Summary
Fracability characterizes the ease of gas shale to form a complex-fracture network with hydraulic-fracturing treatments. Previous methods of fracability evaluation take into account some mechanical properties of gas shale, such as brittleness and fracture toughness. However, very little work has been performed to verify these methods by comparing the predicted fracability against the actual result of fracturing stimulation. Moreover, the prediction models of fracture toughness used in the previous methods are derived from conventional shale rather than from gas shale, which leads to the low resolution of these methods. In this paper, new prediction models for both the Mode-I and the Mode-II fracture toughness of gas shale are developed by use of straight-notched-Brazilian-disk (SNBD) tests and logging data. Furthermore, an improved fracability-evaluation model is proposed on the basis of the new toughness models. The new fracability model takes into account the brittleness, fracture toughness, and minimum horizontal in-situ stress of the gas-shale reservoirs. Compared with the previous models, the new model has better resolution in identifying fracability. The accuracy of the proposed model is verified with the efficiency of field hydraulic-fracturing jobs.
Fracture pressure is the key parameter both for horizontal drilling and hydraulic fracturing in the shale gas reservoir. Reservoir depletion will push for the change of the fracture pressure. Previous studies showed that fracture pressure will be decreased with reservoir depletion, which is not suitable for the anisotropic shale gas reservoir. Aiming at resolving this problem, a novel evaluation was established, which can be used to evaluate the influence on fracture pressure of the reservoir depletion, the anisotropy, and the well inclination. The results showed that the fracture pressure will be increased for the strongly anisotropic reservoir with reservoir depletion, and the fracture pressure has a tiny difference between the various anisotropic reservoirs before reservoir depleted, but a larger difference will appear after reservoir depleted, at the later depletion period, the fracture pressure is higher for the stronger anisotropic one. When the anisotropy and the depletion are both the same, the fracture pressure for the higher deviated well is lower. The method and the results can provide a theoretical basis both for the drilling and hydraulic fracturing in depleted shale gas reservoir.
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.
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