成都理工大学学报（自然科学版）2025，Vol.52Issue(5)：914-930,17.DOI:10.12474/cdlgzrkx.2025072901
基于机器学习的川中地区雷口坡组三段二亚段泥质灰岩储层分布预测
Prediction of the distribution of marly limestone reservoirs in the second sub-member of the third member of the Leikoupo Formation,central Sichuan Basin,based on machine learning
摘要
Abstract
Recently,an industrial gas flow has been obtained from the second sub-member of the third member of the Leikoupo Formation(Lei32)at Well CT1 in the central Sichuan Basin,indicating favorable exploration prospects for marly limestone reservoirs.However,research on well-logging response mechanisms and predictive modeling for such reservoirs remains limited.This study integrates core descriptions,thin-section petrography,and a well-log response analysis to characterize the lithological features of the Lei32 sub-member.A long short-term memory(LSTM)machine learning model was established using well-log data for lithofacies identification of marly limestone.By integrating a single-factor mineral content analysis and sedimentary facies characteristics,a multi-scale predictive framework(point-line-surface)was constructed to forecast marly limestone reservoirs.The results show that the Lei32 sub-member is primarily composed of limestone,marly limestone,dolomite,and gypsum salt rocks.The marly limestone reservoir is characterized by nanoscale to micrometer-scale pores and microfractures,indicating a typical low-porosity,low-permeability system,with reservoir thickness ranging from 40 to 130 m.Compared with CIFLog software calculations and petrographic identifications,the LSTM model achieved a prediction accuracy of(87.3±0.5)%.Spatial prediction results indicate that the reservoir's thickness ranges from 80 to 120 m in the Xichong,Nanchong,and Yilong areas,which are favorable for exploration.In contrast,Zhongjiang,Ziyang,Anyue,and Hechuan exhibit reservoir thicknesses of 60 to 80 m,suggesting potential exploration targets.This study provides useful a reference for the hydrocarbon exploration of marly limestone reservoirs in the Lei32 sub-member of the central Sichuan Basin.关键词
川中地区/雷三2 亚段/泥质灰岩储层/机器学习/预测模型Key words
central Sichuan area/Lei32/marly limestone reservoir/machine learning/prediction model分类
天文与地球科学引用本文复制引用
任杉,杨绍海,闫春桥,金鑫,郭嘉欣,刘树根,宋金民,李柯然,杨迪,叶玥豪,李泽奇,王斌,邵兴鹏,周佳庆..基于机器学习的川中地区雷口坡组三段二亚段泥质灰岩储层分布预测[J].成都理工大学学报（自然科学版）,2025,52(5):914-930,17.基金项目
国家科技重大专项(2025ZD1400403) （2025ZD1400403）
国家自然科学基金面上项目(42572132,41872150). （42572132,41872150）
