TY  - EJOU
AU  - Wang, Qing 
AU  - Wang, Haige 
AU  - Huang, Hongchun 
AU  - Zhuo, Lubin 
AU  - Ji, Guodong 

TI  - An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells
T2  - Fluid Dynamics \& Materials Processing

PY  - 2023
VL  - 19
IS  - 10
SN  - 1555-2578

AB  - 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.
KW  - Shale gas drilling; sticking fault; artificial intelligence; risk early warning technology

DO  - 10.32604/fdmp.2023.025349