石油物探2025，Vol.64Issue(2)：388-396,9.DOI:10.12431/issn.1000-1441.2023.0445
基于流动单元智能划分的湖泊-三角洲致密砂岩储层渗透率测井评价
A permeability prediction method from well logs for tight sandstone reservoirs based on intelligent division of flow units
摘要
Abstract
In the lake-delta depositional system,tight sandstone reservoirs are characterized by complex pore structures,diverse pore types,and low permeability,and log interpretation and formation evaluation is facing the challenges.Permeability is a key parameter for reservoir evaluation and productivity prediction,and traditional calculation methods from log interpretation are not accurate and cannot meet production requirements.Aiming at this problem,two closely related controls on reservoir permeability are analyzed:microscopic pore structure and macroscopic flow unit,and a new permeability prediction method based on rock type and flow zone indicator(FZI)are proposed.First,core experimental results are analyzed,rock types are determined,core FZIs are calculated,rock types are classified using the cumulative frequency method,and the permeability model for each rock type is constructed.Then,sensitive well logs are selected to form labels,and a deep neural network(DNN)is used to predict reservoir flow unit index(FZI).Finally,log porosities and FZIs are input into the model for each type to calculate permeability.The application in low-porosity low-permeability reservoirs in the HG Formation,the XH sag,China,shows good results with logarithmic error of 0.18,which is smaller than of other DNN methods.The new method includes both data-driven machine learning methods and mechanism-based or knowledge-driven physical model construction,which embodies the idea of data and model jointly driven intelligence,and significantly improved the accuracy of permeability evaluation of tight sandstone reservoirs.Furthermore,it is also referential to permeability prediction for tight sandstone reservoirs in other lake-delta sedimentary systems.关键词
湖泊-三角洲沉积/致密砂岩储层/流动单元指数/深度神经网络/数模双驱智能/渗透率评价Key words
lake-delta deposition/tight sandstone reservoir/flow unit index/deep neural network/data and model jointly driven intelligence/permeability calculation分类
地质学引用本文复制引用
赵天沛,赵勇,谭茂金,李久娣,李博,王安龙,叶俊琦..基于流动单元智能划分的湖泊-三角洲致密砂岩储层渗透率测井评价[J].石油物探,2025,64(2):388-396,9.基金项目
国家自然科学基金重点项目(42430810)、面上项目(42174149)、联合基金项目(U24B6001)、国家科技重大专项(2024ZD1000403,2024ZD1002703)和海洋油气勘探国家工程研究中心主任基金(2024)共同资助. This research is financially supported by the National Natural Science Foundation of China(Grant Nos.42430810,42174149,U24B6001),the National Science and Technology Major Project(Grant Nos.2024ZD1000403,2024ZD1002703),and the Director's Fund of the National Engineering Research Center of Offshore Oil and Gas Exploration(2024). （42430810）
