History Matching of Production and Time-Lapse Seismic Data into Channelized Reservoir Models Using Ensemble Smoother.

摘要

Ensemble-based algorithms have been successfully implemented for history matching of geological models. However, their performance is optimal only if the prior-state vector is linearly related to the predicted data and if the joint distribution of the prior-state vector is multivariate Gaussian. Moreover, the number of degrees of freedom is as large as the ensemble size, so the assimilation of large amounts of production or seismic data might lead to the ensemble collapse which results in inaccurate predictions of future performance. In this paper, we introduce a methodology that combines model classification with multidimensional scaling (MDS) and the ensemble smoother algorithm to efficiently history match fluvial and channelized reservoir models. The dynamic responses (production and seismic data) of the different ensemble members are used to compute a dissimilarity matrix. This dissimilarity matrix is then transformed into a lower-dimensional space by the use of MDS.