Two-stage genetic algorithm for parallel machines scheduling problem: Cyclic steam stimulation of high viscosity oil reservoirs
| Autor: | Manuel Chi-Chim, Jorge Martinez-Munoz, Leonid Sheremetov |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mutation operator Mathematical optimization Job shop scheduling business.industry Computer science Heuristic Tardiness Crossover Steam injection 02 engineering and technology Scheduling (computing) 020901 industrial engineering & automation Software Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
| Zdroj: | Applied Soft Computing. 64:317-330 |
| ISSN: | 1568-4946 |
| Popis: | In this paper, the problem of optimal assignment of trailer-mounted steam generators for cyclic steam stimulation (CSS) of petroleum wells is formulated as a parallel uniform machines scheduling (PMS) problem with release dates. The total weighed tardiness is used as the goal of the optimization process. The distinctive features of the proposed PMS formulation include: jobs with variable weights, variable machine setup time, constraint capacity of drilling pads (where machines are allocated), and modified tardiness criterion. A two-stage scheduling algorithm combining heuristic and genetic algorithms is proposed for solving it. A chromosome representation, crossover and mutation operators generating only feasible solutions and thus avoiding the use of any repair mechanism are discussed. The performance of the algorithm is tested on a real-world data set from the oilfield asset located in the coastal swamps of the Gulf of Mexico. The experiments indicate that the proposed approach gives good results in optimization of the operational costs and petroleum recovery. The algorithm is implemented as a part of the software platform for optimization of CSS and currently is in use by oilfield engineers. |
| Databáze: | OpenAIRE |
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