Real-Time Optimization of Smart Wells
Abstract/Contents
- Abstract
- Smart wells are wells that have downhole instrumentation, such as sensors and valves, on the production tubing. These wells provide the ability for both downhole monitoring and control. Downhole monitoring can be achieved through the use of sensors while controls realized with downhole valves. Once a smart well is deployed, valves can be used to independently control each segment / branch of the well in a reactive mode, such as shutting off a zone once it starts producing water, or in a defensive mode, which requires the a priori determination of valve settings. Using the latter approach, which is the method applied in this work, valve settings are determined through an optimization procedure. We show with this procedure that well instrumentation can provide over 50%gain in cumulative oil recovery over the uninstrumented case for systems considered herein which the geology is assumed to be known. Because the geology is not known in real applications, we couple the valve optimization procedure with history matching techniques, in which we use idealized sensor data to update the reservoir description. Up to 90% of the gain attainable with known geology is achieved for the unconditionally and conditionally generated models considered. In addition, we show that it is beneficial to use multiple history-matched models for the optimization in some cases. This is because multiple history-matched models capture the geologic uncertainty better than single history-matched models. We also introduce efficient alternative procedures to improve the speed of the overall technique. These include the use of a Levenberg-Marquardt algorithm for the optimizations.
Description
| Type of resource | text |
|---|---|
| Date created | June 2004 |
Creators/Contributors
| Author | Aitokhuehi, Inegbenose |
|---|---|
| Primary advisor | Durlofsky, Louis J. |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Aitokhuehi, Inegbenose. (2004). Real-Time Optimization of Smart Wells. Stanford Digital Repository. Available at: https://purl.stanford.edu/xs120yv1469
Collection
Master's Theses, Doerr School of Sustainability
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