Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in...

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Autores principales:	Gulbahar Yazmyradova, Nik Nur Anis Amalina Nik Mohd Hassan, Nur Farhana Salleh, Maman Hermana, Hassan Soleimani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rock physics inversion artificial neural network Malay Basin Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/256da7d9bd4e4da39bebe6d2c4975497
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id	oai:doaj.org-article:256da7d9bd4e4da39bebe6d2c4975497
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spelling	oai:doaj.org-article:256da7d9bd4e4da39bebe6d2c49754972021-11-11T15:17:19ZReservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach10.3390/app1121102482076-3417https://doaj.org/article/256da7d9bd4e4da39bebe6d2c49754972021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10248https://doaj.org/toc/2076-3417The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.Gulbahar YazmyradovaNik Nur Anis Amalina Nik Mohd HassanNur Farhana SallehMaman HermanaHassan SoleimaniMDPI AGarticlerock physicsinversionartificial neural networkMalay BasinTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10248, p 10248 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rock physics inversion artificial neural network Malay Basin Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	rock physics inversion artificial neural network Malay Basin Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Gulbahar Yazmyradova Nik Nur Anis Amalina Nik Mohd Hassan Nur Farhana Salleh Maman Hermana Hassan Soleimani Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
description	The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.
format	article
author	Gulbahar Yazmyradova Nik Nur Anis Amalina Nik Mohd Hassan Nur Farhana Salleh Maman Hermana Hassan Soleimani
author_facet	Gulbahar Yazmyradova Nik Nur Anis Amalina Nik Mohd Hassan Nur Farhana Salleh Maman Hermana Hassan Soleimani
author_sort	Gulbahar Yazmyradova
title	Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
title_short	Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
title_full	Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
title_fullStr	Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
title_full_unstemmed	Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach
title_sort	reservoir characterisation of high-pressure, high-temperature zone of malay basin using seismic inversion and artificial neural network approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/256da7d9bd4e4da39bebe6d2c4975497
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Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

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