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

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Petroleum Science 2024年第2期21卷 1135-1148页

作者： Tie Yan Rui Xu Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng School of Petroleum Engineering Northeast Petroleum UniversityDaqing163318HeilongjiangChina Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya572025HainanChina

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

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