Reservoir evaluation using petrophysics informed machine learning:A case study  

作　　者：Rongbo Shao Hua Wang Lizhi Xiao 

机构地区：[1]College of Artificial Intelligence,China University of Petroleum,Beijing,102249,PR China [2]School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu,611731,PR China [3]National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum,Beijing,102249,PR China

出　　处：《Artificial Intelligence in Geosciences》2024年第1期46-63,共18页地学人工智能（英文）

基　　金：supported by the Strategic Cooperation Technology Projects of CNPC and CUPB (ZLZX2020-03);National Key Research and Development Program (2019YFA0708301);National Key Research and Development Program (2023YFF0714102);Science and Technology Innovation Fund of CNPC (2021DQ02-0403).

摘　　要：relationships between logging data and reservoir parameters.We compare our method’s performances using two datasets and evaluate the influences of multi-task learning,model structure,transfer learning,and petrophysics informed machine learning(PIML).Our experiments demonstrate that PIML significantly enhances the performance of formation evaluation,and the structure of residual neural network is optimal for incorporating petrophysical constraints.Moreover,PIML is less sensitive to noise.These findings indicate that it is crucial to integrate data-driven machine learning with petrophysical mechanism for the application of artificial intelligence in oil and gas exploration.

关 键 词：Machine learning Reservoir parameters evaluation Data-mechanism-driven Well logs 

分 类 号：H31[语言文字—英语]