Junliang Yuan

Deep drilling can lead to the encounter of complex geological conditions, with significant overburden pressure leading to a narrow safety window for mud density. In this study, we deviated wellbore instability conditions using the Mohr–Coulomb and tensile failure criteria, solving for collapse, shear, and fracture pressure using Newton’s method. The safe mud density window is defined between the maximum value of pore and collapse pressures and the minimum value of shear and fracture pressures. The analysis of the Anderson fault stress model, utilizing this method, enables a comprehensive investigation of how the safety mud density window varies with wellbore inclination and azimuth angles under various stress conditions. Additionally, applications in Chinese oilfields illustrate that this methodology can accurately calculate and analyze extremely narrow safety mud density windows at depths ranging from 2000 to 3000 m. In conclusion, this method enables rapid and accurate prediction of mud density limits, improving wellbore stability and reducing drilling risks.

Seismic high-resolution processing is crucial to enhance the accuracy and reliability of seismic data, particularly in the exploration and development of complex hydrocarbon reservoirs. Conventional high-resolution processing methods, exemplified by sparse spike deconvolution (SSD), typically rely on the assumption of sparse reflectivity. Recent advancements in deep learning have introduced a deep convolutional neural network (DCNN) into high-resolution processing and relaxed it from rigorous physical assumptions. However, DCNN high-resolution processing often suffers from limited generalization capabilities, primarily due to the scarcity of labeled data and variations in data quality. In this study, we identify a frequency learning bias in DCNN high-resolution processing, in which the network initially prioritizes dominant-frequency components before gradually addressing lower and higher frequencies. This bias results in inadequate learning of high-frequency components. We develop a frequency-dependent multiscale network (FMN) informed by the frequency multiscale transformation theory. The FMN aims to improve the frequency learning direction of neural networks, enabling them to simultaneously and efficiently learn information across low-, dominant-, and high-frequency components. Our synthetic and 3D field data experiments demonstrate that our FMN outperforms SSD and DCNN methods, providing superior generalization and higher fidelity in producing high-resolution results.

In view of the low accuracy of existing tomographic detection methods, in order to improve the accuracy of tomographic detection, a tomographic detection method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, then use the RPN structure to generate the candidate frame, and project the candidate frame generated by the RPN to the feature map to obtain the corresponding feature matrix, and finally, through the ROI pooling layer, each of the feature matrix is scaled to a fixed-size feature map, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. Faster R-CNN realizes end-to-end training, combines the advantages of ResNet-50 and Faster R-CNN, and has a precise positioning efficiency. The accuracy of detecting faults reaches 90%. The data enhancement is further optimized, the generalization ability of the network is improved, the detection results of the network are optimized, and the accuracy of fault detection is effectively improved, and the feasibility of the method is verified by actual seismic data.

Drilling fluid loss in depleted reservoir has been an import issue faced by further tapping the potential of old oil fields. Accurate evaluation of the fracture pressure is the foundation to avoid mud loss. Traditional views suggest that tensile failure is the only fracture mode and the fracture pressure should be determined by a tensile failure criterion, which are not suitable for wells drilled in the depleted reservoir. In this paper, the analysis focuses on the fracture mode and fracture pressure in depleted reservoir, and case studies show that three fracture modes may first occur, and the fracture mode will be changed with reservoir depletion which highly depends on reservoir depletion degree, well azimuth and deviation angle, and the in situ stress state; different failure criteria at different stages of reservoir depletion should be selected to accurately evaluate the fracture pressure. For the vertical well, fracture pressure is no longer a single linear reduction with reservoir depletion; instead, a three-step and two-step reduction may appear, and for the directional well, the fracture pressure is not always decreased; the other patterns such as increase and first increase then decrease may also appear for the wells drilled in reverse and strike fault stress regimes.

Long‐term oil and gas exploitation in reservoir will lead to pore pressure depletion. The pore pressure depletion will result in changes of horizontal in‐situ stresses both in reservoirs and caprock formations. Using the geophysics logging data, the magnitude and orientation changes of horizontal stresses in caprock and reservoir are studied. Furthermore, the borehole stability can be affected by in‐situ stresses changes. To address this issue, the dehydration from caprock to reservoir and roof effect of caprock are performed. Based on that, the influence scope and magnitude of horizontal stresses reduction in caprock above the depleted reservoirs are estimated. The effects of development on borehole stability in both reservoir and caprock are studied step by step with the above geomechanical model.