WO2024003093A1 - Procédé de caractérisation de porosité totale de roche - Google Patents

Procédé de caractérisation de porosité totale de roche Download PDF

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WO2024003093A1
WO2024003093A1 PCT/EP2023/067546 EP2023067546W WO2024003093A1 WO 2024003093 A1 WO2024003093 A1 WO 2024003093A1 EP 2023067546 W EP2023067546 W EP 2023067546W WO 2024003093 A1 WO2024003093 A1 WO 2024003093A1
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porosity
image
pores
rock
resolved
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PCT/EP2023/067546
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English (en)
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Nishank SAXENA
Matthias Appel
Ronny HOFMANN
John Justin Freeman
Bochao ZHAO
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Shell Internationale Research Maatschappij B.V.
Shell Usa, Inc.
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Publication of WO2024003093A1 publication Critical patent/WO2024003093A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
    • G01N2015/0846—Investigating permeability, pore-volume, or surface area of porous materials by use of radiation, e.g. transmitted or reflected light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24—Earth materials
    • G01N33/241—Earth materials for hydrocarbon content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]

Definitions

  • the present invention relates to a method of characterizing a total porosity of rock.
  • the present invention relates to a method of characterizing a total porosity of a rock, having primary and secondary porosity systems, using a three-dimensional image of the rock.
  • BACKGROUND OF THE INVENTION [0002] Accurate determination of petrophysical characteristics of rock within a hydrocarbon- containing reservoir is important in determining whether to select the hydrocarbon-containing reservoir for development, as well as developing and managing the hydrocarbon-containing reservoir.
  • Digital rock physics utilizes digital images of formation rocks to simulate rock multiphysics at the pore scale and predict properties of complex rocks.
  • Porosity is among the most fundamental of rock properties and is utilized to estimate the fluid capacity of a hydrocarbon-containing formation and to calculate further rock properties such as permeability.
  • Current digital rock physics technologies estimate the porosity of a rock sample by capturing a three-dimensional image of the rock, for example by 3-D x-ray computer tomography, segmenting the resulting image into solid and void space voxels utilizing various image segmentation algorithms, and calculating the porosity of the rock from the total number of void space voxels relative to the total overall number of voxels.
  • a method for estimating a total porosity of rock comprising: obtaining a three-dimensional image of a rock wherein the image is comprised of a plurality of voxels; processing the three-dimensional image to segment the image to identify resolved pores having a dimension greater than or equal to the size of a voxel, solid material having a dimension greater than or equal to the size of a voxel, and partial pores having a dimension less than the size of a voxel; estimating an image porosity of the rock based on the resolved pores of the segmented three-dimensional image; deriving a non- wetting liquid capillary pressure curve using the resolved pores of the segmented three- dimensional image; determining a resolved porosity
  • a method for estimating a total porosity of rock comprising: obtaining a three-dimensional image of a rock wherein the image is comprised of a plurality of voxels; processing the three-dimensional image to segment the image to identify resolved pores having a dimension greater than or equal to the size of a voxel, solid material having a dimension greater than or equal to the size of a voxel, and porous material; estimating an image porosity of the rock based on the resolved pores of the segmented three-dimensional image; deriving a non-wetting liquid capillary pressure curve using the resolved pores of the segmented three-dimensional image; determining a resolved porosity correction factor from the non-wetting liquid capillary pressure curve; determining a porous material correction factor from a volume fraction of porous material identified in the segmented three-dimensional image; and calculating a saturation of the non-wetting liquid using the image porosity, the resolved po
  • a backpropagation-enabled method for estimating a total porosity of rock from a three-dimensional image of rock comprising the steps of: obtaining a three-dimensional image of rock, the three- dimensional image wherein the image is comprised of a plurality of voxels; segmenting the three-dimensional image to identify solid material, primary porosity, and secondary porosity; applying a backpropagation-enabled trained model to estimate a saturation of a non-wetting fluid using: an image porosity and a resolved porosity correction factor based on the primary porosity and a non-wetting liquid capillary pressure curve for the primary porosity; and a secondary porosity correction factor.
  • Figs.1A – 1D are schematic illustrations of a rock image having primary and secondary porosity systems
  • Fig.2A is a 2D slice from a 3D micro-CT image of a rock having primary and secondary porosity systems
  • Fig.2B is a segmented image of Fig.2A
  • Fig.3 – 5 are examples comparing laboratory measurements to simulation data.
  • the present invention provides a method for accurately estimating a total porosity of a rock based on an original three-dimensional pore-scale image of the rock having limited resolution relative to the actual pore structure of the rock.
  • the present inventors found that, contrary to state-of-the-art assumptions in digital rock physics modelling, a substantial fraction of the pore volume of hydrocarbon-containing rocks is contained in pores of a size below the image resolution provided by three-dimensional pore-scale imaging technology commonly used to provide images of such rocks.
  • conventional digital rock physics modelling substantially underestimates the porosity of hydrocarbon-containing rocks by failing to account for pores that are smaller than the image resolution of the pore-scale imaging technology.
  • Fig.1A shows a porous material 16, the porous material having a solid portion and pores.
  • Fig.1B illustrates a voxel grid 22 showing the size of a voxel (depicted herein by a single square of the voxel grid 22), as compared to the size of features in the rock sample. As can be seen by Fig.1B, many features of the porosity system are smaller than the voxel size.
  • Fig.1C schematically illustrates a synthetic micro-CT image with a binary segmentation of solid material 24 and pores 26.
  • Fig.1D schematically illustrates a micro-CT image that has been processed to segment the image to identify the primary porosity system and the secondary porosity system.
  • the primary porosity system has resolved pores 28 with a dimension greater than or equal to the size of the voxel, and solid material 32 having a dimension greater than or equal to the size of a voxel.
  • the secondary porosity system illustrated in Fig.1D has partial pores 34 having a dimension less than the size of a voxel and porous material 36.
  • a total porosity is determined for a rock having both a primary pore system and a secondary pore system.
  • Fig.2A shows a 2D slice from a 3D micro-CT image of a rock having a primary porosity system and a secondary porosity system.
  • the rock has pores 12 and solid material 14.
  • porous material 16 in this case shale
  • partial pores 18 As illustrated, Fig.2A reveals that the micro-CT image contains a significant volume of porous material 16 and partial pores 18.
  • partial pore we mean a voxel in a micro-CT image that is a composite of pores and solid material. Partial pores often occur at the interface between resolved pores and solid material, as well as at the interface between resolved pores and porous materials.
  • porous material we mean solid materials that themselves have pores, clay minerals, and/or silt size grains.
  • Fig.2B is the corresponding segmented image of Fig.2A, which has been processed to identify the primary porosity system and the secondary porosity system.
  • the primary porosity system has resolved pores 28 with a dimension greater than or equal to the size of the voxel, and solid material 32 having a dimension greater than or equal to the size of a voxel.
  • the secondary porosity system illustrated in Fig.2B has partial pores 34 having a dimension less than the size of a voxel and porous material 36.
  • the present invention provides a method for estimating the porosity of a hydrocarbon-containing rock based upon a three-dimensional digital image provided by pore- scale imaging technology, where the porosity estimated in accordance with the present method includes a primary porosity system and a secondary porosity system.
  • an image porosity is estimated based on the resolved pores of a segmented image. From this, a non-wetting liquid capillary pressure curve can be determined for the resolved pores.
  • Capillary pressure is the pressure difference across the interface between two immiscible fluids in a constricted channel (e.g., air and a non-wetting liquid such as mercury).
  • Non-wetting liquid injection capillary pressure curves describe the relation between the pressure applied on the non-wetting liquid to overcome capillary pressure and thereby enter pore throats and the fractional bulk volume of the rock that is occupied by the non-wetting liquid at that pressure.
  • the total porosity of a hydrocarbon-containing rock may be estimated by utilizing capillary pressure curves based on an image of a rock to determine a resolved porosity correction factor to be applied to porosity determined from resolved pores in the image, where the resolved pore correction factor accounts for pores having a size below the image resolution of the image.
  • the method of the present invention further accounts for the impact of a secondary porosity system by partial pore porosity correction factor from a volume fraction of partial pores identified in the segmented three-dimensional image and/or a porous material correction factor from a volume fraction of porous material identified in the segmented three-dimensional image.
  • the present invention also provides a backpropagation-enabled method for estimating the total porosity of rock from a three-dimensional image of rock. The three-dimensional image is segmented to identify solid material, primary porosity, and secondary porosity.
  • a backpropagation-enabled trained model is applied to estimate a saturation of a non-wetting fluid using an image porosity and a resolved porosity correction factor based on the primary porosity and a non-wetting liquid capillary pressure curve for the primary porosity, and a secondary porosity correction factor.
  • the backpropagation-enabled process involves iterations to improve the total porosity determination.
  • the training set of images of rock may include, for example, 2D projection images obtained from a pore-scale imaging technology, 3D images reconstructed from 2D projection images, synthetic 2D images, synthetic 3D images, and combinations thereof.
  • the training set of images is obtained from a cloud-based tool adapted to store 2D projection images from a pore-space imaging technology, especially micro-CT and thin sections.
  • the tool is adapted to process the 2D projection images to produce a reconstructed 3D image.
  • the tool is also adapted to store the resulting 3D images.
  • backpropagation-enabled processes include, without limitation, artificial intelligence, machine learning, and deep learning. It will be understood by those skilled in the art that advances in backpropagation-enabled processes continue rapidly. The method of the present invention is expected to be applicable to those advances even if under a different name.
  • a preferred embodiment of a backpropagation-enabled process is a deep learning process, including, but not limited to a convolutional neural network.
  • the backpropagation-enabled process may be supervised, semi-supervised, unsupervised or a combination thereof. In one embodiment, a supervised process is made semi- supervised by the addition of an unsupervised technique.
  • the training set of images is labelled to provide examples of pore spaces, solid material, partial pores and/or porous material of interest.
  • a pore space, solid material, partial pores and/or porous material of interest may be identified by, for example, drawing a polygon around the image of interest in the image.
  • the trained process will then identify areas of interest having similar latent space characteristics.
  • the labels may have a dimension of 1D – 3D.
  • the supervised backpropagation-enabled process is a classification process.
  • the classification process may be conducted voxel-wise, slice-wise and/or volume-wise.
  • the unsupervised backpropagation-enabled process is a clustering process.
  • the clustering process may be conducted voxel-wise, slice-wise and/or volume-wise.
  • the unsupervised backpropagation-enabled process is a generative process.
  • the generative process may be conducted voxel-wise, slice-wise and/or volume-wise.
  • the backpropagation-enabled process is a segmentation process.
  • the training step includes validation and testing.
  • the petrophysical characteristics of a rock, particularly the porosity of the rock are estimated.
  • the rock may be a rock from any hydrocarbon-containing formation, or a portion of a hydrocarbon-containing formation, for which the petrophysical characteristics of the formation, or portion thereof, are of interest.
  • the rock may be a sandstone, a carbonate, a shale, and combinations thereof from a hydrocarbon-containing formation.
  • the rock may be obtained by conventional means for obtaining rock samples from a hydrocarbon formation.
  • a core sample of the rock is obtained by coring a portion of the formation from within a well in the formation.
  • a sample of the rock may be obtained from drill cuttings produced in drilling a well in the formation.
  • the rock sample should be of sufficient size to obtain a three-dimensional image of sufficient volume at the scale that the image is generated.
  • the rock sample should be of sufficient size such that porosity provided by pores within the bulk of the sample is substantially greater than porosity provided by pores at the edge of the sample at the scale or field of view of the image to be generated.
  • a three-dimensional image comprised of a plurality of voxels is obtained from the rock sample.
  • the three-dimensional image of the rock may be obtained utilizing pore-scale imaging technology.
  • a three-dimensional image of the rock may be obtained by x-ray computer tomography, including, without limitation, x-ray micro-computed tomography (micro-CT) and x- ray nano-computed tomography (nano-CT), acoustic microscopy, or magnetic resonance imaging.
  • micro-CT x-ray micro-computed tomography
  • nano-CT x-ray nano-computed tomography
  • acoustic microscopy or magnetic resonance imaging.
  • the three-dimensional image of the rock is obtained by micro-CT to provide sufficient field of view of the rock to avoid edge pores distorting the overall porosity and pore throat size of the resulting image, as well as to reduce scanning time and computational requirements that higher resolution tomography (e.g., nano-CT) would require.
  • the three-dimensional image is obtained from a cloud- based tool adapted to store 2D projection images from a pore-space imaging technology, especially micro-CT and thin sections.
  • the tool is adapted to process the 2D projection images to produce a reconstructed 3D image.
  • the tool is also adapted to store the resulting 3D images.
  • the three-dimensional image of the rock obtained by pore-scale imaging technology has a resolution.
  • the voxels of the three-dimensional image define the resolution of the image.
  • the image is comprised of a plurality of voxels, where the volume defined by each voxel represents a maximum resolution of the image.
  • the resolution of the image should be selected to provide a voxel size at which the dominant pore throats for fluid flow in the rock are sufficiently resolved and at which a sufficient field of view is provided so as to be representative of the whole rock for a given petrophysical property to be analysed (e.g., porosity).
  • the dominant pore throat size (Dd) is the size of pore throats of pores that a non-wetting liquid enters at the pore entry pressure (Pd), where the pore entry pressure is the minimum pressure required before the non-wetting liquid can begin to invade the pore structure of the rock.
  • the resolution of a micro-CT image may be chosen based on the size of the rock sample, the relative average pore size of the type of rock, the time required for the imaging, and the computational power required to store and conduct further computational activity on the image data.
  • the image resolution is chosen to be detailed enough that a non-wetting liquid capillary injection curve can be plotted based on a segmented image produced from the image while maintaining a sufficient field of view to avoid edge pores distorting the overall porosity and pore throat size of the resulting image.
  • the image resolution is selected to require as little computational power to store and conduct further computational activity on the image while providing sufficient detail to construct a capillary injection curve based on the segmented image.
  • the image resolution may be selected based on the type of rock, where sandstones generally have a larger pore structure than carbonates, and require less image resolution than carbonates, and carbonates have a larger pore structure than shales, and require less image resolution than shales.
  • the resolution of the micro-CT image may range from 0.1 ⁇ m 3 to 30 ⁇ m 3 per voxel.
  • the micro-CT image preferably is produced at a resolution of from 1 ⁇ m 3 to 25 ⁇ m 3 per voxel, or from 2.5 ⁇ m 3 to 15 ⁇ m 3 per voxel; for carbonates the resolution of the micro-CT image may range from 0.5 ⁇ m 3 to 20 ⁇ m 3 , or from 1 ⁇ m 3 to 10 ⁇ m 3 ; and for shales the resolution of the micro-CT (or nano-CT) image may range from 0.1 ⁇ m 3 to 10 ⁇ m 3 , or from 0.5 ⁇ m 3 to 5 ⁇ m 3 .
  • the acquired image may be processed to reduce noise and image artifacts.
  • Noise may be filtered from the acquired image by filtering using a local means filter to reduce noise.
  • Imaging artifacts, predominant at the outer edges of the acquired image, may be reduced by processing the image while excluding the outer edges of the image.
  • the three-dimensional image obtained of the rock is processed to segment the image to identify the primary porosity system and the secondary porosity system.
  • voxels are identified as representing either pore space in the rock or solid material in the rock, thereby producing a binary image in which pore voxels have a value of 0 and solid material voxels have a value of 1 (or vice versa).
  • the image obtained of the rock may be a grayscale image
  • processing the voxels of the image to segment the image into voxels representing pore space or solid material may be effected by assigning a voxel a designation as pore space or as solid material based on a threshold, wherein voxels having an image intensity above the threshold may be assigned a value representing a pore (or solid material) and voxels having an image intensity below the threshold may be assigned a value representing solid material (or a pore).
  • a threshold may be calculated using Otsu’s method as described in Otsu, N., A Threshold Selection Method from Gray-level Histogram, IEEE pp.62-66, Trans.
  • Partial pores and/or porous materials are identified a multi-phase segmentation technique.
  • the three-dimensional image of the rock may be processed to segment the voxels into pore space voxels and solid material voxels utilizing segmentation algorithms known in the art.
  • the image may be segmented using a fuzzy c-means clustering algorithm in accordance with the method as described in Chuang, K.-S. et al. (“Fuzzy C-Means Clustering with Spatial Information for Image Segmentation” Comput. Med. Imaging Graph.30: 9-15; 2006).
  • the image may be segmented using an Otsu algorithm.
  • Segmentation using segmentation algorithms is preferably conducted automatically using data processing systems.
  • an image porosity of the rock is estimated from the segmented three-dimensional image of the rock.
  • the image porosity of the rock may be estimated by summing the number of voxels in the segmented image that represent resolved pores, summing the total number of voxels in the segmented image (or obtaining the total number of voxels from the imaging parameters), then dividing the sum of the number of voxels in the segmented image that represent resolved pores by the total number of voxels in the segmented image.
  • a sum of the number of voxels in the segmented image that represent resolved pores may be determined by adding up the number of voxels assigned a binary value (e.g., 1 or 0) representing resolved pores.
  • a sum of the total number of voxels in the segmented image may be determined by adding up the total number of voxels assigned a binary value, both resolved pore voxels and solid material voxels.
  • a resolved porosity correction factor is determined from the segmented three- dimensional image to correct the image porosity of the rock to obtain a corrected resolved porosity of the rock.
  • ⁇ I ⁇ ⁇ where 0 ⁇ ⁇ ⁇ 1 (1)
  • ⁇ I ⁇ ⁇ where 0 ⁇ ⁇ ⁇ 1 (1)
  • ⁇ I ⁇ ⁇ where 0 ⁇ ⁇ ⁇ 1 (1)
  • a non-wetting liquid capillary pressure curve is determined from the segmented three-dimensional image of the rock for resolved pores distinguishable in the segmented image at pressures up to an image-limited pressure.
  • mercury or Woods metal is selected as the non-wetting liquid.
  • a non- wetting liquid capillary pressure curve may be determined from the segmented image by plotting the porosity of the rock occupied by the non-wetting liquid at selected pressures up to the image- limiting pressure based upon simulations of the non-wetting liquid filling the pore space of the image.
  • a simulation may be conducted in which voxels of pore space of pore bodies having a pore throat size of D or larger are assumed to be filled with the non-wetting liquid at the given pressure, the voxels that are “filled” with the liquid are summed, and the porosity of the rock occupied by the non-wetting liquid is calculated by dividing the sum of the number of pore space voxels filled with liquid by the total number of voxels in the image.
  • the porosity of the rock occupied by the non-wetting liquid may then plotted against the given pressure for a number of selected given pressures above the entry pressure (Pd) up to the image- limited pressure (P max ).
  • the entry pressure (P d ) is the pressure at which the non-wetting liquid initially enters pores in the rock
  • the image-limited pressure (P max ) is the minimum pressure required to overcome the capillary pressure of the narrowest pore throat distinguishable in the segmented image.
  • the resolved porosity correction factor ⁇ therefore, can be determined from the pore geometric factor G and the pore resolution parameter N.
  • the pore resolution parameter N is determined from the non-wetting liquid capillary pressure curve derived from the image and the image resolution as determined from the size of the voxels.
  • the size of the voxels may be determined from the parameters of the three-dimensional imaging (i.e., the resolution of the image).
  • the pore geometric factor G is determined from the non-wetting liquid capillary pressure curve derived from the image.
  • the pore geometric factor G may be determined by plotting a best fit curve to the non-wetting liquid capillary pressure curve simulated from the segmented image and determining the pore geometric factor from the shape of the curve.
  • the best fit curve may be plotted by the least squares method or by any conventional curve-fitting method.
  • the pore geometric factor G and the pore throat resolution parameter N are utilized to determine the resolved porosity correction factor.
  • the resolved porosity correction factor ⁇ is then applied to the image porosity of the rock to obtain a corrected resolved porosity.
  • V PP and V PM The volume fraction of partial pores and porous materials are referred to herein as V PP and V PM , and the porosity within these volumes as ⁇ PP and ⁇ PM , respectively.
  • ⁇ TR ⁇ ⁇ + ⁇ PP V PP ⁇ R + ⁇ PM V PM (8)
  • ⁇ ⁇ is calculated by fitting model to MICP simulation results assuming only resolved pores are accessible to invading mercury.
  • Segmented micro-CT images often contain pores that are disconnected from the primary or secondary pore systems. The reasons for disconnected pores can be both geological (e.g., micrites in carbonates) and non-geological (e.g., imaging artefacts).
  • MICP model examples include, without limitation, those described by Leverett (“Capillary Behavior in Porous Solids” Transactions of the AIME, 142: 152–169; 1941), Thomeer (“Introduction of a Pore Geometrical Factor Defined by the Capillary Pressure Curve” Journal of Petroleum Technology 12: 73–77; 1960) and Brooks and Corey (“Hydraulic properties of porous media” Hydrology Papers, Colorado State University, v.3, 37 pp; 1964).
  • MICP expression in equation (10) can also be expressed in terms of pressure for or in terms of saturation in the rock [0061]
  • the mercury will begin to enter the partial pores and/or pores contained within porous minerals. Partial pores are likely to contain larger pores than porous minerals with lower entry pressures. This is because they are distinguished from solid minerals (e.g., quartz, carbonates) by a drop in grey value in 3D micro- CT image around pore-grain boundaries, while porous minerals such as shale are known to have high entry pressures.
  • Partial pores contain pores that are smaller than voxel size but need to be sufficiently voluminous to be identified Therefore, for P max ⁇ P ⁇ P d-PM , equations (13) and (14) generalize to: where P d-PP and P d-PM are entry pressure for mercury to invade pores in partial pores and porous mineral phases, respectively. G d-PP and G d-PM are Thomeer parameters for partial pores and porous mineral phases, respectively.
  • P ⁇ P d-PM equations (15) and (16) further generalize to: [0062]
  • the generalized equations (15) – (18) require several input parameters. These parameters can be obtained by acquiring 3D images by zooming into various phases and subsequent estimation of the local MICP behaviour of individual phases.
  • volume fractions of partial pore and porous minerals, V PP and V PM can be directly obtained from segmented images (Fig.2B).

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Abstract

La présente invention concerne un procédé d'estimation d'une porosité totale de roche à partir d'une image 3D. L'image est segmentée pour identifier une porosité primaire et une porosité secondaire. Pour la porosité primaire, la segmentation identifie des pores résolus et un matériau solide dont les dimensions sont supérieures ou égales à la taille du voxel. Pour la porosité secondaire, l'impact des pores partiels dont la dimension est inférieure à la taille du voxel et/ou des matériaux poreux est déterminé. Une porosité d'image basée sur les pores résolus de l'image segmentée est déterminée et une courbe de pression capillaire de liquide non mouillant est produite pour calculer un facteur de correction de porosité résolu. Des corrections de système de porosité secondaire sont déterminées à l'aide d'un facteur de correction de porosité de pore partielle et/ou d'un facteur de correction de matrice poreuse à partir d'une fraction volumique de pores partiels et/ou d'un matériau poreux, respectivement, identifiés dans l'image segmentée. La saturation est calculée à l'aide de la porosité d'image, du facteur de correction de porosité résolu et du ou des facteurs de correction de porosité secondaire.
PCT/EP2023/067546 2022-06-29 2023-06-28 Procédé de caractérisation de porosité totale de roche WO2024003093A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120163688A1 (en) * 2010-12-22 2012-06-28 Chevron U.S.A. Inc. System and method for multi-phase segmentation of density images representing porous media
US11119025B2 (en) 2018-12-18 2021-09-14 Shell Oil Company Method for characterizing the porosity of rock
WO2021216638A1 (fr) * 2020-04-22 2021-10-28 Shell Oil Company Procédé d'estimation de la saturation en fluide d'une roche

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120163688A1 (en) * 2010-12-22 2012-06-28 Chevron U.S.A. Inc. System and method for multi-phase segmentation of density images representing porous media
US11119025B2 (en) 2018-12-18 2021-09-14 Shell Oil Company Method for characterizing the porosity of rock
WO2021216638A1 (fr) * 2020-04-22 2021-10-28 Shell Oil Company Procédé d'estimation de la saturation en fluide d'une roche

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
BROOKSCOREY: "Hydrology Papers", vol. 3, 1964, COLORADO STATE UNIVERSITY, article "Hydraulic properties of porous media", pages: 37
CHUANG, K.-S. ET AL.: "Fuzzy C-Means Clustering with Spatial Information for Image Segmentation", COMPUT. MED. IMAGING GRAPH., vol. 30, 2006, pages 9 - 15, XP024903619, DOI: 10.1016/j.compmedimag.2005.10.001
LEVERETT: "Capillary Behavior in Porous Solids", TRANSACTIONS OF THE AIME, vol. 142, 1941, pages 152 - 169
OTSU, N.: "Trans. SMC-9", 1979, IEEE, article "A Threshold Selection Method from Gray-level Histogram", pages: 62 - 66
SAXENA NISHANK ET AL: "Estimating electrical cementation and saturation exponents using digital rock physics", JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, ELSEVIER, AMSTERDAM, NL, vol. 198, 3 December 2020 (2020-12-03), XP086434571, ISSN: 0920-4105, [retrieved on 20201203], DOI: 10.1016/J.PETROL.2020.108198 *
SAXENA NISHANK ET AL: "Estimating Pore Volume of Rocks from Pore-Scale Imaging", TRANSPORT IN POROUS MEDIA, SPRINGER NETHERLANDS, DORDRECHT, vol. 129, no. 1, 11 May 2019 (2019-05-11), pages 403 - 412, XP036832637, ISSN: 0169-3913, [retrieved on 20190511], DOI: 10.1007/S11242-019-01295-X *
THOMEER: "Journal of Petroleum Technology", vol. 12, 1960, article "Introduction of a Pore Geometrical Factor Defined by the Capillary Pressure Curve", pages: 73 - 77
YANG EOMZI ET AL: "Construction of Reliable Flow Simulation Domain and Estimation of Permeability Based on Nuclear Magnetic Resonance and 3D X-ray Computed Tomography for Reservoir Carbonate Rocks", TRANSPORT IN POROUS MEDIA, SPRINGER NETHERLANDS, DORDRECHT, vol. 143, no. 3, 25 June 2022 (2022-06-25), pages 739 - 763, XP037926762, ISSN: 0169-3913, [retrieved on 20220625], DOI: 10.1007/S11242-022-01807-2 *

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