Ordovician carbonate reservoirs in the Shunbei area of the Tarim Basin are characterized by large burial depth, small vertical fault displacement of the strike-slip fault zone, small karst development scale, strong reservoir heterogeneity, complex and diverse seismic reflection types, which lead to unclear seismic response characteristics of fault zone and reservoir, and further restrict the fine description of ultra-deep (>7300 m) fault controlled reservoirs. On the basis of high-precision 3D seismic data analysis, combined with drilling, logging, and comprehensive geological interpretation data in the Shunbei area, seismic geological models of different types of reservoirs and fault zones are established. The changes in seismic response characteristics of fault zones and reservoirs are simulated and analyzed by changing reservoir size, scale, fault and fracture cavity combination modes, as well as other factors. On this basis, two seismic identification models of fault-controlled reservoirs in the Shunbei area are summarized and established. The seismic identification mode of seismic reflection marker wave of fault-controlled reservoir is mainly “fault seismic response characteristics & weak reflection” and red trough anomaly under T₇⁴. The internal seismic identification modes are mainly “fault seismic response characteristics & random reflection”, “fault seismic response characteristics & beading”, “fault seismic response characteristics & linear weak reflection”, and “fault seismic response characteristics & random reflection”. Among them, except for “fault seismic response characteristics & random reflection”, the coincidence rate of reservoir prediction of other three types of seismic response characteristics is more than 70%. The coincidence rate of the other three types of seismic response characteristics of a reservoir is more than 70%. The research results provide a reference for the prediction and description of ultra-deep carbonate fault-controlled reservoirs.

Intracratonic strike-slip faults play a vital role in hydrocarbon migration and accumulation, making their accurate detection crucial for subsurface structure interpretation and reservoir characterization. Although numerous approaches have been developed for automatic fault interpretation, they are still unable to fully recognize intracratonic strike-slip faults with subtle reflection variations in seismic images. We develop a supervised convolutional neural network (CNN) to automatically and precisely delineate these faults in 3D seismic images. The primary obstacle in using a CNN for this task is the lack of diverse seismic training samples and corresponding high-quality labels. To address this, we develop an efficient two-step workflow for automatically generating a tremendous amount of 3D seismic training sample pairs: (1) guided by the prior geologic structure patterns of the study area, we use parameterized vector fields to simulate various velocity models with accurate fault labels incorporating realistic intracratonic strike-slip faulting features, such as the transtensional, transpressional, and strike-slip translational types and (2) following seismic imaging laws, we synthesize seismic training samples from simulated velocity models through the point-spread function-based convolution method. One synthetic and two field examples demonstrate that the CNN trained with this seismic data set containing prior geologic patterns and seismic imaging features outperforms the conventional fault detection approaches in accurately and continuously delineating intracratonic strike-slip faults.

Many wells drilled on the ultradeep strike-slip fault zone in the Tarim Basin have obtained oil and gas discoveries, indicating that reservoir formation is controlled by the fault zone, and oil and gas are enriched in high-quality reservoirs along the fault zone. The fine description and evaluation of strike-slip fault zones are important for the prediction of high-quality reservoirs and the optimization of exploration targets. However, strike-slip faults have the characteristics of steep fracture surface, small vertical fault displacement, and sometimes even no vertical fault displacement. It is difficult for seismic exploration to obtain complete seismic wave information of the fault zone, resulting in difficulties in seismic data imaging and fault interpretation. The identification and description of strike-slip fault zones are mainly based on qualitative prediction methods. This research uses 3D seismic reflection data to investigate the quantitative seismic characterization of ultradeep carbonate strike-slip fault zones and proposes a technical approach and method for their quantitative characterization in ultradeep carbonate rock. The technical process mainly includes three aspects. The forward modeling is used to define the seismic response characteristics of strike-slip faults with varying scales, and the quantitative standards of the vertical fault displacement and transverse width of strike-slip fault zones are established. By leveraging the varying detection abilities of different seismic attributes in response to strike-slip faults of different scales, we select various seismic attributes to predict strike-slip faults based on their scales, vertical fault displacement, and transverse width. Finally, prediction results are combined with the quantitative standards of fault zone established by forward modeling to establish the quantitative relationship between seismic attributes and characterization parameters of strike-slip faults such as scale, vertical fault displacement, and transverse width. The technical process of the seismic quantitative characterization of ultradeep carbonate strike-slip fault zone has been applied in S-3D seismic data and achieved good results. Establishing a clear quantitative relationship between specific seismic attributes and parameters characterizing strike-slip faults can help reduce the variability in evaluation results that arise from differences in interpreters’ experience. This relationship provides a solid foundation for effective quantitative characterization and detailed reservoir description within strike-slip fault zones.