Machine Learning Provides Fracture Analysis, Mapping for CCUS
This paper describes a method with multitiered analysis to leverage machine-learning techniques to process passive seismic monitoring data, pumping and injection pressure, and rate for fracture and fault analysis.
The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis.
This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives:
- Improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way
- Enable the optimization of carbon-storage reservoirs by creating a capability for real-time forecasting of the behavior of such reservoirs
- Improve the ability to understand and communicate subsurface behavior during carbon-storage operations to nonexperts
In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks.