Multi-period Optimization for Long-Term Oilfield Production Planning

Jadier Aristizabal (), María Mar Prieto (), Lizzet Vargas (), Diego Pradilla () and Jorge M. Gómez ()
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Jadier Aristizabal: Universidad de los Andes
María Mar Prieto: Universidad de los Andes
Lizzet Vargas: Universidad de los Andes
Diego Pradilla: Universidad de los Andes
Jorge M. Gómez: Universidad de los Andes

Journal of Optimization Theory and Applications, 2023, vol. 197, issue 1, No 4, 97 pages

Abstract: Abstract In this work, a multi-period nonlinear programming formulation is presented to obtain the optimal long-term oilfield production planning, based on a two-phase, one-dimensional, and Cartesian-coordinated phenomenological reservoir model. The phenomenological model contains a set of second-order partial differential equations, which are approximated by a collocation on finite element method. This CFE method prevents mathematical stability limitations due to stiffness problems, resulting in an algebraic equation system added as an optimization set of constraints. This is a significant and innovating approach as there are only a handful of similar studies in the literature that integrate phenomenological models as mathematical constraints in the optimization problem. However, these works do not solve the model using long-term production planning coupled with a simultaneous strategy. Also, formulation applied to two study cases allowed solving the optimization problem within an adequate time without requiring a high-performance computing platform. Results show the economic impact of simultaneously considering the constraints and the state variables evolution throughout the reservoir’s life span to obtain the optimal long-term production planning.

Keywords: Collocation on finite elements; Long-term horizons; Nonlinear programming; Oilfield production planning; Phenomenological model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10957-023-02191-7

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