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.
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oil well test data;
Document Type: Research Article
Centro de Investigación en Matemáticas, Guanajuato, Mexico
Applied Mathematics and Statistics, University of California, Santa Cruz, CA, USA
Centro de Innovación Matemática, UNAM, Querétaro, Mexico
March 11, 2016