Connectionist approach estimates gas-oil relative permeability in petroleum reservoirs: Application to reservoir simulation

Relative permeability of the petroleum reservoirs is a key parameter for various aspects of the petroleum engineering area like as reservoir simulation, history matching and etc. Due to this fact, various approaches such as experimental, theoretical and numerical approaches have been studied however; such experimental methods are time consuming, complicated and expensive. Based on the addressed disadvantages, robust, rapid, simple and accurate model is needed to represent gas/oil relative permeability through petroleum reservoirs. In this research communication we utilized the concept of various intelligent approaches such as least square support vector machine (LSSVM) which is high attended branches of artificial intelligent approaches. To develop and test the proposed LSSVM approach massive experimental relative permeability data from literature survey was faced to the addressed model. The suggested LSSVM method has low deviation from relevant measured values and statistical factors of the addressed model solutions were calculated. According to the determined statistical factors, the results of the proposed LSSVM approach prove and certify the high performance and low uncertainty of the addressed model in prediction gas/oil relative permeability in petroleum reservoirs. Finally, the suggested LSSVM model could help us to prepare more precise and accurate relative permeability curves without extensive experiment and furthermore, could lead to provide high performance reservoir simulation with low uncertainty.