An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

Ahmed H. Fsheikh, Mary Fanett Wheeler, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.
Original languageEnglish (US)
Title of host publicationSPE Reservoir Simulation Symposium
PublisherSociety of Petroleum Engineers (SPE)
ISBN (Print)9781627480246
DOIs
StatePublished - Feb 18 2013

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