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 pr...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.展开更多
基金The project is supported by CNPC Key Core Technology Research Projects(2022ZG06)received by Qing Wangproject 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.
文摘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.