An integrated framework for optimal monitoring and history matching in CO $$_{2}$$ storage projects

Abstract

Monitoring is an important component of geological carbon storage operations because it provides data that can be used to estimate key quantities such as CO\(_{2}\) plume location. The design of the monitoring strategy is complicated, however, because the monitoring plan must be established prior to the availability of extensive flow data. In this work, we present and apply a framework that integrates monitoring well optimization and (subsequent) history matching. The monitoring well optimization entails finding the locations of monitoring wells such that, with the data acquired at those locations, the expected uncertainty reduction in a particular flow quantity is maximized. This optimization requires the simulation of a large set of prior models, though these simulations need only be performed once for a given injection scenario. Once the monitoring wells are in place and CO\(_{2}\) injection begins, history matching is performed using the monitoring data. This is accomplished here using an ensemble smoother with multiple data assimilation. The overall framework is applied to variogram-based geomodels that are representative of an actual storage project under development in the USA. Two injection scenarios are considered with two different (synthetic) ‘true’ models, which provide the observed data. History matched models are constructed using data from both optimally located and heuristically placed monitoring wells. Posterior uncertainty, evaluated in terms of the cumulative distribution function for a metric related to plume extent over the ensemble of history matched models, is shown to be minimized through use of optimized monitoring wells. These results demonstrate the importance of optimizing the monitoring plan, and the degree of uncertainty reduction that can be realistically achieved.