eprintid: 17769
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/77/69
datestamp: 2023-12-19 03:24:05
lastmod: 2023-12-19 03:24:05
status_changed: 2023-12-19 03:08:39
type: article
metadata_visibility: show
creators_name: Biswas, K.
creators_name: Nazir, A.
creators_name: Tauhidur Rahman, M.
creators_name: Khandaker, M.U.
creators_name: Idris, A.M.
creators_name: Islam, J.
creators_name: Rahman, M.A.
creators_name: Jallad, A.-H.M.
title: A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization
ispublished: pub
keywords: algorithm; Article; cellular automaton; energy; information processing; mathematical computing; nonlinear system; particle swarm optimization; process optimization; torque; validation process; algorithm; computer simulation; energy conservation; oil industry; procedures, Algorithms; Computer Simulation; Conservation of Energy Resources; Oil and Gas Industry
note: cited By 0
abstract: 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. © 2022 Biswas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
date: 2022
publisher: Public Library of Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123688368&doi=10.1371%2fjournal.pone.0261427&partnerID=40&md5=59939f3c52ddfa76312e61b04678a1a4
id_number: 10.1371/journal.pone.0261427
full_text_status: none
publication: PLoS ONE
volume: 17
number: 1 Janu
refereed: TRUE
issn: 19326203
citation:   Biswas, K. and Nazir, A. and Tauhidur Rahman, M. and Khandaker, M.U. and Idris, A.M. and Islam, J. and Rahman, M.A. and Jallad, A.-H.M.  (2022) A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization.  PLoS ONE, 17 (1 Janu).   ISSN 19326203