Intelligent Approach for Gas-Oil Separation Plant Oil Recovery Enhancement

Abstract

The present practice is to operate the gas-oil separation plant (GOSP) at the predetermined set of conditions obtained during the design stage. These predetermined sets of conditions are fixed and do not account for the effects due to changes in the ambient temperature (Ta), resulting in low recovery and profitability. The variation of Ta highly affects the separation process, where Ta varies greatly from summer to winter. Thus, this study proposes an intelligent approach to maximize profitability by improving the oil recovery through optimization of low-pressure production trap (LPPT) and high-pressure production trap (HPPT) accounting for the changes in the Ta. This work also proposes an advisory system for guiding the operation team to set the HPPT/LPPT pressure at an optimal value that accounts for the changes in Ta for maximizing the oil recovery. To generate the data accounting for the variation in Ta, a GOSP model was developed using the OmegaLand dynamic simulator. A typical Saudi Aramco GOSP parameter was used for the design. The oil recovery was obtained for the various runs of simulation for the representative range of HPPT/LPPT pressure over a wide range of Ta. Then, artificial intelligence (AI) techniques were applied to determine the optimal pressure of LPPT and HPPT units, and an intelligent advisory system is developed based on the correlation obtained for the optimal set of pressure according to the variation in Ta. Results show that at constant HPPT and LPPT pressure, liquid recovery decreases with an increase in Ta, suggesting that readjustment in HPPT or LPPT operating pressure can counter the temperature changes to improve the oil recovery. The analysis of the results reveals that at a fixed value of Ta and LPPT pressure, the oil recovery increases with an increase in HPPT pressure up to the optimal value of HPPT pressure and then decreases above the value of optimal HPPT pressure. Similarly, when the HPPT pressure and Ta are fixed, the oil recovery increases with an increase in LPPT pressure until it reaches the optimal value and then decreases above the value of optimal LPPT pressure. The improvement in the oil recovery signifies the existence of optimal pressure conditions for HPPT/LPPT separators at which maximum oil recovery can be obtained. This study shows the novel way to incorporate the changes in the ambient condition by optimizing LPPT/HPPT operating pressure for enhancing the liquid recovery of the GOSP plant. The advisory system developed from this study maximizes the oil recovery by determining the optimal set of operating conditions for the HPPT/LPPT separators.