Bayesian experimental design for CO₂ sequestration with leakage risk assessment

Abstract

Purpose This study aims to examine the sequestration of carbon dioxide (CO2) in abandoned geological formations where leakages are permitted up to only a certain threshold to meet the international CO2 emissions standards. Technically, the author addresses a Bayesian experimental design problem to optimally mitigate uncertainties and to perform risk assessment on a CO2 sequestration model, where the parameters to be inferred are random subsurface properties while the quantity of interest is desired to be kept within safety margins. Design/methodology/approach The author starts with a probabilistic formulation of learning the leakage rate and later relaxes it to a Bayesian experimental design of learning the formations geophysical properties. The injection rate is the design parameter and the learned properties are used to estimate the leakage rate by means of a nonlinear operator. The forward model governs a two-phase two-component flow in a porous medium with no solubility of CO2 in water. The Laplace approximation is combined with Monte Carlo sampling to estimate the expectation of the Kullback–Leibler divergence that stands for the objective function. Practical implications The injection duration given by the reservoir capacity is extended while the environment is preserved despite the leakage.