Modeling of Immiscible Fluid Flow in Mixed-Wetted Porous Media Using the Lattice Boltzmann Method
Bibliographic record
Abstract
ABSTRACT: The phenomenon of immiscible fluid flow in mixed-wetted porous media has long fascinated researchers due to its prevalence in unconventional oil reservoirs and its profound implications for the spreading behavior of fracturing fluids during invasion and flowback processes. In this study, we employed the multi-component Shan-Chen lattice Boltzmann method (LBM) to investigate the complex dynamics of immiscible fluid flow within these media. To validate our numerical approach, we simulated the forced imbibition dynamics in parallel double wettability pores and compared the results with theoretical models. Our findings reveal that wetting fluids tend to preferentially invade regions of stronger wettability in porous media, especially at low capillary numbers. In contrast, subsequent fluid injection fails to invade weak-wettable areas once a breakthrough occurs in strongly wettable zones. Additionally, we identified a critical capillary number that determines the ability of injected fluids to overcome capillary resistance and flow into weakly wetted regions, thereby enhancing fluid displacement. These insights offer valuable understanding for optimizing fluid flow in unconventional reservoirs and designing more efficient porous media systems, ultimately contributing to more sustainable and effective oil recovery techniques. 1. INTRODUCTION Understanding the immiscible fluid flow processes (including imbibition and drainage) of multi-component systems, such as oil-water, in porous media is of paramount importance across diverse industrial and technological domains. These processes are involved in enhanced oil recovery (EOR) (Singh et al., 2019), geologic storage of CO2 (Bachu, 2008), and proton exchange membrane fuel cells (PEMFC) (Anderson et al., 2010), among other applications. The key influencing factors in these processes encompass wettability, capillary number, fluid viscosity, and pore-throat structure (Holtzman & Segre, 2015). In particular, wettability is critical in governing slow imbibition/drainage dynamics within porous media (Bakhshian et al., 2020). During imbibition processes in predominantly hydrophilic rocks, the corner flow and wetting film phenomena are commonly observed (Zhao et al., 2016). Conversely, in the case of hydrophobic rocks, typical occurrences during drainage include the Haines jump and snap-off sensations (Alpak et al., 2019). However, it is imperative to acknowledge that reservoir rocks rarely exhibit uniform wettability; rather, they often manifest a mixed-wetting state, characterized by the coexistence of both hydrophilic and hydrophobic rock surfaces (AlRatrout et al., 2018). Therefore, to comprehend microscopic mechanisms like EOR through water flooding and the spreading behavior of fracturing fluids throughout the invasion and flowback processes requires studying the impact of mixed-wettability on two-phase flow patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".