Generating Multiple History-Matched Reservoir Model Realizations Using Wavelets

Abstract

Abstract This paper focuses on an automated way to generate multiple history-matched reservoir models with the inclusion of both geological uncertainty and varying levels of trust in the production data, using wavelet methods. As opposed to previously developed automated history-matching algorithms, this methodology not only ensures geological consistency in the final models, but also includes uncertainty in the production data. A data distribution, say a permeability field, can be (reversibly) transformed into wavelet space where it fully described by a set of wavelet coefficients. It was found that different subsets of the collection of wavelet coefficients can be constrained separately to: (a) the production history, and (b) the geological constraints. This means the history match need only be performed once, after which multiple realizations can be generated by adjusting just the second subset of coefficients. The ability to include both geological and production data uncertainty into reservoir model automatically is of great consequence to reservoir modeling and hence to reservoir management, risk analysis and making key economic decisions. The more complete and realistic reservoir model will lead to better reservoir production and development decisions.