Fracture Monitoring and Characterization Using Unsupervised Microseismic Data Analysis

Abstract

Abstract The study shows the use of unsupervised manifold learning on microseismic data for fracture monitoring and characterization. Manifold learning condenses complex, high-dimensional data into more concise, lower-dimensional representations that encapsulate valuable underlying patterns and structures of the data. The study leverages Uniform Manifold Approximation and Projection (UMAP) methodology to efficiently convert high-dimensional data into a graph-based, lower-dimensional representation. This transformative approach adeptly captures meaningful patterns and structures while preserving both local and global distances; thereby, the topology. In this study, the unsupervised manifold learning is applied on accelerometer and hydrophone data obtained during an intermediate field-scale hydraulic stimulation experiment conducted at the Sanford Underground Research Facility in South Dakota to measure, monitor, and characterize fracture propagation in near-real-time. Each micro-earthquake location identified using travel-time information is assigned a fracture type using unsupervised manifold learning, and later is assigned a fracture-plane label using semi-supervised manifold learning. Our findings highlight the precision and accuracy of our proposed method in preserving fracture network clusters and fracture-plane labels using signals from both accelerometers and hydrophones. These remarkable results underscore the potential of the Uniform Manifold Approximation and Projection (UMAP) technique as a versatile unsupervised-learning tool for unveiling the intrinsic structural features embedded within microseismic signals generated during hydraulic fracturing for purposes of fracture monitoring and characterization. Next, this study addresses the challenge of reliably assigning fracture-plane labels to microseismic events created during hydraulic stimulation. Most events lack clear labels due to scattering and uncertainty in event locations. To tackle this, the study introduces a semi-supervised learning strategy based on UMAP that utilizes a sparse dataset with pre-labeled fracture-plane categorizations. By combining event coordinates and Fourier spectra from geophone signals in a low-dimensional manifold, this approach achieves precision and recall rates exceeding 92% while using only about 20% of labeled events. This method remains robust even in the presence of location errors, making it valuable for enhancing fracture network characterization in various applications, including hydrocarbon and geothermal production.