Application of derivative-free methodologies to generally constrained oil production optimisation problems
| Autor: | David Echeverría Ciaurri, Obiajulu J. Isebor, Louis J. Durlofsky |
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| Rok vydání: | 2011 |
| Předmět: |
Numerical Analysis
Mathematical optimization Optimization problem Computer science Applied Mathematics Derivative-free optimization Constrained optimization Reservoir simulation Function (mathematics) Pattern search Net present value Nonlinear programming Constraint (information theory) Random search Modeling and Simulation Genetic algorithm General Earth and Planetary Sciences Production (economics) Closed-loop reservoir modeling Penalty method Metaheuristic Oil production optimization General Environmental Science |
| Zdroj: | ICCS |
| ISSN: | 2040-3615 2040-3607 |
| DOI: | 10.1504/ijmmno.2011.039425 |
| Popis: | Oil production optimization involves the determination of optimum well controls (e.g., well pressures, injection rates) to maximize an objective function such as cumulative oil production or net present value. In practice, this problem additionally requires the satisfaction of physical and economic constraints. Thus, the overall problem represents a challenging nonlinearly constrained optimization. The cost function and constraints involve calls to a reservoir simulator. Therefore, in many situations gradient information cannot be obtained efficiently. This fact motivates the use of derivative-free (non-invasive, black-box) optimization methods. This work entails a comparative study of a number of these methods applied to the solution of generally constrained production optimization problems. The derivativefree techniques considered include two pattern search methods (generalized pattern search and Hooke-Jeeves direct search) and a genetic algorithm. A gradient-based algorithm, in which derivatives are estimated numerically, is also considered. The performance of the derivative-free algorithms is shown to be quite satisfactory and can be improved significantly when implemented within a distributed computing environment. In order to address the solution of the generally constrained production optimization problem, different constraint handling techniques are investigated. Penalty functions can be used successfully for this purpose, but they typically involve a tuning/iterative process that is not exempt, in theory, from potential pitfalls. The results indicate that the filter method combined with pattern search suitably addresses this issue while keeping the scheme efficient. We have also explored a parameterless penalty method for genetic algorithms that appears promising when hybridized with pattern search techniques. In total, the results of this study demonstrate the applicability of derivative-free methods for challenging reservoir management problems. |
| Databáze: | OpenAIRE |
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