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

Chen, Bailian; Xu, Jianchun

doi:10.1115/1.4043244

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

Journal Article · Wed Mar 20 00:00:00 EDT 2019 · Journal of Energy Resources Technology

DOI:https://doi.org/10.1115/1.4043244· OSTI ID:1503202

Chen, Bailian ^[1]; Xu, Jianchun ^[2]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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.

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Research Organization:: Los Alamos National Lab. (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, Vol. 141, Issue 9; ISSN 0195-0738

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 7 works

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