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The complex and variable structure of subsurface oil reservoirs as well as the small pore throat size of reservoirs make it extremely important to investigate the effect of oil–water–rock interactions for enhancing oil recovery. In this paper, the powder wettability of oil sand with different polar solvents was investigated using the improved Washburn capillary rise method, and the surface free energy of oil sand was calculated in combination with the OWRK method. In addition, the wettability of anionic surfactants HABS and PS solutions on the surface of oil sand was determined, and it showed that their wetting rates showed different trends after CMC (critical micelle concentration). The C×cosθ value of HABS decreased significantly with increasing concentration, whereas PS showed little changes. This may be related to the aggregate structure formed by HABS on the oil sand surface. Meanwhile, the interfacial free energy between crude oil and oil sand was obtained by crude oil-to-oil sand wetting experiments, and found that the wetting rate of crude oil to oil sand was much lower than that of solvents and surfactants. In combination with the above results and the oil–water interfacial tension (IFT), the oil–water–rock three-phase contact angle and the work of adhesion between the crude oil and the solid were obtained by Young's equation. From the three-phase contact angle results, it can be found that the contact angle values of both HABS and PS are obviously higher than that of the simulated water, and both HABS and PS have the ability to significantly reduce the work of adhesion, which shows a strong ability to strip the oil film on the surface of the solid. The research results of this paper are helpful to understand the oil displacement mechanism of chemical flooding in reservoir pores, which is of great significance for improving oil recovery. [ABSTRACT FROM AUTHOR]

Predicting multiphase flow in complex fractured reservoirs is essential for developing unconventional resources, such as shale gas and oil. Traditional numerical methods are computationally expensive, and deep learning methods, as an alternative approach, have become an increasingly popular topic. Fourier neural operator (FNO) networks have been shown to be a hundred times faster than convolutional neural networks (CNNs) in predicting multiphase flow in conventional reservoirs. However, there are few relevant studies on applying FNO to predict multiphase flow in reservoirs with complex fractures. In the present study, FNO-net and U-net (CNN-based) were successfully applied to predict pressure and gas saturation fields for the 2D heterogeneous fractured reservoirs. The tested results show that FNO can accurately depict the influence of fine fractures, while the CNN-based method has relatively poor performance in the treatment of fracture systems, both in terms of accuracy and computational speed. In addition, by adding initial conditions and boundary conditions to the loss function of FNO, we prove the necessity of adding physical constraints to the data-driven model. This work contributes to improving the understanding of the applicability of FNO-net, and provides new insights into deep learning methods for predicting multiphase flow in complex fractured reservoirs. [ABSTRACT FROM AUTHOR]