, enabling analysis of the evolution of reservoir properties—such as porosity, permeability, tortuosity, and coordination number—through diagenetic processes.
How to cite: Lu, Z., Xi, K., and Wu, Y.: Deep Learning–Based 3D Multi-Mineral Digital Rock Modeling and Diagenetic Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7042, https://doi.org/10.5194/egusphere-egu26-7042, 2026.
Accurate evaluation of pore systems in shale reservoirs is critical for understanding fluid flow, gas storage capacity, and overall reservoir performance. Extensive research has been conducted on the micro- and nano-pore structure of shale reservoirs using various experimental methods and shale samples. However, discrepancies in sample preparation standards and experimental parameters—such as observation techniques and data interpretation—raise concerns about the reliability and comparability of these results across different shale reservoirs. This review systematically evaluates the current methods for characterizing shale pore systems, with particular emphasis on commonly used techniques such as scanning electron microscopy (SEM), gas adsorption, and CT scanning. Key challenges related to sample preparation (e.g., sample size) and experimental conditions (e.g., voltage and current) are discussed, as these factors can introduce significant inaccuracies into pore structure characterization, even for well-established methods. Additionally, we examine the difficulties in integrating these methods to achieve a comprehensive understanding of shale pore parameters, including the quantification of organic and inorganic porosity, full-scale pore size distribution, and pore connectivity. Addressing these challenges requires the establishment of standardized processing workflows to enhance the comparability of results and minimize experimental errors. We also highlight often-overlooked issues, such as the potential discrepancy between pore structures observed under laboratory conditions and those at in-situ depths. The review concludes with recommendations for future research, including the development of advanced experimental techniques and more efficient data processing strategies to improve pore characterization in shale reservoirs. This review provides a new perspective for future research and addresses a critical gap in understanding the impact of experimental conditions on pore structure characterization outcomes.
How to cite: Gou, Q., Xu, S., Xiong, Q., and Li, Z.: A multiple review on identification and fine evaluation of shale pores: Prospects and challenges, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2531, https://doi.org/10.5194/egusphere-egu26-2531, 2026.
The accurate petrophysical characterization of unconventional reservoirs, particularly deeply buried fractured–vuggy carbonate systems, remains challenging due to extreme heterogeneity, ultra-low permeability, and strong scale dependency of reservoir properties. Conventional formation evaluation workflows often fail to reconcile geological observations at the core scale with log- and seismic-scale petrophysical interpretations in a physically consistent manner.In this study, we propose an integrated geological–geophysical petrophysical characterization workflow that links core and thin-section observations, electrofacies classification, and seismic data through an electrofacies-constrained seismic waveform-guided inversion framework. Rock types identified from core and petrographic analyses are translated into electrofacies at the log scale to represent pore-structure variability. Electrofacies are incorporated as conditional constraints in a Bayesian seismic waveform-guided porosity inversion framework. Electrofacies-dependent porosity priors derived from well-log statistics restrict the admissible pore-structure space, while seismic waveform similarity controls the lateral propagation of high-frequency porosity features, implicitly embedding seismic facies within the inversion.The resulting porosity volume exhibits enhanced vertical resolution and improved lateral continuity, allowing thin-layer and non-layered heterogeneities to be resolved beyond the limitations of conventional impedance-based inversion. Recognizing that permeability is not directly observable at the seismic scale, permeability is subsequently derived from the inverted porosity volume under electrofacies control, ensuring that pore connectivity and flow characteristics are consistently represented at the appropriate scale.This workflow establishes a causally consistent and scalable solution for advanced petrophysical characterization and formation evaluation of heterogeneous unconventional reservoirs. By integrating geological constraints with seismic waveform-driven inversion, the proposed method effectively bridges core-, log-, and seismic-scale information and demonstrates strong potential for application in complex carbonate systems as well as other unconventional plays.
How to cite: Zhao, A., Tian, F., and Cao, W.: Multi-Scale Petrophysical Modeling and Characterization of Heterogeneous Carbonate Reservoirs Based on Facies-Constrained Seismic Waveform Integration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6054, https://doi.org/10.5194/egusphere-egu26-6054, 2026.
