DEVELOPMENT OF COMPOSITIONAL MODEL FOR PREDICTING VISCOSITY OF CRUDE OILS USING POLYNOMIAL NEURAL NETWORKS (PNN) INDUCED BY GROUP METHOD OF DATA HANDLING (GMDH)

• Main Author: Wen Pin, Yong
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi PETRONAS 2011
• Subjects: T Technology (General)
• Online Access: http://utpedia.utp.edu.my/10407/1/2011%20-%20Devolopment%20of%20compositional%20model%20for%20predicting%20viscosity%20of%20crude%20oils%20using%20polynomial.pdf; http://utpedia.utp.edu.my/10407/

Viscosity or the intemal resistance of the fluids to flow is the most important transport property that controls and influences the flow of oil through porous media and pipes. Accurate predictions of reservoir fluids are required in equation of state (EOS) based reservoir simulators. Due to time and money spent of experimental viscosity measurements, reliable viscosity models are developed for predicting crude oils viscosity. Throughout the years, although many of the common correlations were developed, laboratory measurements still cannot be replaced due to the complexities, varied composition and reservoir characteristics difference from different reservoirs. This study estimates crude oil viscosity by using a group method of data handling (GMDH) based on polynomial neural network (PNN). GMDH is an inductive algorithm for computer-based mathematical modeling using neural network with active neurons that optimizes model coefficients for predetermine mathematical equation and selects the optimal model complexity. The new model was built and tested using experimental measurements collected through literature search. The database consists of crude oils composition, viscosity, temperature and pressure from Middle East, North Sea and the others. Overall, the proposed model improved the prediction as compared to other viscosity model. 111