Modeling and Optimization of Gas Metering Using Genetic Algorithm

Abstract

In this study, the performance of the continuous gas-assisted continuous process in a sample well in southern Iran is simulated. For this purpose, the upstream, vertical, and two-phase gas-liquid movement along the well is modeled. Presenting a detailed model requires considering all the parameters influencing the fluid behavior. For this purpose, a mathematical model including a mechanical model to calculate the pressure gradient is combined with the energy balance of the equilibrium calculations. The mathematical model is solved in such a way that the values ​​of pressure, temperature, and fluid percentage composition at each point of the problem solving space are determined in series, which eliminates the need to know the temperature or pressure profiles to determine another profile. Using the mathematical model and the existing solution algorithm, the temperature and pressure profiles in the problem solving space, including the length of the brain tube, were determined and compared with actual values, indicating acceptable accuracy of the proposed mathematical model. In order to determine the optimal operating conditions, a comprehensive objective function is defined that includes operational, design and economic factors. Experimental objective function is solved using genetic algorithm and the effect of process parameters such as well head pressure, diameter of the brain tube and depth of gas injection on the performance of gas processing process have been investigated and the optimal parameters have been determined.