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A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization
Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic al...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794190/ https://www.ncbi.nlm.nih.gov/pubmed/35085239 http://dx.doi.org/10.1371/journal.pone.0261427 |
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author | Biswas, Kallol Nazir, Amril Rahman, Md. Tauhidur Khandaker, Mayeen Uddin Idris, Abubakr M. Islam, Jahedul Rahman, Md. Ashikur Jallad, Abdul-Halim M. |
author_facet | Biswas, Kallol Nazir, Amril Rahman, Md. Tauhidur Khandaker, Mayeen Uddin Idris, Abubakr M. Islam, Jahedul Rahman, Md. Ashikur Jallad, Abdul-Halim M. |
author_sort | Biswas, Kallol |
collection | PubMed |
description | Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO’s exploration phase is enhanced, and the SHO’s hunting mechanisms are modified with PSO’s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization. |
format | Online Article Text |
id | pubmed-8794190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87941902022-01-28 A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization Biswas, Kallol Nazir, Amril Rahman, Md. Tauhidur Khandaker, Mayeen Uddin Idris, Abubakr M. Islam, Jahedul Rahman, Md. Ashikur Jallad, Abdul-Halim M. PLoS One Research Article Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO’s exploration phase is enhanced, and the SHO’s hunting mechanisms are modified with PSO’s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization. Public Library of Science 2022-01-27 /pmc/articles/PMC8794190/ /pubmed/35085239 http://dx.doi.org/10.1371/journal.pone.0261427 Text en © 2022 Biswas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Biswas, Kallol Nazir, Amril Rahman, Md. Tauhidur Khandaker, Mayeen Uddin Idris, Abubakr M. Islam, Jahedul Rahman, Md. Ashikur Jallad, Abdul-Halim M. A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title | A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title_full | A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title_fullStr | A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title_full_unstemmed | A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title_short | A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
title_sort | hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794190/ https://www.ncbi.nlm.nih.gov/pubmed/35085239 http://dx.doi.org/10.1371/journal.pone.0261427 |
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