Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm
In order to solve the problems such as unclear formation mechanism and poor fluid identification effect in deep gas reservoirs with low contrast of Bashijiqike formation in Bozi well area, Tarim basin, the mechanism of formation with low contrastis deeply analyzed based on the analysis data of cast...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Well Logging Technology
2023-12-01
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| Series: | Cejing jishu |
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| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5540 |
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| author | CAO Yuan ZHAO Yuanliang YUAN Xuehua YUAN Long RONG Junqing ZHAO Pan BIE Kang |
| author_facet | CAO Yuan ZHAO Yuanliang YUAN Xuehua YUAN Long RONG Junqing ZHAO Pan BIE Kang |
| author_sort | CAO Yuan |
| collection | DOAJ |
| description | In order to solve the problems such as unclear formation mechanism and poor fluid identification effect in deep gas reservoirs with low contrast of Bashijiqike formation in Bozi well area, Tarim basin, the mechanism of formation with low contrastis deeply analyzed based on the analysis data of cast thin section, high pressure mercury injection and nuclear magnetic resonance experiment. Combined with logging and production dynamic data, fluid sensitive factors such as porosity, resistivity, volume modulus, fluid compression coefficient, fluid index, equivalent fluid volume modulus and fluid volume modulus are selected to identify fluids by random forest algorithm. The results show that the low contrast formation mechanism is different in the region. The reservoirs with low contrast in the southern well area is the result of the combination of formation water salinity, reservoir physical property and pore structure. However, the degree of carbonate cement development is the main factor of the reservoirs with low contrast in the northern well area. The accuracy of the fluid identification model of low contrast gas reservoir based on random forest algorithm is 89.25%, which weakens the multiple solutions caused by a single fluid identification factor and provides a reliable basis for the efficient development of gas fields. |
| format | Article |
| id | doaj-art-ebd4b10948d4494cb45ef70e2713fa3e |
| institution | Kabale University |
| issn | 1004-1338 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Well Logging Technology |
| record_format | Article |
| series | Cejing jishu |
| spelling | doaj-art-ebd4b10948d4494cb45ef70e2713fa3e2025-08-20T03:47:40ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382023-12-0147667167810.16489/j.issn.1004-1338.2023.06.0041004-1338(2023)06-0671-08Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest AlgorithmCAO Yuan0ZHAO Yuanliang1YUAN Xuehua2YUAN Long3RONG Junqing4ZHAO Pan5BIE Kang6Geological Research Institute, China National Logging Corporation, Xi’an, Shaanxi 710077, ChinaExploration Department, PetroChina Tarim Oilfield Company, Korla, Xinjiang 841000, ChinaExploration and Development Research Institute, PetroChina Dagang Oilfield Company, Tianjin 300457, ChinaGeological Research Institute, China National Logging Corporation, Xi’an, Shaanxi 710077, ChinaGeological Research Institute, China National Logging Corporation, Xi’an, Shaanxi 710077, ChinaChangqing Branch, China National Logging Corporation, Xi’an, Shaanxi 710200, ChinaExploration and Development Research Institute, PetroChina Tarim Oilfield Company, Korla, Xinjiang 841000, ChinaIn order to solve the problems such as unclear formation mechanism and poor fluid identification effect in deep gas reservoirs with low contrast of Bashijiqike formation in Bozi well area, Tarim basin, the mechanism of formation with low contrastis deeply analyzed based on the analysis data of cast thin section, high pressure mercury injection and nuclear magnetic resonance experiment. Combined with logging and production dynamic data, fluid sensitive factors such as porosity, resistivity, volume modulus, fluid compression coefficient, fluid index, equivalent fluid volume modulus and fluid volume modulus are selected to identify fluids by random forest algorithm. The results show that the low contrast formation mechanism is different in the region. The reservoirs with low contrast in the southern well area is the result of the combination of formation water salinity, reservoir physical property and pore structure. However, the degree of carbonate cement development is the main factor of the reservoirs with low contrast in the northern well area. The accuracy of the fluid identification model of low contrast gas reservoir based on random forest algorithm is 89.25%, which weakens the multiple solutions caused by a single fluid identification factor and provides a reliable basis for the efficient development of gas fields.https://www.cnpcwlt.com/#/digest?ArticleID=5540fluid identificationdeep reservoirrandom forestlow contrastbozi well area |
| spellingShingle | CAO Yuan ZHAO Yuanliang YUAN Xuehua YUAN Long RONG Junqing ZHAO Pan BIE Kang Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm Cejing jishu fluid identification deep reservoir random forest low contrast bozi well area |
| title | Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm |
| title_full | Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm |
| title_fullStr | Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm |
| title_full_unstemmed | Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm |
| title_short | Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm |
| title_sort | fluid identification of deep low contrast gas reservoirs based on random forest algorithm |
| topic | fluid identification deep reservoir random forest low contrast bozi well area |
| url | https://www.cnpcwlt.com/#/digest?ArticleID=5540 |
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