The volcanic rocks of the Es3 Formation (the third member of the Eocene Shahejie) in the Liaohe Eastern Sag can be divided into four facies and twelve subfacies. The porosity spectrum, porosity bin, variation coefficient (VC), and porosity width derived from electrical imaging log data were applied to study and characterize the heterogeneity of four facies and nine subfacies, both qualitatively and quantitatively. However, the VC and porosity width cannot be used to quantitatively classify heterogeneity when the VC is small and the porosity width is large. In the present study, the authors propose a new parameter, P_vcd = variation coefficient × porosity width. Based on this parameter, using a combination of porosity spectra, porosity bin features, VC, and porosity width, lithofacies heterogeneity is divided into three categories. The first is weak heterogeneity, which has a P_vcd < 1.1, a VC < 0.15, and a porosity width < 6. The second is moderate heterogeneity, which has a P_vcd 1.1–1.6, a VC 0.15–0.25, and a porosity width 6–9. The third is strong heterogeneity, which has a P_vcd > 1.6, a porosity VC > 0.25, and a porosity width > 9. In these three cases, the porosity spectra mainly display unimodal features, the porosity bins are concentrated, the bimodal features with tails and porosity bins are less concentrated, or the multimodal features with tails and porosity bins are scattered, respectively. Favorable reservoirs of volcanic rocks are controlled by lithofacies. In the study area, the favorable reservoirs appear to be the diatreme subfacies with low or medium heterogeneity, the pyroclastic flow subfacies with low heterogeneity, the compound lava flow subfacies with low or medium heterogeneity, and the outer zone subfacies with strong heterogeneity.

Relative permeability, as a key parameter characterizing multiphase fluid flow behavior in porous media, holds significant importance across various fields, such as reservoir evaluation and engineering geology. However, measuring relative permeability is time-consuming and costly. Considering the analogy between fluid flow and electrical current conduction provides a novel approach for studying multiphase flow characteristics using resistivity data. An integrated oil–water relative permeability and resistivity co-measurement experiment was specifically designed for a complex argillaceous sandstone reservoir in a block, referred to as Block A. Research has shown that as the resistance coefficient increases, the water and oil relative permeability decrease and increase, respectively. As the porosity–permeability comprehensive index increases and the shale content decreases, corresponding to the same resistance coefficient, the water and oil relative permeability show increasing and decreasing trends, respectively. The integration of tortuous capillary tube theory and three-water model concepts, combined with the flow-current similarity principle, has enabled the development of a novel relative permeability–resistivity correlation model that is applicable to complex argillaceous sandstone formations. The application of actual data from the study area shows that the relative errors of the water- and oil-phase relative permeability calculated by the proposed model are both small, at 16% and 8.6%, respectively. The model is validated to better characterize multiphase fluid flow in rocks, offering a new approach for accurately calculating relative permeability based on formation resistivity data.

The G Block of Daqing Oilfield is a crucial area for sustainable development and stable production. In addressing the technical bottlenecks of high-resolution logging data interpretation for reservoir evaluation in the Block, this study proposes a resistivity curve prediction method based on machine learning algorithms. Traditional interpretation models relying on DLS logging data face two major challenges when applied to 0.2 m high-resolution logging: first, the interpreted effective thickness of the reservoir tends to be overestimated, and second, the accuracy of fluid property identification declines. Additionally, the lack of corresponding well-test data for new logging datasets further constrains the development of interpretation models. To tackle these challenges, this study employs the XGBoost algorithm to construct a high-precision resistivity prediction model. Through systematic analysis of various logging parameter combinations, the optimal feature set comprising HAC, MSFL, and GR curves was identified. Training and testing results demonstrate that the model achieves a mean absolute error (MAE) of 0.94 Ω·m and a root mean square error (RMSE) of 1.79 Ω·m in predicting resistivity. After optimization, the model’s performance improved significantly, with MAE and RMSE reduced to 0.75 Ω·m and 1.31 Ω·m, respectively. To evaluate the model’s reliability, an external validation test was conducted on Well GFX2, yielding MAE and RMSE values of 0.91 Ω·m and 1.43 Ω·m, confirming the model’s strong generalization capability. Furthermore, the RLLD-AC and RLLD-DEN crossplots constructed from the predicted results exhibit excellent fluid identification performance in practical applications, achieving an accuracy rate exceeding 89%, which aligns well with production test data. The findings of this study provide new technical support for fine reservoir characterization in the study area and offer significant practical guidance for development plan adjustments.

Shale oil reservoirs contain complex pore networks where fluid distribution and displacement are governed by intricate capillary and wettability effects. This study combines pore-scale computational fluid dynamics (CFD) simulations and laboratory-scale experiments to investigate fluid flow and oil recovery behavior in shale formations. Using the Volume of Fluid method, we simulate two-dimensional (2 D) displacement in porous media with varying heterogeneity and wettability. Results show that uniform pore structures exhibit a broader swept region, whereas non-uniform structures promote preferential flow along high-permeability pathways, leading to lower recovery. Experimental validation was performed using high-temperature, high-pressure static imbibition tests on shale core samples from four lithofacies regions, supported by 2 D nuclear magnetic resonance and interfacial tension (IFT) measurements. CO2 and surfactant-enhanced fluids demonstrated superior oil mobilization in organic matter pores, while water-based fluids primarily recovered oil from clay-bound micropores. An energy-based analysis revealed that capillary-driven imbibition accounts for over 70% of total recovery, surpassing elastic expansion and gas dissolution contributions. IFT measurements confirmed the effectiveness of low-IFT fluids in altering wettability and improving pore accessibility. Together, the integrated CFD–experimental approach offers mechanistic insights into pore-scale flow dynamics and recovery processes in heterogeneous shale systems, providing guidance for the design of targeted enhanced oil recovery strategies.

Oil–water relative permeability is a critical parameter for reservoir fluid property evaluation and productivity prediction, playing a vital role in reservoir identification and development. However, the correlation between relative permeability and resistivity remains unclear for complex conductive mineral-bearing argillaceous sandstone reservoirs. In this study, twenty-eight artificial core samples were prepared and compacted under simulated reservoir temperature and pressure conditions. Simultaneous oil–water relative permeability and resistivity measurements were performed on these samples at two brine salinities (7000 ppm and 3000 ppm). The analysis reveals that under the same resistivity index, both conductive-mineral content and shale content exhibit a negative correlation with water-phase relative permeability but a positive correlation with oil-phase relative permeability. In contrast, porosity–permeability conditions and injected-water salinity show a positive correlation with water-phase relative permeability and a negative correlation with oil-phase relative permeability. Furthermore, the effect of salinity on the relative permeability–resistivity relationship intensifies as porosity–permeability conditions improve. Based on the consistency between movable water seepage and conductive paths, a novel relative permeability–resistivity model was developed for the target reservoirs by integrating seepage and conductive theories, with a generalized Archie’s equation adopted to calibrate the effects of conductive minerals and shale on rock conductivity. The model parameters are optimized using the Newton–Raphson iteration algorithm, and empirical correlations between the undetermined coefficients and reservoir parameters are developed. Validated against two independent cores with the most complex mineral compositions (SH2-3 and SH3-3), the model yields average relative errors of 9.8% and 13.4% for water-phase relative permeability, and 4.4% and 5.4% for oil-phase relative permeability, respectively, demonstrating its high calculation accuracy. It enables efficient prediction of reservoir relative permeability using resistivity data, providing a new theoretical and technical support for low-resistivity oil reservoir identification and productivity assessment.

Breakthrough pressure (BP) is a key parameter for evaluating the sealing capacity of shale caprocks, whereas direct laboratory measurements are time-consuming and costly, limiting their use in continuous regional assessment. This study develops a conventional-log-based workflow for BP prediction in the Quan-4 Member (K1q4) caprock underlying the Qingshankou shale oil interval in the Gulong Sag, Songliao Basin. Four routinely available logs—gamma ray (GR), acoustic interval transit time (AC), bulk density (DEN), and compensated neutron log (CNL)—were integrated with core-measured BP data. A GR-AC multiple-regression baseline and five machine-learning algorithms, including stochastic gradient descent (SGD), extremely randomized trees (ERT), Random Forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), were compared under a unified workflow. The training set was used for normalization, model fitting, grid search, and internal five-fold cross-validation, whereas the held-out test set and external prediction wells were kept separate for performance evaluation. The results show that BP generally increases with GR and DEN and decreases with AC and CNL, indicating that clay content, compaction, and pore connectivity jointly control the logging response of caprock sealing capacity. Among the evaluated models, AdaBoost achieved the best overall performance, with RMSE, MAE, and R² values of 1.33 MPa, 0.97 MPa, and 0.89 on the held-out test set, and 1.19 MPa, 0.92 MPa, and 0.94 in external prediction wells. Train–test diagnostics, learning curves, and SHAP analysis indicate stable performance and physically plausible feature contributions within the present dataset. The proposed workflow can therefore provide a practical supplement to laboratory BP measurements for caprock evaluation in the study area, although broader application still requires well-level independent validation and explicit prediction-uncertainty quantification.