Continuous Relative Permeability Model for Compositional Simulation

Khebzegga, Ouassim; Iranshahr, Alireza

doi:10.1007/s11242-020-01440-x

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…Permeability and electrical properties are crucial petrophysical characteristics. The fluid effective permeability of sandstone is affected by many factors, such as fluid saturation for each fluid, rock porosity, pore structure, and wettability. − ,− The effects of these factors cannot be described accurately. In hydrophilic sandstone with little irreducible water (only particle surface water is present), the flow path of water is similar to the conductive path.…”

Section: Conduction Model and Fluid Flow Model Of Complex Pore Struct...mentioning

confidence: 99%

“…The values of fluid relative permeability are mainly measured by water–oil relative permeability steady-state experiments and unsteady-state experiments. , Methods to improve the accuracy of fluid relative permeability-measured experiments have been studied in depth by Saraf, Schneider, Brace, and Oak . The effect of capillary force, gravity, starting pressure gradient, temperature, and viscosity on the relative permeability calculation has been studied by Civan, Zheng, Esmaeili, and Khebzegga …”

Section: Introductionmentioning

confidence: 99%

“…6 The effect of capillary force, gravity, starting pressure gradient, temperature, and viscosity on the relative permeability calculation has been studied by Civan, 7 Zheng, 8 Esmaeili, 9 and Khebzegga. 10 As rock relative permeability experiments are time-consuming and rock samples cannot present the reservoir properties completely, a method of building a relative permeability curve by a capillary pressure curve was proposed by Burdine, 11 and a fractal model for relative permeability curve was put forward by Li. 12 Models building on the relationship between the experimental relative permeability and experimental irreducible water saturation are applied for reservoir relative permeability calculation with actual well logging data; 13,14 in this way, reservoir fluid-producing evaluations can be achieved.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Relative Permeability from the Electrical Parameters of Sandstone with a Complex Pore Structure

Xie

Yin²,

Guan³

et al. 2020

Energy Fuels

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Relative permeability is a crucial parameter for reservoir fluid-producing evaluation. In clay-free rock, water flow and electric current are dependent on the same controlling factors. The impact of pore structure on rock electrical properties and fluid flow is significant when calculating relative permeability from electrical properties in complex pore structure sandstone. In this paper, the pore geometry of complex pore structure sandstone is equivalent to tubular-and micromembrane-like shapes. A conduction model and a fluid flow model for two shapes of pores are studied. The study result shows that the flow path of water is the same as the conductive path in tubular-like-shaped pores; however, in micromembrane-like-shaped pores, water does not flow but electric current flows. Based on the above, the relative permeability calculation method from electrical properties in complex pore structure sandstone is proposed. A total of 16 rock samples selected from a complex pore structure reservoir of Dongying Formation in Nanpu Sag are used to verify the validity of the relative permeability calculation method. Rock samples are classified into five pore size distribution types according to NMR experimental data; for each type, relative permeability is calculated using the new method. The result shows that the new method-calculated relative permeability is in accordance with the experimental values and parameter calculation accuracy is improved. The relative permeability calculation method from electrical properties in complex pore structure sandstone has good application prospects and can provide important guidance for high-precision evaluation of complex pore structure reservoirs and research of rock mechanical characteristics.

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Section: Conduction Model and Fluid Flow Model Of Complex Pore Struct...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Relative Permeability from the Electrical Parameters of Sandstone with a Complex Pore Structure

Xie

Yin²,

Guan³

et al. 2020

Energy Fuels

View full text Add to dashboard Cite

show abstract

“…Although some challenging nonlinear features are inherent to the physics -such as inflection points of fractional flow curves -it is desirable to avoid kinks or strong curvature induced by the numerical treatment of governing equations. Moreover, many works have shown that smoother discretizations lead to improvements in efficiency [12][13][14][15]. To this end, a total velocity formulation with hybrid upwinding (HU) has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Smooth Implicit Hybrid Upwinding for Compositional Multiphase Flow in Porous Media

Bosma,

Hamon,

Mallison

et al. 2021

Preprint

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In subsurface multiphase flow simulations, poor nonlinear solver performance is a significant runtime sink. The system of fully implicit mass balance equations is highly nonlinear and often difficult to solve for the nonlinear solver, generally Newton(-Raphson). Although the physical problem is inherently nonlinear, discretization schemes introduce several strong nonlinearities that can cause Newton iterations to diverge or converge very slowly. This frequently results in time step cuts, leading to wasted iterations and computationally expensive simulations. Much literature has looked into how to improve the nonlinear solver through enhancements or safeguarding updates. In this work, we take a different approach; we aim to improve convergence with a smoother finite volume discretization scheme which is more suitable for the Newton solver.Building on recent work, we propose a novel total velocity hybrid upwinding scheme with weighted average flow mobilities (WA-HU TV) that is unconditionally monotone and extends to compositional multiphase simulations. Analyzing the solution space of a one-cell problem, we demonstrate the improved properties of the scheme and explain how it leverages the advantages of both phase potential upwinding and arithmetic averaging. This results in a flow subproblem that is smooth with respect to changes in the sign of phase fluxes, and is well-behaved when phase velocities are large or when co-current viscous forces dominate. Additionally, we propose a WA-HU scheme with a total mass (WA-HU TM) formulation that includes phase densities in the weighted averaging.The proposed WA-HU TV consistently outperforms existing schemes, yielding benefits from 5% to over 50% reduction in nonlinear iterations. The WA-HU TM scheme also shows promising results; in some cases leading to even more efficiency. However, WA-HU TM can occasionally also lead to convergence issues. Overall, based on the current results, we recommend the adoption of the WA-HU TV scheme as it is highly efficient and robust.

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“…Numerical simulation is the primary tool used for predicting field-scale multiphase flow by solving spatially and temporally discretized mass and energy balance equations [3,4,5]. However, numerical simulation for multiphase flow problems is computationally expensive due to the multiphysics problem nature [6], highly nonlinear governing partial differential equations (PDEs) [7], multiscale heterogeneity in the permeability field [8], and need for high spatial resolution of the grids [9,10]. The inherent uncertainty in the subsurface geology necessitates probabilistic assessments and history matching [11], which often require prohibitively large numbers of simulation runs.…”

Section: Introductionmentioning

confidence: 99%

CCSNet: a deep learning modeling suite for CO$_2$ storage

Wen,

Hay,

Benson

2021

Preprint

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Numerical simulation is an essential tool for many applications involving subsurface flow and transport, yet often suffers from computational challenges due to the multi-physics nature, highly non-linear governing equations, inherent parameter uncertainties, and the need for high spatial resolutions to capture multi-scale heterogeneity. We developed CCSNet, a general-purpose deep-learning modeling suite that can act as an alternative to conventional numerical simulators for carbon capture and storage (CCS) problems where CO 2 is injected into saline aquifers in 2d-radial systems. CCSNet consists of a sequence of deep learning models producing all the outputs that a numerical simulator typically provides, including saturation distributions, pressure buildup, dry-out, fluid densities, mass balance, solubility trapping, and sweep efficiency. The results are 10 3 to 10 4 times faster than conventional numerical simulators. As an application of CCSNet illustrating the value of its high computational efficiency, we developed rigorous estimation techniques for the sweep efficiency and solubility trapping.

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Continuous Relative Permeability Model for Compositional Simulation

Cited by 16 publications

References 27 publications

Estimating the Relative Permeability from the Electrical Parameters of Sandstone with a Complex Pore Structure

Estimating the Relative Permeability from the Electrical Parameters of Sandstone with a Complex Pore Structure

Smooth Implicit Hybrid Upwinding for Compositional Multiphase Flow in Porous Media

CCSNet: a deep learning modeling suite for CO$_2$ storage

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