Implementation of SVM framework to estimate PVT properties of reservoir oil

Through this work, a novel mathematical-based approach was proposed to develop reliable models for calculation of PVT properties of crude oils at various reservoir conditions. For this purpose, a new soft computing approach namely Least Square Support Vector Machine (LSSVM) modeling optimized with Coupled Simulated Annealing (CSA) optimization technique was implemented. The constructed models are evaluated by carrying out extensive experimental data reported in open literature. Results obtained by the proposed models were compared with the corresponding experimental values. Moreover, in-depth comparative studies have been carried out between these models and all other predictive models. The results indicate that the proposed models are more robust, reliable and efficient than existing techniques for prediction of PVT properties. Results from present research show that implementation of CSA-LSSVM in crude oil PVT calculations can lead to more accurate and reliable estimation of reservoir oil PVT properties.

► Accurate models to estimate saturation pressure and OFVF were developed.
► Least Square Support Vector Machine (LSSVM) was employed for modeling.
► Coupled Simulated Annealing (CSA) was adapted for hyper-parameter selection.
► Comparative studies were carried out between proposed models and all other models.