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

作　　者：Qing Wang Haige Wang Hongchun Huang Lubin Zhuo Guodong Ji 

机构地区：[1]CNPC Engineering Technology R&D Company Limited,Planning and Support Institute,Beijing,102206,China

出　　处：《Fluid Dynamics & Materials Processing》2023年第10期2569-2578,共10页流体力学与材料加工（英文）

基　　金：The project is supported by CNPC Key Core Technology Research Projects(2022ZG06)received by Qing Wang;project funded by China Postdoctoral Science Foundation(2021M693508)received by Qing Wang.Basic Research and Strategic Reserve Technology Research Fund Project of Institutes directly under CNPC received by Qing Wang.

摘　　要：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 pro-blems 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 pro-posed training-based model can effectively be applied to practical situations.

关 键 词：Shale gas drilling sticking fault artificial intelligence risk early warning technology 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]