The methodology encompasses a seven-phase analytical framework, including: (1) data collection and preprocessing; (2) technical assessment of treatment and re-injection technologies; (3) economic modeling using NPV and IRR; (4) environmental impact assessment (including emissions and water footprint); (5) decision-making via multi-criteria optimization; (6) Monte Carlo simulation and scenario analysis; and (7) final model validation with real-world Texas field data.
The model forecasts PW generation and regional water demand from 2025 to 2070, identifying optimal scenarios for either re-injection or treatment. Cost analysis includes CAPEX, OPEX, regulatory compliance, carbon taxation, and potential reuse revenue (e.g., for hydraulic fracturing or irrigation). The simulation engine enables dynamic sensitivity testing oil prices, treatment costs, and evolving regulations.
Results indicate that while re-injection remains cost-effective in the short term, PW treatment becomes increasingly favorable under scenarios of rising freshwater demand, tightening regulations, and technological advancements. The model serves as a decision support tool for industry stakeholders, offering both economic transparency and environmental accountability.
This work contributes to AIChE’s mission by demonstrating how chemical engineering principles, economic modeling, and sustainability metrics can converge to inform policy and practice in resource management. It offers a replicable framework for other oil-producing regions facing similar water management dilemmas.