Intelligent Identification Method for Drilling Conditions Based on Stacking Model Fusion

Yonghai Gao (), Xin Yu, Yufa Su, Zhiming Yin, Xuerui Wang and Shaoqiang Li
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Yonghai Gao: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Xin Yu: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Yufa Su: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Zhiming Yin: China National Offshore Oil Corporation Research Institute Co., Ltd., Beijing 100028, China
Xuerui Wang: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Shaoqiang Li: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China

Energies, 2023, vol. 16, issue 2, 1-12

Abstract: Due to the complex and changing drilling conditions and the large scale of logging data, it is extremely difficult to process the data in real time and identify dangerous working conditions. Based on the multi-classification intelligent algorithm of Stacking model fusion, the 24 h actual working conditions of an XX well are classified and identified. The drilling conditions are divided into standpipe connection, tripping out, tripping in, Reaming, back Reaming, circulation, drilling, and other conditions. In the Stacking fusion model, the accuracy of the integrated model and the base learner is compared, and the confusion matrix of the drilling multi-condition recognition results is output, which verifies the effectiveness of the Stacking model fusion. Based on the variation in the parameter characteristics of different working conditions, a real-time working condition recognition diagram of the classification results is drawn, and the adaptation rules of the Stacking fusion model under different working conditions are summarized. The stacking model fusion method has a good recognition effect under the standpipe connection condition, tripping in condition, and drilling condition. These three conditions’ accuracy, recall rate, and F1 value are all above 90%. The stacking model fusion method has a relatively poor recognition effect on ‘other conditions‘, and the accuracy rate, recall rate, and F1 value reach less than 80%.

Keywords: drilling; stacking model fusion; machine learning; intelligent identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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