| dc.description.abstract | Water pumping systems are widely used in Oil & Gas operations primarily due to water transferring and re-injection in oil treatment process. The energy consumed by these systems is significant, particularly in fields with a high water-to-oil ratio of water per oil produced. Assessing the performance of a process relies on two Key Performance Indicators (KPIs): the Energy Performance Indicator (EnPI), which assesses the energy performance of the system, and the Profitability Indicator (PI) which evaluates the profits derived from the process. These indicators are influenced by several process factors such as operational performance, restrictions in the system, system control, costs, and capacity of utilization.
Process and facility engineers are responsible for optimally managing resources and assets, focusing on enhancing the energy efficiency of the process. However, determining the optimal conditions for achieving desired KPIs is often challenging. It is difficult to find optimal solutions in a subjective manner, such as intuition and trial-and-error, which often rely on people’s experiences and may cause process disturbances, economic penalties, or higher energy consumption. Therefore, it is necessary to propose a systematic decision-making approach to find optimal solutions considering the interaction of process variables (based on first principles or experimental relations) and economic or performance indicators.
In this study, a methodology was proposed to determine the optimal conditions for a water-pumping system. The first step involves adjusting the pump performance curves based on power consumption. Next, the system was simulated using a modular block-based hierarchy process model developed by IDAES (Lee, 2021). An optimization problem is then formulated to minimize or maximize the KPI. The results were simulated using Aspen HYSYS to provide insight into the performance of each KPI.
This work proposes a framework for process model optimization that can be applied to operational decision-making that is both tractable and flexible. For the water pumping systems, three different scenarios were evaluated: the initial conditions, a discrete function to overhaul one pump, and modifications in the pressure conditions and performance curves. For each scenario, optimal solutions were found to improve the KPIs.
The optimization results showed that adjusting the variables in the system could reduce the EnPI by 2%–4% and increase the PI by 2%– 5%. The findings of this optimization study demonstrate the inverse relationship between PI and EnPI; therefore, improving the energy efficiency of pumps not only enhances production, but also reduces energy intensity. Consequently, applying this analysis to other high-energy-intensive systems such as steam processes, heat exchangers, power systems, and similar contexts is recommended. By extending this approach to different domains, valuable insights can be gained, leading to enhanced energy efficiency and improved system performance. | |
