Abstract
The accurate prediction of surfactant concentration profiles deep within reservoirs remains a critical challenge for the successful implementation of surfactant-enhanced oil recovery (EOR). Traditional static models often fail to capture the dynamic depletion of surfactants caused by phase partitioning and rock adsorption during long-distance transport. Herein, we integrated experimental results from long-distance physical simulations to monitor the spatiotemporal evolution of surfactant concentrations. By correlating the partition coefficient and adsorption capacity with the migration distance, we derived a dynamic governing equation that relates the surfactant concentration to its transport trajectory. This mechanistic model revealed the nonlinear kinetic characteristics of surfactant depletion. Surfactant loss induced by adsorption exhibited a monotonic decline with distance, whereas loss due to partitioning into the oil phase distinctly increased and then decreased. This study further identified the dominant control zones for surfactant effectiveness: the surfactant concentration in the near-well region (0-100 m) is primarily governed by the initial injection concentration, whereas that in the mid- to far-well regions (100-300 m) is significantly sensitive to the cumulative injected pore volumes. This study shifted the design paradigm from a simple concentration-based approach to a volume-prioritized strategy for sustaining effective surfactant concentrations in zones deep within the reservoir. These results provide a robust theoretical framework and practical guidance for optimizing chemical injection parameters in complex EOR operations.