Scaling and Sustaining a Hybrid Al–Physics Waterflood Optimization Solution from Pilot to Full Field

Abstract

Abstract Waterflood optimization solutions commonly demonstrate value at pilot scale but often fail to transition into sustainable, full-field deployment, particularly in large, mature assets where data quality challenges, fragmented insights, computational limits, and operational constraints dominate. Strategic application of artificial intelligence (AI), combined with physics-based reservoir understanding, provides an opportunity to overcome these barriers and deliver business value. Following a successful proof-of-concept implementation in 2025 (SPE-229785-MS, SPE-229209-MS, SPE-229199-MS), this paper presents a structured and deployment-focused approach for scaling a hybrid AI–physics waterflood optimization solution evolving from pilot sectors to multi-sector and finally to full-field operational implementation in the mature sandstone reservoir of the Greater Burgan Field, Kuwait. The methodology combines reduced-physics reservoir models with AI-driven pattern recognition and injector-producer connectivity analysis to support tactical waterflood decisions, including injection allocation, production balancing, remedial operations, and pattern development. The solution is positioned within a broader operational framework that recognizes the need to align tactical optimization with day-to-day field operations and longer-term development objectives. Following pilot validation, the workflow was progressively expanded through standardized data quality control and reconciliation, the use of automated workflows for surveillance inputs across sectors, and systematic performance benchmarking as the number of active wells increased. A phased scale-up strategy was adopted, prioritizing data stabilization and workflow standardization as prerequisites for automation. Subsequent phases focused on automated surveillance and mapping, constraint-recognized optimization that explicitly incorporates gas saturation behaviour and pressure variability, and the selective introduction of generative AI capabilities to support scenario-based operational decision-making aspects of reservoir production management. This progression ensured repeatability, scalability, and operational confidence while maintaining computational efficiency and practicality. Field-scale deployment demonstrated that pilot-level performance could be preserved and operationalized when data integrity, allocation consistency, and workflow automation were established upfront. The solution delivered significant reductions in surveillance turnaround time, enabled consistent injection-balancing recommendations across multiple field sectors, and supported diagnostics and forecasting for hundreds of producers and injectors within acceptable computational limits. Scale-up also revealed critical failure modes related to data heterogeneity, production injection allocation uncertainty, pressure gaps, and sector boundary effects; these were mitigated through standardized quality control, reconciliation processes, and constraint-recognized workflow design. The results confirm that sustainable, field-scale AI-enabled waterflood optimization depends less on increasing model complexity and more on foundational data readiness, robust workflows, and performance-aware execution. By integrating hybrid AI–physics approaches with design-thinking principles, this work provides a practical framework for bridging the gap between pilot success and full-field adoption, while establishing a foundation for future progression toward increasingly autonomous waterflood operations.