Open Access

ARTICLE

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Qing Wang^*, Haige Wang, Hongchun Huang, Lubin Zhuo, Guodong Ji

CNPC Engineering Technology R&D Company Limited, Planning and Support Institute, Beijing, 102206, China

* Corresponding Author: Qing Wang. Email:

Fluid Dynamics & Materials Processing 2023, 19(10), 2569-2578. https://doi.org/10.32604/fdmp.2023.025349

Received 07 July 2022; Accepted 27 September 2022; Issue published 25 June 2023

Abstract

Sticking is the most serious cause of failure in complex drilling operations. In the present work a novel “early warning” method based on an artificial intelligence algorithm is proposed to overcome some of the known problems associated with existing sticking-identification technologies. The method is tested against a practical case study (Southern Sichuan shale gas drilling operations). It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state; furthermore, the results from four groups of verification samples are also consistent with the actual downhole state. This shows that the proposed training-based model can effectively be applied to practical situations.

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Keywords

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