An Integrated Framework for Optimal Monitoring and History Matching in CO2 Storage Projects

Abstract

Monitoring of the CO2 plume location is an essential component of any carbon storage project. The optimal placement of monitoring wells is challenging because this must be accomplished before CO2 injection is started, when geological uncertainty is high. In addition, this optimization is closely linked with the history matching procedure, as the measurements from the monitoring wells represent the key observations used for data assimilation. In this work, we present and apply a framework that integrates the monitoring-well optimization and history matching problems. Our approach is ensemble-based and is expressed within a Bayesian setting. The monitoring-well optimization entails finding the locations of monitoring wells such that, with the data acquired at those locations, the expected variance of a quantity of interest is minimized (related approaches were used previously by He at al., 2018 ; Sun & Durlofsky, 2019 , in data-space inversion settings). This quantity of interest, which must be aligned with the goals of the history matching, is here taken to be the volume of CO2 beyond a specified distance from the injector. Through use of prior simulation data and a genetic algorithm-based minimization, we thus find the locations of monitoring wells such that, when the data from these wells are used in history matching, we maximize uncertainty reduction.

The overall framework is applied to idealized multi-Gaussian geomodels based on an actual storage project under development in the US. A large number of prior simulation runs are required for monitoring-well optimization, and these are conducted using an existing deep-neural-network surrogate model, developed by Wen et al. (2021) . Following this optimization, history matching is performed using an ensemble smoother with multiple data assimilation. Several different (synthetic) “true” models, which provide the observed data, are considered. The requisite history matching simulations are again accomplished using the surrogate flow model. We generate history matching results for optimal monitoring-well locations and for heuristically or randomly placed monitoring wells. In all cases posterior uncertainty, evaluated in terms of the cumulative distribution function for plume extent over all history-matched models, is found to be minimized through use of optimized monitoring wells. This confirms the consistency and applicability of the integrated workflow. We note finally that the overall framework is general, and it is compatible with different optimization procedures, history matching methods, and flow simulators.