Hubbry Logo
Relative permeabilityRelative permeabilityMain
Open search
Relative permeability
Community hub
Relative permeability
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Relative permeability
Relative permeability
from Wikipedia

In multiphase flow in porous media, the relative permeability of a phase is a dimensionless measure of the effective permeability of that phase. It is the ratio of the effective permeability of that phase to the absolute permeability. It can be viewed as an adaptation of Darcy's law to multiphase flow.

Formulation

[edit]

For two-phase flow in porous media given steady-state conditions, we can write

where is the flux, is the pressure drop, is the viscosity. The subscript indicates that the parameters are for phase .

is here the phase permeability (i.e., the effective permeability of phase ), as observed through the equation above.

Relative permeability, , for phase is then defined from , as

where is the permeability of the porous medium in single-phase flow, i.e., the absolute permeability. Relative permeability must be between zero and one.

In applications, relative permeability is often represented as a function of water saturation; however, owing to capillary hysteresis one often resorts to a function or curve measured under drainage and another measured under imbibition.

Under this approach, the flow of each phase is inhibited by the presence of the other phases. Thus the sum of relative permeabilities over all phases is less than 1. However, apparent relative permeabilities larger than 1 have been obtained since the Darcean approach disregards the viscous coupling effects derived from momentum transfer between the phases (see assumptions below). This coupling could enhance the flow instead of inhibit it. This has been observed in heavy oil petroleum reservoirs when the gas phase flows as bubbles or patches (disconnected). [1]

Modelling assumptions

[edit]

The above form for Darcy's law is sometimes also called Darcy's extended law, formulated for horizontal, one-dimensional, immiscible multiphase flow in homogeneous and isotropic porous media. The interactions between the fluids are neglected, so this model assumes that the solid porous media and the other fluids form a new porous matrix through which a phase can flow, implying that the fluid-fluid interfaces remain static in steady-state flow, which is not true, but this approximation has proven useful anyway.

Each of the phase saturations must be larger than the irreducible saturation, and each phase is assumed continuous within the porous medium.

Based on data from special core analysis laboratory (SCAL) experiments,[2] simplified models of relative permeability as a function of saturation (e.g. water saturation) can be constructed. This article will focus on an oil-water system.

Saturation scaling

[edit]

The water saturation is the fraction of the pore volume that is filled with water, and similarly for the oil saturation . Thus, saturations are themselves scaled properties or variables. This gives the constraint

The model functions or correlations for relative permeabilities in an oil-water system are therefore usually written as functions of only water saturation, and this makes it natural to select water saturation as the horizontal axis in graphical presentations. Let (also denoted and sometimes ) be the irreducible (or minimal or connate) water saturation, and let be the residual (minimal) oil saturation after water flooding (imbibition). The flowing water saturation window in a water invasion / injection / imbibition process is bounded by a minimum value and a maximum value . In mathematical terms the flowing saturation window is written as

Normalization of water saturation values

By scaling the water saturation to the flowing saturation window, we get a (new or another) normalized water saturation value

and a normalized oil saturation value

Endpoints

[edit]

Let be oil relative permeability, and let be water relative permeability. There are two ways of scaling phase permeability (i.e. effective permeability of the phase). If we scale phase permeability w.r.t. absolute water permeability (i.e. ), we get an endpoint parameter for both oil and water relative permeability. If we scale phase permeability w.r.t. oil permeability with irreducible water saturation present, endpoint is one, and we are left with only the endpoint parameter. In order to satisfy both options in the mathematical model, it is common to use two endpoint symbols in the model for two-phase relative permeability. The endpoints / endpoint parameters of oil and water relative permeabilities are

These symbols have their merits and limits. The symbol emphasize that it represents the top point of . It occurs at irreducible water saturation, and it is the largest value of that can occur for initial water saturation. The competing endpoint symbol occurs in imbibition flow in oil-gas systems. If the permeability basis is oil with irreducible water present, then . The symbol emphasizes that it is occurring at the residual oil saturation. An alternative symbol to is which emphasizes that the reference permeability is oil permeability with irreducible water present.

The oil and water relative permeability models are then written as

The functions and are called normalised relative permeabilities or shape functions for oil and water, respectively. The endpoint parameters and (which is a simplification of ) are physical properties that are obtained either before or together with the optimization of shape parameters present in the shape functions.

There are often many symbols in articles that discuss relative permeability models and modelling. A number of busy core analysts, reservoir engineers and scientists often skip using tedious and time-consuming subscripts, and write e.g. Krow instead of or or krow or oil relative permeability. A variety of symbols are therefore to be expected, and accepted as long as they are explained or defined.

The effects that slip or no-slip boundary conditions in pore flow have on endpoint parameters, are discussed by Berg et alios.[3][4]

Corey-model

[edit]

An often used approximation of relative permeability is the Corey correlation [5] [6] [7] which is a power law in saturation. The Corey correlations of the relative permeability for oil and water are then

Example of Corey-correlation for imbibition flow with = and .

If the permeability basis is normal oil with irreducible water present, then .

The empirical parameters and are called curve shape parameters or simply shape parameters, and they can be obtained from measured data either by analytical interpretation of measured data, or by optimization using a core flow numerical simulator to match the experiment (often called history matching). is sometimes appropriate. The physical properties and are obtained either before or together with the optimizing of and .

In case of gas-water system or gas-oil system there are Corey correlations similar to the oil-water relative permeabilities correlations shown above.

LET-model

[edit]

The Corey-correlation or Corey model has only one degree of freedom for the shape of each relative permeability curve, the shape parameter N. The LET-correlation[8] [9] adds more degrees of freedom in order to accommodate the shape of relative permeability curves in SCAL experiments[2] and in 3D reservoir models that are adjusted to match historic production. These adjustments frequently includes relative permeability curves and endpoints.

Example of LET-correlation for imbibition flow with L,E,T all equal to 2 and .

The LET-type approximation is described by 3 parameters L, E, T. The correlation for water and oil relative permeability with water injection is thus

and

written using the same normalization as for Corey.

Only , , , and have direct physical meaning, while the parameters L, E and T are empirical. The parameter L describes the lower part of the curve, and by similarity and experience the L-values are comparable to the appropriate Corey parameter. The parameter T describes the upper part (or the top part) of the curve in a similar way that the L-parameter describes the lower part of the curve. The parameter E describes the position of the slope (or the elevation) of the curve. A value of one is a neutral value, and the position of the slope is governed by the L- and T-parameters. Increasing the value of the E-parameter pushes the slope towards the high end of the curve. Decreasing the value of the E-parameter pushes the slope towards the lower end of the curve. Experience using the LET correlation indicates the following reasonable ranges for the parameters L, E, and T: L ≥ 0.1, E > 0 and T ≥ 0.1.

In case of gas-water system or gas-oil system there are LET correlations similar to the oil-water relative permeabilities correlations shown above.

Evaluations

[edit]

After Morris Muskat et alios established the concept of relative permeability in late 1930'ies, the number of correlations, i.e. models, for relative permeability has steadily increased. This creates a need for evaluation of the most common correlations at the current time. Two of the latest (per 2019) and most thorough evaluations are done by Moghadasi et alios[10] and by Sakhaei et alios.[11] Moghadasi et alios[10] evaluated Corey, Chierici and LET correlations for oil/water relative permeability using a sophisticated method that takes into account the number of uncertain model parameters. They found that LET, with the largest number (three) of uncertain parameters, was clearly the best one for both oil and water relative permeability. Sakhaei et alios[11] evaluated 10 common and widely used relative permeability correlations for gas/oil and gas/condensate systems, and found that LET showed best agreement with experimental values for both gas and oil/condensate relative permeability.

See also

[edit]

References

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Relative permeability is a dimensionless in and that quantifies the conductance of a specific phase through a when multiple immiscible phases are present, defined as the ratio of the effective permeability of that phase to the absolute permeability of the medium. Absolute permeability measures the medium's capacity to transmit a single saturating , typically under single-phase conditions, while effective permeability accounts for the reduced flow due to interactions with other phases, such as , , and gas in hydrocarbon reservoirs. Values of relative permeability range from 0 (no flow) to 1 (single-phase flow), and their sum for all phases is typically less than or equal to 1 in multiphase systems. This concept extends to multiphase flow scenarios, where the flow rate of each phase is proportional to its , , and relative permeability, allowing for the modeling of simultaneous movement in porous rocks. In , relative permeability is crucial for simulating displacement during production processes like waterflooding or gas injection, as it directly influences recovery factors and production rates by reflecting how saturation levels affect phase mobilities. It varies with saturation, wettability of the rock surface, pore geometry, and saturation history (e.g., drainage vs. ), often visualized through relative permeability curves that plot these functions against saturation. Relative permeability data are obtained experimentally through core flooding tests on rock samples under controlled conditions, providing empirical curves essential for simulation models. These measurements reveal heterogeneities across reservoirs, influenced by factors like chemistry and rock properties, which must be integrated with data for accurate predictions of volumes and flow behavior over the reservoir's lifecycle. In practice, relative permeability endpoints—such as the residual saturation where a phase's relative permeability reaches zero—define critical thresholds for trapping and mobility, impacting strategies.

Fundamentals

Definition

Relative permeability, denoted as krik_{ri}, is defined as the ratio of the effective permeability of a fluid phase kik_i to the absolute permeability kk of the , mathematically expressed as kri=kik,k_{ri} = \frac{k_i}{k}, where 0kri10 \leq k_{ri} \leq 1. This quantifies the reduction in a phase's to flow through the medium due to the presence of other immiscible phases, serving as a key parameter in extending from single-phase to . The concept of relative permeability was introduced by Morris Muskat in the late 1930s, building on earlier work in porous media flow to address multiphase systems. Muskat's seminal book, The Flow of Homogeneous Fluids Through Porous Media (1937), formalized this extension, enabling the modeling of simultaneous flow of multiple fluids. Relative permeability is primarily applied in porous media such as rocks, soils, and filters, where multiple immiscible fluids—like and or gas and —coexist and interact during flow processes.

Physical Interpretation

Relative permeability emerges at the pore scale from the between immiscible fluid phases for limited pore space within a , where the presence of one phase obstructs the flow paths of the other, thereby reducing the effective conductivity for each phase relative to single-phase flow. This is governed by interfacial tension, which creates menisci that trap portions of the non-preferred phase in smaller pores or dead-end spaces, limiting connectivity and overall flow . Phase further influences this , as the distribution of fluids across pore throats and bodies determines the available pathways; for instance, the non-wetting phase tends to occupy larger pores during drainage, fragmenting the phase's network and hindering its mobility. Wettability, the preferential affinity of the solid surface for one fluid phase over another, profoundly affects phase distribution and the resulting flow hindrance in multiphase systems. In water-wet rocks, where the solid prefers water, the wetting phase spreads in thin films along pore walls, maintaining connectivity even at low saturations and allowing it to access a larger fraction of the pore space, which enhances its relative permeability compared to the non-wetting oil phase. Conversely, in oil-wet rocks, the solid's preference for oil leads to the wetting oil occupying central pore bodies and larger throats, displacing water to corners and films, which restricts water's flow paths and reduces its relative permeability while potentially improving oil mobility. This wettability-driven redistribution alters the effective pore network available to each phase, directly impacting the hindrance imposed on flow. Hysteresis in relative permeability arises from the path-dependent nature of multiphase displacement processes, where the saturation history—specifically drainage (non-wetting phase invasion) versus (wetting phase invasion)—leads to different phase configurations and at the same saturation. During drainage, the non-wetting phase advances through larger pores, leaving behind connected wetting-phase films, which results in higher relative permeability for the non-wetting phase compared to , where forces trap disconnected non-wetting phase ganglia in smaller pores, reducing its mobility and altering the available flow paths for the wetting phase. This mechanism causes the relative permeability curves to diverge between drainage and paths, reflecting irreversible changes in phase occupancy and connectivity due to the sequence of saturation alterations.

Mathematical Framework

Formulation

The formulation of relative permeability arises from extending Darcy's law, originally developed for single-phase flow, to describe multiphase fluid transport in porous media. In multiphase flow, the volumetric flux qi\mathbf{q}_i of phase ii is given by the extended Darcy's law: qi=krikμiPi,\mathbf{q}_i = -\frac{k_{ri} k}{\mu_i} \nabla P_i, where kk is the absolute permeability of the porous medium, krik_{ri} is the relative permeability of phase ii (a dimensionless scalar between 0 and 1), μi\mu_i is the dynamic viscosity of phase ii, and Pi\nabla P_i is the pressure gradient in phase ii. This equation accounts for the reduced conductance of each phase due to the presence of other immiscible phases occupying the pore space. For a two-phase system, such as and in a , separate flux equations apply to each phase: qo=krokμoPo,qw=krwkμwPw,\mathbf{q}_o = -\frac{k_{ro} k}{\mu_o} \nabla P_o, \quad \mathbf{q}_w = -\frac{k_{rw} k}{\mu_w} \nabla P_w, where subscripts oo and ww denote and , respectively, and the relative permeabilities krok_{ro} and krwk_{rw} depend on the saturation SjS_j of the respective phases (with So+Sw=1S_o + S_w = 1). These functions capture how the effective pathway for each phase diminishes as the other phase's saturation increases. The concept extends to general multiphase flow involving nn immiscible phases, where the relative permeability of phase ii is expressed as kri=f(S1,S2,,Sn)k_{ri} = f(S_1, S_2, \dots, S_n), subject to the constraint j=1nSj=1\sum_{j=1}^n S_j = 1. This functional dependence reflects the complex interactions among phases in sharing the porous medium's conductance.

Key Assumptions

The formulation of relative permeability extends to multiphase flow in porous media under several core assumptions that enable the independent application of the law to each phase while simplifying pore-scale complexities. A primary assumption is steady-state flow, where fluid saturations and velocities remain constant over time and uniform across the medium, allowing equilibrium conditions for measurement and modeling. The fluids are treated as immiscible, with no significant between phases, and the flow is assumed horizontal, neglecting gravitational segregation effects that could otherwise alter saturation distributions. The is idealized as homogeneous and isotropic, implying uniform pore structure and without directional variations in permeability. Each phase is presumed continuous and interconnected within the porous matrix above its irreducible saturation, below which flow ceases due to . Standard models further neglect interphase momentum transfer, known as , and adsorption of fluid components onto the solid matrix, assuming negligible interaction beyond saturation-dependent hindrance. These assumptions limit applicability in certain scenarios; for instance, in heavy oil reservoirs with high viscosity contrasts, unmodeled viscous coupling can result in apparent relative permeabilities exceeding 1, as observed in depletion tests where momentum transfer enhances phase mobility beyond independent flow predictions. Similarly, capillary-induced hysteresis—arising from path-dependent saturation changes during drainage and imbibition—is often not fully captured, leading to discrepancies in dynamic simulations where wetting history affects permeability curves.

Normalization and Parameters

Endpoints

In relative permeability analysis for in porous media, particularly oil- systems, the endpoints define the boundary conditions of the relative permeability curves at extreme phase saturations, serving as essential parameters for model and . The relative permeability to at irreducible saturation, denoted as KrotK_{rot}, represents the value of the oil relative permeability (krok_{ro}) when the saturation (SwS_w) equals the irreducible saturation (SwirS_{wir}). At this point, is immobile and occupies the smallest pores without contributing to flow, allowing to achieve near-maximum mobility relative to single-phase conditions. Similarly, the relative permeability to at residual saturation, KrwrK_{rwr}, is the value of the relative permeability (krwk_{rw}) when the saturation (SoS_o) reaches the residual saturation (SorwS_{orw}), where ganglia become trapped and cease to flow, enabling to dominate the pore space. These critical saturations mark the thresholds of phase mobility: SwirS_{wir} is the minimum water saturation at which water flow effectively stops due to capillary forces binding it in place, while SorwS_{orw} is the minimum oil saturation remaining after or displacement processes, reflecting trapping mechanisms like snap-off and bypassing. In water-wet systems, common in many reservoirs, SwirS_{wir} typically ranges from 0.15 to 0.30, and SorwS_{orw} from 0.20 to 0.40, depending on rock wettability, pore geometry, and fluid properties; these values directly impact initial fluid distribution and ultimate recovery potential. Endpoints are generally normalized relative to the absolute permeability (kk) of the rock or the effective single-phase permeability under reservoir conditions, ensuring dimensionless consistency in multiphase models. For instance, KrotK_{rot} is often approximately 0.8 to 1.0 in water-wet oil-water systems, indicating that irreducible water reduces oil mobility by only a modest amount, while KrwrK_{rwr} typically falls in the range of 0.2 to 0.4, as residual oil continues to impede water flow significantly even at high water saturations. These ranges arise from empirical correlations, such as those linking KrotK_{rot} to SwirS_{wir} via Krot1.312.62Swir+1.1Swir2K_{rot} \approx 1.31 - 2.62 S_{wir} + 1.1 S_{wir}^2 for SwirS_{wir} between 0.2 and 0.5, highlighting the influence of initial water saturation on endpoint values.

Saturation Scaling

Saturation scaling in relative permeability involves normalizing the actual fluid saturations to a standard range, typically between 0 and 1, to facilitate consistent modeling and comparison across diverse rock types. For the phase in a two-phase oil- , the normalized saturation SwnS_{wn} is defined as Swn=SwSwir1SwirSorw,S_{wn} = \frac{S_w - S_{wir}}{1 - S_{wir} - S_{orw}}, where SwS_w is the actual saturation, SwirS_{wir} is the irreducible saturation, and SorwS_{orw} is the residual saturation to . This transformation maps the mobile saturation range—bounded by the irreducible and residual endpoints—onto a , enabling the relative permeability curves krwk_{rw} and krowk_{row} to be expressed as functions of SwnS_{wn} rather than the absolute SwS_w. The primary purpose of this normalization is to scale relative permeability curves to a universal form, mitigating the influence of rock-specific residual saturations that vary due to differences in pore , wettability, and properties across formations. By doing so, it allows for the direct comparison, averaging, and upscaling of datasets from experiments or core samples to field-scale simulations, improving the accuracy of performance predictions. For instance, normalized curves can be grouped by wettability states (e.g., water-wet or oil-wet) and rock types, facilitating and the generation of representative inputs for models. In multi-phase systems, such as three-phase oil-water-gas flows, similar normalization principles are extended to account for all mobile phases. The normalized saturation for each phase is adjusted relative to the total , incorporating gas saturation SgS_g alongside and oil, to preserve the functional dependence of relative permeabilities on phase interactions. This approach ensures that saturation paths and effects are captured consistently, even as residual saturations for multiple phases (e.g., SorgS_{org} for residual oil to gas) influence the scaling.

Classical Models

Corey Model

The Corey model, developed by A. T. Corey in , represents a foundational parametric approach to describing two-phase relative permeability curves through simple power-law expressions. It defines the relative permeability for the non-wetting phase, such as , as
kro=Krot(1Swn)Nok_{ro} = K_{rot} (1 - S_{wn})^{N_o}
and for the wetting phase, such as , as
krw=KrwrSwnNw,k_{rw} = K_{rwr} S_{wn}^{N_w},
where KrotK_{rot} and KrwrK_{rwr} are the endpoint relative permeabilities at residual saturations, NoN_o and NwN_w are empirical exponents that control the curve shapes (typically ranging from 2 to 4), and SwnS_{wn} denotes the normalized wetting-phase saturation.
The model's parameters consist of the endpoints KrotK_{rot} (maximum relative permeability) and KrwrK_{rwr} (maximum relative permeability), with the factors NoN_o and NwN_w influencing the ; in oil-wet systems, No<NwN_o < N_w reflects the preferential flow of the wetting phase. This formulation offers significant advantages, including ease of implementation in numerical simulations due to its minimal parameter requirements, and its effectiveness in fitting empirical two-phase from diverse and reservoirs.

LET Model

The LET model, proposed by Lomeland, , and in 2005 as a versatile parametric correlation for relative permeability curves, provides enhanced flexibility over simpler power-law approaches by incorporating three shape parameters to better fit experimental across the full saturation range. This model is particularly suited for capturing complex behaviors in heterogeneous porous media, such as those encountered in reservoirs. The relative permeability to is given by krw=krwrSwnLwSwnLw+Ew(1Swn)Tw,k_{rw} = k_{rwr} \frac{S_{wn}^{L_w}}{S_{wn}^{L_w} + E_w (1 - S_{wn})^{T_w}}, where SwnS_{wn} is the normalized saturation, krwrk_{rwr} is the endpoint relative permeability to , and LwL_w, EwE_w, TwT_w are the model exponents specific to the phase. A similar functional form applies to the oil phase: krow=kror(1Swn)Lo(1Swn)Lo+EoSwnTo,k_{row} = k_{ror} \frac{(1 - S_{wn})^{L_o}}{(1 - S_{wn})^{L_o} + E_o S_{wn}^{T_o}}, with krork_{ror} as the endpoint relative permeability to oil and LoL_o, EoE_o, ToT_o as the corresponding exponents. These equations derive from empirical fitting to core flood data, ensuring smooth transitions without singularities at endpoint saturations. The parameter LL governs the lower portion of the curve, controlling the initial rise from irreducible saturation and typically ranging from 2 to 5 to reflect low-saturation mobility. The entry parameter EE influences the or "knee" of the curve, adjusting the sharpness of the transition and often valued between 1 and 3. The terminal parameter TT shapes the high-saturation tail, determining the approach to the endpoint and commonly set from 1 to 4. These ranges allow customization for various rock types and wettability conditions, with higher values generally indicating more piston-like displacement. Compared to the simpler model, which relies on a single exponent, the LET formulation excels in reproducing S-shaped relative permeability curves—characteristic of mixed-wet systems—and in modeling between drainage and paths through separate parameter sets for each process. This added parametrization improves history matching in simulations without introducing numerical instabilities.

Experimental Methods

Steady-State Methods

Steady-state methods for measuring relative permeability involve core flooding experiments where two immiscible fluids are injected simultaneously into a at a constant fractional flow ratio until equilibrium conditions are reached. These techniques are particularly suited for two-phase systems, such as oil-water or gas-oil, using preserved core samples under reservoir-like conditions to ensure representativeness. The experimental setup typically employs a horizontal core holder to minimize gravitational effects, with the core sample—often a plug from reservoir rock—saturated initially with one phase before co-injecting the two fluids. Immiscible fluids, such as brine and live oil, are circulated in a closed system at controlled reservoir temperature and pressure, allowing for accurate simulation of in-situ conditions. Saturations are monitored via effluent analysis or in-situ techniques like a high-pressure separator for phase volumes. In the procedure, fluids are injected at varying fractional flow ratios (e.g., 5-10 ratios covering the saturation range from irreducible to residual ) while maintaining constant total flow rate or pressure boundaries. is achieved when the across the core and the effluent phase ratios stabilize, indicating uniform saturation distribution; this may require monitoring over time until no further changes occur. Pressure drops and cumulative effluents are recorded at each steady-state point, with average saturation determined via from produced volumes. Relative permeability for each phase, krik_{ri}, is then calculated at the corresponding saturation using , relating effective permeability to the measured flow rates and pressure gradients. These methods offer high accuracy, especially at low flow velocities where viscous forces dominate without significant inertial effects, making them reliable for heterogeneous or mixed-wettability cores. However, they are time-consuming due to the need to reach equilibrium at multiple saturation points, often requiring days per experiment. Additionally, end effects can distort saturation profiles near the core outlet, leading to errors; these are mitigated through corrections such as linear of profiles or high-viscosity fluid use to reduce the end-effect region.

Unsteady-State Methods

Unsteady-state methods measure relative permeability through dynamic displacement experiments in core samples, analyzing transient production and pressure data to derive saturation-dependent permeability reductions. These approaches rely on the Buckley-Leverett theory, which models the propagation of a displacing fluid front in a under immiscible, conditions. The theory assumes piston-like displacement with negligible and effects, providing a foundation for interpreting effluent histories. In the standard procedure, a initially saturated with and irreducible (typically 80% saturation) is flooded with at a constant rate to simulate a waterflood displacement. Effluent volumes of produced and are recorded over time, alongside differential pressure profiles across the core, capturing the evolving saturation distribution during the transient process. Relative permeabilities krik_{ri} are then estimated by history matching experimental to numerical simulations or using analytical techniques like the Johnson-Bossler-Naumann (JBN) method, which applies fractional flow concepts to compute kro/krwk_{ro}/k_{rw} from average saturations and injected pore volumes at . History matching involves iteratively adjusting krik_{ri} curves in a simulator until simulated effluent and pressure responses align with observed , often incorporating to account for non-uniform saturation profiles. Centrifuge variants accelerate the process by spinning the core sample at increasing rotational speeds, generating centrifugal forces that drive drainage or faster than alone, particularly useful for low-permeability rocks or low-saturation endpoints. Introduced by Hagoort in , this method analyzes produced fluid volumes during stepwise speed increases to derive relative permeability via analytical solutions or simulation, enabling measurement of wetting-phase krik_{ri} at residual saturations. These techniques are adaptable to three-phase flows, where extensions of Buckley-Leverett track multiple saturation paths from displacement data, though they require careful handling of phase interactions. Compared to steady-state methods, unsteady-state approaches offer significant time savings, often completing in hours rather than days, and lower costs due to simpler setups and reduced fluid volumes. However, results are highly sensitive to the viscosity ratio of displacing and displaced fluids, which can cause end effects or fingering that distort saturation profiles, necessitating corrections via low flow rates or simulation adjustments. Additionally, the (influenced by flow rate) affects non-equilibrium conditions, potentially leading to rate-dependent krik_{ri} curves that require scaling for field applicability.

Advanced Approaches and Evaluations

Model Comparisons

The model, while effective for fitting simple monotonic relative permeability curves due to its limited parameters, often underperforms in capturing points and S-shaped behaviors observed in experimental data. In contrast, the LET model demonstrates superior performance for both oil-water and gas-oil systems, providing better agreement with laboratory measurements across diverse rock types. This advantage stems from the LET model's three-parameter structure per phase (L for linear, E for exponential, and T for threshold), which allows greater adaptability compared to the 's single-exponent form. Performance evaluations typically employ error (RMSE) as a key metric for , where LET consistently yields lower values—for instance, an average RMSE of 0.0138 for /condensate and 0.0110 for gas phases in gas- systems—outperforming by reducing fitting errors in complex datasets. Additionally, LET excels in handling effects during drainage-imbibition cycles, enabling more accurate representation of non-wetting phase trapping without excessive parameterization. Historical studies prior to 2020, particularly in special core analysis (SCAL), underscore LET's flexibility for interpreting data from coreflood experiments, with applications in upscaling and history matching showing its robustness across wettability conditions. For example, evaluations from 2011 to 2018 highlighted LET's ability to fit diverse empirical datasets more reliably than rigid models like , establishing it as a preferred choice in simulations.

Machine Learning and Modern Predictions

Machine learning approaches have emerged as powerful tools for predicting relative permeability curves, particularly when experimental data is limited. Artificial neural networks (ANNs) and models enable the estimation of oil-water relative permeability from core flooding experiments and CT-scan data, achieving high accuracy in curve prediction by integrating rock and properties as inputs. For instance, a model developed by Arigbe et al. utilizes real-time inputs such as , permeability, and viscosities to forecast non-wetting and phase relative permeabilities, demonstrating strong agreement with field validation datasets. Similarly, neural networks optimized by genetic algorithms (RBFNN-GA) have been applied to predict permeability in heterogeneous formations by incorporating saturation and data, offering improved integration of characteristics over traditional empirical methods. Recent advancements in extend to hysteresis modeling and stress-dependent predictions. In 2025, a physically constrained ANN framework was introduced to model relative permeability using limited experimental data on drainage and cycles, ensuring continuous and physically realistic outputs by enforcing connectivity constraints on phase saturations. For stress-sensitive scenarios, theory-based models from 2024 incorporate pore structure heterogeneity and effects to predict oil-water relative permeability in porous media, accounting for changes that alter flow paths. Additionally, a generalized equation-of-state (EOS) approach parameterizes relative permeability geometrically using normalized saturation, interfacial area, and spreading coefficients, providing a state-function framework that unifies and three-phase flow predictions. These data-driven techniques offer distinct advantages in handling complex phenomena beyond classical models. Machine learning models effectively capture three-phase interactions and non-Darcy effects in heterogeneous media, where traditional parametric approaches often falter due to oversimplification. Studies from 2020 to 2025 consistently show that architectures, such as transformer-based networks, outperform classical correlations in for fractured or vuggy reservoirs, with mean absolute errors reduced by up to 30% in heterogeneous datasets derived from experimental inputs.

Applications

In Petroleum Engineering

In , relative permeability serves as a fundamental input parameter in numerical reservoir simulation models to describe multiphase . These models, such as those based on finite-difference or finite-volume methods, rely on relative permeability curves to simulate the simultaneous flow of , , and gas, enabling predictions of critical events like water breakthrough in reservoirs. For instance, the relative permeability (krok_{ro}) and relative permeability (krwk_{rw}) curves determine the mobility , which influences and ultimate recovery during primary and secondary production phases. Accurate representation of these curves is essential for forecasting production rates and optimizing well placement, as deviations can lead to significant errors in estimated recoverable reserves. Special core analysis (SCAL) data, obtained from laboratory measurements on core samples, provides the basis for relative permeability functions but requires scaling to field conditions for integration into models. This scaling process adjusts lab-derived curves using endpoint saturations—such as irreducible saturation (SwirS_{wir}) and residual saturation (SorwS_{orw})—to account for heterogeneities in rock properties and wettability variations across the field. During matching, these scaled curves are iteratively refined against production data to ensure the model replicates observed and saturation profiles, thereby improving the reliability of forward predictions for field development. Parameterization techniques, where relative permeability is expressed as functions of normalized saturation, facilitate this upscaling while preserving physical consistency. Relative permeability plays a pivotal role in (EOR) processes, particularly waterflooding, where it governs the displacement efficiency of by injected . In waterflooding simulations, favorable oil- relative permeability ratios promote piston-like displacement, minimizing bypassing and maximizing sweep, as seen in cases where low residual saturation enhances recovery factors up to 50% of original . For three-phase systems involving gas, such as in gas-cap drive reservoirs, relative permeability curves predict gas coning, where gravity segregation causes gas breakthrough at producers, potentially reducing rates by altering phase mobilities. Models incorporating three-phase relative permeability, often derived from two-phase data via empirical correlations, help design mitigation strategies like horizontal wells to delay coning and sustain production. Common analytical forms, such as the model, are frequently employed as inputs for these simulations due to their simplicity and fit to SCAL data.

In Other Fields

In hydrology, relative permeability concepts are applied to model the transport of non-aqueous phase liquids (NAPLs), such as petroleum hydrocarbons, in contaminated aquifers, where the water relative permeability krwk_{rw} influences the mobility and distribution of contaminants during and remediation processes. These models account for multiphase interactions in porous media, enabling predictions of NAPL infiltration and persistence, which are critical for assessing long-term risks to . In (CCS), relative permeability plays a key role in simulating CO2 injection into saline aquifers, particularly through hysteresis effects during , where the non-wetting phase (CO2) relative permeability decreases as displaces it, enhancing residual . Studies show that incorporating can increase trapped CO2 by up to 83% of the initial saturation, improving sequestration efficiency and reducing leakage risks in geological formations. Beyond these areas, relative permeability informs soil remediation techniques like soil vapor extraction (SVE), where air relative permeability evolves with saturation changes to optimize removal from unsaturated zones. In fuel cells, particularly (PEM) types, it governs in porous electrodes and gas diffusion layers, with measurements revealing that liquid water relative permeability controls flooding and performance under operational conditions. Recent applications in gas hydrate reservoirs utilize models that integrate effects to predict gas-water relative permeability, aiding in the evaluation of production strategies for hydrates in pores. In underground , relative permeability models for hydrogen-water systems are essential for simulating cyclic injection and production in aquifers and depleted reservoirs, with effects influencing storage efficiency and cushion gas requirements. As of 2025, approaches have been developed to predict these curves, enhancing assessments of large-scale feasibility.

References

Add your contribution
Related Hubs
User Avatar
No comments yet.