Sharif Digital Repository

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive... 

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide,... 

Compressibility factor (z-factor) values of natural gasses are essential in most petroleum and chemical engineering calculations. The most common sources of z-factor values are laboratory experiments, empirical correlations and equations of state methods. Necessity arises when there is no available experimental data for the required composition, pressure and temperature conditions. Introduced here is a technique to predict z-factor values of natural gasses, sour reservoir gasses and pure substances. In this communication, a novel mathematical-based approach was proposed to develop reliable model for prediction of compressibility factor of sour and natural gas. A robust soft computing... 

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... 

Appropriate execution of drilling operation, in particular for high pressure and high temperature wells, requires accurate knowledge of behavior of the drilling fluid density as a function of pressure and temperature. In this communication, a novel mathematicalbased approach is presented to develop a reliable model for predict the density of four drilling fluid including water-based, oil-based, Colloidal Gas Aphron (CGA) and synthetic. To pursue our objective, a predictive model is proposed using a robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization tool. Moreover, leverage approach, in which...