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Open Access Invited Review Issue
A comprehensive review of shale wettability characterization: Mechanisms, measurements and influencing factors
Capillarity 2025, 17(2): 38-53
Published: 19 September 2025
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Shale reservoir wettability is a critical parameter governing the occurrence, migration and recovery efficiency of oil and gas. This paper systematically reviews the recent advances in shale wettability assessment methods and their influencing factors. These factors are evaluated by various methods, each offering distinct advantages and limitations. The contact angle method provides rapid macroscopic wettability assessment but is constrained by the properties of mineral and fluid. Spontaneous imbibition effectively characterizes macroscopic wettability but is time-consuming owing to the inherently low porosity and permeability of shale formations. Nuclear magnetic resonance enables the dynamic monitoring of fluid distribution across multi-scale pores, whereas it is constrained by high technical complexity and cost. Numerical simulations investigate wettability from the perspectives of interfacial mechanics and at the molecular scale, while their parameterization and accuracy still depend on experimental validation. The spatial distribution of hydrophilic minerals alongside oleophilic organic matter leads to mixed-wettability states. Increased total organic carbon content enhances the oil-wetting propensity, while higher maturity further promotes the development of hydrophobic organic pores. Elevated temperature generally strengthens the water-wet characteristics, whereas increased pressure induces a preference for oil-wetting. High salinity fluids, particularly those containing divalent cations, asphaltenes and aromatic compounds, enhance oil affinity. Macroscopic wetting behavior is ultimately determined by the connectivity and relative abundance of organic versus inorganic pores. Future studies should integrate multidisciplinary approaches combining advanced experimental characterization with computational modeling to enhance dynamic wettability prediction under real reservoir conditions.

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