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

作　　者：Tie Yan Rui Xu Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 

机构地区：[1]School of Petroleum Engineering,Northeast Petroleum University,Daqing,163318,Heilongjiang,China [2]Sanya Offshore Oil&Gas Research Institute,Northeast Petroleum University,Sanya,572025,Hainan,China

出　　处：《Petroleum Science》2024年第2期1135-1148,共14页石油科学（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(No.52174001);the National Natural Science Foundation of China(No.52004064);the Hainan Province Science and Technology Special Fund “Research on Real-time Intelligent Sensing Technology for Closed-loop Drilling of Oil and Gas Reservoirs in Deepwater Drilling”(ZDYF2023GXJS012);Heilongjiang Provincial Government and Daqing Oilfield's first batch of the scientific and technological key project “Research on the Construction Technology of Gulong Shale Oil Big Data Analysis System”(DQYT-2022-JS-750)。

摘　　要：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 realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

关 键 词：Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction 

分 类 号：TE24[石油与天然气工程—油气井工程] TP18[自动化与计算机技术—控制理论与控制工程]