Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/125945
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Type: Journal article
Title: (μ + Λ) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation
Other Titles: (mu + Lambda) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation
Author: Alrashdi, Z.
Sayyafzadeh, M.
Citation: Journal of Petroleum Science and Engineering, 2019; 177:1042-1058
Publisher: Elsevier BV
Issue Date: 2019
ISSN: 0920-4105
1873-4715
Statement of
Responsibility: 
Zaid Alrashdi, Mohammad Sayyafzadeh
Abstract: Field development optimisation is a critical task in the modern reservoir management processes. The optimum setting provides the best exploitation strategy and financial returns. However, finding such a setting is difficult due to the non-linearity between the reservoir response and the development strategy parameters. Therefore, growing attention is being paid to computer-assisted optimisation algorithms, due to their capabilities in handling optimisation problems with such complexities. In this paper, the performance of (μ + Λ) Evolution Strategy (ES) Algorithm is compared to Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and (μ, Λ) Covariance Matrix Adaptation Evolution Strategy (CMA-ES) using five different optimisation cases. The 1st and 2nd cases are well placement and trajectory optimisation, respectively, which have rough objective function surfaces and a small number of dimensions. The 3rd Case is well control optimisation with a small number of dimensions, while the 4th case is a high-dimensional control optimisation. Lastly, the 5th case is joint optimisation that includes the number of wells, type, trajectory, and control, which has a high dimensional rugged surface. The results show that the use of ES as the optimisation algorithm delivers promising results in all cases, except case 3. It converged to a higher NPV compared to the other algorithms with the same computational budget. The obtained solutions also outperformed the ones delivered by reservoir engineering judgments.
Keywords: Evolution strategy; Well placement optimisation; Nonconventional well; Control optimisation; Joint optimisation
Description: Available online 20 February 2019
Rights: © 2019 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.petrol.2019.02.047
Published version: http://dx.doi.org/10.1016/j.petrol.2019.02.047
Appears in Collections:Aurora harvest 4
Australian School of Petroleum publications

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