Optimized History Matching with Stochastic Image Transforming of a Deltaic Reservoir

Maria Helena Caeiro, Amilcar Soares, Vasily Demyanov, Mike Christie

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

3 Citations (Scopus)

Abstract

The focus of this paper is to demonstrate an integrated characterization approach for a complex deltaic reservoir by using petrophysical properties and dynamic production data. Accurately characterizing deltaic reservoirs requires a non-stationary approach to the reservoir description. By linking the non-stationary characterization step with the dynamic flow modeling, the probability of success in locating infill wells increases. In this work, is presented a hybrid method which integrates the optimization firstly in the space of the anisotropy model parameters and secondly refines it in the space of the static models with a regional perturbation technique. It is an iterative methodology for optimized history matching, using adaptive stochastic sampling in the multiparameter space, and direct sequential simulation as the engine for the image transformation of the porosity and permeability models of the reservoir. The results show coherence when compared with the true case model. The approach provides the simultaneous simulation of the morphology and the property value.
Original languageEnglish
Title of host publicationMathematics of Planet Earth
PublisherSpringer
Pages571-574
Number of pages4
ISBN (Electronic)9783642324086
ISBN (Print)9783642324079
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Earth System Sciences
PublisherSpringer
ISSN (Print)2193-8571
ISSN (Electronic)2193-858X

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Earth and Planetary Sciences(all)

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    Caeiro, M. H., Soares, A., Demyanov, V., & Christie, M. (2013). Optimized History Matching with Stochastic Image Transforming of a Deltaic Reservoir. In Mathematics of Planet Earth (pp. 571-574). (Lecture Notes in Earth System Sciences). Springer. https://doi.org/10.1007/978-3-642-32408-6_125