Weathered-crust karst carbonate is developed in the third submember of the fourth member of the Middle Triassic Leikoupo Formation (Lei₄³ submember) in the Longgang area of Sichuan Basin, China, and acts as an important oil and gas reservoir. To reconstruct the paleogeomorphology of the weathered-crust karst, we analyzed the seismic thickness of the interval from the erosional unconformity surface on the top of the Leikoupo Formation to the karst at the bottom of the third member of the Xujiahe Formation (Xu₃ member), based on existing three-dimensional seismic and drilling data, and the sedimentary characteristics of the Leikoupo and Xujiahe formations. There are three secondary types of geomorphological unit, namely the karst highlands, the karst transitional zone, and the karst basin, and these have distinct karst water development patterns and hydrological conditions that determine the intensity of karstification and reservoir quality. Among them, the karst highlands have a poor reservoir capacity due to their long-term exposure above the water table, which resulted in severe denudation. The karst transitional zone features the superimposition of multiple periods of strong karstification, and this has resulted in high-quality reservoir conditions in the karst monadnocks but poor reservoir conditions in the karren. The karst basin represents a drainage area that experienced weak karstification, and the reservoir capacity is generally poor, although some good reservoirs were developed in shoals. Paleogeomorphological maps provide excellent guides to finding karst monadnocks in karst transitional zones, and monadnocks should serve as the main targets of exploration in the weathered-crust karst reservoir on top of the Lei₄³ submember.

Geologists have made several advances in applying multifractal theory in geology; however, some questions such as a large statistical workload and low efficiency remain unanswered. Thus, this study proposes an image recognition–based method for calculating fault multifractality. First, grayscale processing and binarization of the fault distribution map were performed. The image was then gridded, and the grids were numbered. Subsequently, computer image recognition technology was used to count the number of faults in each grid as a replacement for manual counting. Finally, the fractal dimensions of the faults were calculated using a multifractal box-counting algorithm. This method was successfully applied to fracture studies of the Maokou Formation in southeast Sichuan. Compared to the conventional approach, the proposed method demonstrated considerably improved work efficiency and accuracy. The results showed that the faults in the study area exhibited good statistical self-similarity in the scale range, indicating fractal characteristics. The fractal dimensions of faults with different orientations and the planar distribution of the fractal dimension contours indicate tectonic stages and stress magnitude in the study area. The results indicate that the tectonic setting of southeast Sichuan was formed primarily during the Indosinian, Yanshanian, and Himalayan periods. From the Indosinian to the early Yanshanian periods, NE-trending faults with relatively large fractal dimensions developed under NW–SE compressional tectonic stress. From the Late Yanshanian to Early Himalayan, EW-trending faults were formed by relatively weak N–S compressional stress and had the lowest fractal dimensions. The NW-trending faults formed by intense NE–SW compressional tectonic stress in the Late Himalayan region had the highest fractal dimensions. To promote oil and gas migration and ensure that faults do not destroy the caprock, oil and gas reservoirs must be in a relatively mild tectonic environment. Thus, the fractal dimensions of faults in favorable areas should be neither too high nor too low. The relationship between the fractal dimensions of faults and well test results in southeast Sichuan indicates that the region along the wells “ls1–xia14–guan3” (with fractal dimensions of 1.49–1.57) in the study area is a relatively favorable region for oil and gas preservation.

The classification method of karst formations is widely used in engineering and environmental geology but is seldom used in petroleum geology. In this study, the classification method of karst formations is applied to the sealing study of shale gas roof and floor carbonate rocks, and the influence on shale gas accumulation and drilling is discussed. The Paleozoic black shale in southern China is primarily formed by marine and transitional faces, and the intergrowth between shale and carbonate rocks is a basic geological feature of the Paleozoic strata in southern China. Carbonate karst is an unavoidable problem in shale gas exploration in southern China. Around the black shale target layer, in the Upper Paleozoic trait region, the study starts from the development strength of karst strata, through geological profile survey, spring flow statistics, test, and other methods and means; the shale and the carbonate rock contacted with it are taken as a whole to explore the impact of karst strata on shale gas drilling. The Upper Paleozoic karst strata in the study area were divided into two kinds, four types, and six subtypes. It was determined that the limestone continuous karst strata of the Sidazhai Formation and the second member of the Nandan Formation are the sensitive layers of shale gas drilling, whereas the first number of Nandan and Wuzhishan formations are shale reservoir-forming packers. In addition, a method for evaluating the karst sensitivity of shale gas exploration is summarized. The karst avoidance, karst-sensitive, and karst-insensitive areas for shale gas exploration were divided. Combined with the surface and underground conditions and the basic geological conditions of shale gas not being significantly different, shale gas drilling should avoid the fold core and fault zones.

High-precision time-frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this paper, a TFA method based on analytical mode decomposition (AMD) and the Hilbert-Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data.