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

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

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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 identifi-cation 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 algo-rithm(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 identi-fication.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 li-thology 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 applica-bility,which is worthy of being widely used in logging interpretation.

Intelligent drillingClosed-loop drillingLithology identificationRandom forest algorithmFeature extraction

Tie Yan、Rui Xu、Shi-Hui Sun、Zhao-Kai Hou、Jin-Yu Feng

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School of Petroleum Engineering,Northeast Petroleum University,Daqing,163318,Heilongjiang,China

Sanya Offshore Oil & Gas Research Institute,Northeast Petroleum University,Sanya,572025,Hainan,China

School of Petroleum Engineering,Northeast Petroleum University,Daqing 163318,Heilongjiang,China

国家自然科学基金国家自然科学基金Hainan Province Science and Technology Special FundHeilongjiang Provincial Government and Daqing Oilfield's first batch of the scientific and technological key project

5217400152004064ZDYF2023GXJS012DQYT-2022-JS-750

2024

10.1016/j.petsci.2023.09.011

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(2)
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