Stochastic inversion method of time-lapse controlled source electromagnetic data for CO2 plume monitoring

Abstract

Carbon dioxide storage in deep saline aquifers can potentially reduce the CO2 concentration in the atmosphere due to anthropogenic activities with limited environmental impact. To minimize the risks of leakage, it is necessary to monitor the CO2 distribution in the reservoir. Here, we present a stochastic optimization method namely the Ensemble Smoother, to invert time-lapse marine controlled source electromagnetic data for predicting the CO2 plume location. Electromagnetic surveys have been successfully used in subsurface monitoring studies in different geosciences applications due to their sensitivity to the changes in the fluid in porous rocks. The inverse method is based on a Bayesian approach, in which an ensemble of stochastically generated prior models representing the spatial distribution of CO2 saturation is updated according to the mismatch between the measured data and the predicted electromagnetic response of the prior models. The proposed method generates an ensemble of updated realizations, the posterior models of the spatial distribution of CO2 saturation, that match the observations. The variance of the posterior models represents the uncertainty of the CO2 distribution. Compared to deterministic inversion methods, our proposed method is better suited to solve non-linear inverse problems with the added benefit of uncertainty quantification. The method is tested and validated on a real dataset, the Johansen formation, offshore Norway, for which we created synthetic time-lapse electromagnetic data for the entire injection period. The inversion of electromagnetic data shows that the proposed method can accurately predict the prediction of the CO2 plume location and quantify the associated uncertainty.