There are multiporosity media in tight oil reservoirs after stimulated reservoir volume (SRV) fracturing. Moreover, multiscale flowing states exist throughout the development process. The fluid flowing characteristic is different from that of conventional reservoirs. In terms of those attributes of tight oil reservoirs, considering the flowing feature of the dual-porosity property and the fracture network system based on the discrete-fracture model (DFM), a mathematical flow model of an SRV-fractured horizontal well with multiporosity and multipermeability media was established. The numerical solution was solved by the finite element method and verified by a comparison with the analytical solution and field data. The differences of flow regimes between triple-porosity, dual-permeability (TPDP) and triple-porosity, triple-permeability (TPTP) models were identified. Moreover, the productivity contribution degree of multimedium was analyzed. The results showed that for the multiporosity flowing states, the well bottomhole pressure drop became slower, the linear flow no longer arose, and the pressure wave arrived quickly at the closed reservoir boundary. The contribution ratio of the matrix system, natural fracture system, and network fracture system during SRV-fractured horizontal well production were 7.85%, 43.67%, and 48.48%, respectively in the first year, 14.60%, 49.23%, and 36.17%, respectively in the fifth year, and 20.49%, 46.79%, and 32.72%, respectively in the 10th year. This study provides a theoretical contribution to a better understanding of multiscale flow mechanisms in unconventional reservoirs.

The efficient development of tight gas reservoirs is significantly enhanced by multi-stage and multi-cluster fracturing techniques in conjunction with horizontal well technology, leading to substantial increases in reservoir drainage volume and individual well productivity. This study presents a tailored fine-fracturing approach for horizontal wells in tight gas reservoirs, supported by a gas–water two-phase numerical simulation model. Utilizing the orthogonal experimental design method, we simulated and optimized various fracture distribution schemes to refine fracturing parameters for maximum efficiency. The optimization was further validated through a comparison with actual well completion and development dynamics. The quantitative results highlight the optimal fracture distribution for horizontal wells, with a horizontal section length of 1400 to 1600 m and 14 to 16 fracturing stages. The pattern features a “dense at both ends and sparse in the middle” strategy, with stage spacing of 80 to 110 m, and a “longer in the middle and shorter at both ends” fracture half-length of 100 to 140 m, achieving a fracture conductivity of 30 μm²·cm. To ensure the economic feasibility of the proposed fracturing strategy, we conducted an economic evaluation using the net present value (NPV) method, which confirmed the robustness of the optimization outcomes in terms of both technical performance and economic viability. The reliability of these optimization outcomes has been confirmed through practical application in the development of horizontal wells in the study area. This research approach and methodology can provide theoretical guidance for the design of hydraulic fracturing operations and the integration of geological and engineering practices in similar unconventional oil and gas reservoirs.

To address the technical challenge of real-time interpretation of sandstone reservoir porosity during drilling, a data-driven approach is employed by integrating logging data with machine learning algorithms to deeply mine existing logging data and predict the porosity range of encountered reservoirs. Initially, the acquired logging data is cleaned, and correlation analysis is conducted on the feature parameters. Porosity values were discretized into intervals according to field conditions. Subsequently, porosity-intelligent interpretation models are established using One-vs.-One Support Vector Machines (OVO SVMs), Random Forest (RF), XGBoost, and CatBoost algorithms. Model parameters are optimized using grid search and cross-validation methods. Finally, the test data is interpreted based on the four models with optimized parameters. Results indicate that all four models achieve training accuracies exceeding 95% and test accuracies exceeding 85%. Considering precision, recall, and F1 score comprehensively, the RF model is selected for the case study, with all three indicators exceeding 96%. These findings demonstrate that data-driven methods based on machine learning can accurately interpret sandstone reservoir porosity within specified intervals. For porosity interpretation of sandstone reservoirs in different blocks, interpretation models should be developed using multiple machine learning algorithms, and the best performing model should be selected for practical deployment. This method can be integrated with geological steering drilling technology during horizontal well drilling to ensure that the wellbore trajectory passes through higher-quality reservoir intervals, thereby providing certain guidance for maximizing the encounter rate of reservoir sweet spots.

This study addresses the unclear mechanisms by which preferential flow channels (PFCs), formed during long-term waterflooding, affect nano-gel microsphere (NGM) flooding efficiency, utilizing CMG reservoir numerical simulation software. A dynamic evolution model of PFCs was established by coupling CROCKTAB (stress–porosity hysteresis) and CROCKTABW (water saturation-driven permeability evolution), and the deep flooding mechanism of NGMs (based on their gel properties such as swelling, elastic deformation, and adsorption, and characterized by a “plugging-migration-replugging” process) was integrated. The results demonstrate that neglecting PFCs overestimates recovery by 8.7%, while NGMs reduce permeability by 33% (from 12 to 8 mD) in high-conductivity zones via “bridge-plug-filter cake” structures, diverting flow to low-permeability layers (+33% permeability, from 4.5 to 6 mD). Field application in a Chang 6 tight reservoir (permeability variation coefficient 0.82) confirms a >10-year effective period with 0.84% incremental recovery (from 7.31% to 8.15%) and favorable economics (ROI ≈ 10:1), providing a theoretical and engineering framework for gel-based conformance control in analogous reservoirs.