Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters

Most carbonate reservoirs have poor physical properties, low porosity and permeability, strong heterogeneity and significant anisotropy. It is difficult to accurately identify the fluid properties of complex oil and gas reservoirs by single logging method. Therefore, to address the challenge of flui...

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Main Authors: CAI Ming, GAO Ziran, ZHANG Yuanjun, YE Chang, WU Dong, CHEN Xu, MIAO Yuxin, ZHANG Chengguang
Format: Article
Language:zho
Published: Editorial Office of Well Logging Technology 2025-04-01
Series:Cejing jishu
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Online Access:https://www.cnpcwlt.com/en/#/digest?ArticleID=5729
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author CAI Ming
GAO Ziran
ZHANG Yuanjun
YE Chang
WU Dong
CHEN Xu
MIAO Yuxin
ZHANG Chengguang
author_facet CAI Ming
GAO Ziran
ZHANG Yuanjun
YE Chang
WU Dong
CHEN Xu
MIAO Yuxin
ZHANG Chengguang
author_sort CAI Ming
collection DOAJ
description Most carbonate reservoirs have poor physical properties, low porosity and permeability, strong heterogeneity and significant anisotropy. It is difficult to accurately identify the fluid properties of complex oil and gas reservoirs by single logging method. Therefore, to address the challenge of fluid identification in carbonate rocks, this study takes the Cambrian dolomite reservoirs in the Tarim basin as an example. Based on the existing data from the Tarim oilfield, a new characteristic parameter, resistivity per unit porosity RA, is proposed. Furthermore, this paper explores multiple fluid identification methods based on the new characteristic response parameters. These methods include two cross-plot methods, namely RA—C1 and RA—Δt, and the artificial intelligence-based random forest identification method. The results of case studies show that, in terms of the accuracy of the three methods, the random forest intelligent method has an edge over the cross-plot methods. Specifically, the fluid identification accuracy of the RA—C1 cross-plot method reaches 86.79%, that of the RA—Δt cross-plot method is 84.55%, and that of the random forest intelligent method is 88.56%. However, the cross-plot methods exhibit higher accuracy in identifying gas-bearing water layers and water layers. In conclusion, the comprehensive application of these methods can significantly improve the accuracy of fluid identification in carbonate rocks, providing an effective approach to solve the practical problem of accurately discriminating the fluid types in carbonate reservoirs during oil and gas exploration and development.
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institution Kabale University
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language zho
publishDate 2025-04-01
publisher Editorial Office of Well Logging Technology
record_format Article
series Cejing jishu
spelling doaj-art-56a8db7cee9040ffadc5e5d8e9e127a62025-08-20T03:47:33ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382025-04-0149225526510.16489/j.issn.1004-1338.2025.02.0131004-1338(2025)02-0255-11Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response ParametersCAI Ming0GAO Ziran1ZHANG Yuanjun2YE Chang3WU Dong4CHEN Xu5MIAO Yuxin6ZHANG Chengguang7Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaKey Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaKey Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaKey Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaInformation Centre, Engineering Technology R&D Company Limited, CNPC, Beijing 102206, ChinaTarim Oilfield Company, PetroChina, Korla, Xinjiang 841000, ChinaKey Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaKey Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, Hubei 430100, ChinaMost carbonate reservoirs have poor physical properties, low porosity and permeability, strong heterogeneity and significant anisotropy. It is difficult to accurately identify the fluid properties of complex oil and gas reservoirs by single logging method. Therefore, to address the challenge of fluid identification in carbonate rocks, this study takes the Cambrian dolomite reservoirs in the Tarim basin as an example. Based on the existing data from the Tarim oilfield, a new characteristic parameter, resistivity per unit porosity RA, is proposed. Furthermore, this paper explores multiple fluid identification methods based on the new characteristic response parameters. These methods include two cross-plot methods, namely RA—C1 and RA—Δt, and the artificial intelligence-based random forest identification method. The results of case studies show that, in terms of the accuracy of the three methods, the random forest intelligent method has an edge over the cross-plot methods. Specifically, the fluid identification accuracy of the RA—C1 cross-plot method reaches 86.79%, that of the RA—Δt cross-plot method is 84.55%, and that of the random forest intelligent method is 88.56%. However, the cross-plot methods exhibit higher accuracy in identifying gas-bearing water layers and water layers. In conclusion, the comprehensive application of these methods can significantly improve the accuracy of fluid identification in carbonate rocks, providing an effective approach to solve the practical problem of accurately discriminating the fluid types in carbonate reservoirs during oil and gas exploration and development.https://www.cnpcwlt.com/en/#/digest?ArticleID=5729carbonate rockfluid identificationcrossplot methodrandom forestresistivity per unit porositydolomite reservoir
spellingShingle CAI Ming
GAO Ziran
ZHANG Yuanjun
YE Chang
WU Dong
CHEN Xu
MIAO Yuxin
ZHANG Chengguang
Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
Cejing jishu
carbonate rock
fluid identification
crossplot method
random forest
resistivity per unit porosity
dolomite reservoir
title Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
title_full Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
title_fullStr Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
title_full_unstemmed Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
title_short Research on Fluid Identification Methods for Cambrian Dolomite Reservoirs Based on New Characteristic Response Parameters
title_sort research on fluid identification methods for cambrian dolomite reservoirs based on new characteristic response parameters
topic carbonate rock
fluid identification
crossplot method
random forest
resistivity per unit porosity
dolomite reservoir
url https://www.cnpcwlt.com/en/#/digest?ArticleID=5729
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