A New Predictive Method for CO2-Oil Minimum Miscibility Pressure
Gas injection processes are among the effective methods for enhanced oil recovery. Miscible and/or near miscible gas injection processes are among the most widely used enhanced oil recovery techniques. The successful design and implementation of a miscible gas injection project are dependent upon th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/8868592 |
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| _version_ | 1832566287343026176 |
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| author | Dangke Ge Haiying Cheng Mingjun Cai Yang Zhang Peng Dong |
| author_facet | Dangke Ge Haiying Cheng Mingjun Cai Yang Zhang Peng Dong |
| author_sort | Dangke Ge |
| collection | DOAJ |
| description | Gas injection processes are among the effective methods for enhanced oil recovery. Miscible and/or near miscible gas injection processes are among the most widely used enhanced oil recovery techniques. The successful design and implementation of a miscible gas injection project are dependent upon the accurate determination of minimum miscibility pressure (MMP), the pressure above which the displacement process becomes multiple-contact miscible. This paper presents a method to get the characteristic curve of multiple-contact. The curve can illustrate the character in the miscible and/or near miscible gas injection processes. Based on the curve, we suggest a new model to make an accurate prediction for CO2-oil MMP. Unlike the method of characteristic (MOC) theory and the mixing-cell method, which have to find the key tie lines, our method removes the need to locate the key tie lines that in many cases is hard to find a unique set. Moreover, unlike the traditional correlation, our method considers the influence of multiple-contact. The new model combines the multiple-contact process with the main factors (reservoir temperature, oil composition) affecting CO2-oil MMP. This makes it is more practical than the MOC and mixing-cell method, and more accurate than traditional correlation. The method proposed in this paper is used to predict CO2-oil MMP of 5 samples of crude oil in China. The samples come from different oil fields, and the injected gas is pure CO2. The prediction results show that, compared with the slim-tube experiment method, the prediction error of this method for CO2-oil MMP is within 2%. |
| format | Article |
| id | doaj-art-8ada6abed11d456e8f60cad29d97867f |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-8ada6abed11d456e8f60cad29d97867f2025-02-03T01:04:36ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/88685928868592A New Predictive Method for CO2-Oil Minimum Miscibility PressureDangke Ge0Haiying Cheng1Mingjun Cai2Yang Zhang3Peng Dong4Petrochina Dagang Oilfield, Tianjin, ChinaPetrochina Dagang Oilfield, Tianjin, ChinaPetrochina Dagang Oilfield, Tianjin, ChinaPetrochina Dagang Oilfield, Tianjin, ChinaChina University of Petroleum, Beijing, ChinaGas injection processes are among the effective methods for enhanced oil recovery. Miscible and/or near miscible gas injection processes are among the most widely used enhanced oil recovery techniques. The successful design and implementation of a miscible gas injection project are dependent upon the accurate determination of minimum miscibility pressure (MMP), the pressure above which the displacement process becomes multiple-contact miscible. This paper presents a method to get the characteristic curve of multiple-contact. The curve can illustrate the character in the miscible and/or near miscible gas injection processes. Based on the curve, we suggest a new model to make an accurate prediction for CO2-oil MMP. Unlike the method of characteristic (MOC) theory and the mixing-cell method, which have to find the key tie lines, our method removes the need to locate the key tie lines that in many cases is hard to find a unique set. Moreover, unlike the traditional correlation, our method considers the influence of multiple-contact. The new model combines the multiple-contact process with the main factors (reservoir temperature, oil composition) affecting CO2-oil MMP. This makes it is more practical than the MOC and mixing-cell method, and more accurate than traditional correlation. The method proposed in this paper is used to predict CO2-oil MMP of 5 samples of crude oil in China. The samples come from different oil fields, and the injected gas is pure CO2. The prediction results show that, compared with the slim-tube experiment method, the prediction error of this method for CO2-oil MMP is within 2%.http://dx.doi.org/10.1155/2021/8868592 |
| spellingShingle | Dangke Ge Haiying Cheng Mingjun Cai Yang Zhang Peng Dong A New Predictive Method for CO2-Oil Minimum Miscibility Pressure Geofluids |
| title | A New Predictive Method for CO2-Oil Minimum Miscibility Pressure |
| title_full | A New Predictive Method for CO2-Oil Minimum Miscibility Pressure |
| title_fullStr | A New Predictive Method for CO2-Oil Minimum Miscibility Pressure |
| title_full_unstemmed | A New Predictive Method for CO2-Oil Minimum Miscibility Pressure |
| title_short | A New Predictive Method for CO2-Oil Minimum Miscibility Pressure |
| title_sort | new predictive method for co2 oil minimum miscibility pressure |
| url | http://dx.doi.org/10.1155/2021/8868592 |
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