Characterization of natural fracture development in coal reservoirs using logging machine learning inversion, well test data and simulated geostress analyses

Natural fractures directly affect the permeability and mechanical strength of reservoirs, and their development degree has an important impact on the design and implementation of engineering and development projects. Although there is some correlation between logging data and fracture development, studies using algorithms to optimize logging predictions are still scarce. Meanwhile, there is a scarcity of calculations and analyses concerning the distribution of geostress at the block scale, and the pivotal role that geostress plays as a tectonic factor in the development of fractures. In this study, machine learning methods are used to predict reservoir fracture development, and regional geostress distribution patterns derived from well test data and finite element methods are combined for verification. The support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) and back propagation neural network (BPNN) algorithms are used to predict the fracture development of No.15 coal reservoir in the block. The results showed that the accuracy of SVM is 83.3 %, RF is 91.6 %, XGBoost is 93.7 % and BPNN is 95.8 %. The BPNN can effectively predict the reservoir fracture development of the block. Combined with the regional finite element stress-strain analysis and geostress measurement, the prediction of No.15 coal geostress distribution and fracture development model is established. Under comprehensive verification, the established distribution of the degree of regional fracture development under the control of geostress is consistent with the results of the BPNN prediction of fracture development. These results show that the regional geostress calculated in association with finite element analysis (FEA) can reflect the development of fracture in coalbed methane (CBM) reservoirs, and the neural network has good performance in predicting regional fracture development. This work provides a new approach to the application of machine learning in the field of geological engineering, and the comprehensively validated model provides geologists and geological engineers with ideas in algorithmic practice.