Intelligent Identification Method for Drilling Conditions Based on Stacking Model Fusion

• Published in: Energies (Basel) Vol. 16; no. 2; p. 883
• Main Authors: Gao, Yonghai, Yu, Xin, Su, Yufa, Yin, Zhiming, Wang, Xuerui, Li, Shaoqiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Accuracy; Algorithms; Artificial intelligence; Corrosion; Decision trees; Drilling; Efficiency; Identification; intelligent identification; Machine learning; Neural networks; stacking model fusion; Support vector machines; Working conditions

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 con...