Abstract:

Uncertainty involved in estimating reservoir parameters from a well test interpretation originates from the fact that different reservoir models may appear to match the pressure data equally well. A successful well test analysis requires the selection of the most appropriate model to represent the reservoir behavior. This step is now performed by graphical analysis using the pressure derivative plot and confidence intervals. The selection by graphical analysis is influenced by human bias and, as a result, the result may vary according to the interpreter. Confidence intervals can provide a quantitative evaluation of the adequacy of a single model but is less useful to discriminate between models.

This study describes a new quantitative method, the sequential predictive probability method, to discriminate between candidate reservoir models. This method was originally proposed in the field of applied statistics to construct an effective experimental design and is modified in this study for effective use in model discrimination in well test analysis. This method is based on Bayesian inference, in which all information about the reservoir model and, subsequently, the reservoir parameters deduced from well test data are expressed in terms of probability.

The sequential predictive probability method provides a unified measure of model discrimination regardless of the number of the parameters in reservoir models and can compare any number of reservoir models simultaneously.

Eight fundamental reservoir models, which are the infinite acting model, the sealing fault model, the no flow outer boundary model, the constant pressure outer boundary model, the double porosity model, the double porosity and sealing fault model, the double porosity and no flow outer boundary model, and the double porosity and constant pressure outer boundary model, were employed in this study and the utility of the sequential predictive probability method for simulated and actual field well test data was investigated.

The sequential predictive probability method was found to successfully discrirninate between these models, even in cases where neither graphical analysis nor confidence intervals would work.

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Copyright 1993, Toshiyuki Anraku: Please note that the reports and theses are copyright to their original authors. Authors have given written permission for their work to be made available here. Readers who download reports from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the author, Toshiyuki Anraku.