摘要
【研究目的】地层岩性的准确识别对于水合物层的确定至关重要,应用集成学习方法构建测井数据与水合物地层岩性相关关系,探讨对冻土区水合物地层岩性精准预测的方法、技术。传统测井岩性识别方法存在依赖专家经验、主观性强且可重复性差,新区域应用时需重新构建解释模型,自适应能力欠缺、时效性不佳等问题。【研究方法】以青藏高原冻土区水合物地层为研究对象,构建ADASYN-XGBoost集成学习模型,开展地层岩性识别研究,并将识别结果与未优化的极端梯度提升模型、随机森林、K最近邻分类算法、梯度提升决策树算法、支持向量机等典型机器学习算法进行对比。【研究结果】结果表明,ADASYN-XGBoost模型对冻土区水合物地层岩性识别的准确率最高,高达97.8%,较XGBoost、RF、KNN、GBDT、SVM模型的准确率有明显提升。【结论】基于集成学习算法优化构建的冻土区水合物地层岩性识别模型,可为冻土区水合物地层岩性分类问题提供理论依据与技术支持。
[Objective]The accurate identification of stratigraphic lithology plays a critical role in delineating gas hydrate−bearing layers.This study applies ensemble learning methods to establish correlations between logging data and gas hydrate stratigraphic lithology,exploring techniques for precise prediction of stratigraphic lithology in gas hydrate−bearing layers within permafrost regions.Conventional well−logging lithology identification methods suffer from limitations such as heavy reliance on expert knowledge,high subjectivity,and poor reproducibility.Moreover,these traditional approaches lack adaptability,often requiring the reconstruction of interpretation models when applied to new regions,which impedes timely application.[Methods]This study focuses on gas hydrate reservoirs in the permafrost regions of the Qinghai−Xizang Plateau,where an ADASYN−XGBoost ensemble learning model is developed for lithology identification.The performance of this model is compared with that of several non−optimized machine learning algorithms,including Extreme Gradient Boosting(XGBoost),Random Forest(RF),K−Nearest Neighbors(KNN),Gradient Boosting Decision Tree(GBDT),and Support Vector Machine(SVM).[Results]The results indicate that the ADASYN−XGBoost model achieves the highest lithology identification accuracy of 97.8%for gas hydrate formations in permafrost regions,significantly surpassing the accuracy rates obtained by the XGBoost,RF,KNN,GBDT,and SVM models.[Conclusions]The ensemble learning−based lithology identification model proposed in this study offers a theoretical foundation and technical support for addressing the challenges associated with lithological classification of gas hydrate reservoirs in permafrost regions.
作者
肖昆
徐艺宸
邹长春
卢振权
李红星
胡旭东
XIAO Kun;XU Yichen;ZOU Changchun;LU Zhenquan;LI Hongxing;HU Xudong(National Key Laboratory of Uranium Resources Exploratory-Mining and Nuclear Remote Sensing,East China University of Technology,Nanchang 330013,Jiangxi,China;Key Laboratory of Nuclear Resources and Environment,East China University of Technology,Nanchang 330013,Jiangxi,China;School of Geophysics and Information Technology,China University of Geosciences(Beijing),Beijing 100083,China;Oil and Gas Survey,China Geological Survey,Beijing 100083,China)
出处
《地质通报》
北大核心
2025年第12期2249-2265,共17页
Geological Bulletin of China
基金
江西省自然科学基金项目《基于机器学习的江西地区铀矿层测井识别方法研究》(编号:20232BAB203072)
江西省主要学科学术和技术带头人培养计划《江西地区页岩气储层岩石地球物理响应特征数值模拟研究》(编号:20204BCJ23027)
国家自然科学基金项目《祁连山冻土区孔隙型天然气水合物储层电阻率模型及饱和度评价方法研究》(批准号:42404150)。
