Integrated Well Placement and Completion Optimization using Heuristic Algorithms: A Case Study of an Iranian Carbonate Formation
Determination of optimum location for drilling a new well not only requires engineering judgments but also consumes excessive computational time. Additionally, availability of many physical constraints such as the well length, trajectory, and completion type and the numerous affecting parameters inc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Tehran
2018-06-01
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| Series: | Journal of Chemical and Petroleum Engineering |
| Subjects: | |
| Online Access: | https://jchpe.ut.ac.ir/article_66102.html |
| Summary: | Determination of optimum location for drilling a new well not only requires engineering judgments but also consumes excessive computational time. Additionally, availability of many physical constraints such as the well length, trajectory, and completion type and the numerous affecting parameters including, well type, well numbers, well-control variables prompt that the optimization approaches become imperative;. The aim of this study is to figure out optimum well location and the best completion condition using coupled simulation optimization on an Iranian oil field located in southwest of Iran. The well placement scenarios are considered in two successive time intervals during of the field life, i.e., exploration and infill drilling phase. In the former scenario, the well-placement optimization is considered to locate the drilling site of a wildcat well, while the later scenario includes the optimum drilling location of a well is determined after 10-years primary production of nine production wells. In each scenario, two stochastic optimization algorithms namely particle swarm optimization, and artificial bee colony will be applied to evaluate the considered objective function. The net present value to drill production wells through the field life is considered as an objective function during our simulation-optimization approach. Our results show that the outcome of two population-based algorithms (i.e., particle swarm optimization and artificial bee colony) is marginally different from each other. The net present value of the infill drilling phase attains higher value using artificial bee colony algorithm. |
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| ISSN: | 2423-673X 2423-6721 |