A multistage sampling method for rapid quantification of uncertainty in history matching geological models

X. Ma*, M. Al-Harbi, A. Datta-Gupta, Yalchin Efendiev

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

Abstract

The paper presents a novel method for rapid quantification of uncertainty in history matching reservoir models using a two-stage Markov Chain Monte Carlo (MCMC) method. Our approach is based on a combination of fast linearized approximation to the dynamic data and the MCMC algorithm. In the first stage, we use streamline-derived sensitivities to obtain an analytical approximation in a small neighborhood of the previously computed dynamic data. The sensitivities can be conveniently obtained using either a finite-difference or streamline simulator. The approximation of the dynamic data is then used to modify the instrumental proposal distribution during MCMC. In the second stage, those proposals that pass the first stage are assessed by running full flow simulations to assure rigorousness in sampling. The uncertainty analysis is carried out by analyzing multiple models sampled from the posterior distribution in the Bayesian formulation for history matching. We demonstrate that the two-stage approach increases the acceptance rate, and significantly reduces the computational cost compared to conventional MCMC sampling without sacrificing accuracy. Finally, both twodimensional synthetic and three-dimensional field examples demonstrate the power and utility of the two-stage MCMC method for history matching and uncertainty analysis.

Original languageEnglish (US)
Pages2038-2052
Number of pages15
StatePublished - Nov 20 2006
EventSPE Annual Technical Conference and Exhibition, ATCE 2006: Focus on the Future - San Antonio, TX, United States
Duration: Sep 24 2006Sep 27 2006

Other

OtherSPE Annual Technical Conference and Exhibition, ATCE 2006: Focus on the Future
CountryUnited States
CitySan Antonio, TX
Period09/24/0609/27/06

ASJC Scopus subject areas

  • Fuel Technology
  • Energy Engineering and Power Technology

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