Simultaneous determination of capillary pressure and relative permeability curves from core-flooding experiments with various fluid pairs

New guidelines and suggestions for taking reliable effective relative permeability measurements in heterogeneous rocks are presented. The results are based on a combination of high resolution of 3D core-flooding simulations and semi-analytical solutions for the heterogeneous cores. Synthetic “data sets” are generated using TOUGH2 and are subsequently used to calculate effective relative permeability curves. A comparison between the input relative permeability curves and “calculated” relative permeability is used to assess the accuracy of the “measured” values. The results show that, for a capillary number (Ncv = kLpc × A/H2μCO2qt) smaller than a critical value, flows are viscous dominated. Under these conditions, saturation depends only on the fractional flow as well as capillary heterogeneity, and is independent of flow rate, gravity, permeability, core length, and interfacial tension. Accurate whole-core effective relative permeability measurements can be obtained regardless of the orientation of the core and for a high degree of heterogeneity under a range of relevant and practical conditions. Importantly, the transition from the viscous to gravity/capillary dominated flow regimes occurs at much higher flow rates for heterogeneous rocks. For the capillary numbers larger than the critical value, saturation gradients develop along the length of the core and accurate relative permeability measurements are not obtained using traditional steady-state methods. However, if capillary pressure measurements at the end of the core are available or can be estimated from independently measured capillary pressure curves and the measured saturation at the inlet and outlet of the core, accurate effective relative permeability measurements can be obtained even when there is a small saturation gradient across the core.

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A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves

10.2118/205989-ms ◽

2021 ◽

Author(s):

Carlos Esteban Alfonso ◽

Frédérique Fournier ◽

Victor Alcobia

Keyword(s):

Machine Learning ◽

Capillary Pressure ◽

Relative Permeability ◽

Fractional Flow ◽

Flow Curves ◽

Rock Types ◽

Rock Typing ◽

Relative Permeability Curves ◽

Permeability Data

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.

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Simulation Protocol for Core Flooding: Relative Permeability and Capillary Pressure Analysis

10.4043/24715-ms ◽

2014 ◽

Cited By ~ 2

Author(s):

S Kumar ◽

Mariyamni Awang ◽

Ghulam Abbas ◽

Khurram Farouque ◽

Sheraz Ahmed

Keyword(s):

Capillary Pressure ◽

Relative Permeability ◽

Core Flooding ◽

Simulation Protocol ◽

Pressure Analysis

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Uncertainty span for relative permeability and capillary pressure by varying wettability and spatial flow directions utilizing pore scale modelling

E3S Web of Conferences ◽

10.1051/e3sconf/202014601002 ◽

2020 ◽

Vol 146 ◽

pp. 01002

Author(s):

Thomas Ramstad ◽

Anders Kristoffersen ◽

Einar Ebeltoft

Keyword(s):

Continental Shelf ◽

Pore Structure ◽

Capillary Pressure ◽

Relative Permeability ◽

Phase Flow ◽

Pore Scale ◽

Full Field ◽

Scale Modelling ◽

Relative Permeability Curves ◽

Norwegian Continental Shelf

Relative permeability and capillary pressure are key properties within special core analysis and provide crucial information for full field simulation models. These properties are traditionally obtained by multi-phase flow experiments, however pore scale modelling has during the last decade shown to add significant information as well as being less time-consuming to obtain. Pore scale modelling has been performed by using the lattice-Boltzmann method directly on the digital rock models obtained by high resolution micro-CT images on end-trims available when plugs are prepared for traditional SCAL-experiments. These digital rock models map the pore-structure and are used for direct simulations of two-phase flow to relative permeability curves. Various types of wettability conditions are introduced by a wettability map that opens for local variations of wettability on the pore space at the pore level. Focus have been to distribute realistic wettabilities representative for the Norwegian Continental Shelf which is experiencing weakly-wetting conditions and no strong preference neither to water nor oil. Spanning a realistic wettability-map and enabling flow in three directions, a large amount of relative permeability curves is obtained. The resulting relative permeabilities hence estimate the uncertainty of the obtained flow properties on a spatial but specific pore structure with varying, but realistic wettabilities. The obtained relative permeability curves are compared with results obtained by traditional SCAL-analysis on similar core material from the Norwegian Continental Shelf. The results are also compared with the SCAL-model provided for full field simulations for the same field. The results from the pore scale simulations are within the uncertainty span of the SCAL models, mimic the traditional SCAL-experiments and shows that pore scale modelling can provide a time- and cost-effective tool to provide SCAL-models with uncertainties.

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