Abstract:

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells under geological uncertainty. In order to reduce the excessive computational requirements of the base method, a statistical proxy based on cluster analysis is applied into the optimization process. This proxy provides an estimate of the cumulative distribution function (cdf) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxybased performance estimate in conjunction with the proxy cdf enables the systematic selection of the most appropriate scenarios for full simulation. The proxy is extended for application to the optimization of multiple nonconventional wells opened at different times. The proxy in this case is referred to as dynamic proxy. For optimization of a single nonconventional well, it is shown that by simulating only 10 or 20% of the scenarios, optimization results very close to those achieved by simulating all cases are obtained. For multiple wells drilled at different times, the dynamic proxy is effective though a relatively high percentage (e.g., 50%) of the cases must be simulated.

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Copyright 2006, JÈrÙme Onwunalu: 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, JÈrÙme Onwunalu.