Imaging Hydraulic Fractures Under Energized Steel Casing by Convolutional Neural Networks

Imaging hydraulic fractures is of paramount importance to subsurface resource extraction, geologic storage, and hazardous waste disposal. The use of electrically conductive proppants and current energized steel casing provides a promising approach to monitor the distribution of fractures. In this article, a borehole-to-surface system is employed to energize the steel casing and measure electric and magnetic fields on the ground. A convolutional neural network (CNN) is then trained to learn the relationship between the measured field pattern and the parameterized fracture, namely, the lateral extent and direction. To accelerate the generation of training data with limited accuracy loss, an approximate hollow casing is modeled by the impedance transition boundary condition with a tenfold magnified radius and reduced conductivity. Two training strategies are then presented with a grid search of the network's hyperparameters. The well-trained CNN shows good generalization to unseen fracture conductivity, the true casing model, as well as white Gaussian noise. Finally, we apply the CNN to image irregular fractures and obtain reliable results even under strong noise, indicating a promising imaging technique for more complicated fractures.