Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory

Abstract Rock typing is an extremely critical step in the estimation of carbonate reservoir quality and reserves in the Middle East. In order to recognize the rock types of carbonate reservoirs in the Mishrif Formation better, classify the reservoirs accurately, and establish the permeability model...

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Autores principales:	Xinxin Fang, Hong Feng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fa390ad8f7ad4167b4366028ddd2688c
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spelling	oai:doaj.org-article:fa390ad8f7ad4167b4366028ddd2688c2021-12-02T18:14:16ZStudy on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory10.1038/s41598-021-98154-x2045-2322https://doaj.org/article/fa390ad8f7ad4167b4366028ddd2688c2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98154-xhttps://doaj.org/toc/2045-2322Abstract Rock typing is an extremely critical step in the estimation of carbonate reservoir quality and reserves in the Middle East. In order to recognize the rock types of carbonate reservoirs in the Mishrif Formation better, classify the reservoirs accurately, and establish the permeability model in line with the study area precisely, it is necessary to study the recognition method conforming to the actual situation of the study area. The practice shows that the current recognition methods based on capillary pressure curve, flow unit and NMR logging data can effectively distinguish rock types, but a large number of accurate experimental data are required, which can only be applied in a few cored well, however, cannot be applied in the whole oil field. In this study, based on core, thin section, logging data, the sedimentary characteristics of carbonate reservoir, logging response of four rock types as well as porosity and permeability characteristics of Mishrif Formation in W are comprehensively studied. Based on Bayesian stepwise discriminant theory in multivariate statistics, the Bayesian discrimination model based on conventional logging data is established. The examining results showed that, compared with the description of logging and coring, the accuracy of Bayesian discriminant model and cross confirmation rate have achieved more than 80% for the original sample. Reliability verification showed that the matching degree of the rock type recognized in the non-cored well with the core and mud logging was as high as 90%, which matched the depositional environment of the entire region. The study results confirm the validity and generalizability of the Bayesian method to identify and predict rock types, which can be applied to the entire Middle East region to solve the problem of the lack of core data to accurately evaluate the quality of non-cored wells and accurately predict production, meeting the needs of actual reservoir evaluation and production development in the Middle East.Xinxin FangHong FengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xinxin Fang Hong Feng Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
description	Abstract Rock typing is an extremely critical step in the estimation of carbonate reservoir quality and reserves in the Middle East. In order to recognize the rock types of carbonate reservoirs in the Mishrif Formation better, classify the reservoirs accurately, and establish the permeability model in line with the study area precisely, it is necessary to study the recognition method conforming to the actual situation of the study area. The practice shows that the current recognition methods based on capillary pressure curve, flow unit and NMR logging data can effectively distinguish rock types, but a large number of accurate experimental data are required, which can only be applied in a few cored well, however, cannot be applied in the whole oil field. In this study, based on core, thin section, logging data, the sedimentary characteristics of carbonate reservoir, logging response of four rock types as well as porosity and permeability characteristics of Mishrif Formation in W are comprehensively studied. Based on Bayesian stepwise discriminant theory in multivariate statistics, the Bayesian discrimination model based on conventional logging data is established. The examining results showed that, compared with the description of logging and coring, the accuracy of Bayesian discriminant model and cross confirmation rate have achieved more than 80% for the original sample. Reliability verification showed that the matching degree of the rock type recognized in the non-cored well with the core and mud logging was as high as 90%, which matched the depositional environment of the entire region. The study results confirm the validity and generalizability of the Bayesian method to identify and predict rock types, which can be applied to the entire Middle East region to solve the problem of the lack of core data to accurately evaluate the quality of non-cored wells and accurately predict production, meeting the needs of actual reservoir evaluation and production development in the Middle East.
format	article
author	Xinxin Fang Hong Feng
author_facet	Xinxin Fang Hong Feng
author_sort	Xinxin Fang
title	Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title_short	Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title_full	Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title_fullStr	Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title_full_unstemmed	Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title_sort	study on discriminant method of rock type for porous carbonate reservoirs based on bayesian theory
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fa390ad8f7ad4167b4366028ddd2688c
work_keys_str_mv	AT xinxinfang studyondiscriminantmethodofrocktypeforporouscarbonatereservoirsbasedonbayesiantheory AT hongfeng studyondiscriminantmethodofrocktypeforporouscarbonatereservoirsbasedonbayesiantheory
_version_	1718378430978850816

Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory

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