Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/125945
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.