ABSTRACT

In geological carbon sequestration, structurally trapped CO2 that is mobile will be susceptible to leakage if the cap rock is compromised. Thus, the environmental and economic risks associated with a sequestration project can be reduced by facilitating other storagemechanisms andminimizing the amount of structurally trapped CO2. Data assimilation is also essential if we are to reduce the uncertainty in the CO2 plume location. In this work we develop and apply computational optimization procedures for minimizing a measure of the mobility of free CO2 at the top of the storage aquifer and for data assimilation. Mobility minimization is accomplished by determining optimum locations and time-varying injection rates for four horizontal CO2 injection wells. A particle swarm optimization algorithm is used for this purpose. For data assimilation (or history matching), aquifer geology is represented in terms of a relatively small number of parameters using a Karhunen-Loève (K-L) expansion. Sensor and injection data are assumed to provide the data to be matched, and a generalized pattern search method is used for the resulting minimization. A procedure for optimizing the placement of monitoring wells, with the goal of maximizing the efficacy of the history matching procedure, is also presented. Optimization results for both deterministic and uncertain aquifer models (in the latter case, the aquifer is represented using multiple realizations) are presented for a variety of cases, and reduction in the mobility of free CO2 at the top of the aquifer is consistently achieved. In addition, the data assimilation procedure is shown to improve predictions for plume location relative to results from prior geological models.