Improved Hydraulic Fracture Characterization Using Representation Learning

Abstract

Abstract Representation learning is a technique for transforming high-dimensional data into lower-dimensional representations that capture meaningful patterns or structures in the data. Uniform manifold approximation and projection (UMAP) enables representation learning that uses a combination of nearest neighbor search and stochastic gradient descent in the low-dimensional graph-based representation to preserve local structure and global distances present in high-dimensional data. We introduce a new technique in representation learning, where high-dimensional data is transformed into a lower-dimensional, graph-based representation using UMAP. Our method, which combines nearest neighbor search and stochastic gradient descent, effectively captures meaningful patterns and structures in the data, preserving local and global distances. In this paper, we demonstrate our expertise by utilizing unsupervised representation learning on accelerometer and hydrophone signals recorded during a fracture propagation experiment at the Sanford Underground Research Facility in South Dakota. Our UMAP-based representation executes a five-step process, including distance formulation, connection probability calculation, and low-dimensional projection using force-directed optimization. Our analysis shows that the short-time Fourier Transform of signals recorded by a single channel of the 3D accelerometer is the best feature extraction technique for representation learning. For the first time, we have successfully identified the distinct fracture planes corresponding to each micro-earthquake location using accelerometer and hydrophone data from an intermediate-scale hydraulic stimulation experiment. Our results from the EGS Collab project show the accuracy of this method in identifying fracture planes and hypocenter locations using signals from both accelerometers and hydrophones. Our findings demonstrate the superiority of UMAP as a powerful tool for understanding the underlying structure of seismic signals in hydraulic fracturing.