Cargando…

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

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 w...

Descripción completa

Detalles Bibliográficos
Autores principales: Fang, Xinxin, Feng, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452684/
https://www.ncbi.nlm.nih.gov/pubmed/34545164
http://dx.doi.org/10.1038/s41598-021-98154-x
_version_ 1784570122463084544
author Fang, Xinxin
Feng, Hong
author_facet Fang, Xinxin
Feng, Hong
author_sort Fang, Xinxin
collection PubMed
description 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 Online
Article
Text
id pubmed-8452684
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-84526842021-09-21 Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory Fang, Xinxin Feng, Hong Sci Rep Article 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. Nature Publishing Group UK 2021-09-20 /pmc/articles/PMC8452684/ /pubmed/34545164 http://dx.doi.org/10.1038/s41598-021-98154-x Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Fang, Xinxin
Feng, Hong
Study on discriminant method of rock type for porous carbonate reservoirs based on Bayesian theory
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452684/
https://www.ncbi.nlm.nih.gov/pubmed/34545164
http://dx.doi.org/10.1038/s41598-021-98154-x
work_keys_str_mv AT fangxinxin studyondiscriminantmethodofrocktypeforporouscarbonatereservoirsbasedonbayesiantheory
AT fenghong studyondiscriminantmethodofrocktypeforporouscarbonatereservoirsbasedonbayesiantheory