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.
Cite This Article
APA Style
Wang, Q., Wang, H., Huang, H., Zhuo, L., Ji, G. (2023). An artificial intelligence algorithm for the real-time early detection of sticking phenomena in horizontal shale gas wells. Fluid Dynamics & Materials Processing, 19(10), 2569-2578. https://doi.org/10.32604/fdmp.2023.025349
Vancouver Style
Wang Q, Wang H, Huang H, Zhuo L, Ji G. An artificial intelligence algorithm for the real-time early detection of sticking phenomena in horizontal shale gas wells. Fluid Dyn Mater Proc. 2023;19(10):2569-2578 https://doi.org/10.32604/fdmp.2023.025349
IEEE Style
Q. Wang, H. Wang, H. Huang, L. Zhuo, and G. Ji "An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells," Fluid Dyn. Mater. Proc., vol. 19, no. 10, pp. 2569-2578. 2023. https://doi.org/10.32604/fdmp.2023.025349