@Article{fdmp.2023.025349,
AUTHOR = {Qing Wang, Haige Wang, Hongchun Huang, Lubin Zhuo, Guodong Ji},
TITLE = {An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {19},
YEAR = {2023},
NUMBER = {10},
PAGES = {2569--2578},
URL = {http://www.techscience.com/fdmp/v19n10/53296},
ISSN = {1555-2578},
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.},
DOI = {10.32604/fdmp.2023.025349}
}