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WO2010059987A2 - Procédé permettant de déterminer les caractéristiques physiques des roches par images tomographiques informatisées - Google Patents

Procédé permettant de déterminer les caractéristiques physiques des roches par images tomographiques informatisées Download PDF

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Publication number
WO2010059987A2
WO2010059987A2 PCT/US2009/065401 US2009065401W WO2010059987A2 WO 2010059987 A2 WO2010059987 A2 WO 2010059987A2 US 2009065401 W US2009065401 W US 2009065401W WO 2010059987 A2 WO2010059987 A2 WO 2010059987A2
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WIPO (PCT)
Prior art keywords
image
pixels
rock
gray scale
pore space
Prior art date
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Ceased
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PCT/US2009/065401
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English (en)
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WO2010059987A3 (fr
Inventor
Jack Dvorkin
Naum Derzhi
Meghan Armbruster
Qian FANG
Zbigniew Wojcik
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Ingrain Inc
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Ingrain Inc
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Publication date
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Priority to AU2009316427A priority Critical patent/AU2009316427B2/en
Priority to EP09764136.9A priority patent/EP2359334B1/fr
Publication of WO2010059987A2 publication Critical patent/WO2010059987A2/fr
Anticipated expiration legal-status Critical
Publication of WO2010059987A3 publication Critical patent/WO2010059987A3/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/005—Testing the nature of borehole walls or the formation by using drilling mud or cutting data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/419—Imaging computed tomograph

Definitions

  • the invention relates generally to the field of estimating material properties of porous media. More specifically, the invention relates to methods for estimating such properties using computer tomographic (CT) images of porous media such as subsurface rock formation.
  • CT computer tomographic
  • U.S. Patent No. 6,516,080 issued to Nur includes preparing a "thin section" from a specimen of rock formation.
  • the preparation typically includes filling the pore spaces with a dyed epoxy resin.
  • a color micrograph of the section is digitized and converted to an n-ary index image, for example a binary index image.
  • Statistical functions are derived from the two-dimensional image and such functions are used to generate three-dimensional representations of the rock formation. Boundaries can be unconditional or conditioned to the two-dimensional n-ary index image. Desired physical property values are estimated by performing numerical simulations on the three-dimensional representations.
  • permeability is estimated by using a Lattice-Boltzmann flow simulation.
  • multiple, equiprobable three-dimensional representations are generated for each n-ary index image, and the multiple estimated physical property values are averaged to provide a result.
  • a rock physics transform is a mathematical formula or algorithm that relates one property of a rock formation to another.
  • Such transforms can be based on an idealized mathematical model of rock, such as the differential effective medium that models rock as a solid with ideal-shape inclusions or the Hertz-Mindlin model that models rock as a composite made of perfect elastic spheres.
  • Such transforms can also be based on a sufficient number of experimental data (e.g., well log measurements or laboratory measurements) using a statistically fit expression that approximates such data.
  • An example of the latter is the Raymer transform between porosity ⁇ and the compressional wave (P -wave) velocity of the rock (V p ).
  • V p (1 - ⁇ ) 2 V ps + ⁇ V pf , where V ps is the P-wave velocity in the mineral (matrix or solid) phase of the rock (e.g., quartz) and V pf is the P-wave velocity in the pore fluid (e.g., water).
  • the elastic (compressional) wave velocity is directly related to the bulk K and shear G moduli by the expression
  • V p i](K + AG 13) / p , where p is the bulk density of the rock.
  • the foregoing moduli can be obtained by laboratory measurement, and can also be obtained by calculations made from an image of a rock sample.
  • Another example is the relationship between the absolute permeability k and the porosity ⁇ of a rock formation called the Kozeny-
  • the parameters that enter these two equations, one for permeability and the other for the formation factor, can be obtained by laboratory measurement and also by calculations based on an image of a rock sample.
  • the permeability ( k ) and formation factor ( F) equation examples above one may conduct a large number of laboratory tests on samples that represent the formation under examination. Alternatively, such data can be obtained by digital calculations on a digitally imaged rock sample.
  • Obtaining and calibrating rock physics transforms using physical samples and using measurements made on actual rock samples requires extensive laboratory and/or well measurements. There exists a need to use images such as the foregoing CT scan images to estimate relationships between formation parameters without the need for extensive laboratory or well measurements.
  • a method for estimating a relationship between physical properties of a porous material from a sample thereof includes making a three dimensional tomographic image of the sample of the material.
  • the image is segmented into pixels each representing pore space or rock grains.
  • the image is divided into sub-volumes.
  • a porosity is estimated for each sub-volume from the image thereof.
  • At least one petrophysical parameter is modeled from the image of each sub-volume.
  • a relationship between the porosity and the at least one modeled petrophysical parameter is determined.
  • the relationship and the modeled petrophysical parameter for each sub-volume are stored in a computer or displayed.
  • FIG. 1 shows an example of obtaining cuttings during drilling of a wellbore and analysis thereof during the drilling.
  • FIG. 2 shows a flow chart of an example process for CT image segmentation.
  • FIG. 3 shows a flow chart of an example analysis procedure according to the invention.
  • FIG. 4 shows a continuation of the flow chart of FIG. 3.
  • drill cuttings obtained during the drilling of a wellbore through subsurface formations. It should be clearly understood that drill cuttings is only one example of samples of rock formation that may be used with the present invention. Any other source of a rock formation sample, e.g., whole cores, sidewall cores, outcrop quarrying, etc. may provide suitable samples for analysis using methods according to the invention. Consequently, the invention is not limited in scope to analysis of drill cuttings.
  • a drilling unit or "rig" 10 is disposed at the Earth's surface.
  • the rig 10 includes lifting equipment (not shown separately) for raising and lowering one of several types of device used to rotate a drill string 14.
  • the device, shown at 18 in the present example may be a top drive, although the use of a tope drive is not a limit on the scope of the invention.
  • the drill string 14 is assembled by threadedly coupling segments of drill pipe end to end.
  • a drill bit 16 is disposed at the lower end of the drill string 14 and cuts through subsurface rock formations 11 to form a wellbore 12.
  • the rig 10 is operated to cause some of the axial load (weight) of the drill string 14 to be applied to the drill bit 16.
  • the top drive 18 rotates the drill string 14 and the drill bit 16 at the lower end thereof.
  • the combination of axial load and rotation causes the drill bit 16 to cut through the formations 11.
  • the rig 10 includes a tank or pit 22 having drilling fluid ("mud") 20 stored therein.
  • a pump 24 lifts the mud 20 and discharges it through suitable flow lines 26 so that the mud 20 passes through an internal passage in the drill string 14, whereupon it is discharged through suitable orifices or courses in the drill bit 16.
  • the discharged mud 20 cools and lubricates the drill bit 16 and lifts the cuttings generated by the bit 16 to the Earth's surface.
  • the cuttings and mud thus lifted enter separation and cleaning devices, shown generally at 28 and including, for example, devices known as "degassers” and “shale shakers” to remove the cuttings and contamination from the mud 20.
  • the mud after such cleaning is returned to the pit 22 for subsequent use in drilling the wellbore 12.
  • the cuttings removed from the separation and cleaning device 28 may be transported to a computer tomographic ("CT") scanner 30, which may use x-rays for analysis of internal structure of the cuttings, for generation of three dimensional (3D) images of the cuttings.
  • CT computer tomographic
  • the images so generated may be in numerical form and their content will be further explained below.
  • the cuttings may be saved for further analysis or may be suitably discarded.
  • An example of a suitable CT scanner for making images usable with methods according to the invention is sold under model designation MicroXCT Series 3D tomographic x-ray transmission microscope by Xradia, Inc., 5052 Commercial Circle, Concord, CA 94520.
  • an analysis of the cuttings from the CT scan images may provide, substantially in real time during the drilling of the wellbore, an estimate of certain properties of the subsurface formations being drilled, for example fluid mobility of one or more constituent fluids in the pore spaces of the rock formations 11.
  • images generated by the CT scanner 30 may be transferred to a computer 32 having program instructions for carrying out image analysis and subsequent formation property modeling as described below.
  • drill cuttings are only one type of rock sample that may be analyzed according to the invention.
  • the drill bit 16 may be an annular type configured to drill whole cores of the rock formations 11.
  • percussion sidewall core samples may be obtained during drilling or when the drill string 14 is withdrawn from the wellbore 12 such as for "wireline" well evaluation techniques. Accordingly, the scope of the invention is not limited to analysis of drill cuttings. As explained above, the invention is also not limited to use with rock samples obtained from a wellbore drilled through subsurface rock formations.
  • CT scan imaging of a porous material sample is used in the invention to produce a numerical object that represents the material sample digitally in the computer 32 for subsequent numerical simulations of various physical processes, such as viscous fluid flow (for permeability estimation); stress loading (for the effective elastic moduli); electrical current flow (for resistivity); and pore size distribution for nuclear magnetic resonance relaxation time properties, including distribution of relaxation time.
  • various physical processes such as viscous fluid flow (for permeability estimation); stress loading (for the effective elastic moduli); electrical current flow (for resistivity); and pore size distribution for nuclear magnetic resonance relaxation time properties, including distribution of relaxation time.
  • such analysis can be performed while drilling operations are underway, substantially in real time.
  • the CT scan image produced by the CT scanner 30 may be a 3D numerical object consisting of a plurality of 2D sections of the imaged sample.
  • Each 2D section consists of a grid of values each corresponding to a small region of space defined within the plane of the grid. Each such small region of space is referred to as a "pixel" and has assigned thereto a number representing the image darkness (or for example the density of the material) determined by the CT scan procedure.
  • the value ascribed to each pixel of the 2D sections is typically an integer that may vary between zero and 255 where 0 is, e.g., pure white, and 255 is pure black. Such integer is typically referred to as a "gray scale" value.
  • 0 to 255 is associated with eight digital bits in a digital word representing the gray scale value in each pixel.
  • Other gray scale ranges may be associated with longer or shorter digital words in other implementations, and the range of 0 to 255 is not intended to limit the scope of the invention.
  • the numerical object is preferably processed so that all the pixels allocated to the void space in the rock formation (pore space) are represented by a common numerical value, e.g., by only 255s, and all the pixels associated with the rock matrix (or rock grains) are represented by a different numerical value, for example, zeroes.
  • image segmentation is called image segmentation.
  • the resulting numerical object can be normalized so that the pore spaces are represented by, for example, ones and the rock grains are represented by zeroes.
  • the foregoing may be described as converting the image into a binary index.
  • the image may be converted into an index having any selected number, n, of indices. It has been determined that sufficiently accurate modeling of some rock petrophysical parameters or properties, e.g. permeability, may be obtained using a binary index, in which one value represents pore space and another single value represents rock grains.
  • thresholding where all pixels having a gray scale value below a selected threshold value (e.g., a gray scale value of 150 on a scale of 0 to 255) are identified as grains, while all other pixels are identified as pore space.
  • a selected threshold value e.g., a gray scale value of 150 on a scale of 0 to 255
  • pore space all pixels having a gray scale value below a selected threshold value (e.g., a gray scale value of 150 on a scale of 0 to 255) are identified as grains, while all other pixels are identified as pore space.
  • a type of image segmentation known as "region growing” can be used. Region growing may be described as follows. Consider a 2D section of a CT scan image made of a porous rock formation such as sandstone, which has primarily quartz rock grains.
  • a substantial number of “seeds” (each seed consists of one or more pixels having a similar pixel gray scale level, e.g., 250 ⁇ 5) is placed within the image. All pixels within a seed are assigned the same gray scale level which may be an average (e.g., arithmetic) of the gray levels of all the pixels within the seed. The seeds in the image frame do not overlap spatially. Next, two or more adjacent seeds are merged and are identified as a "region” if the gray scale levels of the adjacent seeds have gray scale values within a selected difference threshold of each other. Each identified region is assigned a uniform (fixed) gray level, which can be a weighted average of the gray scale values of all the seeds that have been merged into the identified region.
  • the unprocessed CT image is transformed into internally uniform regions plus unclassified pixels that were not assigned to any of the identified regions (because such pixels included gray scale values outside the allocation threshold criteria).
  • Each of such unclassified pixels can be assigned to an adjacent region with the closest gray scale level. If the resulting number of regions is greater than two, however, the foregoing method simply fails to allocate the CT image correctly into grains and pores.
  • the next element in image classification according to the invention is to grow each of the two initially formed seeds by allocating to such seeds all adjacent pixels having gray scale levels within a selected tolerance, e.g., 130 - 5 for pore spaces and 60 + 5 for rock grains.
  • the foregoing process can continue by incrementally increasing the gray scale lower limit for rock grains and incrementally reducing the gray scale upper limit for pore spaces until the two limits meet. The result is that all pixels will be allocated to either pore space or to rock grains, thus providing a fully segmented image.
  • a possible advantage of the foregoing procedure is that instead of forming multiple regions, the foregoing technique grows only two distinctive regions from start to end, thus avoiding the situation where multiple distinctive regions appear and then have to be reclassified into either pores or grains. If the resulting segmented image appears noisy (cluttered), it can be smoothed by any of conventional filters.
  • two user-selected thresholds ti and ⁇ are selected to determine initial regions for pore space and rock grains, respectively.
  • the initial thresholds may be selected, for example, by analysis of a histogram of the gray scale values in the CT image. For every pixel p t having a gray scale level represented by B(P 1 ):
  • clusters If there are two or more contiguous pixels in any subset of the image frame that are classified according to the threshold procedure above, such contiguous pixels may be referred to as "clusters.” All of the pixels allocated as explained above then become the image seeds from which region growing proceeds.
  • each pixel classified as a pore its eight neighbors (spatially contiguous pixels) in the 2D image plane are interrogated. If any of the interrogated neighbor pixels is not already identified as pore or rock grain, and the gray scale level of such pixel is within a preselected tolerance level of (or initially selected different between) the gray scale level assigned to the "pore" seed (as in Step 2 above), the interrogated neighbor pixel is then classified as a pore and is allocated to the "pore" cluster.
  • the foregoing tolerance value for each of the pore space and the rock grain may be increased by a selected increment (for example five gray scale numbers), and the contiguous pixel interrogation and classification may be repeated.
  • the foregoing tolerance increase and repeated adjacent pixel interrogation may be repeated until all or substantially all the pixels in the
  • 2D image frame are allocated to either rock grain or pore space.
  • all pixels in the image frame are interrogated may be are allocated to pore space or to rock grains, depending on whether the gray scale value in each pixel exceeds the respective segmentation threshold.
  • the allocated pixels are then segmented into seeds where two or more contiguous pixels are allocated to either pore space or rock grain.
  • pixels adjacent to the each of the seeds are interrogated.
  • Previously unallocated pixels having a gray scale value falling within an initially selected threshold difference (or tolerance) of the adjacent cluster pixel gray scale value are allocated to the seed at 50.
  • the image frame is interrogated to determine if all or substantially all the image frame pixels have been allocated to either pore space or rock grain.
  • the number of allocated pixels is counted and at 60 if all or substantially all the pixels in the image frame have been allocated, a new 2D image frame can be selected, at 58, and the above process repeated. Typically the next 2D image frame will be adjacent to the most recently analyzed 2D image frame. The above process can be repeated until all available 2D image frames have been analyzed. If all pixels in the image frame have not been allocated, at 52, the tolerance or difference threshold values used at 50 may be increased and the interrogation of pixels adjacent to the existing seeds can be repeated, at 48, and the remainder of the process can be repeated.
  • the result of the foregoing procedure is a segmented 3D image of the rock sample including image elements for rock grain and for pore space.
  • image can be stored or displayed in a computer and can be used as input to one or more rock property characterization models.
  • image segmentation may prove advantageous when implementing analysis techniques according to the invention, which require a segmented image as input for further analysis. Such techniques may be explained as follows. In many cases, a relatively large range for each input parameter (e.g., porosity) can be obtained from a single sample of rock formation, for example drill cuttings (FIG. 1), sidewall cores, outcrop quarry or whole drill cores. Referring to FIGS.
  • the sample of rock formation, obtained at 60 should be imaged, such as by CT- scanning, to obtain a high-resolution 3D image of the pore space and rock grains (matrix). The foregoing is shown at 62.
  • the image should be segmented to allocate image portions to the rock matrix and the pore space. Image segmentation may be performed as explained above with reference to FIG. 2.
  • an estimate of the porosity ⁇ may be obtained, at 66.
  • a numerical simulation of a physical experiment may be conducted on the sample image.
  • Such numerical experiments and methods to conduct these experiments may include but are not limited to: (a) single-phase fluid flow using the Lattice-Boltzmann numerical method (LBM) to obtain the absolute permeability; (b) elastic deformation using the finite-element method (FEM) to obtain the imaged sample's elastic moduli and elastic-wave velocity; and (c) electrical current flow using FEM to obtain the imaged sample's electrical resistivity and formation factor.
  • LBM Lattice-Boltzmann numerical method
  • FEM finite-element method
  • electrical current flow using FEM to obtain the imaged sample's electrical resistivity and formation factor.
  • the original segmented image volume may be subdivided into a selected number of sub-volumes, as shown at 70.
  • Subdividing the image can be performed by dividing the original volume into a number of evenly spaced volumes or by randomly selecting a sufficient number of sub-volumes.
  • One example is to divide a cubic image volume into sub-cubes. Examples of sub-cubes include dividing the original image volume into eight, twenty seven, sixty four or one hundred twenty five cubic sub-volumes.
  • a value of porosity may be determined for each sub-volume, as shown at 72.
  • the porosity and/or other attributes of sub-volumes typically includes a relatively large range of each petrophysical parameter of interest, and such range is typically sufficient to obtain a meaningful transform for each such parameter.
  • numerical simulation of any one or more petrophysical parameters of interest can be performed on each of the sub-volumes (just as performed on the entire image volume), at 72.
  • the results may be plotted or otherwise allocated in the computed outputs (e.g., absolute permeability k) with respect to the input parameters (e.g., porosity ⁇ ) at 76.
  • the resulting data points often are sufficient in number to establish a relationship between them (i.e., a transform).
  • the data points above are used, at 78, for the one or more modeled petrophysical parameters, to obtain a relationship such as a best-fit analytical expression between porosity and the petrophysical parameter(s).
  • the porosity may be related to permeability.
  • the foregoing relationship determination may be repeated with respect to formation resistivity factor, at 80.
  • the foregoing relationship determination may also be repeated for bulk, elastic and/or shear moduli, at 82.
  • the Lattice-Boltzmann method can be used to numerically solve Navier-Stokes equations for flow simulation for permeability modeling. Such solution may be used to calculate permeability of simulated 3D volumes.
  • the Lattice-Boltzmann method is a robust tool for flow simulation, particularly in media with complex pore geometry. See, for example. Ladd, Numerical Simulations of Particulate Suspensions via a discretized Boltzmann Equation, Part 1: Theoretical Foundation, J. Fluid Mech., v271, 1994, pp. 285-309; Gunstensen et al, "Lattice Boltzmann Model of Immiscible Fluids, Phys. Rev.
  • the Lattice-Boltzmann method simulates fluid motion as collisions of imaginary particles, which are much larger than actual fluid molecules, but wherein such particles show almost the same behavior at a macroscopic scale.
  • the algorithm used in the Lattice-Boltzmann method repeats collisions of these imaginary particles until steady state is reached, and provides a distribution of local mass flux.
  • the Lattice-Boltzmann method is applied successfully for many pore structures, including cylindrical tubes, random densely packed spheres, and 3D rock samples digitized by CT scanning as explained above. See, for example, U.S. Patent No.

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Abstract

L’invention concerne un procédé permettant d'estimer les relations entre les propriétés physiques d’un matériau poreux à partir d'un échantillon de celui-ci consistant à réaliser une image tomographique en trois dimensions de l'échantillon du matériau. L'image est segmentée en pixels chacun représentant un espace poreux ou des grains de roche. L'image est divisée en sous-volumes. On estime la porosité pour chaque sous-volume. On modélise au moins un paramètre pétrophysique à partir de l’image de chaque sous-volume. On détermine une relation entre la porosité et ledit paramètre pétrophysique modélisé selon, par exemple, un procédé statistique le plus adapté. La relation et le paramètre pétrophysique modélisé pour chaque sous-volume sont stockés dans un ordinateur ou affichés.
PCT/US2009/065401 2008-11-24 2009-11-20 Procédé permettant de déterminer les caractéristiques physiques des roches par images tomographiques informatisées Ceased WO2010059987A2 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
AU2009316427A AU2009316427B2 (en) 2008-11-24 2009-11-20 Method for determining rock physics relationships using computer tomographic images thereof
EP09764136.9A EP2359334B1 (fr) 2008-11-24 2009-11-20 Procédé de détermination de relations <i>in situ</i> entre des propriétés physiques d'un milieu poreux à partir d'un échantillon de celui-ci

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US12/276,430 US8155377B2 (en) 2008-11-24 2008-11-24 Method for determining rock physics relationships using computer tomographic images thereof
US12/276,430 2008-11-24

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WO2010059987A2 true WO2010059987A2 (fr) 2010-05-27
WO2010059987A3 WO2010059987A3 (fr) 2011-06-23

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2544884C1 (ru) * 2011-02-28 2015-03-20 Шлюмбергер Текнолоджи Б.В. Способ определения репрезентативных элементов площадей и объемов в пористой среде

Families Citing this family (60)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8331626B2 (en) * 2008-05-21 2012-12-11 Ingrain, Inc. Method for estimating material properties of porous media using computer tomographic images thereof
US8170799B2 (en) * 2008-11-24 2012-05-01 Ingrain, Inc. Method for determining in-situ relationships between physical properties of a porous medium from a sample thereof
US8583410B2 (en) * 2010-05-28 2013-11-12 Ingrain, Inc. Method for obtaining consistent and integrated physical properties of porous media
KR101048605B1 (ko) * 2010-11-26 2011-07-12 한국지질자원연구원 컴퓨터 단층촬영장치를 이용한 지질시료 코어 내 이질물질 부피측정장치 및 그 방법
US8861814B2 (en) * 2010-12-22 2014-10-14 Chevron U.S.A. Inc. System and method for multi-phase segmentation of density images representing porous media
US9507047B1 (en) 2011-05-10 2016-11-29 Ingrain, Inc. Method and system for integrating logging tool data and digital rock physics to estimate rock formation properties
EP2732135B1 (fr) 2011-07-12 2016-08-24 Ingrain, Inc. Procédé de simulation d'écoulement multiphase/multicomposant fractionnaire dans des milieux poreux
US20130262028A1 (en) 2012-03-30 2013-10-03 Ingrain, Inc. Efficient Method For Selecting Representative Elementary Volume In Digital Representations Of Porous Media
US9746431B2 (en) * 2012-05-11 2017-08-29 Ingrain, Inc. Method and system for multi-energy computer tomographic cuttings analysis
US9285301B2 (en) * 2012-07-13 2016-03-15 Ingrain, Inc. Digital rock analysis systems and methods with reliable multiphase permeability determination
US9140117B2 (en) 2012-07-13 2015-09-22 Ingrain, Inc. Method for evaluating relative permeability for fractional multi-phase, multi-component fluid flow through porous media
WO2014025970A1 (fr) 2012-08-10 2014-02-13 Ingrain, Inc. Procédé pour améliorer la précision de valeurs de propriété de roche obtenues à partir d'images numériques
US20140052420A1 (en) * 2012-08-20 2014-02-20 Ingrain Inc. Digital Rock Analysis Systems and Methods that Estimate a Maturity Level
US10514372B2 (en) * 2012-08-23 2019-12-24 Halliburton Energy Services, Inc. Digital rock analysis systems and methods that reliably predict a porosity-permeability trend
BR112015006701B1 (pt) 2012-09-27 2021-11-03 Ingrain, Inc Método para analisar alterações em propriedades de rochas resultantes de um tratamento.
EP2914956A1 (fr) 2012-11-01 2015-09-09 Ingrain, Inc. Caractérisation de roche et d'autres échantillons par processus et système de préparation d'échantillons utilisant des matières de montage aptes à être coulées
US9070049B2 (en) * 2013-03-15 2015-06-30 Bp Corporation North America Inc. Systems and methods for improving direct numerical simulation of material properties from rock samples and determining uncertainty in the material properties
US10550690B2 (en) 2013-05-16 2020-02-04 Dassault Systemes Simulia Corp. Mass exchange model for relative permeability simulation
US10669844B2 (en) 2013-07-11 2020-06-02 Halliburton Energy Services, Inc. Characterizing a wellbore depth interval from rock fragments
EA031381B1 (ru) 2013-08-06 2018-12-28 Бипи Корпорейшн Норт Америка Инк. Прямое численное моделирование петрофизических свойств на основе изображений в имитированных условиях напряженно-деформированного состояния
US20150062300A1 (en) * 2013-08-30 2015-03-05 Halliburton Energy Services, Inc. Wormhole Structure Digital Characterization and Stimulation
US20160369601A1 (en) * 2013-12-30 2016-12-22 Sergey Sergeevich Safonov Method for estimating petrophysical properties of a hydrocarbon reservoir
US10001446B2 (en) 2014-11-07 2018-06-19 Ge Energy Oilfield Technology, Inc. Core sample analysis
US9970888B2 (en) * 2014-11-07 2018-05-15 Ge Energy Oilfield Technology, Inc. System and method for wellsite core sample analysis
US10031148B2 (en) 2014-12-31 2018-07-24 Ge Energy Oilfield Technology, Inc. System for handling a core sample
US10261204B2 (en) 2014-12-31 2019-04-16 Ge Energy Oilfield Technology, Inc. Methods and systems for scan analysis of a core sample
US10302814B2 (en) 2015-08-20 2019-05-28 Baker Hughes, A Ge Company, Llc Mechanisms-based fracture model for geomaterials
US20180247450A1 (en) * 2015-09-03 2018-08-30 Schlumberger Technology Corporation A computer-implemented method and a system for creating a three-dimensional mineral model of a sample of a heterogenous medium
US10422736B2 (en) 2015-09-16 2019-09-24 Halliburton Energy Services, Inc. Method for determining porosity associated with organic matter in a well or formation
US10546072B2 (en) 2016-03-28 2020-01-28 Baker Huges, A Ge Company, Llc Obtaining micro- and macro-rock properties with a calibrated rock deformation simulation
US10102311B2 (en) * 2016-03-28 2018-10-16 Baker Hughes, A Ge Company, Llc Obtaining micro- and macro-rock properties with a calibrated rock deformation simulation
US10429532B2 (en) * 2016-03-31 2019-10-01 Schlumberger Technology Corporation System and methodology for estimating formation elastic properties using decomposed and undecomposed signal
US10621292B2 (en) 2016-04-18 2020-04-14 International Business Machines Corporation Method, apparatus and computer program product providing simulator for enhanced oil recovery based on micron and submicron scale fluid-solid interactions
EP3574291B1 (fr) 2017-01-26 2023-12-06 Dassault Systemes Simulia Corp. Visualisations d'écoulements à phases multiples basées sur un temps d'occupation de fluide
US10691846B2 (en) 2017-03-01 2020-06-23 International Business Machines Corporation Capillary network simulations based on a low-dimensional representation of porous media
US10648292B2 (en) 2017-03-01 2020-05-12 International Business Machines Corporation Cognitive enhanced oil recovery advisor system based on digital rock simulator
CN108802073B (zh) * 2017-05-05 2021-03-12 中国石油化工股份有限公司 一种基于数字岩心的岩石电性参数获取方法及装置
WO2019040049A1 (fr) * 2017-08-22 2019-02-28 Halliburton Energy Services, Inc. Nouvelle méthodologie indépendante de la porosité pour prédiction de perméabilité fondée sur des images de micro-résistivité et des résistivités de latérolog
US11714040B2 (en) 2018-01-10 2023-08-01 Dassault Systemes Simulia Corp. Determining fluid flow characteristics of porous mediums
CN108397193A (zh) * 2018-01-31 2018-08-14 连琼娥 一种冲击式石油勘探机
US11009497B2 (en) 2018-06-22 2021-05-18 Bp Corporation North America Inc. Systems and methods for estimating mechanical properties of rocks using grain contact models
US11530598B2 (en) 2018-08-21 2022-12-20 Dassault Systemes Simulia Corp. Determination of oil removed by gas via miscible displacement in reservoir rock
US11613984B2 (en) 2019-09-04 2023-03-28 Dassault Systemes Simulia Corp. Determination of hydrocarbon mobilization potential for enhanced oil recovery
US11443149B2 (en) 2020-04-14 2022-09-13 Schlumberger Technology Corporation Cuttings imaging for determining geological properties
CN115552461A (zh) * 2020-05-08 2022-12-30 Bp北美公司 源自二维图像的材料性质
US11530999B2 (en) * 2020-05-29 2022-12-20 Halliburton Energy Services, Inc. Pore contribution corrected quantifying of surface roughness
US11847391B2 (en) 2020-06-29 2023-12-19 Dassault Systemes Simulia Corp. Computer system for simulating physical processes using surface algorithm
WO2022032057A1 (fr) 2020-08-06 2022-02-10 Schlumberger Technology Corporation Imagerie de déblais pour déterminer des propriétés géologiques
WO2022053991A1 (fr) * 2020-09-10 2022-03-17 Khalifa University of Science and Technology Décodeur de morphologie destiné à prédire une perméabilité de roche hétérogène par vision 3d guidée par apprentissage automatique
US11907625B2 (en) 2020-12-29 2024-02-20 Dassault Systemes Americas Corp. Computer simulation of multi-phase and multi-component fluid flows including physics of under-resolved porous structures
US12098992B2 (en) * 2021-03-31 2024-09-24 Saudi Arabian Oil Company Evaluating source rock permeability using 3D model
CN112989481B (zh) * 2021-05-17 2021-08-20 中国科学院武汉岩土力学研究所 复杂地质隧道施工围岩稳定可视化图像数据处理方法
CN113433157B (zh) * 2021-06-24 2023-12-15 西南石油大学 基于核磁共振t2谱建立随机单元等效岩心模型的方法
US12131526B2 (en) 2022-01-07 2024-10-29 Schlumberger Technology Corporation Systems and methods for segmenting rock particle instances
US12241369B2 (en) * 2022-01-07 2025-03-04 Schlumberger Technology Corporation Systems and methods for performing lithological classification of cuttings based on calibrated photographs of rock particles
AR128863A1 (es) 2022-03-24 2024-06-19 Schlumberger Technology Bv Sistema de microscopía digital para objetos 3d
CN119630993A (zh) 2022-07-21 2025-03-14 斯伦贝谢技术有限公司 根据数字图像的地层孔隙度估计
CN115760883B (zh) * 2022-12-21 2025-12-09 武汉大学 基于随机森林算法的颗粒材料ct图像分割方法及装置
US20240427050A1 (en) * 2023-06-20 2024-12-26 Saudi Arabian Oil Company Identification of Borehole Flow Members
CN119177841B (zh) * 2024-11-22 2025-02-25 陕西炬烽建筑劳务有限公司 用于水利工程勘探的钻孔和采样一体化设备

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5877995A (en) * 1991-05-06 1999-03-02 Exxon Production Research Company Geophysical prospecting
US5335724A (en) * 1993-07-28 1994-08-09 Halliburton Company Directionally oriented slotting method
US6516080B1 (en) * 2000-04-05 2003-02-04 The Board Of Trustees Of The Leland Stanford Junior University Numerical method of estimating physical properties of three-dimensional porous media
US6807487B2 (en) * 2001-05-11 2004-10-19 Nonlinear Seismic Imaging, Inc. Mapping permeable reservoir formations by measuring the elastic nonlinear interactions of a seismic wave as it propagates through the reservoir rock matrix and its pore fluids
US6742603B2 (en) * 2001-06-18 2004-06-01 Exxonmobil Research And Engineering Company Hydrothermal drilling method and system
US6933719B2 (en) * 2003-07-03 2005-08-23 Exxonmobil Research And Engineering Co. Fluid flow properties from acoustically stimulated NMR
GB2414072B (en) 2004-05-12 2006-07-26 Schlumberger Holdings Classification method for sedimentary rocks

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of EP2359334A2 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2544884C1 (ru) * 2011-02-28 2015-03-20 Шлюмбергер Текнолоджи Б.В. Способ определения репрезентативных элементов площадей и объемов в пористой среде

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