DV4.6 Design of multi-site and multi-scale monitoring schemes

• Published in: NGI-rapport 2024
• Main Authors: Furre, Anne-Kari, Kettlety, Tom, Ringrose, Philip, Wienecke, Susann, Williams, John, Singh, Ajendra, Voss, Peter H, Johnston, Rodney, Dee, Stephen
• Format: Report
• Language: English
• Subjects: Broadband seismometer; CO2 storage; Denmark; Distributed acoustic sensing; Fibre optic; Geomechanical model; Geomechanical risk; India; Machine learning; Monitoring; Norway; Norwegian North Sea; Offshore; Onshore; Risk; Risk management; Seismicity; The Netherlands

This report describes a monitoring scheme applicable for CO2 storage projects at different maturity levels and scales. It is the final delivery from ACT SHARP (“Accelerating CCS Technologies - Stress history and reservoir pressure for improved quantification of CO2 storage containment risks”) Work package 4 (WP4) – “Monitoring”. It builds on previous deliveries from WP4 and other SHARP work packages on multi-scale geomechanical rock failure risks (both onshore and offshore), machine-learning approaches for seismicity detection, and optimal use of fibre optics. In this report we introduce the concept of Geomechanical Readiness Level (GRL), a scale intended to help storage operators evaluate the readiness of their potential injection site with respect to available data characterising the stress conditions at the site. We discuss how selected potential storage sites in the North Sea and onshore India place in the GRL scale, and how work performed both within SHARP and by individual operators has matured each site to their present GRL level. We also present an overview of monitoring tools for detecting geomechanical pore pressure and stress changes and discuss their different applications. The focus in SHARP has especially been on potential fibre optics applications, which have the potential to detect a variety of subsurface changes (pressure, temperature, seismic responses, strain), but which also comes with limitations (directivity, deployment limitations, noise, handling large data volumes). An important delivery from WP4 has been developing efficient data analysis tools, capable of combining observations from different sources and handling large datasets with machine learning. Finally, we describe how an optimal monitoring programme needs to be tailored to the site(s) in question, selecting optimised monitoring tools depending on the relevant risks and geomechanical setting.