Demonstration of a Semi-Automated Decision Support Toolbox to Aid Operators in the Design of Efficient Environmental Offshore Monitoring Programs for Co2 Storage Sites

Abstract

Optimised marine monitoring strategies for CCS can be developed through several computational approaches. Many of these methods exist already including simulating hypothetical release events through models of hydrodynamics and biogeochemistry, quantifying highly sensitive criteria to distinguish from anomalous background conditions as an anomaly indicator, and the use of complex algorithms or machine learning to increases the certainty of detecting anomalies whilst utilising the most cost effective mobile or stationary monitoring platform distributions. However, one of the biggest challenges is the accessibility of these approaches, including closed source programs and inaccessible data, along with the need for programming skills and high-powered supercomputers. We integrate a number of these approaches and make them accessible through the ACTOM toolbox, the end-product of which is to aid users in defining a monitoring plan that will satisfy local stakeholders The toolbox uses pre-existing hydrodynamic and marine biogeochemical data that can be readily attained from existing high-resolution models and simulations, combined with available in-situ measurements, allowing the methodologies to be applied coherently to multiple offshore storage sites and to answer specific stakeholder led questions around required sensitivity and assurance levels. The ACTOM toolbox is designed to provide value over a range of field cases with diverse subsurface geology and environmental marine characteristics. Here we present the technical details of the ACTOM toolbox, and its components. The preparation steps for various sites, and how the toolbox can address regional concerns and be tuned to local characteristics is presented in another contribution to GHGT-16.

Keywords: Measurement Monitoring and Verification (MMV), Offshore Storage, Monitoring, Marine, Assurance