测井技术2025，Vol.49Issue(1)：1-9,9.DOI:10.16489/j.issn.1004-1338.2025.01.001
基于SMOTE采样和集成学习的低渗透率储层流体性质识别方法
Fluid Property Identification Method for Low Permeability Reservoirs Based on SMOTE Sampling and Integrated Learning
摘要
Abstract
At present,low-permeability reservoirs are the focus of oil and gas development in China.The identification of their fluid properties is of great guiding significance for oilfield exploration and development.The petrophysical characteristics of low-permeability reservoirs are complex,and the logging response characteristics are not obvious,resulting in difficulties in fluid property identification.Integrated learning,with its powerful non-linear ability and high efficiency,has become a powerful tool for intelligent reservoir evaluation.However,its final effect is limited by the quality of samples.Aiming at the problems of uneven distribution and scarcity of labeled data in low-permeability reservoirs,a method for identifying fluid properties in low-permeability reservoirs based on SMOTE sampling and integrated learning technology is proposed.The SMOTE sampling is used to reasonably expand the core labeled data to meet the training requirements of the integrated learning model.Then,the integrated learning model is optimized to achieve the identification of fluid properties in low-permeability reservoirs.The application results of the method for identifying fluid properties in low-permeability reservoirs based on SMOTE sampling and integrated learning in the Y9XX well group of the Dongying depression show that,this method can effectively identify the fluid properties of low-permeability reservoirs,with an accuracy rate of 87.44%.On this basis,a blind well test is carried out on Y94X well in the Dongying depression,and the final classification results meet the accuracy requirements of actual logging interpretation.The fluid identification model combining SMOTE sampling and integrated learning provides a basis for the wide application of subsequent machine learning in reservoir evaluation.关键词
流体性质识别/集成学习/SMOTE采样/样本不均匀/东营凹陷Key words
fluid property identification/integrated learning/SMOTE sampling/sample unevenness/Dongying depression引用本文复制引用
杨文凯,孙建孟,杜钦波,张宇昆,罗歆..基于SMOTE采样和集成学习的低渗透率储层流体性质识别方法[J].测井技术,2025,49(1):1-9,9.基金项目
国家自然科学基金项目"深部低阻砂岩气藏渗流与导电机理模拟分析研究"(42174143) （42174143）
中国石油集团测井有限公司开放基金课题"基于电成像数据的地层产状计算算法研究"(CNLC2022-9C06) （CNLC2022-9C06）
