AGER,2018, 2(1): 14-33 基于随机时间成本权衡问题的油气田开发项目双目标优化

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基于随机时间成本权衡问题的油气田开发项目双目标优化

      本文基于随机时间成本权衡问题（STCTP）分析扩展了油气田开发的离散项目持续时间-成本分析的范围。当使用双目标优化方法来确定最小总成本解决方案时，STCTP能提供较为深入的分析，并可在较大的潜在项目持续时间范围内额外推导出非支配总项目成本解决方案的帕累托边界。本文将STCTP项目工作的持续时间和成本进行概率分布并在区间（0,1）中进行随机采样。通过与项目持续时间分布的随机数相关联的公式控制对项目成本分布抽样的分数，并应用了一系列复杂的时间-成本关系。开发了约束STCTP的模因算法，包括十个共通启发式演算法，其中一部分集中用于局部开发，另一部分集中用于研究可行解空间。这种双重聚焦方式有效地实现了以下双重目标：确定最低总计划成本解决方案，或解决方案所在的区域；开发帕累托边界。通过应用八种不同项目时间成本关系分析案例，借助于元启发式分析，在Visual Basic for Applications中编码并在Excel中运行的模块化STCTP算法，并有效地实现双目标。从混沌序列调整的长尾分布中获得启发式算法并应用动态调整因子，提高可行解空间抽样的有效性。元启发式配置文件还有助于微调算法的配置，以进一步提高特定工作项目时间-成本关系的准确性。
Advances in Geo-Energy Research 论文：http://www.astp-agr.com/index.php/Index/Index/detail?id=45

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Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems

David A. Wood

(Published: 2018-01-16)

Corresponding Author and Email: David A. Wood, dw@dwasolutions.com

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Citation: Wood, D.A. Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems. Advances in Geo-Energy Research, 2018, 2(1): 14-33, doi: 10.26804/ager.2018.01.02.

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Article Type: Original article

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Abstract:

Conducting stochastic-time-cost-tradeoff-problem (STCTP) analysis beneficially extends the scope of discrete project duration-cost analysis for oil and gas field development projects. STCTP can be particularly insightful when using a dual-objective optimization approach to locate minimum-total-project-cost solutions, and to additionally derive a Pareto frontier of non-dominated-total-project-cost solutions across a wide range of potential project durations. For STCTP project-work-item durations and costs are expressed as probability distributions and sampled with random numbers (0, 1). By controlling the fractional numbers used to sample the work-item cost distributions by formulas linked to the random numbers used to sample the work-item duration distribution, a wide range of complex time-cost relationships are readily applied. The memetic algorithm developed for constrained STCTP involves ten metaheuristics configured to focus partly on local exploitation and partly on exploration of the feasible solution space. This dual focus effectively delivers the dual objective of: 1) locating the global minimum total-projectcost solution, if it exists, or the region in the vicinity of where that solution exists; and, 2) developing a Pareto frontier. Analysis of an example project, applying eight distinct work-item time-cost relationships, demonstrates with the aid of metaheuristic profiling, that the memetic STCTP algorithm coded in Visual Basic for Applications and operated in Microsoft Excel effectively delivers on both objectives. Dynamic adjustment factors applied by some metaheuristics, derived from fat-tailed distributions adjusted by chaotic sequences, aid the efficient sampling of the feasible solution space. The metaheuristic profiles also help to fine tune the configuration of the algorithm to further enhance performance for specific work-item time-cost relationships.

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Keywords: Stochastic project time-cost tradeoff problems TCTP, dual-objective nondominated sorting optimization, memetic optimization algorithm with chaotic sampling, metaheuristic profiling, pareto frontier, oil/gas project schedule-cost uncertainty model.