Surfactant Concentrations at Different Distances from the Injection Well

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.

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Publication Details

Journal
ACS Omega
Published
2026-03-13
DOI
https://doi.org/10.1021/acsomega.5c13231
Primary Topic
Surfactants and Colloidal Systems
Type
article
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article

Surfactant Concentrations at Different Distances from the Injection Well

Yang Zhang, Jing Li, Liwei Niu
ACS Omega
Surfactants and Colloidal Systems
article

Surfactant Concentrations at Different Distances from the Injection Well

Yang Zhang, Jing Li, Liwei Niu
article en

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.

ACS OmegaVol. 11(11)
Petroleum University of Technology (IR), Daqing Oilfield of CNPC, Hebei Petroleum University of Technology, Northeast Petroleum University (CN)
Openalex Percentile: Top 22%
Surfactants and Colloidal Systems
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