Insights on Foam Transport from a Texture-Implicit Local-Equilibrium Model with an Improved Parameter Estimation Algorithm

Zeng, Yongchao; Muthuswamy, Aarthi; Ma, Kun; Wang, Le; Farajzadeh, R.; Puerto, Maura; Vincent-Bonnieu, S.; Eftekhari, Ali Akbar; Wang, Ying; Chen, Da; Joyce, Jeffrey C.; Biswal, Sibani Lisa; Hirasaki, George J.

doi:10.1021/acs.iecr.6b01424

Cited by 68 publications

(57 citation statements)

References 65 publications

(151 reference statements)

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“…They concluded that, presence of oil has less destabilizing effect on foams in the low foam-quality regime compared to the foams in the high foam-quality regime. Similar phenomena have been described in (Batôt et al 2016;Zeng et al 2016).…”

Section: Properties Of Eor-foamsupporting

confidence: 86%

An Overview of the Present Stability and Performance of EOR-Foam

Hamza

Sinnathambi

Merican

et al. 2017

JSM

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Section: Properties Of Eor-foamsupporting

confidence: 86%

An Overview of the Present Stability and Performance of EOR-Foam

Hamza

Sinnathambi

Merican

et al. 2017

JSM

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“…The shear-thinning function F 4 is defined as g /(ΦS g ) the interstitial foamedgas velocities, σ the surface tension between gas and water in presence of surfactant and e c the shear-thinning exponent. The F 4 function definition remains controversial since some authors [30,31,32] consider the total velocity instead of the gas velociy to define the capillary number.…”

Section: Implicit-texture Foam Modelmentioning

confidence: 99%

Calibrating and interpreting implicit-texture models of foam flow through porous media of different permeabilities

Gassara

Douarche

Braconnier

et al. 2017

Journal of Petroleum Science and Engineering

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This paper proposes a robust methodology to calibrate steady-state models of foam flow through porous reservoirs from foam displacements on core samples. The underlying approach is an equivalence between foam mobility and foam lamellas density (or texture) at local-equilibrium. This calibration methodology is applied to foam displacements at different qualities and velocities on a series of sandstones. Its advantage lies in a deterministic transcription of flow measurements into texture data, by comparison with commonly-applied least-square-fit methods that may yield non unique solutions.Scaling trends of foam parameters with porous medium permeability are then identified and discussed with the help of theoretical representations of foam flow in a confined medium. Although they remain to be further confirmed from other well-documented experimental data sets, these scaling laws can increase the reliability of reservoir simulators for the assessment of foam-based improved recovery processes in heterogeneous reservoirs.

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“…Later on, Ma et al [20] determined the same three model parameters (M ref , S * -SE w , Θ ) by using a least squares minimization method as other authors, but adding a constraint on S * -SE w to avoid non unique (M ref , S * -SE w ) solutions. Then, Zeng et al [38] converted the three-parameter (M ref , S * -SE w , Θ ) optimization problem (Ma et al [20]) to a single-parameter (M ref ) problem by using steady state apparent viscosity data and searching the M ref value that fits a linear function of the saturation with (S * -SE w , Θ ) as parameters. The same authors also adjusted the shear-thinning function separately from total-flow-rate-scan experiments.…”

Section: Comparison With Other Calibration Methodsmentioning

confidence: 99%

Equivalence Between Semi-empirical and Population-Balance Foam Models

et al. 2017

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Models for simulating foam-based displacements in enhanced oil recovery (EOR) processes fall into two categories: population-balance (PB) models that derive explicitly foam texture, or bubble size, evolution in porous media from pore-level mechanisms related to lamellas generation and coalescence, and semi-empirical (SE) models that account implicitly for foam texture effects through a gas mobility reduction factor that depends on fluid saturation, interstitial velocity, surfactant concentration, and other factors. This mobility reduction factor has to be calibrated from a large number of experiments on a case by case basis in order to match the physical effect of each considered parameter on foam behavior. This paper develops a method for identifying the SE models from the physics of foams as derived from PB models at local equilibrium (LE). The identification of both foam flow models leads to a method for calibrating SE models from the PB model translation of foam flow data. Application to a set of foam quality-scan experiments at fixed total flow rate shows that the SE and PB models at LE match equally well the measurements and generate almost the same results in both the so-called high-and low-quality regimes. We demonstrate that the two approaches are equivalent at local equilibrium and differ only in the way in which the complex dynamic mechanisms of lamellas are handled. This physical approach of foam flow could circumvent some difficulties in the direct calibration of SE models from foam mobility (or apparent viscosity) data.

show abstract

Insights on Foam Transport from a Texture-Implicit Local-Equilibrium Model with an Improved Parameter Estimation Algorithm

Cited by 68 publications

References 65 publications

An Overview of the Present Stability and Performance of EOR-Foam

An Overview of the Present Stability and Performance of EOR-Foam

Calibrating and interpreting implicit-texture models of foam flow through porous media of different permeabilities

Equivalence Between Semi-empirical and Population-Balance Foam Models

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