• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    METHOD FOR BUILDING A MODEL OF A POROUS MEDIA SAMPLE, SYSTEM FOR BUILDING A MODEL OF A POROUS MEDIA SAMPLE, AND METHOD FOR SEGMENTING A DIGITAL IMAGE OF POROUS MEDIAThis disclosure in question describes methods for building and / or improving 3D digital models of porous media by combining high and low resolution data to capture large and small pores in single models. High resolution data includes laser scanning fluorescence microscopy (LSFM), nano computed tomography (CT) scans and focused ion beam scanning electron microscopic (FIB-SEM). Low-resolution data includes conventional CT scans, micro-tomography scans and synchrotron computed tomography scans.
    公开号:BR112013020554A2
    申请号:R112013020554-7
    申请日:2012-02-28
    公开日:2020-07-28
    发明作者:Neil F. Hurley；Tuanfeng Zhang；Weishu Zhao；Mustafa Al Ibraham
    申请人:Prad Research And Development Limited；
    IPC主号:
    专利说明:

    C METHOD FOR BUILDING A MODEL OF A POROUS MEDIA SAMPLE, SYSTEM FOR BUILDING A MODEL OF A POROUS MEDIA SAMPLE, AND METHOD FOR SEGMENTING A DIGITAL IMAGE
    POROUS MEDIA BACKGROUND Computed tomography images (Computed Tomographic, CT) are commonly used to visualize pore rock systems. CT scans are two-dimensional (2D) cross sections generated by an X-ray source that either rotates around the sample or the sample rotates around the source beam. The gross density is calculated from X-ray attenuation coefficients and serial sections are used to build three-dimensional (3D) images. The digital models are built from conventional scans, micro CT, nano CT and CT - synchrotron. The resolution, inversely related to. sample size, is on the scale from millimeter to micron to submicron, depending on the device used. Petrophysical calculations, such as porosity and: 20 permeability, are strongly influenced by segmentation of pixels in rock vs. pore. Segmentation is especially difficult if a fraction of the pores is less than the resolution of the CT acquisition system. Summary This summary is provided to provide an overview
    . 2 The variety of concepts that are described further down in the detailed description.
    This summary is not intended to identify major or essential features of the claimed matter, nor is it intended to be used as an aid in limiting the scope of the claimed matter.
    According to some modalities, a method of constructing a model of a sample of porous media is described.
    The method includes: receiving the low resolution image data generated using a lower resolution measurement performed on a sample of DPporous media; receive high resolution image data that represent characterizations of aspects (such as shape, size and pore spacing, etc.) of a smaller sample of the porous media, the high resolution data being generated using a higher resolution measurement performed on sample 'smaller; and distribute the characterizations of aspects of the - smaller sample from the high resolution data to the low resolution data, thus generating an improved model of the porous media. . According to some modalities, the distribution. includes the use of a multiple-point statistical method, such as discrete variable geostatistics, or continuous variable geostatistics.
    According to some modalities, the porous media are an underground rock formation "carrying hydrocarbon.
    According
    . 3 some modalities, before distribution, the low resolution image data is segmented into a binary image, the segmentation being based in part on the high resolution measurement characterizations.
    According to some modalities, high-resolution image data is generated using one or more measurements, such as: laser scanning fluorescent microscopy, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, interferometry of vertical scan, nano CT scans, and focused ion beam scanning electron microscopy and low resolution image data is generated using one or more measurements, such as: three-dimensional micro CT, conventional three-dimensional CT and three-dimensional synchrotron CT scans and digital macro photography. 'According to some modalities, a system for. construction of a model of a sample of porous media is described.
    The system includes a processing system adapted and programmed to receive low resolution image data generated using a lower measurement. resolution performed on a first sample of the porous media, receiving high resolution image data that represent characterizations of aspects of a small sample of the porous media, the high resolution data being generated using a higher resolution measurement performed
    . 4 i in the second small sample and distribute the characterizations of aspects of the second small sample from the high resolution data to the low resolution data, thus generating an improved model of the porous media. According to some modalities, the porous media are a reservoir rock formation carrying hydrocarbons and the system includes a sampling system adapted to collect a core test sample from the underground rock formation.
    According to some modalities, a method of segmenting a digital image of porous media is described. The method includes: receiving a low resolution digital image generated using a lower resolution measurement performed on a first sample of the Dporous media; receiving a high resolution digital image generated using a higher resolution measurement performed on a second] small sample of the porous media; identify macropores. high resolution digital image; and segment the low resolution digital image, thus generating a digital binary image having two possible values for each 'pixel, the segmentation being based on the macropores' identified.
    Other features and advantages of the disclosure in question will become more readily apparent from the following detailed description when taken in conjunction with the accompanying drawings.
    . 5 Brief Description of the Drawings The disclosure in question is further described in the detailed description which follows in reference to the observed plurality of drawings by means of non-limiting examples of the disclosure modalities in question, in which similar reference numerals represent similar parts throughout of the various views of the drawings, and in which: Fig. 1 illustrates a volume of representative element (in the acronym in English for representative element volume, REV) of porosity according to some modalities.
    Fig. 2 illustrates a cross-sectional view of a schematic thin section of rock having two pores and impregnated with epoxy and mounted on glass according to some modalities. | Fig. 3 is a flow diagram for 2D model. composite using laser scanning fluorescence microscopy (for laser scanning fluorescence microscopy, LSFM), micro CT, y scans, multi-point statistics,. MPS, and representative element areas (in the acronym in English for representative element area, REA) according to some modalities.
    Fig. 4 illustrates an LSFM (confocal) scan of a porous rock according to some modalities.
    . 6 Figs. 5 and 6 illustrate a comparison of a lower resolution micro CT scan image with a higher resolution LSFM (confocal) image of the same rock surface according to some modalities.
    Fig. 7 is a flow chart for a 3D composite model using laser scanning fluorescence microscopy (LSFM), micro CT scans, multi-point statistics (MPS) and representative element volumes (REV) according to some modalities.
    Fig. 8 illustrates the recording of a confocal scan and a micro CT scan of the same volume of rock according to some modalities; and Fig. 9 shows systems for building an improved model of a sample of porous media according to some modalities.
    Detailed Description | The data shown here are by way of example and: for the purpose of illustrative discussion of the disclosure modalities in question only and are presented in order to provide what is believed to be the most useful and readily understood description of the principles and aspects - conceptual of the disclosure in question.
    In this regard, no attempt is made to show structural details of the disclosure in question in more detail than is necessary for the fundamental understanding of the disclosure in question, the description taken with the drawings making
    . It is evident to those skilled in the art how the various forms of disclosure in question can be realized in practice.
    In addition, similar reference numbers and designations in the various drawings indicate similar elements.
    Laser scanning fluorescence microscopy (LSFM) creates images of polished rock fragments that are impregnated under vacuum pressure with fluorescent epoxy.
    The sample is located on a mobile platform and LSFM scans produce an x-y grid of light intensities measured in regularly spaced planes on the z axis.
    The smallest pores, a function of the laser wavelength and microscope optics, are about 0.25 microns in size.
    3D volumes are about 10 to 20 microns thick in carbonate rocks and about 50 to 250 microns thick in sandstones.
    Scans cover tens of mm in Ú surface area. »The volumes of representative elements (REV) and the areas (REA) are the smallest volumes and areas, respectively, which can be modeled to yield 1 consistent results within the acceptable limits of. variance of the modeled property (in non-limiting examples, porosity and permeability). REVS and REAs allow samples of adequate size to be chosen to ensure that heterogeneity in porous media is captured.
    . 8 According to some modalities, a combination is | described from (a) high resolution 2D or 3D LSFM images acquired for REA's or VER's in rocks, with (Db) CT scans that capture relatively larger 3D volumes at lower resolution.
    LSFM scans are used as training images for 2D and 3D multi-point statistics to distribute high-resolution micropores over lower-resolution CT scan volumes which are used as hard data to condition the simulations.
    The end result is a 3D composite “total porosity” model that captures large and small pores.
    An advantage of the technique is that high resolution data helps to solve the segmentation problem for CT scan data.
    In addition, although we apply this approach to rocks, the same techniques apply to any porous media swept over more than one resolution range. 'Digital models of rocks and pores.
    There are many examples of numerical rock models built using techniques including reconstructions made from thin 2D sections or electron microscope images. scanning (SEM), computer generated sphere packages, laser scanning fluorescence microscopy and various types of CT scans (conventional, micro CT, nano CT and synchrotron microtomography).
    . E) CT scans.
    The most common way to view | 3D pore systems is from CT scans.
    Samples for micro CT are selected based on CT scans of the entire core.
    Full core CT scans provide an overview of heterogeneity in the witnessed range.
    Based on CT numbers that are direct indications of core density, sample locations from different core areas are marked.
    The samples are then cut using suitable tools.
    No special procedures are needed to clean samples before micro CT scans.
    Microtomography uses X-rays to acquire cross sections of a 3D object that can be used to create virtual models.
    Micro CT scans are small in design compared to medical scanners and are ideally suited for imagining such smaller objects. as core samples a few millimeters in size.
    Micro CT scanners are used to obtain accurate 3D details on rock morphology avoiding AND approximations necessary to reconstruct 3D images via. process-based or statistical methods.
    Micro CT scanners achieve a resolution of about 1 to 5 microns.
    For further analysis, with a resolution below the micron range, nano CT scanners can be used.
    “10" Laser scanning fluorescence microscopy. Ú The laser scanning fluorescence microscopy (LSFM) offers a high resolution technique (about 0.25 micron) for the construction of 3D digital rock models. Confocal and Multiphotons are the most common, although the emerging field of super resolution fluorescence microscopy can provide improved images of rocks and other porous media, up to a few nm to tens of nm in scale. See “Huang, B., Bates, M. , and Zhuang, X., 2009, “Super-resolution fluorescence microscopy:" ”Annual Review of Biochemistry, v. 78, Pp. 993-1016 ”"., Such techniques improve the resolution of fluorescence microscopy using standardized excitation or single fluorescence molecule localization. Confocal microscopy, the most common type of LSFM, uses spot lighting and a perforation placed in front] of a detector to eliminate out-of-focus light. As each measurement is a single point, confocal devices scan along parallel line grids to provide 2D images of sequential planes at specified depths within a sample.
    . The depth of penetration of LSFM is limited because the reflected light is absorbed and scattered by material above the focal plane. Depths of optical sectioning in sandstones ranged from 50 to 250 microns. See, “Fredrich, J.T., 1999, 3D imaging of porous media using laser scanning
    - 11 confocal microscopy: Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, v. 24, Issue 7, p. 551- 561 ”. In carbonate rocks, the sweep depths are 10 to 20 microns. Our experiments successfully imaged depths as large as 500 microns using pore molds of carbonate rocks, where the rock material was removed with acid. Fortunately, area coverage is not limited because tile sweeps can be made from relatively large areas (tens of mm ) Of polished sections of rock.
    Multiphoton microscopy uses two-photon excitation to image living tissue to a very high depth, about a millimeter. See "Wikipedia, 2010a, website http://en.wikipedia.org/wiki/Confocal microscopy, accessed October 31, 2010". Like confocal microscopy, this technique excites fluorescent dyes injected into rocks. “The principle is based on the idea that. two photons of comparably lower energy than O needed to excite a photon can also excite a fluorophore in a quantum event. Each photon carries about half the energy needed to excite the - molecule. An excitation results in the subsequent emission of a fluorescent photon, at a higher energy than either of the two excitatory photons ”. The resolution is limited by diffraction to about 250 nm, similar to confocal microscopy.
    . 12 Confocal and multiphoton microscopy is widely used in the earth sciences and semiconductor industries.
    Multiple point statistics. Multi-point (or multipoint) statistical methods (MPS) are a new family of spatial statistical interpolation algorithms proposed in the 1990s that are used to generate conditional simulations of discrete variable fields, such as geological faces, through training images . See, "Guardiano, F., and Srivastava, RM 1993, Multivariate geoetatistics: Beyound bivariate moments: Geoestatistics-Troia, A. Soares, Dordrecht, Netherlands, Kluwer Academic Publications, v 1, p 133-144" ". MPS is winning popularity in reservoir modeling due to its ability to generate realistic models that can be limited by different types of data.Unlike conventional 2-point geostatistics or variogram-based approaches, MPS uses a training image to quantify the complex depositional patterns believed to exist in studied reservoirs. These training patterns are then reproduced in the final MPS simulations with conditioning for local data collected from the reservoirs. Therefore, MPS allows modelers to use their previous geological interpretations as conceptual models (training images) in the reservoir modeling process and assess uncertainty
    & 13 associated with previous interpretations by helping to use different training images. In addition to categorical variables, MPS can also be used to treat continuous variable training images, such as spatial porosity distribution. Two families of MPS algorithms are available to handle these different types of training images: Snesim for categorical variables and Filtersim for continuous variables.
    The Snesim and Filtersim algorithms honor absolute or “hard” restrictions based on data acquired in wells or outcrops and other interpreted trend maps of the reservoir under study. Training images are the primary driver of any MPS approach. A question raised implicitly by the current MPS algorithms is how to generate training images. Training images are designed to model or reproduce. real geological characteristics and should, as far as possible, be derived from existing geologically significant images. Training images' can be derived from various sources, such as - hand-made sketches, aerial photographs, satellite images, seismic volumes, models based on geological objects, models of physical scale or models based on geological processes .
    Training images of categorical variables
    . 14 are easier to generate than continuous variable training images. An object-based approach is commonly used to generate training images with categorical variables. A region-based approach, combined with the addition of desired constraints, can be used to generate training images for continuous variables. See Zhang T., Bombarde, S., Strebelle, S., and Oatney, E., 2006, porosity modeling of a carbonate reservoir using continuous multiple-point statistics simulation: SPE Journal v. 11, p. 375-379. Areas and volumes of representative elements. Representative element volumes (REV) provide a new way of dealing with heterogeneity and scaling issues in reservoir modeling. In summary, REV is the smallest volume that can be modeled to yield consistent results within acceptable limits i of variance of a modeled property, such as' porosity. Using this approach, we can scale up rock properties from fine to coarse scales by determining the smallest volume that needs to be modeled, executing the flow model and using the B results in larger scale simulations. Once we have modeled a REV, we do not need to model larger volumes because we capture heterogeneity of the special rock type on that scale.
    The concept of REV was first discussed in 1972.
    . 15 See “Bear, J., 1972,“ Dynamics of fluids in porous media: ”Elsevier, New York, 746 p” (hereinafter “Bear 1972”). Bear defined AU; as a volume in a porous medium with a centroid of P (Fig. 3). AU; it is considered to be much larger than a single pore or grain.
    AU, is the volume of empty space and n; is the ratio of empty space to volume, that is, fractional porosity.
    In large values of AU; i, there are minimal fluctuations in porosity as a function of volume.
    However, as the volume decreases, the porosity fluctuations increase, especially when AU; approach the size of a single pore, which has a fractional porosity of 1. If the centroid P is located in a grain, the porosity is 0 when AU; = O.
    The AU value is defined as the REV below which fluctuations in porosity are significant and above which fluctuations in porosity are minimal.
    In summary, the AU dimensions are sufficient in Ú so that “the effect of adding or subtracting one or more. pores have no significant influence on the value of no.
    Using the REV approach, the porous medium is replaced by “a fictional continuum: a substance without a structure that for any point from which we can assign - kinematic and dynamic variables and parameters that are continuous functions of the spatial coordinates of the point and time” (Bear, 1972). Note that REV for porosity may differ from REV for permeability or other parameters.
    In addition, the REV for static vs. static properties dynamic
    . 16 i can vary.
    In practice, the best method is to use the largest REV determined for various parameters.
    In 2D, the term analogous to REV is representative element area (OER). OER is the smallest area of a rock that is representative of the measured rock property.
    REA and REV measure area and volume, respectively.
    Both terms allow us to capture heterogeneity in rock properties.
    REA and REV are both determined using an iterative process, whereby the variance in a given parameter, such as porosity or permeability, is measured for successively larger areas or sample volumes.
    REA and REV are determined as the area or volume, respectively, where standard deviation of the sample mean variance falls below an acceptable cut.
    Sample mean can be lab-derived core analysis porosity. ] In order to determine REV for a property. rock, such as porosity, you can model a large volume, subsample that volume and calculate variance in porosity as a function of subsample volume.
    Within the limitations of the available computing power, multi-point statistics (MPS) can generate models of any size and shape.
    Because of this, MPS models can be used to help compute REVs.
    Fig. 1 illustrates a volume of representative element (REV) of porosity according to some modalities.
    A modeled volume of
    . 17. pore scale 600 x 600 p area, 150 p thick is shown.
    The same volume can be divided into smaller subvolumes of different sizes.
    For example, the modeled volume 110-1 is shown with 10 µm cubes extracted, the modeled volume 110-2 is shown with 50 µm cubes extracted and the modeled volume 110-3 is shown with 150 µm cubes extracted.
    In each case, the porosities of the subvolumes can be determined. subvolumes, regardless of scale, must be independent volumes, not overlapping.
    If the porosity variance is less than a chosen cut, for example, +/- 5%, then this volume can be used as the REV.
    For the purposes of flow modeling, REV produces representative results.
    The generalized approach for determining REV for any rock property is, according to some modalities: (1) modeling a large] block with rock properties of interest, (2). randomly select a subsample of a given small size within the block, (3) randomly select another non-overlapping subsample of the same size; (4) repeat this process many times, (5) increase the size - of the subsample and sample many similar objects; (6) plot rock property vs subsample size, to see how the variance decreases as a function of subsample size; and (7) when the variance is within acceptable limits (for example, + 5%), this is the REV for the
    “18 CG property of rock under study.
    It was found that the volume of representative element is an important concept, but often overlooked. Often, laboratory experimentalists assume that the samples on which they perform measurements are representative without stating this explicitly. As a consequence, measurements obtained from small samples are used directly for field applications or by static or dynamic modeling. This most likely leads to erroneous and misleading results due to differences in properties caused by sample sizes.
    According to some modalities, an integrated workflow is described for imaging, processing and generating physical pore models using a 2D and 3D LSFM (laser scanning fluorescence microscopy). LSFM is much better than thin sections for detection and. quantification of microporosity. LSFM imaged pores are as small as about 0.25 microns in size.
    Fig. 2 illustrates a cross-sectional view of - a thin schematic section of rock 210 having two pores: 212 and 214 impregnated with epoxy 216 and mounted on glass 220 according to some embodiments. The thin sections are about 30 microns thick and they are visualized using a microscope with light transmitted under the platform. If a spherical pore of radius (r) of 30 microns
    - 19 E 212 is cut through the top surface of the thin section, the edge of the pore will be unclear.
    If a spherical pore with a radius of 60 microns 214 is cut through the top surface of the thin section, the pore will appear smaller (dashed lines) than its actual size.
    As can be seen, pores smaller than 30 microns in radius are either invisible or poorly imaged when viewed using conventional microscopy.
    In addition, according to some modalities, a computation is described for volumes (REVsS) and areas (REAs) of representative elements from sub-volumes or sub-areas not overlapping in porous media.
    The REVs and REAs of LSFM 3D and 2D scans, respectively, are used here to target lower resolution CT scans.
    This helps to solve a long-standing segmentation problem in rocks with pores smaller than the resolution of CT scan images. 'According to some modalities, REVs and REAS's. LSFM 3D and 2D scans, respectively, are used here as training images for MPS (multi-point statistics) simulations conditioned to 'segmented lower resolution CT scans. 'Digital rock models can be constructed from thin 2D sections, scanning electron microscope images (for scanning electron microscope, SEM), or computer-generated sphere packages.
    In addition, fluorescent microscope images
    . 20: laser scanning (LSFM) can be used to generate high resolution 3D digital models (about 0.25 microns). Most commonly, computed tomography (CT) scans are used to create these models: (a) conventional CT scans use relatively large samples (commonly cm diameter cylindrical cores) with resolutions that are about one to several millimeters in size, (b) micro CT scans use small samples (commonly 5 mm 10 diameter core plugs) with resolutions that are about 1 to 5 microns in size; synchrotron microtomography "works at similar scales, and (e) nano CT scans use very small samples (commonly from 60 micron diameter core plugs) to detect pore bodies with resolutions that are about 50nm to 60nm in size.] After segmentation, according to some: modalities, software converts images into pore models.
    The resulting pore size and pore neck size distributions and pore connectivity are used to compute petrophysical properties, such as porosity and permeability.
    Segmentation is an image analysis stage used to generate binary images, in which pores are differentiated from minerals.
    Ideally, scans are large enough to be representative element areas (REAs) or volumes (REVsS), that is, areas
    . 21: or smaller volumes that can be modeled to yield consistent results within acceptable limits of variance of the modeled property, for example, porosity or permeability.
    According to some modalities, multiple point statistics (MPS) are used to create simulations of fields of spatial geological properties and reservoir for reservoir modeling. These methods are conditional simulations that use known results, such as those measured in well holes or rock samples, as “hard” or fixed data that are respected during simulations. MPS uses 1d, 2D or 3D “training images” as quantitative templates to model subsurface property fields.
    According to some modalities, digital images of pore systems acquired by LSFM are used 'as training images once an OER or REV has been. sampled. LSFM images are segmented using core buffer porosity. Micro and macropores are differentiated in LSFM scans through the application of clustering, watershedding or similar algorithms, with one. cut to size. The CT scan segmentation is done using macropores identified from LSFM scans. Binary CT scans are used as hard data in MPS simulations. Such achievements capture porosity in thin and thick scales and are suitable for
    . 22: pore network modeling and flow simulation. This approach combines the strengths of LSFM, that is, high resolution, with the strengths of CT scans, that is, “relatively large volumes scanned at lower resolution. Such composite models provide a “total porosity” solution.
    According to some modalities, laser scanning fluorescence microscopy (LSFM) is used to scan one or more 2D planes through samples of rocks impregnated with fluorescent epoxy. After confirming the imaging of an area of representative element (REA) 2D or volume (REV) 3D, the pore models are constructed from the scans. Segmentation is done to combine laboratory-determined core buffer porosity. Clustering, watershedding or other algorithms differentiate between micro vs macroporosity. The amount of U macropores is used to segment raster images from. micro CTs that have resolutions that are too poor to resolve microporosity. LSFM scans are used as training images and segmented CT Ú scans are used as hard data for models. multiple point statistics (MPS). The end result is a composite rock model with coarse and fine porosity.
    Fig. 3 is a flowchart for a composite 2D model using laser scanning fluorescence microscopy (LSFM), micro CT scans, 23 23 statistics. multiple points (MPS) and representative element areas (REAS) according to some modalities.
    In block 310, a rock sample is impregnated with vacuum pressure with fluorescent epoxy.
    The clean, dry rock sample is subjected to a vacuum (for example, 12.8 psi, 0.88 bar), and epoxy is introduced that has been stained with fluorescent dye (for example, Rhodamine B, 1.5 to 200 µm) mixture) and the combined sample and epoxy are subjected to high pressure (eg 1,200 psi, 82.7 bar). This ensures impregnation of even the smallest connected pores.
    Slow-curing, low-viscosity epoxy is recommended.
    The sample is mounted on a glass slide, cut to the appropriate thickness, for example, a thin section is 30 microns, and a thick section is about 5000 microns thick.
    The top surface of the rock sample is then polished.
    In block 312, thin or thick sections are scanned] using LSFM.
    2D LSFM scans on tiles on top of. 10 to 20 microns (carbonate rocks) or 50 to 250 microns (sandstones) are purchased from the thin or thick section.
    Fig. 4 illustrates an LSFM (confocal) scan of a porous rock according to some modalities.
    The * mineral matrix is dark and the porosity is shown in white.
    The entire thin section is 12,600 square microns and Table 410 shows a 400 tile section that is 1,800 square microns.
    A single 412 tile is shown to be 135 square microns.
    The single tile illustrates a
    "24 GC enlarged view of high resolution confocal microscopy microporosity]. It is important to ensure that the sample is level, that is, perpendicular to the laser beam. The section, for example, is scanned using xy steps of about 0, 25 x 0.25 micron at a depth of 5 or 10 microns below the top surface to avoid surface irregularities LSFM scans are saved, such as tif files.
    Referring again to Fig. 3, in block 314 the LSFM images are created and segmented. According to some modalities, the image analysis software (for example, ImageJj or Photoshop) is used to fuse LSFM tiles. The images are segmented by choosing a threshold to combine porosity measured in the corresponding core buffer.
    'In block 316, a confirmation is made that a - “representative element area (OER)” has been scanned. Porosity is computed for sub-areas of the segmented LSFM scan and this process is repeated many times to increase independent sub-area sizes not overlapping. Calculations are interrupted when there are less than 30 non-overlapping sub-areas, to provide better statistics for standard deviation calculations. Cross-graphs of variance in porosity versus subarea size are then made. OER is the subarea that occurs
    "25 when a standard deviation of variance is within +/- 5% of the sample mean (core buffer porosity). If the sample is not large enough to capture the OER, new data must be acquired for a sample bigger.
    In block 318, the amount of micro vs macroporosity is calculated from the LSFM scan.
    Clustering, 2D watershedding or a similar algorithm is used to separate the touching pores.
    The percentage of macro and microporosity area is computed.
    Microporosity can be defined, for example, as pores smaller than an arbitrary cut, or smaller than the resolution limit of low resolution data.
    In block 320, the thick section is imaged using micro CT scans.
    Micro CT scans are acquired from the thick section of rock used previously for LSFM work.
    Processing techniques are applied, such as filtering or smoothing, to minimize ”or eliminate image artifacts.
    In block 322, micro CT scans are segmented using cut determined from LSFM for macroporosity.
    The amount of macroporosity determined from LSFM in block 318 is used to segment the micro CT scan volume.
    This process helps to solve the problem of how to segment micro CT scans.
    The assumption is that the pore percentage area smaller than the LSFM resolution (about 0.25 x 0.25
    . 26 'micron) is negligible.
    Graphs of pore size frequency of LSFM scans show that it is a reasonable assumption.
    In block 324, LSFM scans are registered for micro CT scans.
    LSFM scans are recorded approximately for micro CT scans to make sure that the same portions of rock are imaged.
    Because LSFM scans are used as MPS (multi-point statistic) training images, accurate registration is not required.
    Figs. 5 and 6 illustrate a comparison of a lower resolution micro CT scan image 510 in Fig. 5 with a higher resolution LSFM (confocal) image 610 in Fig. 6 of the same rock surface.
    The pores are dark and the mineral matrix is clear in the CT 510 micro scan image. The pores are clear and the mineral matrix is dark in the image of Ú LSFM (confocal) 610. - Referring again to Fig. 3, in the block 326 micropores are replaced by null values in the LSFM scans according to some modalities.
    Ú In block 328, the edited LSFM scans are * used as training images for MPS modeling.
    Micro CT scan slices are resampled to match LSFM resolution, for example, about 0.25 x 0.25 micron pixels.
    MPS modeling is performed using edited LSFM scans (block 314) as images of
    : 27:: training.
    Resampled segmented micro CT scan slices (block 322) are used as hard data to build the “total porosity” solution. The result is that 2D CT micro scan slices are populated with macro and microporosity.
    3D can be viewed using conventional image analysis software (for example, ImageJ or Photoshop). To deal with computer memory limitations in resampled micro CT scans, one approach is to use a new data structure in which MPS builds submodels in sequence and uninterrupted transitions occur between submodels “using region conditioning concepts.
    See "Zhang, T. 2008, Incorporating geological conceptual models and interpretations into reservoir modeling using multi-point geostatistics: Earth Science Frontiers, v 15, no. 1, p 26-35". Submodels are exchanged a i from the hard disk to RAM according to the window. display that the user wants to see (zoom in / zoom out). Fig. 7 is a flowchart for 3D composite model using laser scanning fluorescence microscopy i (LSFM), micro CT scans, multiple * point statistics (MPS) and representative element volumes (REV's) according to some modalities.
    In block 710, a rock sample is impregnated with vacuum pressure with fluorescent epoxy.
    The clean, dry rock sample is subjected to a vacuum (for example, 12.8 psi, 0.88 bar), and
    : 28. epoxy, it is introduced that has been stained with fluorescent dye (for example, Rhodamine B, 1.5 to 200 mixture) and the combined sample and epoxy are subjected to high pressure (for example, 1,200 psi, 82.7 bar). This ensures impregnation of even the smallest connected pores. Slow-curing, low-viscosity epoxy is recommended. Mount the sample on a glass slide, cut to the appropriate thickness, for example, a thin section is 30 microns and a thick section is about 5,000 microns thick. Polish the top surface of the rock sample.
    In block 712, thin or thick sections are scanned using LSFM. Sweeps of LSFM stacked in z on tiles on top of 10 to 20 microns (carbonate rocks) or 50 to 250 microns (sandstones) are purchased from the thin or thick section (See, Fig. 4). Care must be taken to ensure that the sample is level, that is, perpendicular to the beam. of laser. The section is scanned, for example, using x-y steps of about 0.25 x 0.25 microns and z steps of 0.4 microns. LSFM scans are saved, such as tif files.
    'In block 714, LSFM images are created and segmented. According to some modalities, image analysis software (for example, ImageJj or Photoshop) is used for photofusion LSFM tiles. Segment images by choosing a threshold to match porosity
    . 29 measured in the corresponding core plug.
    LSFM scans are viewed using image analysis software.
    In block 716, confirmation is made that a “representative element volume (REV)” has been scanned.
    Porosity is calculated for subvolumes from the segmented LSFM scan and this process is repeated many times to increase independent non-overlapping subvolume sizes.
    Calculations are interrupted when there are less than 30 non-overlapping subvolumes, to provide better statistics for standard deviation calculations.
    Cross-plots of variance in porosity versus subvolume size are then plotted.
    REV is the subvolume that occurs when a standard deviation of variance is within +/- 5% of the sample mean (core buffer porosity). If the sample is not large enough to: capture the REV, the new data is acquired for one. larger sample.
    In block 718, the amount of micro vs macroporosity is calculated from the LSFM scan. : Clustering, watershedding or a similar algorithm is ”used to separate the touching pores.
    The percentage of macro and microporosity volume is computed.
    Microporosity can be defined, for example, as pores smaller than an arbitrary cut or smaller than The resolution limit of low resolution data.
    ã 30 o In block 720, the thick section is imaged using micro CT scans. Micro CT scans are acquired from the thick section of rock used previously for LSFM work. Processing techniques are applied, such as filtering or smoothing to minimize or eliminate image artifacts.
    In block 722, micro CT scans are segmented using the cut determined from LSFM for macroporosity. The amount of macropores determined from LSFM in block 718 is used to segment the micro CT scan volume. This process helps to solve the problem of how to segment micro CT scans. The assumption is that the percentage pore volume smaller than the LSFM resolution (about 0.25 x 0.25 x 0.4 microns) is negligible. Graphs of pore size frequency of LSFM scans show that this is] a reasonable assumption.
    . In block 724, LSFM scans are registered for micro CT scans. LSFM scans are recorded approximately for micro CT scans to make sure that the same portions of rock are * imaged (Fig. 8). Because LSFM scans are used as MPS training images (multi-point statistics), accurate registration is not required. In block 726, micropores are replaced by null values in LSFM scans according to some
    . 31 CU modalities.
    In block 728, the edited LSFM scans are used as training images for MPS modeling. The micro CT scan slices are resampled to match LSFM resolution, for example, about 0.25 x 0.25 x 0.4 micron pixels. MPS modeling is performed using edited LSFM scans (block 714) as training images. Resampled, segmented micro CT scan slices (block 722) are used as hard data to build the “total porosity” solution.
    Fig. 8 illustrates the recording of a confocal scan and a micro CT scan of the same volume of rock according to some modalities. In the 810 confocal scan, the porosity is clear and the mineral grains are dark.
    The voxel size is about 0.25 micron. In the CT 812 micro scan, the porosity is dark and the mineral grains are 'white / light gray. The voxel size is 7 microns.
    . Both sweeps 810 and 812 cover portions of the same volume of rock. Scans are recorded by dashed lines. The high resolution 810 confocal scan is used as a training image for simulating. multiple porosity statistic in the low resolution 812 micro CT scan. The sizes of scanned volumes are arbitrary.
    Referring again to block 728 of Fig. 7, the result is that the micro CT scan volume is populated
    - 32 CO with macro and microporosity. 3D volumes can be viewed using conventional image analysis software (for example, Image J or Photoshop). To deal with computer memory limitations in resampled micro CT scans, one approach is to use a new data structure in which MPS builds submodels in sequence and uninterrupted transitions occur between submodels using region conditioning concepts. See “Zhang, T. 2008, Incorporating geological conceptual models and interpretations into reservoir modeling using multi-point geostatistics: Earth Science Frontiers, v 15, No. 1, p 26-35” Submodels are switched from the hard drive to RAM according to the display window the user wants to see (zoom in / zoom out). Fig. 9 shows systems for building an intensified model of a sample of porous media according to some modalities. High resolution data - acquired 910 (such as from LSFM, SEM, TEM, AFM, VSI, etc.) is transmitted to a 950 processing center that includes one or more 944 Ú central processing units to perform the data processing procedures Dice, . as described herein, as well as other processing. The processing center includes a 942 storage system, 940 communication and input / output modules, a 946 user display and a user input system
    948. According to some modalities, the
    . 33. Processing 950 can be located at a remote location to the place where the petrographic data was acquired.
    Low resolution data 912, as acquired using conventional micro CT, CT and / or digital macro photography, is transmitted to processing center 950. In Fig. 9, data and / or samples from a porous underground formation 902 are being gathered at a 900 pit location via a 920 steel cable truck deploying a 924 steel cable tool to well 922. According to some modalities, the 924 steel cable tool includes a core sampling tool to collect a or more core samples from the porous formation 902. As described herein, the data processing center is used to intensify the model 914 of the sampled porous material.
    Although the system in Fig. 9 is shown applied to the example of digital rock images of an underground porous formation, in general the techniques described. can be applied to any porous media.
    Although the disclosure in question is described by means of the previous modalities above, it will be understood by those skilled in the art that modification and variation of the - illustrated modalities can be made without departing from the inventive concepts disclosed here.
    In addition, although preferred embodiments are described in connection with various illustrative structures, those skilled in the art will recognize that the system can be configured using a
    . 34 variety of specific structures.
    Therefore, the disclosure in question should not be seen as limited, except for the scope and spirit of the attached claims.
    权利要求:
    Claims (18)
    [1]
    1. METHOD FOR BUILDING A MODEL OF A POROUS MEDIA SAMPLE, the method characterized by the fact that it comprises: receiving low resolution image data generated using a lower resolution measurement performed on a first sample of the porous media; receive high resolution "image data representing characterizations of aspects of a second small sample of the porous media, high resolution data being generated using a higher resolution measurement performed on the second small sample; and distribute the characterizations of aspects of the second sample of high resolution data in low resolution data, thus generating an improved model of the porous media.
    [2]
    2. Method according to claim 1, characterized by the fact that the second small sample forms a subset of the first sample.
    [3]
    3. Method according to claim 1, "characterized by the fact that the lowest resolution measurement achieves a resolution of at least an order of magnitude worse than the highest resolution measurement.
    [4]
    4. Method, according to claim 1, characterized by the fact that the multi-point statistical method includes using one or more techniques selected from a group consisting of: discrete variable geostatistics and continuous variable geostatistics.
    [5]
    5. Method, according to claim 1, characterized by the fact that the characterizations of aspects of the second small sample of the porous media include characterizations of one or more aspects selected from a group consisting of: shape, size and pore spacing .
    [6]
    6. Method, according to claim 1, characterized by the fact that it also comprises, before distribution, segmenting the low resolution image data into a binary image, the segmentation being based in part on the higher resolution measurement characterizations .
    [7]
    7. Method, according to claim 1, characterized by the fact that high resolution image data is generated using one or more measurements selected from a group consisting of: laser scanning fluorescent microscopy, electron i microscopy scanning, transmission electron microscopy, atomic force microscopy, vertical scanning interferometry, nano CT scans and focused ion beam scanning electron microscopy.
    [8]
    8. Method, according to claim 7, characterized by the fact that high resolution image data is generated using one or more measurements selected from a group consisting of: section: two-dimensional thin, two-dimensional thick section and microscopy two-dimensional transmitted laser scan fluorescence.
    [9]
    9. Method, according to claim 1, characterized by the fact that low resolution image data is generated using one or more measurements selected from a group consisting of: three-dimensional micro CT, conventional three-dimensional CT and CT scans of three-dimensional synchrotron, and macro digital photography.
    [10]
    10. Method, according to claim 1, characterized by the fact that it also comprises determining a representative element area size (OER) that is smaller than the second small sample using an iterative process, whereby variance in porosity or permeability is measured for successively larger sample areas; and i20 before distribution, segment the low resolution image data into a binary image, the segmentation being performed to coincide with a computerized macroporosity for an OER.
    [11]
    11. Method according to claim 1, characterized by the fact that it further comprises determining a representative volume element size (REV) that is smaller than the second small sample using a | iterative process, by which variance in porosity or permeability is measured for successively larger sample volumes; and before distribution, segment the low-resolution image data into a binary image, the segmentation being performed to coincide with the computerized macroporosity for REV.
    [12]
    12. SYSTEM TO BUILD A MODEL FOR A POROUS MEDIA SAMPLE, the system characterized by the fact that it comprises: a processing system adapted and programmed to receive the low resolution image data generated using a lower resolution measurement performed on a first sample of the porous media, receive high resolution image data that represent characterizations of aspects of a small sample of the porous media, the high resolution data being generated using a higher resolution measurement performed on the second small sample, and distribute the characterizations of aspects of the second 'small sample from the high resolution data in the low resolution data, thereby generating an improved model of the porous media.
    [13]
    13. System according to claim 12, characterized by the fact that the characterizations of aspects of the second small sample of the porous media include characterizations of one or more selected aspects | from a group consisting of: shape, size and pore spacing.
    [14]
    14. System according to claim 12, characterized by the fact that the porous medium is an underground rock formation.
    [15]
    15. System, according to claim 12, characterized by the fact that the processing system is further programmed to segment the low resolution image data into a binary image, the segmentation being based in part on the characterizations of the highest measurement resolution.
    [16]
    16. System, according to claim 12, characterized by the fact that it also comprises a high resolution imaging system adapted to generate high resolution image data using one or more measurements selected from a group consisting of: microscopy laser scanning fluorescence, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, vertical scanning interferometry, nano CT, and focused ion beam scanning electron microscopy.
    [17]
    17. METHOD TO SEGMENT A DIGITAL IMAGE OF POROUS MEDIA, the method characterized by the fact that it comprises: receiving a low resolution digital image generated using a lower resolution measurement performed on a first sample of the porous media; 'receiving a high resolution digital image generated using a higher resolution measurement performed on a second small sample of the porous media; identify macropores from the high resolution digital image; and segment the low resolution digital image, thereby generating a digital binary image having two possible values for each pixel, the segmentation being based on the identified macropores.
    [18]
    18. Method, according to claim 17, characterized by the fact that it also comprises: characterizing aspects of the second small sample from the high-resolution digital image; and distribute the characterizations in the low resolution digital image, thereby generating an improved model of the porous media.
    2 sound Fig. 1
    . 2/8 A = n (30) a A = n (80) 210
    SST RNA Fig. 2
    类似技术:
    公开号 | 公开日 | 专利标题
    BR112013020554A2|2020-07-28|method for building a model of a sample of porous media, system for building a model of a sample of porous media, and methods for segmenting a digital image of porous media
    US20110004447A1|2011-01-06|Method to build 3D digital models of porous media using transmitted laser scanning confocal mircoscopy and multi-point statistics
    Bultreys et al.2016|Imaging and image-based fluid transport modeling at the pore scale in geological materials: A practical introduction to the current state-of-the-art
    RU2544884C1|2015-03-20|Method of determining representative elements of areas and volumes in porous medium
    Kong et al.2018|Quantification of the morphology of shelly carbonate sands using 3D images
    Wildenschild et al.2013|X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems
    Peng et al.2012|Using X-ray computed tomography in pore structure characterization for a Berea sandstone: Resolution effect
    Freire-Gormaly et al.2016|Pore structure characterization of Indiana limestone and pink dolomite from pore network reconstructions
    BR112013020555A2|2020-07-28|method for characterizing a sample of porous medium including a plurality of pore bodies and a plurality of pore throats, system for characterizing a sample of porous medium including a plurality of pore bodies and a plurality of pore throats, and method for characterizing a sample of porous underground rock formation including a plurality of pore bodies and a plurality of pore throats
    AU2009319885A1|2010-06-03|Method for determining permeability of rock formation using computer tomographic images thereof
    Guan et al.2019|Effects of image resolution on sandstone porosity and permeability as obtained from X-ray microscopy
    US10115188B2|2018-10-30|Porous material analysis system and method for hydrology, petroleum and environment engineering
    Al Ibrahim et al.2012|An automated petrographic image analysis system: Capillary pressure curves using confocal microscopy
    Chandra et al.2021|A critical review on pore to continuum scale imaging techniques for enhanced shale gas recovery
    Zhang2015|MPS-driven digital rock modeling and upscaling
    Goral et al.2016|Correlative X-ray and electron microscopy for multi-scale characterization of heterogeneous shale reservoir pore systems
    Ishutov et al.2017|3D printing Berea sandstone: testing a new tool for petrophysical analysis of reservoirs
    Olkowicz et al.2019|Focus stacking photogrammetry for micro‐scale roughness reconstruction: A methodological study
    Petford et al.2001|Investigation of the petrophysical properties of a porous sandstone sample using confocal scanning laser microscopy
    Petford et al.1999|Pore structure determination using confocal scanning laser microscopy
    Fredrich et al.1997|Statistical characterization of the three-dimensional microgeometry of porous media and correlation with macroscopic transport properties
    Munawar et al.2021|Upscaling reservoir rock porosity by fractal dimension using three-dimensional micro-computed tomography and two-dimensional scanning electron microscope images
    Zhang et al.2019|Multiscale characterization of shale diffusivity using time-lapsed X-ray computed tomography and pore-level simulations
    Tomutsa et al.2004|Nanoscale pore imaging and pore scale fluid flow modeling in chalk
    Roth et al.2016|Fully controlled sampling workflow for multi-scale X-ray imaging of complex reservoir rock samples to be used for digital rock physics
    同族专利:
    公开号 | 公开日
    US8908925B2|2014-12-09|
    RU2576501C2|2016-03-10|
    US20120281883A1|2012-11-08|
    RU2013143805A|2015-04-10|
    WO2012118866A3|2012-11-29|
    WO2012118866A2|2012-09-07|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US3013467A|1957-11-07|1961-12-19|Minsky Marvin|Microscopy apparatus|
    US4107364A|1975-06-06|1978-08-15|The Procter & Gamble Company|Random laid bonded continuous filament cloth|
    JPS61140844A|1984-12-14|1986-06-27|Hitachi Ltd|Observing device for three-dimensional structure|
    FR2673238B1|1991-02-26|1999-01-08|Schlumberger Services Petrol|PROCESS FOR CHARACTERIZING THE TEXTURE HETEROGENEITIES OF GEOLOGICAL FORMATIONS CROSSED BY A BOREHOLE.|
    US5838634A|1996-04-04|1998-11-17|Exxon Production Research Company|Method of generating 3-D geologic models incorporating geologic and geophysical constraints|
    DE69829072D1|1997-12-01|2005-03-24|Schlumberger Technology Bv|METHOD AND DEVICE FOR FORMING, TESTING AND MODIFYING GEOLOGICAL UNDERGROUND MODELS|
    US6750974B2|2002-04-02|2004-06-15|Gsi Lumonics Corporation|Method and system for 3D imaging of target regions|
    WO1999054784A1|1998-04-21|1999-10-28|University Of Connecticut|Free-form nanofabrication using multi-photon excitation|
    US6337568B1|1999-10-25|2002-01-08|Tarek A. Tutunji|System and method for enhanced vertical resolution magnetic resonance imaging logs|
    US6516080B1|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|
    US6985612B2|2001-10-05|2006-01-10|Mevis - Centrum Fur Medizinische Diagnosesysteme Und Visualisierung Gmbh|Computer system and a method for segmentation of a digital image|
    US20050159067A1|2003-11-07|2005-07-21|Mitsui Chemicals, Inc.|Hydrophilic nonwoven fabric|
    WO2005104002A1|2004-03-31|2005-11-03|Exxonmobil Upstream Research Company|Method for simulating and estimating sandstone properties|
    US7516055B2|2004-08-20|2009-04-07|Chevron U.S.A. Inc|Multiple-point statistics simulation with enhanced computational efficiency|
    US7630517B2|2005-07-13|2009-12-08|Schlumberger Technology Corporation|Computer-based generation and validation of training images for multipoint geostatistical analysis|
    US7363160B2|2005-09-12|2008-04-22|Schlumberger Technology Corporation|Technique for determining properties of earth formations using dielectric permittivity measurements|
    US7718351B2|2006-03-14|2010-05-18|Agency For Science, Technology & Research|Three-dimensional fabrication of biocompatible structures in anatomical shapes and dimensions for tissue engineering and organ replacement|
    US7363158B2|2006-04-07|2008-04-22|Chevron U.S.A. Inc.|Method for creating a stratigraphic model using pseudocores created from borehole images|
    US8725477B2|2008-04-10|2014-05-13|Schlumberger Technology Corporation|Method to generate numerical pseudocores using borehole images, digital rock samples, and multi-point statistics|
    US9581723B2|2008-04-10|2017-02-28|Schlumberger Technology Corporation|Method for characterizing a geological formation traversed by a borehole|
    US9134457B2|2009-04-08|2015-09-15|Schlumberger Technology Corporation|Multiscale digital rock modeling for reservoir simulation|
    US8311788B2|2009-07-01|2012-11-13|Schlumberger Technology Corporation|Method to quantify discrete pore shapes, volumes, and surface areas using confocal profilometry|
    US20110004447A1|2009-07-01|2011-01-06|Schlumberger Technology Corporation|Method to build 3D digital models of porous media using transmitted laser scanning confocal mircoscopy and multi-point statistics|
    US8384017B2|2009-08-06|2013-02-26|Schlumberger Technology Corporation|Subsurface nuclear measurement systems, methods and apparatus|
    US8908925B2|2011-02-28|2014-12-09|Schlumberger Technology Corporation|Methods to build 3D digital models of porous media using a combination of high- and low-resolution data and multi-point statistics|
    WO2012118867A2|2011-02-28|2012-09-07|Schlumberger Technology Corporation|Method to determine representative element areas and volumes in porous media|
    WO2012118868A2|2011-02-28|2012-09-07|Schlumberger Technology Corporation|Petrographic image analysis for determining capillary pressure in porous media|US9134457B2|2009-04-08|2015-09-15|Schlumberger Technology Corporation|Multiscale digital rock modeling for reservoir simulation|
    US9581723B2|2008-04-10|2017-02-28|Schlumberger Technology Corporation|Method for characterizing a geological formation traversed by a borehole|
    US8908925B2|2011-02-28|2014-12-09|Schlumberger Technology Corporation|Methods to build 3D digital models of porous media using a combination of high- and low-resolution data and multi-point statistics|
    WO2012118868A2|2011-02-28|2012-09-07|Schlumberger Technology Corporation|Petrographic image analysis for determining capillary pressure in porous media|
    US9684084B2|2012-05-01|2017-06-20|Saudi Arabian Oil Company|Three-dimensional multi-modal core and geological modeling for optimal field development|
    US9377546B2|2012-05-06|2016-06-28|Schlumberger Technology Corporation|Automatic extraction and characterization of fault and fracture populations|
    US9140117B2|2012-07-13|2015-09-22|Ingrain, Inc.|Method for evaluating relative permeability for fractional multi-phase, multi-component fluid flow through porous media|
    RU2015104162A|2012-08-10|2016-09-27|Ингрейн, Инк.|METHOD FOR INCREASING THE ACCURACY OF VALUES OF ROCK PROPERTIES OBTAINED FROM DIGITAL IMAGES|
    US20140052420A1|2012-08-20|2014-02-20|Ingrain Inc.|Digital Rock Analysis Systems and Methods that Estimate a Maturity Level|
    US10514372B2|2012-08-23|2019-12-24|Halliburton Energy Services, Inc.|Digital rock analysis systems and methods that reliably predict a porosity-permeability trend|
    CN104823202B|2012-09-27|2019-04-23|英格染股份有限公司|Computed tomographysystem and method for the analysis variation of the rock property caused by handling|
    CA2886953A1|2012-10-19|2014-04-24|Conocophillips Company|Method for modeling a reservoir using 3d multiple-point simulations with 2d training images|
    EP2755021B1|2013-01-15|2016-06-22|Carl Zeiss Microscopy Ltd.|Method of analyzing a sample and charged particle beam device for analyzing a sample|
    US10815796B2|2013-01-30|2020-10-27|Raytheon Technologies Corporation|Coating process for gas turbine engine component with cooling holes|
    CN105378219B|2013-07-11|2019-05-10|英格染股份有限公司|Wellbore depth interval is characterized from landwaste|
    WO2015021088A1|2013-08-06|2015-02-12|Schlumberger Canada Limited|Methods for determining a saturation-height function in oil and gas reservoirs|
    CA2920525A1|2013-08-08|2015-02-12|Ingrain, Inc.|Conditioning of expanded porosity|
    EP3033484A4|2013-08-13|2017-03-08|Services Petroliers Schlumberger|Digital core sensitivity analysis|
    WO2015084447A1|2013-12-05|2015-06-11|Schlumberger Canada Limited|Method and system of showing heterogeneity of a porous sample|
    US10914864B2|2014-07-16|2021-02-09|Schlumberger Technology Corporation|Multiscale method for reservoir models|
    US10018817B2|2015-03-04|2018-07-10|Aramco Services Company|Adaptive optics for imaging through highly scattering media in oil reservoir applications|
    US10055884B2|2015-04-30|2018-08-21|Saudi Arabian Oil Company|Three-dimensional fluid micromodels|
    CN105067395B|2015-06-30|2017-09-01|中国石油天然气股份有限公司|It is a kind of for nanometer CT mineral standard entropy sample and its preparation method and application|
    WO2017041281A1|2015-09-11|2017-03-16|Irock Technologies Co., Ltd|Porous media anaylysis system and method|
    EP3394394A4|2015-12-22|2019-08-14|Landmark Graphics Corporation|Image based rock property tensor visualization of a geocellular grid in a dynamic 3d environment|
    US10621292B2|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|
    CN105954496B|2016-06-13|2018-02-02|中国石油天然气股份有限公司|The pore throat of sandstone reservoir and the reconstructing method of fluid distrbution|
    CN109923579A|2016-09-28|2019-06-21|港大科桥有限公司|The recovery of pixel resolution in scanning imagery|
    CN106338421A|2016-10-09|2017-01-18|东北石油大学|Planar radial flow rock core preparation device and method|
    US10274625B2|2016-11-09|2019-04-30|Chevron U.S.A. Inc.|System and method for porosity estimation in low-porosity subsurface reservoirs|
    WO2018089783A1|2016-11-11|2018-05-17|University Of South Florida|Automated stereology for determining tissue characteristics|
    US10648292B2|2017-03-01|2020-05-12|International Business Machines Corporation|Cognitive enhanced oil recovery advisor system based on digital rock simulator|
    US10691846B2|2017-03-01|2020-06-23|International Business Machines Corporation|Capillary network simulations based on a low-dimensional representation of porous media|
    CN106770406A|2017-03-03|2017-05-31|南京大学|Carbonate mineral based on light splitting technology contrast cathodoluminescence image acquiring technology high|
    RU178298U1|2017-08-03|2018-03-29|Богдан Валентинович Соколенко|Profiler|
    WO2019055774A1|2017-09-15|2019-03-21|Saudi Arabian Oil Company|Inferring petrophysical properties of hydrocarbon reservoirs using a neural network|
    CN107767338A|2017-10-11|2018-03-06|上海电力学院|The fusion method of different accuracy shale data|
    CN108320307B|2018-01-19|2020-09-08|中国石油天然气股份有限公司|Method and device for determining effective unit volume of reservoir rock sample|
    US10983237B2|2018-04-13|2021-04-20|Saudi Arabian Oil Company|Enhancing seismic images|
    US10891462B2|2018-06-29|2021-01-12|Saudi Arabian Oil Company|Identifying geometrical properties of rock structure through digital imaging|
    CN110954949A|2018-09-27|2020-04-03|中国石油化工股份有限公司|Compact sandstone soft porosity distribution inversion method|
    RU2718409C1|2018-10-23|2020-04-02|Автономная некоммерческая образовательная организация высшего образования "Сколковский институт науки и технологий"|System for recovery of rock sample three-dimensional structure|
    CN109709116B|2018-11-23|2021-11-02|中国石油天然气股份有限公司|Stepping rotating sample table, and micro-particle three-dimensional surface imaging method and system|
    WO2021086388A1|2019-10-31|2021-05-06|Halliburton Energy Services, Inc.|Scale-coupled multiscale model simulation|
    法律状态:
    2020-08-11| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-08-18| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-12-08| B11B| Dismissal acc. art. 36, par 1 of ipl - no reply within 90 days to fullfil the necessary requirements|
    2021-11-03| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    优先权:
    申请号 | 申请日 | 专利标题
    US201161447417P| true| 2011-02-28|2011-02-28|
    US61/447,417|2011-02-28|
    PCT/US2012/027039|WO2012118866A2|2011-02-28|2012-02-28|Methods to build 3d digital models of porous media using a combination of high- and low-resolution data and multi-point statistics|
    US13/407,526|US8908925B2|2011-02-28|2012-02-28|Methods to build 3D digital models of porous media using a combination of high- and low-resolution data and multi-point statistics|
    US13/407,526|2012-02-28|
    [返回顶部]