New Well Optimization in Mature Fields
Abstract/Contents
- Abstract
- Since most of worldwide oil reserves are known, optimal recovery of mature fields has become a crucial issue. Reservoir management teams have to face the challenging problem of determining the best placement, trajectory and type for new wells. Several approaches have already been studied. We will focus on MLOPT (Yeten, 2003 [4]), an optimization algorithm which aims at finding well type, location and trajectory in order to maximize an objective function, typically oil recovery or NPV. The master engine of this method is a Genetic Algorithm.A new procedure is described in order to improve the efficiency of MLOPT for multiple well optimization. This approach is iterative and divides the original problem into several single well optimizations. A field case is also given where the new procedure appears to be effective and shows a promising incremental recovery.The single well optimization has then been improved in order to obtain a faster and better solution. Physical characteristics of the field are used to speed up the algorithm by selecting potential good wells a priori without running expensive numerical simulations. Our approach is two fold. A function is used as a proxy to reservoir simulation if its value is below an appropriately chosen criterion. The choice of this criterion is discussed. In addition, a simple rule-based constraint based on the field properties is applied to reduce the search space by rejecting probable bad wells. These two methods significantly improve the original algorithm and provide a better solution with fewer simulation runs.
Description
| Type of resource | text |
|---|---|
| Date created | June 2003 |
Creators/Contributors
| Author | Rigot, Vincent |
|---|---|
| Primary advisor | Gerritsen, Margot |
| 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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- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
- Rigot, Vincent. (2003). New Well Optimization in Mature Fields. Stanford Digital Repository. Available at: https://purl.stanford.edu/qz327py5003
Collection
Master's Theses, Doerr School of Sustainability
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