Fracture identification of carbonate reservoirs by deep forest model:An example from the D oilfield in Zagros Basin  

作　　者：Chunqiu Ji Shaoqun Dong Lianbo Zeng Yuanyuan Liu Jingru Hao Ziyi Yang 

机构地区：[1]National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China [2]College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,China [3]College of Science,China University of Petroleum(Beijing),Beijing,102249,China

出　　处：《Energy Geoscience》2024年第3期339-350,共12页能源地球科学（英文）

基　　金：funded by the National Natural Science Foundation of China(Grant No.42002134);China Postdoctoral Science Foundation(Grant No.2021T140735).

摘　　要：Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.

关 键 词：Fracture identification Conventional log Deep forest Deep learning Tight carbonate reservoir 

分 类 号：P618.13[天文地球—矿床学] P631.81[天文地球—地质学]