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Stochastic Simplex Approximate Gradient for Robust Life-cycle Production Optimization: Applied to Brugge Field

Journal Article · · Journal of Energy Resources Technology
DOI:https://doi.org/10.1115/1.4043244· OSTI ID:1503202
 [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. China Univ. of Petroleum, Qingdao, Shandong (China)

In oil and gas production industry, production optimization is a viable technique to maximize the oil/gas production or net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for robust life-cycle production optimization. Recently, however, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Finally, the purpose of this study is to provide a theoretical discussion on why StoSAG is generally superior to EnOpt and provide reasonable example (Brugge field) where StoSAG generates estimates of optimal well controls that give a life-cycle net present value (NPV) significantly higher than the NPV obtained from EnOpt.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1503202
Report Number(s):
LA-UR--19-22126
Journal Information:
Journal of Energy Resources Technology, Journal Name: Journal of Energy Resources Technology Journal Issue: 9 Vol. 141; ISSN 0195-0738
Publisher:
ASMECopyright Statement
Country of Publication:
United States
Language:
English

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Gradient-free strategies to robust well control optimization journal September 2019

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