Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore

Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best appro...

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Autores principales:	Ilius Mondal, Kumar Hemant Singh
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2022
Materias:	Permeability Microfacies MICP NMR Hydraulic flow unit Artificial neural network Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/30596ac81ecd4231afd49dc00c6714be
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spelling	oai:doaj.org-article:30596ac81ecd4231afd49dc00c6714be2021-11-04T04:42:48ZCore-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore2666-759210.1016/j.engeos.2021.10.006https://doaj.org/article/30596ac81ecd4231afd49dc00c6714be2022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666759221000573https://doaj.org/toc/2666-7592Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best approach for estimating permeability, assigning rock types and characterising pore networks. Wireline 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 wells. Artificial intelligence, on the other hand, is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data. This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins. Artificial 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 prediction. Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures. This 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 techniques. The pore structure has been characterised by the combination of conventional core, capillary pressure and nuclear magnetic resonance data. Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.Ilius MondalKumar Hemant SinghKeAi Communications Co., Ltd.articlePermeabilityMicrofaciesMICPNMRHydraulic flow unitArtificial neural networkProduction of electric energy or power. Powerplants. Central stationsTK1001-1841ENEnergy Geoscience, Vol 3, Iss 1, Pp 49-62 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Permeability Microfacies MICP NMR Hydraulic flow unit Artificial neural network Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Permeability Microfacies MICP NMR Hydraulic flow unit Artificial neural network Production of electric energy or power. Powerplants. Central stations TK1001-1841 Ilius Mondal Kumar Hemant Singh Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
description	Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best approach for estimating permeability, assigning rock types and characterising pore networks. Wireline 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 wells. Artificial intelligence, on the other hand, is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data. This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins. Artificial 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 prediction. Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures. This 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 techniques. The pore structure has been characterised by the combination of conventional core, capillary pressure and nuclear magnetic resonance data. Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.
format	article
author	Ilius Mondal Kumar Hemant Singh
author_facet	Ilius Mondal Kumar Hemant Singh
author_sort	Ilius Mondal
title	Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
title_short	Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
title_full	Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
title_fullStr	Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
title_full_unstemmed	Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore
title_sort	core-log integration and application of machine learning technique for better reservoir characterisation of eocene carbonates, indian offshore
publisher	KeAi Communications Co., Ltd.
publishDate	2022
url	https://doaj.org/article/30596ac81ecd4231afd49dc00c6714be
work_keys_str_mv	AT iliusmondal corelogintegrationandapplicationofmachinelearningtechniqueforbetterreservoircharacterisationofeocenecarbonatesindianoffshore AT kumarhemantsingh corelogintegrationandapplicationofmachinelearningtechniqueforbetterreservoircharacterisationofeocenecarbonatesindianoffshore
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Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore

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