Modeling and scaling spontaneous imbibition with generalized fractional flow theory and non-Boltzmann transformation

TL;DR

This paper introduces a generalized fractional flow theory combined with non-Boltzmann scaling to improve modeling and prediction of spontaneous imbibition in various porous media, accounting for complex pore structures and fluid properties.

Contribution

The study develops a novel GFFT model using Hausdorff fractal derivatives and non-Boltzmann scaling, enhancing the accuracy of imbibition predictions across diverse porous materials.

Findings

Non-Boltzmann scaling better collapses SI data than traditional models.

Fractal index alpha varies with contact angle, viscosity, and pore structure.

Alpha values range from 0.88 to 1.54, indicating model adaptability.

Abstract

Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, combined with non-Boltzmann scaling. The model links imbibition distance to time through the power law exponent alpha/2, where alpha is the fractal index (0 < alpha < 2 in this study). We applied the GFFT to various experimental and stimulated datasets of both porous and fractured media, finding that alpha varied with factors such as contact angle (of the imbibing fluid), dynamic viscosity, pore structure, and fracture properties. By analyzing SI data from sandstones, diatomite, carbonate, and synthetic porous media, we demonstrated that the non-Boltzmann scaling provided a better collapse of the SI data than the traditional Boltzmann approach alpha = 1), with alpha values ranging from 0.88 to 1.54. These deviations illustrate the model's adaptability to different porous materials. Using the GFFT, we expect to better predict fluid imbibition rates when properties like porosity, permeability, initial and maximum saturations, viscosity, and wettability are known, offering a more accurate alternative to traditional models.