Ensemblebased multiscale historymatching using

Authors: Théophile Gentilhomme Dean S Oliver Trond Mannseth Guillaume Caumon Rémi Moyen Philippe Doyen

Publish Date: 2015/08/21

Volume: 19, Issue: 5, Pages: 999-1025

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Abstract

Ensemblebased optimization methods are of ten efficiently applied to historymatching problems Although satisfactory matches can be obtained the updated realizations affected by spurious correlations generally fail to preserve prior information when using a small ensemble even when localization is applied In this work we propose a multiscale approach based on gridadaptive secondgeneration wavelets These wavelets can be applied on irregular reservoir grids of any dimensions containing dead or flat cells The proposed method starts by modifying a few low frequency parameters coarse scales and then progressively allows more important updates on a limited number of sensitive parameters of higher resolution fine scales The LevenbergMarquardt ensemble randomized maximum likelihood LMenRML is used as optimization method with a new spacefrequency distancebased localization of the Kalman gain specifically designed for the multiscale scheme The algorithm is evaluated on two test cases The first test is a 2D synthetic case in which several inversions are run using independent ensembles The second test is the Brugge benchmark case with 10 years of history The efficiency and quality of results of the multiscale approach are compared with the gridblockbased LMenRML with distancebased localization We observe that the final realizations better preserve the spatial contrasts of the prior models and are less noisy than the realizations updated using a standard gridblock method while matching the production data equally well

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