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A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis

Botsas, T. and Cumming, J. A. and Jermyn, I. H. (2022) 'A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis.', Journal of the Royal Statistical Society: Series C (Applied Statistics) .

Abstract

In petroleum well test analysis, deconvolution is used to obtain information about the reservoir system. This information is contained in the response function, which can be estimated by solving an inverse problem in the pressure and flow rate measurements. Our Bayesian approach to this problem is based upon a parametric physical model of reservoir behaviour, derived from the solution for fluid flow in a general class of reservoirs. This permits joint parametric Bayesian inference for both the reservoir parameters and the true pressure and rate values, which is essential due to the typical levels of observation error. Using a set of flexible priors for the reservoir parameters to restrict the solution space to physical behaviours, samples from the posterior are generated using MCMC. Summaries and visualisations of the reservoir parameters' posterior, response, and true pressure and rate values can be produced, interpreted, and model selection can be performed. The method is validated through a synthetic application, and applied to a field data set. The results are comparable to the state of the art solution, but through our method we gain access to system parameters, we can incorporate prior knowledge that excludes non-physical results, and we can quantify parameter uncertainty.

Item Type:Article
Full text:Publisher-imposed embargo
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1111/rssc.12562
Publisher statement:© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons, Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Date accepted:06 March 2022
Date deposited:19 April 2022
Date of first online publication:20 April 2022
Date first made open access:09 May 2022

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