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https://hdl.handle.net/2440/125945
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dc.contributor.author | Alrashdi, Z. | - |
dc.contributor.author | Sayyafzadeh, M. | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of Petroleum Science and Engineering, 2019; 177:1042-1058 | - |
dc.identifier.issn | 0920-4105 | - |
dc.identifier.issn | 1873-4715 | - |
dc.identifier.uri | http://hdl.handle.net/2440/125945 | - |
dc.description | Available online 20 February 2019 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Zaid Alrashdi, Mohammad Sayyafzadeh | - |
dc.language.iso | en | - |
dc.publisher | Elsevier BV | - |
dc.rights | © 2019 Elsevier B.V. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.petrol.2019.02.047 | - |
dc.subject | Evolution strategy; Well placement optimisation; Nonconventional well; Control optimisation; Joint optimisation | - |
dc.title | (μ + Λ) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation | - |
dc.title.alternative | (mu + Lambda) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.petrol.2019.02.047 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Sayyafzadeh, M. [0000-0002-4414-372X] | - |
Appears in Collections: | Aurora harvest 4 Australian School of Petroleum publications |
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