Reactive vs Proactive Intelligent Well Injection Evaluation for EOR in a Stratified GOM Deepwater Wilcox Reservoir using Integrated Simulation-Surface Network Modeling

Abstract

Improved field management for monitoring, estimating zone productivity/injectivity, and controlling wells with intelligent completions can broaden application of advanced well designs. We have developed a coupled Simulation-Surface Network modeling workflow to evaluate the potential benefit of intelligent injection profile control with a focus on reactive vs proactive control for Gulf of Mexico (GOM) Deepwater Enhanced Oil Recovery (EOR) schemes. The developed injection control solution can be consistently applied to gas, water injection, and gas followed by water injection, to evaluate relative impacts of intelligent injectors on each option. We did this by defining rules for both proactive and reactive injection ICV controls for a GOM Deepwater Wilcox multilayered reservoir. Proactive controls, based on reservoir zone characteristics, pore volume injected, and recoverable pore volume, are dependent on a static reservoir model realization. Proactive control results demonstrate a diminishing return as we begin to observe fluid breakthroughs that results in part from the inevitable uncertainty of the original static assessment so there should be a benefit in reassessing optimal pore volume injection based on reservoir model updates. Reactive control strategies based on measured production response is a challenge in terms of linking injection control events to production responses that are time-lagged and incomplete for understanding gas and water breakthrough. The integrated model captures the effects of topside facilities, risers, flowlines, pressure/temperature at manifolds and topside, seafloor booster pump performance, wellheads, and wellbore to reservoir interactions and ICV controls to provide a realistic evaluation of achievable development alternatives outcomes.