Development of an Intelligent Monitoring System for Geological Carbon Sequestration (GCS) Systems

To provide stakeholders timely evidence that GCS repositories are operating safely and efficiently requires integrated monitoring to assess the performance of the storage reservoir as the CO2 plume moves within it. As a result, GCS projects can be data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, and accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. How to correlate anomaly detected by a certain sensor to events observed by other devices to verify leakage incidents? How to optimally allocate resources for task-oriented monitoring if reservoir integrity is in question? These are issues that warrant further investigation before real integration can take place. In this work, we are building a web-based, data integration, assimilation, and learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS will be an intelligent monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in high-throughput database, complex event processing, data assimilation, and machine learning technologies. Results will be demonstrated using realistic data and model derived from a GCS site.