Stochastic prediction of fractured caprock by history matching pressure monitoring data

In this study, a novel method to stochastically characterize the effective permeability of a fractured caprock (typically fine-grained sedimentary rock or natural mudstones) is presented. The method requires pressure monitoring data from above and below the caprock, which are used in combination with other readily-available data from laboratory and outcrop observations in a history-matching process that maintains the distribution of underlying fracture attributes to characterize the spatial variation in permeability of the caprock. A “best-fitting” geological model of caprock, representing a most-likely scenario, is selected from an ensemble of stochastic realizations generated using optimized parameters. The method is demonstrated using a representative, moderately permeable, heterogeneous caprock, as a reference “truth” to compare accuracy of caprock predicted using pressures from injection and post-injection phases. The accuracy of “best-fitting” caprock is assessed by studying its sensitivity to four variables, which are caprock thickness, leakage intensity, time length of available pressure history, and injection rate. Improved characterization of effective caprock permeability allows more confident prediction of flux through the sealing interval, over time. That information is useful in quantifying potential impacts of fluid migration, evaluating monitoring designs, and designing mitigation alternatives, should they be needed. It has value both during active injection and in building confidence that a site can be safely closed after injection is complete.