Using flow models with sensitivities to study cost efficient monitoring programs of co2 storage sites

TL;DR

This paper develops a simulation-based methodology combining sensitivities and data mining to optimize cost-effective CO2 storage site monitoring strategies, reducing measurement needs while maintaining information quality.

Contribution

It introduces a novel approach integrating flow models, sensitivities, and SVD to identify minimal measurement sets for efficient CO2 storage monitoring.

Findings

Reduced measurement sets without significant information loss

Effective use of seismic, uplift, and gravitational data

Applicability to VE flow models and potential for 3D extensions

Abstract

A key part of planning CO2 storage sites is to devise a monitoring strategy. The aim of this strategy is to fulfill the requirements of legislations and lower cost of the operation by avoiding operational problems. If CCS is going to be a widespread technology to deliver energy without CO2 emissions, cost-efficient monitoring programs will be a key to reduce the storage costs. A simulation framework, previously used to estimate flow parameters at Sleipner Layer 9 [1], is here extended and employed to identify how the number of measurements can be reduced without significantly reducing the obtained information. The main part of the methodology is based on well-proven, stable and robust, simulation technology together with adjoint-based sensitivities and data mining techniques using singular value decomposition (SVD). In particular we combine the simulation framework with time-dependent (seismic) measurements of the migrating plume. We also study how uplift data and gravitational data give complementary information. We apply this methodology to the Sleipner project, which provides the most extensive data for CO2 storage to date. For this study we utilize a vertical-equilibrium (VE) flow model for computational efficiency as implemented in the open-source software MRST-co2lab. However, our methodology for deriving efficient monitoring schemes is not restricted to VE-type flow models, and at the end, we discuss how the methodology can be used in the context of full 3D simulations.