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

• Main Authors: Nagi J., Kiong T.S., Ahmed S.K., Nagi F.
• Other Authors: 25825455100
• Format: Conference paper
• Published: 2023
• Subjects: 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

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...