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2007, Volume 3, Issue 2: 257-277. Doi: 10.3934/jimo.2007.3.257
This issue Previous Article Existence of closed graph, maximal, cyclic pseudo-monotone relations and revealed preference theory Next Article Management of water storage in two connected dams

Integrated production system optimization using global optimization techniques

  • 1.

    Shell International Production and Exploration B.V, Kesslerpark 1, Postbus 60, 2280 AB Rijswijk, The Netherlands

  • 2.

    Shell International Production and Exploration B.V, Kesslerpark 1, Postbus 60, 2280 AB Rijswijk

  • 3.

    Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Victoria, 3353

  • 4.

    WAM Systems Inc., Plymouth Meeting, PA

  • 5.

    PCS Inc., 129 Glenforest Drive, Halifax, NS, Canada B3M 1J2

Received:  August 31, 2006
Revised:  December 31, 2006
Published:  March 2007
Abstract Related Papers Cited by
    Abstract
  • Many optimization problems related to integrated oil and gas production systems are nonconvex and multimodal. Additionally, apart from the innate nonsmoothness of many optimization problems, nonsmooth functions such as minimum and maximum functions may be used to model flow/pressure controllers and cascade mass in the gas gathering and blending networks. In this paper we study the application of different versions of the derivative free Discrete Gradient Method (DGM) as well as the Lipschitz Global Optimizer (LGO) suite to production optimization in integrated oil and gas production systems and their comparison with various local and global solvers used with the General Algebraic Modeling System (GAMS). Four nonconvex and nonsmooth test cases were constructed from a small but realistic integrated gas production system optimization problem. The derivation of the system of equations for the various test cases is also presented. Results demonstrate that DGM is especially effective for solving nonsmooth optimization problems and its two versions are capable global optimization algorithms. We also demonstrate that LGO solves successfully the presented test (as well as other related real-world) problems.
    Mathematics Subject Classification: Primary: 65K05, Secondary: 90C25.

    Citation:
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