New insights of foam flow dynamics in a high-complexity 2D micromodel

Yeates, Christopher; Youssef, S.; Lorenceau, Élise

doi:10.1016/j.colsurfa.2019.04.092

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…As previously shown (Géraud et al 2016;Yeates et al 2019), the majority of flow in a polydisperse foam injection into a heterogeneous medium is carried by the largest bubbles, in a series of preferential paths. Furthermore these paths show higher relative flow intensity than for Newtonian fluids (Yeates et al 2019). Correct characterization and modelling of the flow within these preferential paths will constitute a significant part of macroscopic foam flow modelling due to the large contribution of flow solely in these paths.…”

Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 55%

“…Our experimental data acquisition setup and procedure is described in detail in Yeates et al (2019). We create velocity heatmaps by averaging local tracked bubble velocities over multiple sets of 200 highfrequency images.…”

Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 99%

“…Pressure gradient is measured, and images are acquired at steady state (constant pressure gradient over the model). The image processing procedure leading to bubble segmentation as well as the tracking procedure is also described in Yeates et al (2019).…”

Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 99%

“…The 2D micromodel used has previously been studied elsewhere (Yeates et al 2019). It is a 69.7% porosity glass micromodel with an wet-etched depth of 40 µm.…”

Section: Micromodel Structure and Decompositionmentioning

confidence: 99%

“…To evaluate a candidate path, we make use of a system of classification of active/inactive pores of the experimental data, as previously described in Yeates et al (2019), Appendix B. Average pore velocity intensity values are compared to values for pores of similar sizes (split between 20 equidistant intervals of area).…”

Section: Experimental Path Matchmentioning

confidence: 99%

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Accessing Preferential Foam Flow Paths in 2D Micromodel Using a Graph-Based 2-Parameter Model

2020

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This study uses experimental data of pore-scale foam flow inside a high-complexity network to fit a graphbased model describing preferential flow paths based on characteristics of the porous medium. Two experiments, with equal gas fractions but varying injection rates, are modelled in parallel. Proposed paths are solution paths to the k-Shortest Paths with Limited Overlap (k-SPwLO) problem, applied to a graph representation of the porous medium with edge weights representing throat properties. A 1-parameter model, based on throat radius only is tested before integrating a second parameter, describing the alignment of the pores surrounding the throat with respect to injection pressure gradient. The preferential paths in both experiments vary in quantity and in the specific zones described. As such, fitted models characterizing preferential paths for either experiment show separate dependencies to structural parameters. Overall, the graph-based framework was able to capture many high-flow zones in various model parameter combinations, perhaps as consequence of the relatively spiked throat size distribution of the model. The optimized model for the high injection rate experiment markedly shows a non-zero dependence to the pore alignment to pressure gradient as well as throat size, whereas the lower injection rate experiment was best fitted to a model that made sole use of the throat radius. Recent works on 2D micromodels (Géraud et al. 2016; Yeates et al. 2019) have shown that a range of foam behaviors exist that are highly likely to contribute to Darcy-scale flow properties. Indeed, trapped foams Note here that this graph simply displays graph topology by positioning nodes on the centers of mass of the pores and edge lengths or widths are not representative of edge weights.

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Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 55%

Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 99%

Section: Foam Data Acquisition Procedures and Injection Conditionsmentioning

confidence: 99%

“…The 2D micromodel used has previously been studied elsewhere (Yeates et al 2019). It is a 69.7% porosity glass micromodel with an wet-etched depth of 40 µm.…”

Section: Micromodel Structure and Decompositionmentioning

confidence: 99%

Section: Experimental Path Matchmentioning

confidence: 99%

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Accessing Preferential Foam Flow Paths in 2D Micromodel Using a Graph-Based 2-Parameter Model

2020

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High‐pressure high‐temperature fluid displacement by foam injection within a multiblock matrix‐fracture system

Fathollahi,

Khosravi,

Rostami

et al. 2023

Asia-Pacific J Chem Eng

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Foam injection has been suggested and demonstrated as a potential gas‐based solution for enhancing oil recovery from underground reservoirs. While the pore‐scale behavior of foam has been investigated for many years, most of those studies were done at room temperature and pressure. A high‐pressure high‐temperature (HPHT) cell was constructed to relax this contributing simplification and evaluate foam behavior under reservoir conditions. The performance of foam injection into a fractured reservoir was investigated experimentally at vertical and horizontal pathways utilizing a 2D microfluidic device. The results showed foam had a higher lamellae density in the fracture network and could direct fluids to the matrix. This was mainly attributed to the created pressure drop in the fractured media dictated by foam self‐tuning ability, which is a higher viscosity in areas with higher conductivity. Generating a viscous crossflow in horizontal tests leads to enhanced recovery relative to gas injection. But the presence of gravity in the vertical tests causes the drainage of the foam film and the segregation of the gas and surfactant solution due to gravity, which ultimately reduces the stability of the foam. Comparing the performance of gas and foam in the vertical injection scenario reveals that foam injection results in a considerably more enhanced oil recovery. The oleic phase had a detrimental impact on foam strength and reduced fluids diversion from fracture to matrix. The sizes of gas bubbles were also larger in the presence of the oleic phase.

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