基于常规测井数据的火山岩岩性神经网络识别:以松辽盆地南部长岭断陷为例  

Neural network recognition of volcanic rock lithology based on conventional logging data:a case study of Changling fault depression,southern Songliao Basin

作　　者：洪一鸣 王璞珺[1] 李瑞磊[2] 边伟华[1] 黄布宙[3] 郑健 HONG Yi-ming;WANG Pu-jun;LI Rui-lei;BIAN Wei-hua;HUANG Bu-zhou;ZHENG Jian(College of Earth Sciences,Jilin University,Changchun 130061,China;Norheast Oil and Gas Branch of SINOPEC,Changchun 130062,China;College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China)

机构地区：[1]吉林大学地球科学学院,长春130061 [2]中国石油化工股份有限公司东北油气分公司,长春130062 [3]吉林大学地球探测科学与技术学院,长春130026

出　　处：《世界地质》2021年第2期408-418,共11页World Geology

基　　金：国家自然科学基金重大项目课题(41790453)。

摘　　要：松辽盆地南部长岭断陷火石岭组火山岩岩性复杂多变,成岩改造强烈,常规测井交会图法无法予以有效识别,严重阻碍了研究区火山岩油气藏的勘探开发进程。笔者以长岭断陷17口钻遇火石岭组火山岩钻井为基础,建立取芯段火山岩岩性序列,提取了8种火山岩常规测井(GR、LLS、LLD、CNL、DEN、AC)数据258组,总结出不同火山岩的测井响应基本特征。提取的数据随机分为训练数据(70%)和预测数据(30%)。训练数据用于建立BP神经网络岩性预测模型,同时引入Dropout机制减少过拟合现象。预测数据用于验证该模型岩性预测符合率。研究结果表明,该模型岩性预测符合率最高达89.03%,可有效区分研究区主要火山岩岩石类型。Due to the complex lithology and severe diagenetic alteration,the volcanic rocks of the Huoshiling Formation from the Changling fault depression in the southern Songliao Basin can't be effectively identified by conventional logging cross-plot methods,which seriously hinders the exploration and development process of the volcanic oil and gas reservoirs in the study area.Based on volcanic rocks collected by 17 wells of the Huoshiling Formation in the Changling fault depression,the volcanic rock sequences of the core section are established,and 8 kinds of volcanic rocks are selected to extract 258 sets of conventional logging data(GR,LLS,LLD,CNL,DEN,AC).The logging response characteristics of different volcanic rocks are summarized.The extracted data are randomly divided into training data(70%)and prediction data(30%).The training data is used to build up a BP neural network lithological prediction model,and the Dropout mechanism is introduced to reduce overfitting.The prediction data is used to verify the lithological prediction coincidence rate of the model.The research results show that the highest coincidence rate of lithological prediction by this model can reach 89.03%,which can effectively distinguish the main volcanic rock types in the study area.

关 键 词：长岭断陷 火山岩 测井 岩性预测 BP神经网络 DROPOUT 火石岭组 

分 类 号：P631[天文地球—地质矿产勘探] P588[天文地球—地质学]