Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process
| Autor: | Noureddine Zeraibi, Menad Nait Amar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Coefficient of determination
Mean squared error 020209 energy Support vector regression (SVR) Energy Engineering and Power Technology 02 engineering and technology 020401 chemical engineering lcsh:Engineering geology. Rock mechanics. Soil mechanics. Underground construction Geochemistry and Petrology Statistics 0202 electrical engineering electronic engineering information engineering Range (statistics) Leverage (statistics) 0204 chemical engineering lcsh:Petroleum refining. Petroleum products Mathematics Artificial bee colony (ABC) Geology Geotechnical Engineering and Engineering Geology Support vector machine Fuel Technology Mean absolute percentage error Data point CO2- EOR process CO2-Crude oil minimum miscibility pressure lcsh:TP690-692.5 lcsh:TA703-712 Enhanced oil recovery |
| Zdroj: | Petroleum, Vol 6, Iss 4, Pp 415-422 (2020) |
| ISSN: | 2405-6561 |
| DOI: | 10.1016/j.petlm.2018.08.001 |
| Popis: | Minimum miscibility pressure (MMP) is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process (EOR). MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus (RBA). As these tests are time-consuming and their cost is very expensive, several correlations have been developed. However, and although the simplicity of these correlations, they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application. This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression (SVR) with artificial bee colony (ABC). ABC is used to find best SVR hyper-parameters. 201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs: reservoir temperature (TR), critical temperature of the injection gas (Tc), molecular weight of pentane plus fraction of crude oil (MWC5+) and the ratio of volatile components to intermediate components in crude oil ( x v o l / x i n t ). Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error (3.24%) and root mean square error (0.79) and a high coefficient of determination (0.9868). Furthermore, the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP. Finally, the Leverage approach (Williams plot) is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points. |
| Databáze: | OpenAIRE |
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