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一种混合多目标细胞斑点鬣狗优化器,用于井筒轨迹优化。

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization.

机构信息

Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia.

Department of Information Systems and Technology Management, College of Technological Innovation, Abu Dhabi Campus, Zayed University, Abu Dhabi, United Arab Emirates.

出版信息

PLoS One. 2022 Jan 27;17(1):e0261427. doi: 10.1371/journal.pone.0261427. eCollection 2022.

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.

摘要

成本和安全是石油和天然气行业优化井眼轨迹的关键因素,井眼轨迹是一个受约束的非线性优化问题。在这项工作中,利用真实测量深度、井眼剖面能量和扭矩来优化井眼轨迹。采用了许多元启发式算法来优化这些目标,调整了 17 个约束变量,但存在明显的缺点,包括降低了开发/探索能力、陷入局部最优、非支配解的非均匀分布以及无法跟踪孤立的最小值。本工作的目的是提出一种改进的多目标蜂窝斑点鬣狗算法(MOCSHOPSO)来优化真实测量深度、井眼剖面能量和扭矩。为了克服上述困难,该改进方法结合了元胞自动机(CA)和粒子群优化(PSO)。通过添加 CA,增强了 SHO 的探索阶段,并通过 PSO 的速度更新属性修改了 SHO 的狩猎机制。在轨迹优化过程中利用了一些地球物理和操作约束,并从苏伊士湾油田收集了数据。将所提出的算法与标准方法(MOCPSO、MOSHO、MOCGWO)进行了比较,在非支配解的分布、搜索能力、孤立最小值的最小数量以及 Pareto 最优前沿方面都有显著的改进。通过对算法进行一些统计分析,如 IGD、MS、SP 和 ER,验证了这些显著的改进。所提出的算法在 IGD、SP 和 ER 方面取得了最低的值,在 MS 方面取得了最高的值。最后,提出了一种自适应邻域机制,与 L5、L9、C9、C13、C21 和 C25 等固定邻域拓扑相比,表现出更好的性能。希望这个新提出的改进算法能为更好的井眼轨迹优化铺平道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3835/8794190/ed225cff8167/pone.0261427.g001.jpg

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