Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China
The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/3299768 |
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| author | Peiqiang Zhao Yuting Hou Fengqing Ma Jixin Huang Xiaoyu Wang Jiarui Xie Chengxiang Deng Zhiqiang Mao |
| author_facet | Peiqiang Zhao Yuting Hou Fengqing Ma Jixin Huang Xiaoyu Wang Jiarui Xie Chengxiang Deng Zhiqiang Mao |
| author_sort | Peiqiang Zhao |
| collection | DOAJ |
| description | The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method. |
| format | Article |
| id | doaj-art-c19a6ec0b21a4a59920d7e5fb770f8a4 |
| institution | OA Journals |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-c19a6ec0b21a4a59920d7e5fb770f8a42025-08-20T02:19:15ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/3299768Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, ChinaPeiqiang Zhao0Yuting Hou1Fengqing Ma2Jixin Huang3Xiaoyu Wang4Jiarui Xie5Chengxiang Deng6Zhiqiang Mao7State Key Laboratory of Petroleum Resources and ProspectingPetroChina Changqing Oilfield CompanyState Key Laboratory of Petroleum Resources and ProspectingResearch Institute of Petroleum Exploration and DevelopmentState Key Laboratory of Petroleum Resources and ProspectingState Key Laboratory of Petroleum Resources and ProspectingSchool of Geophysics and Measurement-Control TechnologyBeijing Key Laboratory of Earth Prospecting and Information TechnologyThe Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method.http://dx.doi.org/10.1155/2022/3299768 |
| spellingShingle | Peiqiang Zhao Yuting Hou Fengqing Ma Jixin Huang Xiaoyu Wang Jiarui Xie Chengxiang Deng Zhiqiang Mao Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China Geofluids |
| title | Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China |
| title_full | Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China |
| title_fullStr | Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China |
| title_full_unstemmed | Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China |
| title_short | Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China |
| title_sort | genetic mechanisms and identification of low resistivity pay zones a case study of pengyang area ordos basin china |
| url | http://dx.doi.org/10.1155/2022/3299768 |
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