Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates,Indian offshore
作者机构：Indian Institute of Technology BombayIndia
出 版 物：《Energy Geoscience》 (能源地球科学（英文）)
年 卷 期：2022年第3卷第1期
页 面：49-62页
核心收录：
学科分类：081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程]
基 金：The authors gratefully appreciate the support of Oil and Natural Gas Corporation for providing data and permission to carry out the work under the COE-OGE project: RD/0120-PSUCE19-001. We also acknowledge the editors and reviewers for their constructive suggestions and comments
主 题：Permeability Microfacies MICP NMR Hydraulic flow unit Artificial neural network Reservoir characterisation
摘 要：Rock types,pore structures and permeability are essential petrophysical outputs,and they contribute considerably to the highest degree of uncertainty in reservoir *** factors have a strong influence on exploration and field development *** analysis is the best approach for estimating permeability,assigning rock types and characterising pore *** logs are the most often employed method for estimating the parameters at each data point of reservoirs since there are more un-cored wells than cored *** intelligence,on the other hand,is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface *** is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous *** Neural Networks and Self-Organizing Maps are examples of machine learning approaches that can be used in both supervised and unsupervised modes for modelling and *** carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore *** paper outlines a novel method of rock fabric classification,pore structure characterization,flow unit classification and robust reservoir permeability modelling based on an integrated approach that incorporates core measurements,log data and machine learning *** pore structure has been characterised by the combination of conventional core,capillary pressure and nuclear magnetic resonance *** neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.
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