基于机器学习的火山岩识别方法及应用  

Volcanic Rock Identification Method Based on Machine Learning and Its Application

作　　者：朱博含 单玄龙[1] 衣健[1] 石云倩 郭剑南 刘鹏程[1] 王舒扬 李昂[1] Zhu Bohan;Shan Xuanlong;Yi Jian;Shi Yunqian;Guo Jiannan;Liu Pengcheng;Wang Shuyang;Li Ang(Jilin University,Changchun,Jilin 130012,China)

机构地区：[1]吉林大学,吉林长春130012

出　　处：《特种油气藏》2024年第5期41-49,共9页Special Oil & Gas Reservoirs

基　　金：国家自然科学基金面上项目“长白山更新世—全新世碱性熔岩到浮岩阶段喷发方式转换的深部岩浆过程”(41972313);国家自然科学基金“松辽及辽西地区早白垩世高分辨率陆相地质记录及其分布规律”(41790453)。

摘　　要：针对松辽盆地南部查干花地区火石岭组火山岩岩性复杂多变,基于常规测井的二维交会、逐级分类等传统方法难以准确地识别火山岩岩性的问题,提出了利用机器学习算法对火山岩岩性进行智能识别的思路。通过岩心观察、薄片鉴定等手段,明确取心段火山岩岩性。将取心段测井数据集分为训练集和测试集,利用训练集拟合目标函数,将测试集代入模型计算得到预测结果,并利用集成学习融合模型进行盲井预测。该融合模型通过各测井曲线特征建立定量的数学关系,融合了多种机器学习的特点,基于精确的岩性数据集标签使模型学习效率更强。研究表明:该融合模型对盲井的预测准确率达到95.10%,模型泛化能力强,能够对研究区火山岩岩性进行准确地识别与预测。该研究可为火山岩油气勘探提供智能化支持。In the southern part of Songliao Basin,Chaganhua Area,the lithology of the Huoshiling Formation volcanic rocks is complex and variable.Traditional methods such as two-dimensional intersection and step-by-step classification based on conventional well logging data are difficult to accurately identify the lithology of volcanic rocks.To address the issues,a proposal is developed to use machine learning algorithms for intelligent identification of volcanic rock lithology.By observing sample cores and thin section analysis,the lithology of volcanic rocks in the sampled section is determined.The logging data set of the coring section is divided into training set and test set.The training set is used to match the object function,and the test set is brought into the model to predict results,and use integrate models with ensemble learning to conduct blind well prediction.The fusion model establishes a quantitative mathematical relationship between the characteristics of each well log curve,integrates the characteristics of multiple machine learning,and improves the learning efficiency of the model based on accurate lithology data set labels.The results show that the prediction accuracy of the integrate model for blind wells achieves 95.10%.The model has wide applicability,which can accurately identify and predict the lithology of volcanic rocks.This study can provide support for the intelligent exploration of volcanic rock oil and gas.

关 键 词：火山岩 岩性 机器学习 集成学习 GBDT梯度增益树 松辽盆地 

分 类 号：TE122.2[石油与天然气工程—油气勘探]