Generating Multiple History-Matched Reservoir Model Realizations Using Wavelets

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis 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.

Effective Parametrization for Reliable Reservoir Performance Predictions

“…For example, using multiresolution analysis based on Haar wavelets is proposed as a way to reparameterize the permeability field and wavelet coefficients that are sensitive to the history data are identified and used for history matching [42][43][44][45]. Similarly, discrete cosine transform (DCT) is used to construct A in [46], where the advantage of DCT over KLE in representing non-Gaussian channelized features was demonstrated.…”

Performance of Surfactant Flooding in Heterogeneous Two-Layered Porous Media

“…Random Fields of Rock Porosity. The variogram is a measure of dissimilarity between two points in space separated by a distance h [9,10]. It can be used to estimate the random function at unsampled location.…”

Reservoir description using dynamic parameterisation selection with a combined stochastic and gradient search

“…There is a demand for combining this type of estimate with geostatistical information. In the literature there have been proposed different approaches for including geostatistical information in the optimisation process related to this problem, see, for example, [25][26][27][28]. It could be interesting to combine these types of methodologies with the presented method.…”