Intelligent Approach for GOSP Oil Recovery Enhancement

Abstract

This study aims to propose an intelligent operational advisory solution that guides the plant operation team to optimal HPPT/LPPT pressure settings that compensate for the variation in ambient temperature effect to maximize plant revenue. Traditional industry practice is to operate a gas-oil-separation-plant (GOSP) at fixed operating conditions ignoring the variation in the ambient temperature (Ta) leading to a loss in oil recovery and associated revenue. The variation of ambient temperature (Ta) highly affects the separation process, where ambient temperature varies greatly from summer to winter. To develop a correlation, a GOSP model was constructed by OmegaLand dynamic simulator using a typical Saudi Aramco GOSP design. Oil recovery values were determined by running the process simulation for a typical range of high-pressure production trap (HPPT), low-pressure production trap (LPPT), and ambient temperature (Ta). Then, an intelligent approach was built to determine the optimum pressure of LPPT and HPPT units for each ambient temperature condition using an artificial intelligence technique. Results show that liquid recovery decreases with an increase in ambient temperature at constant HPPT and LPPT pressures, indicating adjustment in HPPT or LPPT pressure responding to the temperature variations can improve the oil recovery. At constant LPPT pressure and ambient temperature, the oil recovery increases with an increase in HPPT pressure until it reaches the optimum value and then decreases with further increase in the HPPTpressure suggesting that there is an optimum HPPT pressure at which oil recovery is maximum. At fixed ambient temperature and fixed HPPT pressure, liquid recovery increases with increasing LPPT pressure until it reaches the optimum value, and then it decreases with further increase in the LPPT pressure suggesting that there is an optimum LPPT pressure at which oil recovery is maximum.