A Practical Method of History Matching Reservoir Simulators

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Abstract/Contents

Abstract
A unique approach to automatic history matching is presented. This method requires approximately one-tenth the CPU time required for the optimal control theory automatic history matching methods described in the literature.Two accurate linear models of the simulator's pressure and production response to permeability changes are presented. These models, which are derived from the general hyperbolic decline curve relationship, are used in a history matching scheme to estimate the permeability distribution from historical production and pressure information.This automatic history matching technique is illustrated in four cases of pressure history in a 72 block 7 well model. Three isotropic permeability distributions and one anisotropic permeability distribution comprise the four cases. Additionally, an untested method is proposed to simultaneously calculate the relative permeability curves as well as the absolute permeability distribution. This technique could also be used to determine pseudo-relative permeabilities when used to match a more complex simulation model.

Description

Type of resource text
Date created September 1984

Creators/Contributors

Author Dye, Lester William
Primary advisor Aziz, Khalid
Advisor Horne, Roland N.
Degree granting institution Stanford University, Department of Petroleum Engineering

Subjects

Subject School of Earth Energy & Environmental Sciences
Subject Stanford University Petroleum Research Institute
Genre Thesis

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User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.

Preferred citation

Preferred Citation
Dye, Lester William. (1984). A Practical Method of History Matching Reservoir Simulators. Stanford Digital Repository. Available at: https://purl.stanford.edu/dd647ns9954

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Master's Theses, Doerr School of Sustainability

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