Non-Darcy Measurements in Dry Core and the Effect of Immobile Liquid

Coles, M.E.; Hartman, K.J.

doi:10.2523/39977-ms

Cited by 17 publications

(8 citation statements)

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“…Typical derivative shapes due to phase redistribution (whether in a drawdown or in a build-up) are shown in Figure 8. Curve (5) in Figure 8 corresponds to the denser phase being re-injected into the formation.…”

Section: Impact Of Wellbore Dynamicsmentioning

confidence: 98%

Well Test Analysis in Gas-Condensate Reservoirs

Gringarten

Al-Lamki

Daungkaew

et al. 2000

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractPublished analyses of well tests in gas-condensate reservoirs when pressure drops below the dew point are usually based on a two-zone radial composite model, representing regions of condensate drop-out around the wellbore and of initial gas composition away from the well. Laboratory experiments, on the other hand, suggest that three different mobility zones could exist: (1) an outer zone away from the well, with the initial liquid condensate saturation; (2) a zone nearer to the well, with increased condensate saturation and lower gas mobility; and (2) a zone in the immediate vicinity of the well with high capillary number which increases the gas relative permeability, resulting in a recovery of much of the gas mobility lost from condensate blockage. This paper investigates the existence of this latter zone in well test data. An example of well test analysis is discussed, which illustrates the difficulty of identifying such a zone as, in many cases, build-up and/or drawdown data are dominated by wellbore phase redistribution effects. Where the three zones can be identified, data are analyzed using a three-zone radial composite model to yield a complete characterization of the near-wellbore effects, and in particular the knowledge of the various components of the total skin effect: mechanical skin; rate-dependent two-phase skin; and skin due to gas condensate blockage. The existence of the three zones and the results of the analysis are verified with a compositional simulator where relative permeability depends on capillary number.

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Section: Impact Of Wellbore Dynamicsmentioning

confidence: 98%

Well Test Analysis in Gas-Condensate Reservoirs

Gringarten

Al-Lamki

Daungkaew

et al. 2000

SPE Annual Technical Conference and Exhibition

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“…Several authors [5,48,44,45,12,42] have emphasised the effect of tortuosity on β factor. Although there has been no clear consensus in its definition, tortuosity has been generally defined as an average elongation of fluid paths within a porous medium.…”

Section: Flow Patterns and Analysismentioning

confidence: 99%

The impact of porous media heterogeneity on non-Darcy flow behaviour from pore-scale simulation

Muljadi

Blunt

Raeini

et al. 2016

Advances in Water Resources

184

132

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The effect of pore-scale heterogeneity on non-Darcy flow behaviour is investigated by means of direct flow simulations on 3-D images of a beadpack, Bentheimer sandstone and Estaillades carbonate. The critical Reynolds number indicating the cessation of the creeping Darcy flow regime in Estaillades carbonate is two orders of magnitude smaller than in Bentheimer sandstone, and is three orders of magnitude smaller than in the beadpack. It is inferred from the examination of flow field features that the emergence of steady eddies in pore space of Estaillades at elevated fluid velocities accounts for the early transition away from the Darcy flow regime. The non-Darcy coefficient β, the onset of non-Darcy flow, and the Darcy permeability for all samples are obtained and compared to available experimental data demonstrating the predictive capability of our approach. X-ray imaging along with direct pore-scale simulation of flow provides a viable alternative to experiments and empirical correlations for predicting non-Darcy flow parameters such as the β factor, and the onset of non-Darcy flow

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“…Preliminary results show a correlation of the form β = c 1 k c2 (20) which agrees with the literature. 6,10,19 The values of the constants in Equation (20) for the case of a Gaussian network were found to be c 1 = 2 · 10 −6 and c 2 = −1.0021, when β is in m −1 and k is in m 2 .…”

Section: Pore Network Flow Modulementioning

confidence: 98%

“…Then, the pore network is traversed, calculating each pore's diameter according to Equation (10). (Tests of this approach have shown only tiny disparities between the user's chosen porosity and the porosity of the resulting network.…”

Section: Random Pore Network Generation In 3dmentioning

confidence: 99%

“…For example, failure to anticipate nonDarcy effects leads to overprediction of the pressure profile around production wells, and therefore to unexpected gas condensation. 10 Another effect is the underprediction of pressure drop in the reservoir, resulting in misleading well productivity estimates. 11,12 Accurate simulation of flow in porous media for high velocities, therefore, requires a theoretical prediction or an experimentally measured value for β.…”

Section: Introductionmentioning

confidence: 99%

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<title>Multiscale characterization of porous media properties for hydrocarbon reservoir simulation</title>

et al. 2001

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Fluid flow through porous materials is critical for understanding and predicting the behavior of systems as diverse in function and scale as hydrocarbon reservoirs, aquifers, filters, membrane separators and even catalytic converters. Recently, there have been calls to incorporate more physics in oil reservoir simulations, as well as to enhance computational capability through the use of High Performance Computing (HPC), in order to improve reservoir management. Accurate prediction of reservoir behavior depends on the physical properties of not only the fluid but also the underlying rock formation.Contemporary approaches to solving these flows involve simulation of only a single physical scale. We are currently developing HiMuST (Hierarchical Multiscale Simulator Technology), an integrated multiscale simulation system for flow through heterogeneous porous materials. HiMuST uses a hierarchy of simulation codes to address the issue of rock property characterization at the pore scale and can self-adjust according to available input data.At the microscopic scale, HiMuST employs the Lattice Boltzmann Method, based on magnetic resonance digitizations of actual rock samples. At the mesoscopic scale, a stochastic model represents a pore network as a randomly generated skeleton of cylindrical pipes, based on physical characteristics determined by the microscopic simulation.We present computational and computer science issues involved in the HPC implementation of the codes and in integrating them into a seamless simulation system. Issues such as portability, scalability, efficiency and extensibility of the final product are also discussed, as well as the numerical methods implemented at each scale. Example simulation results are presented.

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Non-Darcy Measurements in Dry Core and the Effect of Immobile Liquid

Cited by 17 publications

References 0 publications

Well Test Analysis in Gas-Condensate Reservoirs

Well Test Analysis in Gas-Condensate Reservoirs

The impact of porous media heterogeneity on non-Darcy flow behaviour from pore-scale simulation

<title>Multiscale characterization of porous media properties for hydrocarbon reservoir simulation</title>

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