Precise estimation of temperature variations throughout gas-production systems can enhance designing the production amenities. Routine method for determining the temperature profiles in gas production system are based on the gas composition and flash calculations. However, if the gas compositions are not available, the gas production system can be modeled by employing a black–oil approach, which is also a method for calculating the oil/gas resources and for modeling the gas reservoir operation. Accordingly, for black oil models and when the natural gas compositions are not accessible, applying robust predictive tools in this research is of high interest in natural production systems. The current study plays emphasis on applying the predictive model based on least square support vector machine (LSSVM) to estimate precisely the proper temperature drop associated a given pressure drop throughout natural gas production systems based on the black-oil approach to acquire an accurate result for the temperature drop of a natural gas streams. Genetic algorithm (GA) was used to optimize hyper parameters (γ and σ2) which are embedded in LSSVM model. Using this method is simple and accurate to determine the temperature drop through the natural gas stream with minimum uncertainty.
کلید واژگان :Pressure Drop; Temperature Drop; Gas Stream; Modeling; Least Squares Support Vector Machine
ارزش ریالی : 1200000 ریال
با پرداخت الکترونیک