Paleokarst systems, found in carbonate rock formations worldwide, have potential for creating vast reservoirs and facilitating hydrocarbon migration. Thus, studying these systems is essential for the exploration and development of carbonate reservoirs. An approach using convolutional neural networks (CNNs) is introduced to automatically and precisely identify cave features within 3D seismic data. An efficient technique is outlined for generating ample amounts of 3D training data, which is comprised of synthetic seismic data and labels for cave features contained in the seismic data, as a solution to bypass the labeling task for training the CNN. This workflow uses point-spread functions to simulate the cave response in the seismic data and allows us to easily generate realistic and diverse synthetic training data sets with different geologic structures and cave features. By training a CNN with these synthetic data sets, it can effectively learn to detect cave features in field seismic volumes. Upon evaluation using multiple examples, this approach outperforms earlier techniques like seismic attributes and other CNN-based paleokarst characterization methods.

To reduce the impact of nontarget horizon regions and improve horizon tracking precision, we propose an attention based two-stage U-net horizon tracking method (ATUM). The ATUM consists of the horizon region label automatic generation and the attention module based two-stage U-Net (ATUN). Horizon region label automatic generation aims to automatically generate the target horizon region label of the target horizon label. In ATUN, the two stages (stages Ⅰ and Ⅱ) consist of the conventional encoder-decoder U-Net, and the two decoder parts are connected by the attention module. Stage Ⅰ treats horizon tracking as an objection detection problem. It takes the seismic data as its input and the automatically generated target horizon region label as its label, and finally obtains the target horizon region. Stage Ⅱ takes the results of stage Ⅰ with the corresponding seismic data as its input, and finally obtains the precise horizon. Two field three-dimensional seismic dataset studies demonstrated the performance of the ATUM for high-precision horizon tracking.

Ultra-deep (7500–9000 m) Ordovician tight limestone heterogeneous fractured-vuggy reservoir is an important target of FuMan Oilfield in Tarim Basin. The strike-slip fault controlled reservoir is related to formation fracture and dissolution caused by geological stress. The seismic wave-field anomaly characteristics with different energy and irregular waveform are displayed in the seismic profile. Accurate identification of fractured-vuggy reservoirs wrapped in tight limestone is the direct scheme to improve production efficiency. Therefore, a new combination method flow of seismic wave-field anomaly recognition is proposed. In this process, the seismic data must be preprocessed initially, and on this basis, robust formation dip scanning is carried out. Secondly, the dip data is applied to the transverse smoothing filter to obtain the formation background data. Eventually, the seismic wave-field anomaly data is the residual between background data and original seismic data. This method has been applied in blocks with different structural characteristics and can effectively improve the resolution of strike-slip fault controlled reservoirs. Based on the results, the drilling success rate is increased to more than 95%, and the high-yield rate of oil tests is increased to 75% in 2021. Multiple applications indicate that the method is robust and can be popularized.

Due to the precision of the first break, time-variant wavelet, and strata dip angle, the popular iterative vertical seismic profiling (VSP) wavefield separation method may not yield high-precision wavefield separation results. The single-stage multi-task U-Network VSP wavefield separation method can avoid the impact of the first break, time-variant wavelet, the strata dip angle, but it faces challenge in complex VSP wavefield due to its network performance. In this paper, based on the iterative VSP wavefield separation method, the U-Network and multi-task deep learning, we propose a two-stage multi-task U-Network VSP wavefield separation method. The two-stage multi-task U-Network VSP wavefield separation method comprises the two-stage multi-task U-Network, the loss function, and the synthetic VSP training data automatic generation. The two-stage multi-task U-Network aims to simultaneously output high-precision downgoing and upgoing wavefield, as well as the residual wavefield, while the synthetic VSP training data automatic generation aims to automatically generate numerous and various VSP training data. Applications of both synthetic and actual VSP data demonstrate that the two-stage multi-task U-Network VSP wavefield separation method can be widely used for high-precision VSP wavefield separation.