Parallel Dantzig–Wolfe decomposition of petroleum production allocation problems

E Torgnes, V Gunnerud, E Hagem, M Rönnqvist and B Foss
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E Torgnes: Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
V Gunnerud: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
E Hagem: Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
M Rönnqvist: 1] Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Trondheim, Norway[2] Norwegian School of Economics and Business Administration (NHH), Bergen, Norway
B Foss: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Journal of the Operational Research Society, 2012, vol. 63, issue 7, 950-968

Abstract: This article discusses the optimization of a petroleum production allocation problem through a parallel Dantzig–Wolfe algorithm. Petroleum production allocation problems are problems in which the determination of optimal production rates, lift gas rates and well connections are the central decisions. The motivation for modelling and solving such optimization problems stems from the value that lies in an increased production rate and the current lack of integrated software that considers petroleum production systems as a whole. Through our computational study, which is based on realistic production data from the Troll West field, we show the increase in computational efficiency that a parallel Dantzig–Wolfe algorithm offers. In addition, we show that previously implemented standard parallel algorithms lead to an inefficient use of parallel resources. A more advanced parallel algorithm is therefore developed to improve efficiency, making it possible to scale the algorithm by adding more CPUs and thus approach a reasonable solution time for realistic-sized problems.

Date: 2012
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