# US 2018/0321127 A1 · Method for Providing a Numerical Model of a Sample of Rock

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- Number: US 2018/0321127 A1
- Document ID: US20180321127A1
- Status: Published application
- Kind: A1
- Application number: 15/763,176
- Filed: 2015-10-02
- Published: 2018-11-08
- Earliest claimed: 2015-10-02
- Assignees: Repsol, S.A.
- Applicants: Repsol, S.A.
- Inventors: María Fernanda León Carrera; Almudena Díaz Aguado; Noelia Rodríguez Morillas; Laurent Fontanelli
- Web page: https://patentgrep.com/patent/US20180321127A1

## CPC

- G01N 15/08: Investigating permeability, pore-volume, or surface area of porous materials
- G01N 23/046: using tomography, e.g. computed tomography [CT]
- G01N 33/24: Earth materials (G01N33/42 takes precedence)
- G01N 2015/0846: {by use of radiation, e.g. transmitted or reflected light}
- G01N 2223/401: image processing
- G01N 2223/616: earth materials
- G01N 2223/649: porosity
- G01V 20/00: Geomodelling in general
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02)

## Abstract

The present invention relates to a method for providing a numerical model of a sample of rock that, when used for flow simulations, it reproduces the porosity and the permeability according to the measurements taken in said sample of the rock. The method is characterized in that the structure and the properties of the numerical model are populated randomly ensuring that the global behavior reproduces the measurements.

## Claims

### Claim 1

A method for providing a numerical model of a sample of rock, in particular a plug sample taken from a sample of rock, said sample of rock taken from a vertical portion of the rock core, according to its longitudinal direction, extracted from a well of an oil or gas reservoir, the method comprising:
performing a CT scan of the plug sample of rock;
measuring a porosity ϕms of the plug sample of rock;
measuring a permeability kms of the plug sample of rock;
generating a 3D numerical model, at least comprising a cell discretization representing a volume of the plug sample of rock, according to the following steps:
a) recovering data from the CT scan providing at least information on a statistical density function ƒ(ρ) of the value ρ, wherein ρ is an attenuation X-ray radiation in the volume of the plug sample of rock;
b) generating a segmentation of the discretization of the first 3D model into sub-volumes responsive to the data retrieved in step a) from the plug sample of rock, said sub-volumes representing components of the plug sample of rock;
c) identifying the components of each sub-volumes;
d) determining a porosity statistical distribution function F(ϕ) for the cells of the 3D model at least responsive to the statistical density function ƒ(ρ);
e) populating a porosity ϕ for each sub-volume of the 3D model, wherein the porosity is spatially distributed among the cells of said sub-volume by means of a Gaussian simulation algorithm responsive to the porosity statistical distribution function F(ϕ) as an approximation of a Gaussian density function;
f) populating a permeability among the cells of each sub-volume comprising a component contributing to the porosity, wherein the permeability is defined as a scalar function responsive to the porosity of the cell k(ϕ);
g) performing a numerical simulation of the 3D model according to conditions relating to the permeability kms in order to obtain a global permeability knum;
h) iteratively repeating steps e) to g) until the global permeability measured on the plug sample of rock kms and the permeability knum calculated from the 3D model differ less than a threshold.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the porosity statistical distribution function F(ϕ) at step d) is determined as follows:
determining ΔCT1=|CTmean−CTmin| and ΔCT2=|CTmax−CTmean| as the ρ values ranges from CTmin to CTmax, and the statistical density function ƒ(ρ) has a mean value at CTmean;
being Δϕ1=|ϕmean−ϕmin| and Δϕ2=|ϕmax−ϕmean| wherein ϕmin is a minimum value of the porosity ϕ, ϕmax is a maximum value of the porosity ϕ; and, ϕmean is a mean value of the porosity statistical distribution function F(ϕ) fixed as the measured porosity ϕms;
provide the porosity statistical distribution function F(ϕ) as a symmetric function of the statistical density function ƒ(ρ) of the value ρ, scaled and shifted according to:
Δφ
2
Δφ
1
=
Δ


CT
1
Δ


CT
2

### Claim 3 (depends on 1)

The method according to claim 1, wherein for determining the porosity statistical distribution function F(ϕ) on step d), the density function ƒ(ρ) is further truncated as ƒc(ρ) by cutting-off a left tail of the density function ƒ(ρ) at a pre-specified value C∈[ϕmin, ϕmax] providing a truncated porosity statistical distribution function Fc(ϕ) corresponding to the porosity values of those components that do not contribute to the porosity of the plug sample of rock; and, when populating the porosity ϕ in step e), said step is limited to those sub-volumes representing a component contributing to the porosity of the plug sample of rock and the Gaussian simulation algorithm is responsive to the truncated porosity statistical distribution function Fc(ϕ).

### Claim 4 (depends on 1)

The method according to claim 1, wherein step h), when the global permeability measured on the plug sample of rock kms and the permeability knum calculated from the 3D model differs more than the threshold, before repeating steps e) to g) step h) further comprises truncating the porosity statistical distribution function by specifying a new value of c∈[ϕmin, ϕmax].

### Claim 5 (depends on 1)

The method according to claim 1, wherein the Gaussian simulation algorithm of step e) is a sequential Gaussian simulation algorithm.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the Gaussian simulation algorithm of step e) is a Gaussian random function simulation algorithm.

### Claim 7 (depends on 1)

The method according to claim 1, wherein a first porosity statistical distribution function F(ϕ) responsive to the statistical density function ƒ(ρ) in step d), when represented within a normalized range [0,1], is calculated as:
measuring a mean porosity ϕmean from the plug sample of rock according to:
Δ


CT
2
Δ


CT
1
=
Δφ
1
Δφ
2

### Claim 8 (depends on 1)

The method according to claim 1, further including evaluating a connectivity of the components contributing to the porosity of the plug sample of rock by:
measuring a permeability kms(s) of the plug sample of rock using two fluids, based on a saturation s of one fluid with respect to the other fluid for a specified range of the E variable;
performing a numerical simulation according to step g) that further provides the numerical values of the permeability knum(s) for at least the specified range of the s variable; and
iteratively repeating steps e) to g) according to step h) until the global permeability measured on the plug sample of rock kms (S) and the permeability knum (s) calculated from the 3D model differ less than the specified threshold for the range of the s variable measured under a specified norm.

### Claim 9 (depends on 1)

The method for providing a 3D numerical model of the sample of rock taken from a vertical portion of the core extracted from a well of an oil or gas reservoir, comprising:
performing a CT scan of the sample of rock;
identifying facies and a number of components of the facies of the sample of rock;
for each facies,
extracting a plug sample of rock from said facies of the sample of rock;
generating a first 3D numerical model according to claim 1, at least comprising a cell discretization representing a volume of the plug sample of rock;
generating a second 3D numerical model, at least comprising a cell discretization representing a volume of the sample of rock, taken as a longitudinal portion of the rock core, wherein the volume of the sample of rock comprises the volumes of the plugs and
the cell discretization of the second 3D numerical model is clustered according to the facies of the 3D sample;
for each cluster representing a facies, the components are stochastically populated to the cell discretization of said second 3D numerical model by means of a MPS algorithm (Multipoint Statistics) responsive to patterns provided by the cell discretization of the plug sample of rock of the first 3D model of its facies and restricted to a condition that the number of components n of the sample of rock and the partial rate for each component ƒi, i=1 . . . n is maintained;
for each cluster representing a facies, the porosity are stochastically populated to the cell discretization of said second 3D numerical model by means of a Gaussian algorithm;
for each cluster representing a facies, the permeability is populated among the cells as a scalar function responsive to the porosity of second 3D numerical model k(ϕ).

### Claim 10 (depends on 9)

The method according to claim 9, wherein before stochastically populating to the cell discretization of the second 3D numerical model from the cell discretization of the first 3D numerical model of the plug sample of rock, said cell discretization of the plug sample of rock is coarsened.

### Claim 11 (depends on 10)

The method according to claim 10, wherein the coarsening of the cell discretization of the plug sample of rock comprises:
generating a coarser cell discretization than the cell discretization of the plug wherein each cell of the coarser discretization comprises one or more cells of the cell discretization of the plug sample of rock;
each cell of the coarser discretization comprises the component with higher frequency among the components of the cell discretization of the plug sample of rock;
a density of each cell of the coarser discretization is calculated as a mean of the density among the cells of the cell discretization of the plug sample of rock;
a porosity of each cell of the coarser discretization is calculated as a mean of the porosity among the cells of the cell discretization of the plug sample of rock;
a permeability of each cell of the coarser discretization is calculated as k(ϕ) from the porosity ϕ value at said cell of the coarser discretization;
provide the coarser cell discretization as a new discretization of the plug sample of rock to be used in the MPS algorithm.

### Claim 12 (depends on 9)

The method according to claim 9, wherein the porosity, the permeability, or both, are calculated in a plurality of sections along the longitudinal direction of the second 3D numerical model providing one or more discrete functions.

### Claim 13 (depends on 1)

A computer program product adapted to carry out a method according to claim 1.