Abstract
The present work introduces a new approach to the optimum wells placement problem in oil fields using evolutionary computation. In particular, our contribution is twofold: we propose an efficient algorithm for initialisation of highly constrained optimisation problems based on Monte-Carlo sampling and we propose a new optimisation technique that uses this population sampling scheme, a space-efficient chromosome and the application of cellular genetic algorithms to promote a large population diversity. Usually, authors define a domain representation having oil wells placed at any arbitrary position of the chromosome. On the other hand, the proposed representation enforces a unique relative wells position for each combination of wells. Therefore, the suggested scheme diminishes the problem size, thus making the optimisation more efficient. Moreover, by also employing a cellular genetic algorithm, we guarantee an improved population diversity along the algorithm execution. The experiments with the UNISIM-I reservoir indicate an enhancement of 6 to 10 times of the final NPV when comparing the proposed representation and the traditional one. Besides, the cellular genetic algorithm with the suggested chromosome performs better than the classical genetic algorithm by a factor of 1.5. The proposed models are valuable not only for the oil and gas industry but also to every integer optimisation problem that employs evolutionary algorithms.
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Cunha, A.A.L., Duncan, G., Bontempo, A., Pacheco, M.A.C. (2018). Optimum Wells Placement in Oil Fields Using Cellular Genetic Algorithms and Space Efficient Chromosomes. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2016. Studies in Computational Intelligence, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-69266-1_2
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