Scipediacontent: Scipediacontent moved page Review 479977689869 to Zhang et al 2026g

2026-09-14T11:08:29Z

Scipediacontent moved page Review 479977689869 to Zhang et al 2026g

Scipediacontent: Scipediacontent moved page Draft content 952575434 to Review 479977689869

2026-09-14T10:16:09Z

Scipediacontent moved page Draft content 952575434 to Review 479977689869

Scipediacontent: Created page with " == Abstract ==
Well trajectory design is a fundamental component of drilling design. Conventional well trajectory parameter selection relies heavily on expert experience,..."

2026-09-14T10:16:06Z

Created page with " == Abstract == <p>Well trajectory design is a fundamental component of drilling design. Conventional well trajectory parameter selection relies heavily on expert experience,..."

New page

== Abstract ==

<p>Well trajectory design is a fundamental component of drilling design. Conventional well trajectory parameter selection relies heavily on expert experience, requiring repeated manual adjustment, resulting in low design efficiency. Traditional trajectory optimization algorithms require pre-defined parameter ranges and fail to fully leverage the offset well data. To address these limitations, this paper presents a proof-of-concept intelligent well trajectory design scheme that fully exploits the formation and offset well data by integrating reinforcement learning and optimization theory. An optimal offset well screening strategy and a reinforcement learning-based PSO (RL-PSO) algorithm are proposed to realize automatic trajectory design without manual parameter configuration. First, multiple features including formation similarities are encoded and quantified; the entropy weight method is applied to calculate the weight of each feature, and the optimal offset well is selected through a multi-dimensional comprehensive evaluation system. Second, based on the trajectory profile and parameter range derived from the optimal offset well, reinforcement learning is adopted to dynamically adjust the inertia weight and acceleration coefficients of the PSO algorithm for adaptive trajectory parameter optimization. Experimental results indicate that compared with traditional design methods, the proposed RL-PSO framework, driven by optimal offset well data can generate more economical well trajectories and improve design efficiency under the studied experimental conditions, providing a feasible intelligent solution for automated well trajectory design.</p>

== Document ==
<pdf>Media:Draft_content_952575434-5002-document.pdf</pdf>

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Zhang et al 2026g - Revision history

Scipediacontent: Scipediacontent moved page Review 479977689869 to Zhang et al 2026g

Scipediacontent: Scipediacontent moved page Draft content 952575434 to Review 479977689869

Scipediacontent: Created page with " == Abstract == Well trajectory design is a fundamental component of drilling design. Conventional well trajectory parameter selection relies heavily on expert experience,..."

Scipediacontent: Created page with " == Abstract ==
Well trajectory design is a fundamental component of drilling design. Conventional well trajectory parameter selection relies heavily on expert experience,..."