Optimal scheduling of intergroup pumping of PV complementary wells based on improved NSGA-III algorithm.

Area of Science:

• Petroleum Engineering
• Renewable Energy Systems
• Artificial Intelligence in Energy

Background:

• Oilfield production faces increased energy consumption due to frequent well start-stop cycles in later stages.
• Photovoltaic (PV) energy offers potential for enhanced efficiency but presents challenges in balancing its intermittency with oil well stability requirements.
• Optimizing intermittent production with PV-grid complementary systems is crucial for energy efficiency in mature oilfields.

Purpose of the Study:

• To propose a novel two-stage intermittent production optimization method for PV-grid complementary systems in oilfields.
• To enhance PV consumption and improve energy efficiency by addressing the volatility of PV generation.
• To develop a dynamic adjustment strategy that minimizes the impact of PV forecasting errors.

Main Methods:

• A spatiotemporal attention-based prediction module was developed for PV fluctuation monitoring and prescheduling.
• An enhanced NSGA-III algorithm with a reference-point-weight adaptive adjustment mechanism was utilized for optimization.
• A two-stage optimization approach involving prescheduling and dynamic adjustment was implemented for well groups.

Main Results:

• The proposed method achieved a 35.18% reduction in operational costs.
• A PV utilization rate of 78.34% was attained.
• An electricity-saving rate of 33.91% was realized compared to conventional methods.

Conclusions:

• The developed method effectively optimizes intermittent oil well production using PV-grid complementary systems.
• The approach significantly improves energy efficiency, PV utilization, and cost reduction in oilfield operations.
• Dynamic adjustment and advanced prediction modules are key to managing PV intermittency in oil production.