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Relative permeability

Relative permeability is a dimensionless in and that quantifies the conductance of a specific through a when multiple immiscible 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 due to interactions with other phases, such as , , and gas in hydrocarbon reservoirs. Values of relative permeability range from 0 (no ) to 1 (single-phase ), 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 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 levels affect mobilities. It varies with , wettability of the rock surface, pore geometry, and history (e.g., vs. ), often visualized through relative permeability curves that plot these functions against . 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 k_{ri}, is defined as the ratio of the effective permeability of a fluid k_i to the absolute permeability k of the , mathematically expressed as k_{ri} = \frac{k_i}{k}, where $0 \leq k_{ri} \leq 1. This quantifies the reduction in a '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 processes.

Physical Interpretation

Relative permeability emerges at the scale from the between immiscible fluid s for limited space within a , where the presence of one obstructs the paths of the other, thereby reducing the effective for each relative to single- . This is governed by interfacial , which creates menisci that trap portions of the non-preferred in smaller pores or dead-end spaces, limiting and overall . further influences this , as the distribution of fluids across pore throats and bodies determines the available pathways; for instance, the non-wetting tends to occupy larger pores during , fragmenting the '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 processes, where the history—specifically (non-wetting invasion) versus (wetting invasion)—leads to different configurations and at the same . During , the non-wetting advances through larger pores, leaving behind connected wetting-phase films, which results in higher relative permeability for the non-wetting compared to , where forces trap disconnected non-wetting ganglia in smaller pores, reducing its and altering the available flow paths for the wetting . This mechanism causes the relative permeability curves to diverge between and paths, reflecting irreversible changes in occupancy and connectivity due to the sequence of 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 \mathbf{q}_i of phase i is given by the extended Darcy's law: \mathbf{q}_i = -\frac{k_{ri} k}{\mu_i} \nabla P_i, where k is the absolute permeability of the porous medium, k_{ri} is the relative permeability of phase i (a dimensionless scalar between 0 and 1), \mu_i is the dynamic viscosity of phase i, and \nabla P_i is the pressure gradient in phase i. 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 : \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 o and w denote and , respectively, and the relative permeabilities k_{ro} and k_{rw} depend on the S_j of the respective s (with S_o + S_w = 1). These functions capture how the effective pathway for each diminishes as the other phase's increases. The concept extends to general multiphase flow involving n immiscible phases, where the relative permeability of phase i is expressed as k_{ri} = f(S_1, S_2, \dots, S_n), subject to the \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 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 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 , 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 -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 saturations, serving as essential parameters for model and . The relative permeability to at irreducible saturation, denoted as K_{rot}, represents the value of the oil relative permeability (k_{ro}) when the saturation (S_w) equals the irreducible saturation (S_{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, K_{rwr}, is the value of the relative permeability (k_{rw}) when the saturation (S_o) reaches the residual saturation (S_{orw}), where ganglia become trapped and cease to flow, enabling to dominate the pore space. These critical saturations mark the thresholds of phase mobility: S_{wir} is the minimum water saturation at which water flow effectively stops due to capillary forces binding it in place, while S_{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, S_{wir} typically ranges from 0.15 to 0.30, and S_{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 potential. Endpoints are generally normalized relative to the absolute permeability (k) of the rock or the effective single-phase permeability under reservoir conditions, ensuring dimensionless consistency in multiphase models. For instance, K_{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 K_{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 K_{rot} to S_{wir} via K_{rot} \approx 1.31 - 2.62 S_{wir} + 1.1 S_{wir}^2 for S_{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 types. For the phase in a two-phase oil- , the normalized S_{wn} is defined as S_{wn} = \frac{S_w - S_{wir}}{1 - S_{wir} - S_{orw}}, where S_w is the actual , S_{wir} is the irreducible , and S_{orw} is the to . This transformation maps the mobile range—bounded by the irreducible and endpoints—onto a , enabling the relative permeability curves k_{rw} and k_{row} to be expressed as functions of S_{wn} rather than the absolute S_w. The primary purpose of this is to scale relative permeability curves to a universal form, mitigating the influence of rock-specific saturations that vary due to differences in , wettability, and properties across formations. By doing so, it allows for the direct comparison, averaging, and upscaling of datasets from experiments or 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 principles are extended to account for all phases. The for each is adjusted relative to the , incorporating gas saturation S_g alongside and oil, to preserve the functional dependence of relative permeabilities on interactions. This approach ensures that saturation paths and effects are captured consistently, even as saturations for multiple phases (e.g., S_{org} for 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
k_{ro} = K_{rot} (1 - S_{wn})^{N_o}
and for the wetting phase, such as , as
k_{rw} = K_{rwr} S_{wn}^{N_w},
where K_{rot} and K_{rwr} are the endpoint relative permeabilities at residual saturations, N_o and N_w are empirical exponents that control the curve shapes (typically ranging from 2 to 4), and S_{wn} denotes the normalized wetting-phase saturation.
The model's parameters consist of the endpoints K_{rot} (maximum relative permeability) and K_{rwr} (maximum relative permeability), with the factors N_o and N_w influencing the ; in oil-wet systems, N_o < N_w reflects the preferential of the wetting . 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 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 k_{rw} = k_{rwr} \frac{S_{wn}^{L_w}}{S_{wn}^{L_w} + E_w (1 - S_{wn})^{T_w}}, where S_{wn} is the normalized , k_{rwr} is the relative permeability to , and L_w, E_w, T_w are the model exponents specific to the . A similar functional form applies to the oil : k_{row} = k_{ror} \frac{(1 - S_{wn})^{L_o}}{(1 - S_{wn})^{L_o} + E_o S_{wn}^{T_o}}, with k_{ror} as the relative permeability to oil and L_o, E_o, T_o as the corresponding exponents. These equations derive from empirical fitting to core flood data, ensuring smooth transitions without singularities at saturations. The parameter L governs the lower portion of the curve, controlling the initial rise from irreducible and typically ranging from 2 to 5 to reflect low-saturation . The entry parameter E influences the or "knee" of the curve, adjusting the sharpness of the transition and often valued between 1 and 3. The terminal parameter T shapes the high-saturation tail, determining the approach to the and commonly set from 1 to 4. These ranges allow customization for various 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 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 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 range from irreducible to residual ) while maintaining constant total or pressure boundaries. is achieved when the across the core and the effluent ratios stabilize, indicating uniform distribution; this may require over time until no further changes occur. Pressure drops and cumulative effluents are recorded at each steady-state point, with average determined via from produced volumes. Relative permeability for each , k_{ri}, is then calculated at the corresponding 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 at multiple points, often requiring days per experiment. Additionally, end effects can distort 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 . Effluent volumes of produced and are recorded over time, alongside differential pressure profiles across the core, capturing the evolving distribution during the transient process. Relative permeabilities k_{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 k_{ro}/k_{rw} from average saturations and injected pore volumes at . History matching involves iteratively adjusting k_{ri} curves in a simulator until simulated effluent and pressure responses align with observed , often incorporating to account for non-uniform 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 k_{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 that distort profiles, necessitating corrections via low s or simulation adjustments. Additionally, the (influenced by ) affects non-equilibrium conditions, potentially leading to rate-dependent k_{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 , 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 / 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 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 and data, offering improved 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 and cycles, ensuring continuous and physically realistic outputs by enforcing constraints on saturations. For stress-sensitive scenarios, theory-based models from 2024 incorporate 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 , 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 (k_{ro}) and relative permeability (k_{rw}) curves determine the , which influences and ultimate recovery during primary and secondary phases. Accurate representation of these curves is essential for forecasting 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 to field conditions for integration into models. This scaling process adjusts lab-derived curves using saturations—such as irreducible (S_{wir}) and (S_{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 profiles, thereby improving the reliability of forward predictions for field development. Parameterization techniques, where relative permeability is expressed as functions of normalized , facilitate this upscaling while preserving physical consistency. Relative permeability plays a pivotal role in (EOR) processes, particularly waterflooding, where it governs the efficiency of by injected . In waterflooding simulations, favorable oil- relative permeability ratios promote piston-like , minimizing bypassing and maximizing sweep, as seen in cases where low residual saturation enhances recovery factors up to 50% of original . For three- 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 mobilities. Models incorporating three- relative permeability, often derived from two-phase data via empirical correlations, help design mitigation strategies like wells to delay coning and sustain . 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 k_{rw} influences the mobility and distribution of contaminants during and remediation processes. These models account for multiphase interactions in porous , 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 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 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.

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