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Prediction of PVT properties in crude oil systems using support vector machines

Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (Pb) and the oil formation volume factor...

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書誌詳細
主要な著者:	Nagi J., Kiong T.S., Ahmed S.K., Nagi F.
その他の著者:	25825455100
フォーマット:	Conference paper
出版事項:	2023
主題:	Bubble point pressue Oil formation volume factor PVT properties Support vector machine Support vector regression Crude oil Forecasting Gears Learning algorithms Mathematical models Multilayer neural networks Petroleum analysis Petroleum reservoir engineering Petroleum reservoirs Regression analysis Support vector machines Sustainable development Vectors Artificial Neural Network Bubble point pressure Bubble points Comparative studies Crude oil system Data sets Empirical correlations Gas oil ratios Input features Machine learning techniques Non-linear regression Oil formation Oil gravity Oil reservoirs Prediction model Pressure-volume-temperature properties Relative density Reservoir temperatures Support vector regressions Training and testing Oil field development
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