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Bayesian deconvolution of oil well test data using Gaussian processes

Author

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  • J. Andrés Christen
  • Bruno Sansó
  • Mario Santana-Cibrian
  • Jorge X. Velasco-Hernández

Abstract

We use Bayesian methods to infer an unobserved function that is convolved with a known kernel. Our method is based on the assumption that the function of interest is a Gaussian process and, assuming a particular correlation structure, the resulting convolution is also a Gaussian process. This fact is used to obtain inferences regarding the unobserved process, effectively providing a deconvolution method. We apply the methodology to the problem of estimating the parameters of an oil reservoir from well-test pressure data. Here, the unknown process describes the structure of the well. Applications to data from Mexican oil wells show very accurate results.

Suggested Citation

  • J. Andrés Christen & Bruno Sansó & Mario Santana-Cibrian & Jorge X. Velasco-Hernández, 2016. "Bayesian deconvolution of oil well test data using Gaussian processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 721-737, March.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:721-737
    DOI: 10.1080/02664763.2015.1077374
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    References listed on IDEAS

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