Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning
It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison resul...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/3223530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557122861137920 |
|---|---|
| author | Lihua Shao Ru Ji Shuyi Du Hongqing Song |
| author_facet | Lihua Shao Ru Ji Shuyi Du Hongqing Song |
| author_sort | Lihua Shao |
| collection | DOAJ |
| description | It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO). |
| format | Article |
| id | doaj-art-b3d3e3919f2e43bbb5730b41e15e2dfe |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-b3d3e3919f2e43bbb5730b41e15e2dfe2025-02-03T05:43:34ZengWileyGeofluids1468-81232021-01-01202110.1155/2021/3223530Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine LearningLihua Shao0Ru Ji1Shuyi Du2Hongqing Song3School of Mathematics and PhysicsSchool of Civil and Resource EngineeringSchool of Civil and Resource EngineeringSchool of Civil and Resource EngineeringIt is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO).http://dx.doi.org/10.1155/2021/3223530 |
| spellingShingle | Lihua Shao Ru Ji Shuyi Du Hongqing Song Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning Geofluids |
| title | Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning |
| title_full | Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning |
| title_fullStr | Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning |
| title_full_unstemmed | Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning |
| title_short | Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning |
| title_sort | prediction of fluid viscosity in multiphase reservoir oil system by machine learning |
| url | http://dx.doi.org/10.1155/2021/3223530 |
| work_keys_str_mv | AT lihuashao predictionoffluidviscosityinmultiphasereservoiroilsystembymachinelearning AT ruji predictionoffluidviscosityinmultiphasereservoiroilsystembymachinelearning AT shuyidu predictionoffluidviscosityinmultiphasereservoiroilsystembymachinelearning AT hongqingsong predictionoffluidviscosityinmultiphasereservoiroilsystembymachinelearning |