Tight sandstone has low porosity and permeability, a complex pore structure, and strong heterogeneity due to strong diagenetic modifications. Limited intervals of Lianggaoshan Formation in the Fuling area are cored due to high costs, thus, a model for predicting diagenetic facies based on logging curves was established based on few core, thin section, X-ray diffraction (XRD), scanning electron microscopy (SEM), cathodoluminescence, routine core analysis, and mercury injection capillary pressure tests. The results show that tight sandstone in the Lianggaoshan Formation has primary and secondary intergranular pores, secondary intragranular pores, and intergranular micropores in the clay minerals. The compaction experienced by sandstone is medium to strong, and the main diagenetic minerals are carbonates (calcite, dolomite, and ferric dolomite) and clay minerals (chlorite, illite, and mixed illite/montmorillonite). Four types of diagenetic facies are recognized: carbonate cemented (CCF), tightly compacted (TCF), chlorite coating and clay mineral filling (CCCMFF), and dissolution facies (DF). Primary pores develop in the CCCMFF, and secondary pores develop in the DF; The porosities and permeabilities of CCCMFF and DF are better than that of CCF and TCF. The diagenetic facies were converted to logging data, and a diagenetic facies prediction model using four machine learning methods was established. The prediction results show that the random forest model has the highest prediction accuracy of 97.5%, followed by back propagation neural networks (BPNN), decision trees, and K-Nearest Neighbor (KNN). In addition, the random forest model had the smallest accuracy difference between the different diagenetic facies (2.86%). Compared with the other three machine learning models, the random forest model can balance unbalanced sample data and improve the prediction accuracy for the tight sandstone of the Lianggaoshan Formation in the Fuling area, which has a wide application range. It is worth noting that the BPNN may be more advantageous in diagenetic facies prediction when there are more sample data and diagenetic facies types.

Reservoir heterogeneity is a key geological problem that restricts oil and gas exploration and development of clastic rocks from the early to late stages. Existing reservoir heterogeneity modeling methods such as multiple-point geostatistics (MPS) can accurately model the two-dimensional anisotropic structures of reservoir lithofacies. However, three-dimensional training images are required to construct three-dimensional reservoir lithofacies anisotropic structures models, and the method to use reservoir heterogeneity model of fewer-dimensional to obtain a three-dimensional model has become a much-focused research topic. In this study, the outcrops of the second member of Qingshuihe Formation (K₁q₂) in the northwestern margin of the Junggar Basin, which are lower cretaceous rocks, were the research target. The three-dimensional reservoir heterogeneity model of the K1q2 outcrop was established based on the unmanned aerial vehicle (UAV) digital outcrops model and MPS techniques, and the “sequential two-dimensional conditioning data” (s2Dcd) method was modified based on a sensitivity parameter analysis. Results of the parametric sensitivity analysis revealed that the isotropic multigrid simulations demonstrate poor performance because of the lack of three-dimensional training images, conditioning data that are horizontally discrete and vertically continuous, and distribution of lithofacies that are characterized by large horizontal continuities and small thicknesses. The reservoir lithofacies anisotropic structure reconstructions performed well with anisotropic multigrids. The simulation sequence of two-dimensional surfaces for generating the three-dimensional anisotropic structure of reservoir lithofacies models should be reasonably planned according to the actual geological data and limited hard data. In additional to this, the conditional probability density function of each two-dimensional training image should be fully utilized. The simulation results using only one two-dimensional section will have several types of noises, which is not consistent with the actual geological background. The anisotropic multigrid simulations and two-dimensional training image simulation sequence, proposed in this paper as “cross mesh, refinement step by step”, effectively reduced the noise generated, made full use of the information from the two-dimensional training image, and reconstructed the three-dimensional reservoir lithofacies anisotropic structures models, thus conforming to the actual geological conditions.

The Eocene fourth member of the Shahejie formation (Es4x) in the Bonan Depression, Bohai Bay Basin, records syn-rift sedimentation under alternating arid and humid climates. It provides insight into how orbital-scale climatic fluctuations influenced tectonics, facies patterns, and reservoir distribution. This study integrates 406 m of core data, 92 thin sections, 450 km² of 3D seismic data, and multiple geochemical proxies, leading to the recognition of five facies associations (LFA): (1) alluvial fans, (2) braided rivers, (3) floodplain mudstones, (4) fan deltas, and (5) saline lacustrine evaporites. Three major depositional cycles are defined within the Es4x. Seismic reflections, well-log patterns, and thickness trends suggest that these cycles represent fourth-order lake-level fluctuations (0.8–1.1 Myr) rather than short 21-kyr precession rhythms. This implies long-term climate and tectonic modulation, likely linked to eccentricity-scale monsoon variability. Hyperarid phases are marked by Sr/Ba > 4, δ¹⁸O > +4‰, and thick evaporite accumulations. In contrast, Sr/Ba < 1 and δ¹⁸O < −8‰ reflect humid conditions with larger lakes and enhanced fluvial input. During wet periods, rivers produced sand bodies nearly 40 times thicker than in dry intervals. Reservoir quality is highest in braided-river sandstones (LFA 2) with 12%–19% porosity, preserved by chlorite coatings that limit quartz cement. Fan-delta sands (LFA 4) have <8% porosity due to calcite cementation, though fractures (10–50 mm) improve permeability. Floodplain mudstones (LFA 3) and evaporites (LFA 5) act as seals. This work presents a predictive depositional and reservoir model for arid–humid rift systems and highlights braided-river targets as promising exploration zones in climate-sensitive basins worldwide.