Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation
With the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil saturation variation utiliz...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/6515846 |
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| _version_ | 1832560769369112576 |
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| author | Ruijie Huang Chenji Wei Jian Yang Xin Xu Baozhu Li Suwei Wu Lihui Xiong |
| author_facet | Ruijie Huang Chenji Wei Jian Yang Xin Xu Baozhu Li Suwei Wu Lihui Xiong |
| author_sort | Ruijie Huang |
| collection | DOAJ |
| description | With the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil saturation variation utilizing machine learning algorithms based on static and dynamic data from actual reservoirs. The dataset in this study generated from 468 wells includes thickness, permeability, porosity, net-to-gross (NTG) ratio, oil production variation (OPV), water production variation (WPV), water cut variation (WCV), neighbouring liquid production variation (NLPV), neighbouring water injection variation (NWIV), and oil saturation variation (OSV). A data processing workflow has been implemented to replace outliers and to increase model accuracy. A total of 10 machine learning algorithms are tested and compared in the dataset. Random forest (RF) and gradient boosting (GBT) are optimal and selected to conduct quantitative analysis of the main controlling factors. Analysis results show that NWIV is the variable with the highest degree of impact on OSV; impact factor is 0.276. Optimization measures are proposed for the development of this kind of sandstone reservoir based on main controlling factor analysis. This study proposes a reference case for oil saturation quantitative analysis based on machine learning methods that will help reservoir engineers make better decision. |
| format | Article |
| id | doaj-art-db899d12e91e485cb812534518037df6 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-db899d12e91e485cb812534518037df62025-02-03T01:26:54ZengWileyGeofluids1468-81232021-01-01202110.1155/2021/6515846Quantitative Analysis of the Main Controlling Factors of Oil Saturation VariationRuijie Huang0Chenji Wei1Jian Yang2Xin Xu3Baozhu Li4Suwei Wu5Lihui Xiong6Research Institute of Petroleum Exploration and DevelopmentResearch Institute of Petroleum Exploration and DevelopmentResearch Institute of Petroleum Exploration and DevelopmentBytedance Inc.Research Institute of Petroleum Exploration and DevelopmentResearch Institute of Petroleum Exploration and DevelopmentResearch Institute of Petroleum Exploration and DevelopmentWith the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil saturation variation utilizing machine learning algorithms based on static and dynamic data from actual reservoirs. The dataset in this study generated from 468 wells includes thickness, permeability, porosity, net-to-gross (NTG) ratio, oil production variation (OPV), water production variation (WPV), water cut variation (WCV), neighbouring liquid production variation (NLPV), neighbouring water injection variation (NWIV), and oil saturation variation (OSV). A data processing workflow has been implemented to replace outliers and to increase model accuracy. A total of 10 machine learning algorithms are tested and compared in the dataset. Random forest (RF) and gradient boosting (GBT) are optimal and selected to conduct quantitative analysis of the main controlling factors. Analysis results show that NWIV is the variable with the highest degree of impact on OSV; impact factor is 0.276. Optimization measures are proposed for the development of this kind of sandstone reservoir based on main controlling factor analysis. This study proposes a reference case for oil saturation quantitative analysis based on machine learning methods that will help reservoir engineers make better decision.http://dx.doi.org/10.1155/2021/6515846 |
| spellingShingle | Ruijie Huang Chenji Wei Jian Yang Xin Xu Baozhu Li Suwei Wu Lihui Xiong Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation Geofluids |
| title | Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation |
| title_full | Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation |
| title_fullStr | Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation |
| title_full_unstemmed | Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation |
| title_short | Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation |
| title_sort | quantitative analysis of the main controlling factors of oil saturation variation |
| url | http://dx.doi.org/10.1155/2021/6515846 |
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