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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,

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.

Article Type:Original article


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.

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.


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