Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan

The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) in...

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Autores principales:	Mughal Muhammad Rizwan, Akhter Gulraiz
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	3d seismic attributes sparse-spike inversion geo-statistical technique probabilistic neural network gas saturated zone. Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/dc1392b72b014a0f976f54c6f6cfc289
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spelling	oai:doaj.org-article:dc1392b72b014a0f976f54c6f6cfc2892021-12-05T14:10:48ZPredicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan2391-544710.1515/geo-2020-0170https://doaj.org/article/dc1392b72b014a0f976f54c6f6cfc2892021-01-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0170https://doaj.org/toc/2391-5447The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) interval of Sawan gas field, Middle Indus Basin, Pakistan. The hydrocarbon bearing zone is well identified through the seismic attribute analysis along a sand channel. The sparse-spike inversion analysis has efficiently captured the variations in reservoir parameter (P-impedance) for gas prospect. Inversion results indicated that the relatively lower P-impedance values are encountered along the predicted sand channel. To further characterize the reservoir, geostatistical techniques comprising multiattribute regression and probabilistic neural network (PNN) analysis are applied to predict the effective porosity of reservoir. Comparatively, the PNN analysis predicted the targeted property more efficiently and applied its estimations on entire seismic volume. Furthermore, the geostatistical estimations of PNN analysis significantly predicted the gas-bearing zones and confirmed the sand channel as a major contributor of gas accumulation in the area. These estimates are in appropriate agreement with each other, and the workflow adopted here can be applied to various South Asian regions and in other parts of the world for improved characterization of gas reservoirs.Mughal Muhammad RizwanAkhter GulraizDe Gruyterarticle3d seismic attributessparse-spike inversiongeo-statistical techniqueprobabilistic neural networkgas saturated zone.GeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 49-71 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	3d seismic attributes sparse-spike inversion geo-statistical technique probabilistic neural network gas saturated zone. Geology QE1-996.5
spellingShingle	3d seismic attributes sparse-spike inversion geo-statistical technique probabilistic neural network gas saturated zone. Geology QE1-996.5 Mughal Muhammad Rizwan Akhter Gulraiz Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
description	The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) interval of Sawan gas field, Middle Indus Basin, Pakistan. The hydrocarbon bearing zone is well identified through the seismic attribute analysis along a sand channel. The sparse-spike inversion analysis has efficiently captured the variations in reservoir parameter (P-impedance) for gas prospect. Inversion results indicated that the relatively lower P-impedance values are encountered along the predicted sand channel. To further characterize the reservoir, geostatistical techniques comprising multiattribute regression and probabilistic neural network (PNN) analysis are applied to predict the effective porosity of reservoir. Comparatively, the PNN analysis predicted the targeted property more efficiently and applied its estimations on entire seismic volume. Furthermore, the geostatistical estimations of PNN analysis significantly predicted the gas-bearing zones and confirmed the sand channel as a major contributor of gas accumulation in the area. These estimates are in appropriate agreement with each other, and the workflow adopted here can be applied to various South Asian regions and in other parts of the world for improved characterization of gas reservoirs.
format	article
author	Mughal Muhammad Rizwan Akhter Gulraiz
author_facet	Mughal Muhammad Rizwan Akhter Gulraiz
author_sort	Mughal Muhammad Rizwan
title	Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
title_short	Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
title_full	Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
title_fullStr	Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
title_full_unstemmed	Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
title_sort	predicting the gas resource potential in reservoir c-sand interval of lower goru formation, middle indus basin, pakistan
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/dc1392b72b014a0f976f54c6f6cfc289
work_keys_str_mv	AT mughalmuhammadrizwan predictingthegasresourcepotentialinreservoircsandintervaloflowergoruformationmiddleindusbasinpakistan AT akhtergulraiz predictingthegasresourcepotentialinreservoircsandintervaloflowergoruformationmiddleindusbasinpakistan
_version_	1718371747789537280

Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan

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