Donghao Liu , Benjamin D. Leibowicz , Jonathan F. Bard , Yuzixuan Zhu , Yuanyuan Guo , Yufen Shao
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引用次数: 0
Abstract
In this paper, we consider an oilfield planning problem with decisions about where and when to invest in wells and facilities to maximize profit. The model, in the form of a mixed-integer linear program, includes an option to expand capacity for existing facilities, annual budget constraints, well closing decisions, and fixed production profiles once wells are opened. While fixed profiles are a novel and important feature, they add another set of time-indexed binary variables that makes the problem difficult to solve. To find solutions, we develop a three-phase sequential algorithm that includes (1) ranking, (2) branching, and (3) refinement. Phases 1 and 2 determine which facilities and wells to open, along with well-facility assignments. Phase 3 ensures feasibility with respect to budget constraints and adjusts construction times and facility capacities to increase profit. We first demonstrate how our algorithm navigates the problem’s complex features by applying it to a case study parameterized with realistic production profiles. Then, we perform computational experiments on small instances and show that our algorithm generally achieves the same objective function values as CPLEX but in much less time. Lastly, we solve larger instances using our three-phase algorithm and several variations to demonstrate its scalability and to highlight the roles of specific algorithmic components.
期刊介绍:
Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.