Pressure-Separated Water Injection Scheduling to Cut Throttling Losses

The water injection network is segmented into multiple pressure zones based on well pressure requirements. Each zone is equipped with dedicated injection pumps and control systems, allowing independent optimization of each segment. This segmentation eliminates the need for uniform high-pressure injection across all wells, thereby reducing throttling losses while maintaining manageable system complexity through modular zone management.

The water injection system transitions from static average pressure injection to dynamic multi-cycle pressure-separated injection. The system dynamically adjusts injection pressure and timing for different well groups across multiple cycles, optimizing pressure delivery to match actual well requirements. This dynamic approach reduces energy waste while the cyclic operation pattern keeps the control system manageable.

The network is divided into pressure zones with dedicated injection systems. Each zone operates at its required pressure level rather than the maximum pressure needed for the highest-demand wells. This segmentation allows distant low-pressure wells to receive adequate injection at lower pressures, significantly reducing the energy consumption associated with pumping water to high pressures for all wells while maintaining comprehensive injection coverage.

Each pressure zone is equipped with injection parameters optimized for its specific requirements. Low-pressure zones receive water at lower pressures appropriate for their wells, while high-pressure zones receive water at higher pressures. This local optimization of injection quality for each zone reduces overall energy consumption while ensuring each area receives the pressure needed for effective water flooding.

The optimization model incorporates multiple variables including pressure levels, injection timing, well grouping configurations, and cycle durations. By systematically varying these parameters and using the improved butterfly algorithm to evaluate different combinations, the model identifies configurations that minimize throttling losses. The algorithm handles the complexity by efficiently searching the parameter space to find optimal settings for pressure-separated multi-cycle injection.

The optimization model uses feedback from pressure measurements and energy consumption data to adjust injection parameters. The improved butterfly algorithm processes this feedback information to refine well groupings, pressure zone definitions, and injection timing. This feedback mechanism allows the system to navigate the complex optimization landscape by learning from operational data and continuously improving the water injection scheme to reduce throttling losses.