A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

• Published in: Petroleum science Vol. 21; no. 2; pp. 1135 - 1148
• Main Authors: Yan, Tie, Xu, Rui, Sun, Shi-Hui, Hou, Zhao-Kai, Feng, Jin-Yu
• Format: Journal Article
• Language: English
• Published: Beijing Elsevier B.V 01.04.2024; KeAi Publishing Communications Ltd
• Subjects: Accuracy; Algorithms; Artificial intelligence; Closed loops; Closed-loop drilling; Datasets; Decision making; Decision trees; Drilling; Feature extraction; Feature selection; Gamma rays; Geology; Identification; Identification methods; Information retrieval; Intelligent drilling; Lithology; Lithology identification; Logging; Methods; Neural networks; Parameter identification; Random forest algorithm; Real time; Support vector machines; Feature extraction; Intelligent drilling; Random forest algorithm; Closed-loop drilling; Lithology identification
• ISSN: 1995-8226; 1672-5107; 1995-8226
• DOI: 10.1016/j.petsci.2023.09.011

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction, low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm (DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling (LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the real-time intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.