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    • 专利摘要:
    METHOD FOR CHARACTERIZING A POROUS MEDIUM SAMPLE INCLUDING A PORE BODY PLURALITY AND A PORE THROUGH PLATALITY, SYSTEM FOR CHARACTERIZING A POROUS MEDIUM SAMPLE INCLUDING A PORE BODY PLURALITY AND A GUARANTEE PLURALITY OF GUARANTEE A POROUS UNDERGROUND ROCK FORMATION SAMPLE INCLUDING PLURALITY OF PORE BODIES AND PLURALITY OF PORE THROATSThe present revealed matter is generically related to methods to characterize two-dimensional (2D) and three-dimensional (3D) samples to determine pore body size and pore throat and capillary pressure curves in porous medium using petrographic image analysis. The entry includes high-resolution petrographic images and laboratory-derived porosity measurements. The output includes: (1) pore body and pore throat size distributions, and (2) simulated capillary pressure curves for pore bodies and pore throats.
    公开号:BR112013020555A2
    申请号:R112013020555-5
    申请日:2012-02-28
    公开日:2020-07-28
    发明作者:Neil F. Hurley；Mustafa Al Ibraham；Weishu Zhao
    申请人:Prad Research And Development Limited；
    IPC主号:
    专利说明:

    à: METHOD FOR CHARACTERIZING A SAMPLE OF POROUS MEDIA: INCLUDING A PLURALID OF PORE BODIES AND PLURALITY OF PORE THROATS, SYSTEM FOR CHARACTERIZING A SAMPLE OF POROUS MEDIA INCLUDING PLURALITY OF PORE BODIES AND PLURALITY OF PORE THROATS, AND METHOD FOR CHARACTERIZING A ROCK FORMING SAMPLE POROUS UNDERGROUND INCLUDING A PLURALITY OF BODIES OF
    PORO AND A PLORALITY OF PORE THROUGH BACKGROUND Properties obtained from Special Core Analysis (Special Core AnaLysis, SCAL) provide an input for reservoir simulators. Such properties include pore body and pore throat size distributions, and capillary pressure curves. Pore size distributions are generally computed from laboratory mercury injection capillary pressure experiments (Mercury injection capillary pressure, MICP). Under ideal conditions, mercury enters the pores with the largest throats first, and fills these pores while the pressure is relatively constant. Pressure is sequentially increased to allow mercury to enter the smaller and smaller pore throats and their pore bodies fixed. MICP measurements are accurate, but are slow, expensive and destroy samples. Additionally,
    : MICP measurements are not useful for BR pore throats greater than 100 microns because these throats are filled at low injection pressures. Summary This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential aspects 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 embodiments, a method for characterizing a sample of porous medium including a plurality of pore bodies and a plurality of pore throats is described. The method includes preparing the sample of the porous medium in such a way that a single plane of the sample can be imaged; generate a high-resolution two-dimensional image of the single plane of the sample prepared from the porous medium; processing the high resolution image in part by performing a watershed image processing technique; identify a plurality of pore throats - based at least in part on the watershed technique; and Í determine a dimension associated with each of the plurality. identified pore throats.
    According to some modalities, the high resolution image is made using confocal microscopy, for example, a a fluorescence scanning microscopy. laser.
    According to some modalities, the porous medium being characterized is a sample of rock as from an underground rock formation that contains hydrocarbon. According to some modalities, the sample of porous medium is prepared from a core sample in thin and / or thick slices, and subjected to vacuum pressure impregnation with fluorescent epoxy.
    According to some modalities, pore throat and pore body size distributions and capillary pressure curves in the porous medium are determined. The high resolution image can be pre-processed and enlarged, for example, using sewing, registration, mixing, clipping and / or rotation. According to some modalities, the image is segmented into grains and pores, thereby generating a binary image that is separated into a porous image and a throat image using one or more binary logical operations. The pore image and image of throats are differentiated using one or more clustering algorithms thereby generating an image of clustered pores and an image of clustered throats, and - pore throat and body size distributions of:: pore are computed with based on grouped images. The pore image and the image of clustered throats are subjected to data analysis in which each pore body is assigned a diameter of a larger pore throat connected to the pore body, and each pore body has a pore area. known, and pores having at least one throat connection are grouped according to the largest throat size, and pore body size for each cluster is computed and used to generate simulated capillary pressure curves using a Washburn equation.
    According to some embodiments, a system for characterizing a sample of porous medium including a plurality of pore bodies and a plurality of pore throats is described. The system includes: a sample preparation system adapted to prepare a sample of the porous medium in such a way that a single sample plane can be imaged; an imaging system adapted to generate a two-dimensional high resolution image of a single plane of a sample prepared from the porous medium; and a processing system adapted and programmed to process a two-dimensional high resolution image generated in part by performing a watershed image processing technique, identifying a plurality of pore throats based at least in part on the watershed technique; and determining a dimension associated with each of the plurality - identified pore throats. . Additional aspects and advantages of the disclosure in question will become more readily apparent from the following detailed description when taken in combination with the accompanying drawings.
    . Brief description of the drawings The present disclosure is further described in the detailed description which follows, with reference to the mentioned plurality of drawings by means of non-limiting examples 5 of modalities of the present disclosure, in which similar reference numerals represent similar parts in all the various views of the drawings and where: Figure 1 represents a process of limitation (binarization).
    Figure 2 represents a watershed example using the output image of figure 1, according to some modalities.
    Figure 3 represents a flow chart of a selective Kuwahara filter procedure to “cure” large pores, according to some modalities.
    Figure 4 represents an example of a clustering algorithm, according to some modalities.
    Figure 5 represents an Expanding Flow Model (in the acronym in English for Expanding Flow Model, EFM) used to understand capillary pressure, according to some modalities.
    - Figures 6 and 7 represent two views of pores and throats, according to some modalities. Figure 8 represents a numerical pressure calculation workflow based on the EFM model, according to some modalities.
    - k Figure 9 shows systems to determine: pore throat and pore body size distributions and simulated capillary pressure curves from petrographic data, according to some modalities.
    Figure 10 represents a workflow of the described methods for determining pore body distribution, pore throat distribution and capillary pressure, according to some modalities.
    Figure 11 is a flow chart that illustrates a 2D workflow according to some modalities.
    Figure 12 is a flow chart that illustrates the basic workflow for determining the representative element area (in the acronym in English for Representative Element Area, REA) according to some modalities.
    Figure 13 illustrates a procedure for relating each pore throat to its adjacent pore bodies, according to some modalities.
    Figure 14 illustrates a procedure for healing large pores, according to some modalities.
    Figures 15 and 16 represent a typical pore image 1510 and throat image 1610 after processing the binary image. . : Figure 17 represents an EFM model for calculating capillary pressure. The pores are approximated by tubes if separated in decreasing throat size (ie, diameter).
    * Figures 18-25 represent results of analysis' of petrographic image for a sample, according to some modalities.
    Figure 26 is a flow chart that illustrates a 3D workflow according to some modalities; and Figure 27 is a flow chart that illustrates the basic workflow for determining representative element volume (in the acronym for Representative Element Volume, REV), according to some modalities.
    Detailed description | The details shown here are for example and for the purpose of illustrative discussion of the modalities of the present disclosure only and are presented in the case of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the present disclosure.
    In this regard, no attempt is made to show structural details of the present disclosure in more detail than necessary for the fundamental understanding of the present disclosure, the description taken with the drawings making it evident to those skilled in the art such as. various forms of the present disclosure can be incorporated into practice.
    In addition, similar reference numbers and - | designations in the various drawings indicate similar elements.
    According to some modalities, an integrated workflow is described to imagine, process and
    * generate pore and body throat size distributions. pore sizes and similar capillary pressure curves for porous media, with pores as small as approximately 0.25 microns in size. According to some modalities, an automated petrographic image analysis system used to compute pore size and pore size distributions 2D and 3D pore body is revealed. The described methods can be applied in any porous medium, although rocks are used as a non-limiting example. Examples of entries include high-resolution petrographic images and laboratory-derived porosity measurements. Laser scanning fluorescence microscopy (LSFM) provides petrographic images in which pores are measured at a resolution of approximately 0.25 microns. examples of outputs include: (1) pore throat and pore body size distributions, plotted as frequency histograms, cumulative frequency graphs, and VS pore volume fraction. Pore diameter graphs, and (2) simulated curves of: capillary pressure for both pore bodies and pore throats. For validation, simulated capillary pressure results are compared with laboratory data.
    To classify pore throats, an Expanding Flow Model (EFM) is introduced to show how fluids behave within pores. This model
    . identifies the larger fixed pore throat as one that controls flow into or out of a specific pore body. Pore throats are extracted using a new image analysis technique, based on watershedding algorithms and differences between processed images. Simulated capillary pressure is computed from the results. Representative element areas (REA) or volumes (REV), that is, the smallest areas or volumes that can be imaged to capture heterogeneity in a sample are calculated. A semi-quantitative measurement of the error associated with the numerical SCAL (Special Testimony Analysis) is determined by examining image quality in terms of contrast, image size compared to REA or REV, and an optional user-defined factor.
    Definition of pore size: pore systems are composed of relatively large voids (pores) connected by smaller voids (pore throats). Pore body size is generally measured as the diameter of the largest sphere that can fit into a pore, while pore throat size is the diameter of the disc: or smaller sphere that can be placed in the throats between: pore bodies. Pore bodies and throats are commonly represented as networks of spheres and tubes. In general, micropores are considered to be those with pore body diameters of approximately 10 microns or less, and pore throat diameters of the order of
    - approximately 1 micron or less.
    o Pore size distribution. Carbonate rocks have pores that vary in size by at least 9 orders of magnitude, from km-scale caves to submicron-scale voids. In contrast, sandstone pore sizes vary by several orders of magnitude. Pore size distributions are usually shown as VS frequency histograms. Radius or pore diameter. The radius is generally 2D, determined using various image and laboratory analysis approaches. Examples of methods for determining pore size distribution include the following: plate shot, petrographic image analysis, mercury injection capillary pressure (MICP), constant rate mercury injection (constant rate mercury injection, CRMI or APEX), microCT scans and nuclear magnetic resonance (in the acronym for Nuclear Magnetic Ressonance, NMR).
    Plating stock is a technique that involves coating plated & carbonate rock stock with fluorescent, water-soluble paint. Photos taken under black light are processed using software. i image analysis to determine 2D pore size distributions. The smallest pores are generally 0.5 mm (500 microns) in size. The largest pores are in a cm scale (tens of thousands of microns). See Hurley, N.F.
    al *. Pantoja, D. and Zimmerman, R.A., 1999, “Flow unit. determination in a vuggy dolomite reservoir ”", Dagger Draw Field, New Mexico: SPWLA transactions, presented at the SWPLA 40th Annual Logging Symposium, Oslo, Norway and Hurley, NF, Zimmermann, RA, and Pantoja, D., 1998, “Quantification of vuggy porosity in a dolomite reservoir from borehole images and colors, ”Dagger Draw Field, New Mexico: SPE 49323, presented at the SPE Annual Technical Conference and exhibition, New Orleans, Louisiana, USA.
    Capillary Mercury Injection Pressure (MICP) involves progressive injection of mercury into a clean sample, commonly a core plug, at constantly increasing pressures. At each increased level of pressure, pore throats of a specific size are invaded by mercury. Mercury invades the pore bodies connected to the outside of the core plug and pore throats the size that are currently being invaded. Under ideal conditions, mercury enters the pores with the largest throats first. Mercury fills these pores while the pressure is kept relatively constant. After filling the pores that have a certain throat size, pressure - is increased so that the mercury enters through smaller throats. This operation continues until the connected pores are filled. Note that isolated pores are not filled with mercury. Nanoscale throats could connect these apparently isolated pores, but the volume percentage o. and your need for extremely high pressure can. prevent them from contributing to the measured permeability of the rock. See Jennings, J., 1987, Capillary pressure techniques: application to exploration and development geology: AAPG Bulletin, v. 71, no. 10, p. 1196-1209; and Pittman, E.D., 1992: Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone: AAPG Bulletin, v. 76, p. 191-198.
    Pore throat size distributions are generally shown as histograms, computed from the MICP results. Note that MICP is not useful for pore throats larger than 100 microns because these throats are filled at very low injection pressures. The ideal pore throat size for MICP is 0.1 to 100 microns.
    Washburn's equation (see Washburn, EW, 1921, “The dynamics of capillary flow." Physical review, v. 17, No. 3, pages 273-283), which is the standard approach used to relate capillary pressure to throat size, takes on cylindrical throats: It is p = 22280 O where P. is capillary pressure, d is throat diameter, y is interfacial tension and 1 is contact angle.
    * For the mercury-air system at room temperature, y = 480 dynes / cm and Tt = 140º.
    Using these constants, with d in microns and P, in psi, the equation becomes:
    np = 222 2) The Thomeer hyperbolic adjustment (see Thomeer, JHM 1960, “Introduction of a pore geometrical factor defined by the capillary pressure curve”: Journal of petroleum Technology, v. 12, No. 3, pages 73-77 (hereinafter “Thomeer 1960” ")) assumes that capillary pressure data are located in a hyperbola given by equation 3 when the data are plotted on a profile-profile scale.
    This model provides a convenient way to think about capillary pressure curves.
    Bimodal or more complex pore systems can be analyzed by fitting more than one Thomeer hyperbole to the curve (eg, see Clerke, EA, Mueller, HWIII, Phillips, EC, Eyvazzadch, RY, Jones, D ,. H., Ramamoorthy , R., and Srivastava, A., 2008: “Application of Thomeer hyperbolas to decode the pore systems, facies and reservoir properties of the upper Jurassic arab D.] 20 limestone, Ghawar Field, Saudi Arabia: The“ Rosetta Stone ”] approach: "” GeoaArabia, v. 13, pages 1113-116). The equation is: log (e "6) = log (O x log (=) 3) Bo, Pq where G is the format factor, B , is the percentage of o. cumulative volume, B., is the percentage of mercury volume "the maximum reached, P. is the capillary pressure and Pa is the inlet pressure, that is, the pressure when mercury enters the major throat.
    In this way, the Thomeer hyperbole is controlled by three main parameters: G, B., and Pa. G controls the shape of the hyperbola, whereas P; and B. control the location of the x and y asymptotes, respectively. Examination of a typical capillary pressure curve shows that the Thomeer hyperbole diverges from the measured curve if there are large pores. This is because measured data has a large error at low pressures due to surface irregularities. 'Closing correction' is applied to correct for large pores. The Thomeer hyperbole is used to obtain realistic inlet pressure values (P; 3). Constant rate Mercury injection (CRMI or APEX) is a technique that maintains a constant injection rate and monitors injection pressure fluctuations. See Yuan, HH, and Swanson, BE.F., “Resolving pore-space characteristics by rate-controlled porosimetry” ": SPE - formation evaluation, v. 4, no. 1, pages 17-24. injection is kept extremely low so that the loss of 'pressure due to flow within the sample is negligible compared to capillary pressure. In that case, the observation of a sudden drop in pressure is the result of the movement
    * mercury from pore throats into. pore bodies, and is accompanied by mercury instantly filling the pore bodies. The additional increase in injection pressure corresponds to the filling of pore throats that have a smaller radius. The volume of pore bodies can be determined from the injection rate and the time it takes to fill the pore bodies. This method provides pore size distributions and pore throats. However, it cannot reach the same high pressures as conventional MICP. Its maximum pressure is a few thousand psi Hg-air and shows details of the larger pores. Micro CT scans are a technique that uses X-ray computed tomography (CT) in small samples (commonly 5 mm diameter core plugs) to detect pore bodies that are 3 microns and larger in size. See Knackstedt, MA, Arns, CH, Sakellariou, A., Senden, TJ, Sheppard, AP, Sok, RM, Pinczewski, WV, and Bunn, GF, 2004, Digital core Laboratory: Properties of reservoir core derived from 3D images: SPE Preprint 87009, presented at Asia-Pacific - conference on integrated modeling for asset management, 29-: i 30 March.
    Nuclear magnetic resonance (NMR) is a technique based on the interaction of hydrogen cores (protons) with a magnetic field and pulses of
    *. radio frequency.
    See Coates, G.R., Xiao, L and Prammer, - M.G., 1999, NMR logging: Principles and applications; Halliburton Energy services, USA, 233 p.
    The NMR transversal relaxation time distribution (T 2 distribution) refers mostly to the pore size distribution in the rock, although transversal relaxation is also related to factors such as surface relaxation and fluid type.
    Research has shown that grain surface relaxation has the greatest influence on T 2 relaxation times for rocks.
    Surface relaxivity (p) is a measure of the ability of grain surfaces to cause nuclear spin relaxation.
    Different rocks have different surface relaxation characteristics.
    The rate of proton grain surface relaxation depends on the frequency at which protons collide with or get close enough to interact with grain surfaces.
    As a result, the surface to volume ratio (S / V) of rock pores influences NMR relaxation times.
    For spherical pores, S / V is proportional to the inverse of the pore radius.
    Larger pores have relatively smaller S / V ratios and times. proportionally longer relaxation times.
    Smaller pores have relatively larger S / V ratios, resulting in shorter relaxation times.
    NMR surface relaxivity is characterized by the following equations:
    o E) - oz ie E = per O) where p is surface relaxivity in units of um / s, S is surface area (um2), V is volume (um3), eg. it is effective relaxivity (one / s), and it is lightning (one). In this way, we can obtain pore size distribution information from T, NMR distributions.
    Laser scanning fluorescence microscopy (for scanning laser fluorescence microscopy, LSFM) provides a high resolution technique (approximately 0.25 microns) to build 3D digital rock models. Confocal and multifoton techniques are more common, although the emerging field of super-resolution fluorescence microscopy can provide enhanced images of rocks and other porous media, down to a few nm to tens of nm in scale. See Huang, B., Bates, M.,; and Zhuang NX. 2009, “Super-resolution fluorescence microscopy:" Annual Review of biochemistry, v. 78, pp. 993-1016. Such techniques increase the resolution of - fluorescence microscopy using standardized excitation '20 or the single molecule location of: fluorescence.
    Confocal microscopy, the most common type of LSFM, uses spot lighting and a hole placed in front of
    .. a detector to remove 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.
    LSFM's penetration depth is limited because reflected light is absorbed and dispersed by material above the focal plane. Experiments have successfully imaged depths as large as 500 microns using pore predictions of carbonate rocks, where the rock material has been removed with acid. Fortunately, the sandy cover is not limited by tiled sweeps that 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, approximately one millimeter. See the Wikipedia Wikipedia website: //cn.wikipedia.org/wiki/two- photon excitation microscopy, accessed on October 23,
    2010. Like confocal microscopy, this technique excites fluorescent dyes injected into rocks. “The principle is - based on the idea that two photons of energy:: comparatively lower than necessary for excitation of a photon can also excite a fluorophor in a quantum event. Each photon carries approximately half the energy needed to excite the molecule. An
    “. excitation results in the subsequent emission of a & fluorescence photon, generally at a higher energy than either of the two excitation photons. ” The resolution is limited in diffraction to approximately 250 nm, similar to confocal microscopy. Multi-photon and confocal microscopy are widely used in the life of science and semiconductor industries. Representative element volumes (REV) provide a new way to deal with issues of heterogeneity and upward scale in reservoir modeling. See Qi, D., 2009, “Upscaling theory and application techniques for reservoir simulation:” Lambert Academic Publishing, Saarbrucken, Germany, 244 p ”(hereinafter“ Qi 2009 ”). In summary, REV is the smallest volume that can be modeled to provide compatible results, within acceptable limits of variance for a modeled property, such as porosity. Using this approach, we can scale up rock properties from fine to coarse scales. We determine the smallest volume to be modeled, make the flow model and use the results in the following larger scale simulations. After the REV has been modeled,. we do not need larger model volumes because: 'we capture “heterogeneity for that specific type of rock on that scale.
    The concept of REV was discussed in 1972. See Bear, J. 1972, Dynamics of fluids in porous media: Elsevier, Nova
    * York, 746 p (hereinafter "Bear 1972"). Bear defined Au; as ". a volume in a porous medium, with a P. AU centroid; 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, the fractional porosity.
    At large AU; i values, there are minimal fluctuations in porosity as a function of volume. However, as the volume decreases, fluctuations in porosity increase, especially as AU; approaches the size of a single pore, which has a fractional porosity of 1. If the centroid P happens to be located in a grain, porosity is O when AU; - 0. 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 so that "the effect of adding or subtracting one or more pores has no significant influence on the value of n." (Bear 1972). Using the REV approach, the porous medium is replaced by “a fictional continuum: a substance without structure, at any point where we can assign it. 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
    * may vary. In practice, the best method is to use the largest x REV determined using various approaches.
    Many reservoir engineers have heard rocks, especially carbonates, described as "so heterogeneous, they are homogeneous". Fundamentally, this is a statement about REV's. below a certain sample size, rocks are heterogeneous and there is considerable dispersion or variance in rock properties (for example, see “Greder, HN, Biver, PY, Danquigny, ST. and Pellerin, FM, 1996,“ Determination of permeability distribution at log scale in vuggy carbonates. ”Article BB, SPWLA 37th Annual Logging Symposium, 16-19 June, 14 p”). Above a certain sample size, dispersion is reduced to an acceptable level, and that sample size is Oo REV. An analogous term for REV in 2D, which is REA (representative element area) has been defined. See Norris, RJ and Lewis, J.JI.M, 1991. The geological modeling of effective permeability in complex heterolithic facies: SPE Preprint 22692, presented at the 66th Annual Technical conference and exhibition, Dallas, TX, October 6-9, p. . 359-374. (hereinafter “Norris 1991”). Norris 1991 applied the concept to - effective permeability modeling from photos of. . outcrops swept in heterolytic rocks. Basically, REA is the smallest area of a rock that is representative of the measured rock property. REA and REV measure area and volume, respectively. The two terms allow us to capture the heterogeneity in rock properties. . OER is determined using an iterative process, so that variance in a given parameter, such as porosity, is measured for successively larger sample areas.
    OER is the area where the standard deviation of the variance from the sample mean is zero, or an acceptably low value.
    Sample mean is porosity of laboratory-derived core analysis.
    Digital image analysis.
    Digital images can be imaged as a matrix of numbers, with each number representing a pixel.
    In an 8-bit image, each pixel has a value between 0 and 255 (that is, 256 or 28 shades of gray). Images can also be 16 bits, 24 bits and so on.
    Note that RGB images can be imaged as three matrices with a matrix for each of the colors red, green and blue.
    A binary image is an image in which the pixels consist of two numbers commonly O for black, and 255 for white.
    Background color VS foreground color is arbitrary.
    For example, in a sample of rock, grains. they can be white and pores can be black, or vice versa. . The process by which a gray scale or RGB image is. converted to a binary image is called limitation or binarization.
    Two fundamentally different types of image processing techniques are used in this present
    development: (1) gray-scale image processing, and - (2) binary image processing (also known as morphological image processing). Each process operates on its own scale. Some processes perform pixel scale operations, while others are applied to a block with a size called the core size (for example, 3 x 3 pixels). The results, in this case, are returned to the central pixel.
    Gray-scale image processing is used in the present disclosure for image enhancement and recovery. The processes used include: (1) unclear mask (in the acronym for unsharp mask, USM), (2) contrast limited adaptive histogram equalization (in the acronym for contrast limited adaptive histogram equalization, CLAHE) and (3 ) limitation. Unclear mask (USM), in contrast to what its name may indicate, is a filter used to sharpen the image by subtracting a blurred view from the original image. Fogging the image can be done in several ways. The common procedure is to apply an indefinite Gaussian image. After subtraction, the image is then equalized back to its original histogram. The filter radius of. f Gaussian undefined image and its weight are defined. USM can be implemented in an indiscrimination mode on the pixels, or in a discrimination mode. A limit that defines the minimum contrast between pixels can be used
    = to test whether to apply the USM mask.
    A threshold is - normally used to minimize artificial noise created from the indiscrimination USM.
    Contrast limited adaptive histograph equalization (CLAHE), modified by Zuiderveld, K., 1994, “Contrast limited adaptive histograph equalization,” in Heckbert, P.S., Graphic Gems IV, San Diego: Academic Press Professional, p. 474-485 from Adaptive Histogram Equalization of Pizer, S.M., Amburn, E.P.
    Austin, OD, Cromartie, R., Geselowitz, A., Greer, T., Romeny, BTH, Zimmerman, JB, and Zuiderveld, K., 1987, “Adaptive histogran equalization and its variations:" Computer vision, Graphics and Image Processing, v.39, no.3, pages 355-368 is a process that is commonly applied to correct irregular lighting.
    Although standard histogram matching operates on the entire image, CLAHE operates on a local scale for matching contrast to a user-specified distribution.
    The kernel size defines the size of this local scale.
    Bilinear smoothing is applied between each local area to produce seamless transitions.
    CLAHE is most commonly used in the medical industry to - augment x-rays and microscopic images.
    CLAHE minimizes. 'artificial noise, which is its main advantage over standard global normalization algorithms.
    Limitation (binarization) is a process by which a digital integer image (8 bits, 16 bits, etc.) is
    SS converted to a binary image.
    Figure 1 represents one. limitation process (binarization). The histogram 112 of the input image 110 is divided into two parts: the background with a value of 0 and the elements (foreground) with a value of 255. The input image 110 is modified from a Wikipedia image, 2010, HTTP: //en.wikipedia.org/wiki/Thresholding (image processing), accessed October 31, 2010. limitation is applied by assigning a black value to any pixel lower than the limit value and a white pixel to whichever is higher.
    A number of algorithms exist for automatic detection of the best limit value.
    See Sahoo, PK, Soltani, S., Wong, AKC, and Chen, YC, 1988, “A survey of thresholding techniques:" Computer vision, Graphics, and Image processing, v. 41, no. 2, page 233- 260 and Sezgin, M. and Sankur, B., 2004, “Survey over image thresholding techniques and quantitative performance evaluation:" Journal of Electronic Imaging, v. 13, no. 1, p. 146-165. The choice between algorithms depends on the type of use.
    In figure 1, The resulting histogram 122 shows the The limit value with the previous gray level tones - below the limit being assigned a value of O and the tones of. : previous gray level above the limit being assigned a value of 255. Output image 120 is also shown.
    Note that in the example in figure 1 where the histogram has two distinct heaps or peaks, the limit value was
    * a; selected between the two peaks.
    . Morphological image processing operates on binary images, and the ultimate goal is to obtain measurements. Processes used in the present disclosure include: (1) Kuwahara edge preservation filter, (2) logical operators, (3) watershed transformation, (4) clustering algorithms and (5) measurement calculations. The Kuwahara edge smoothing filter (Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M., 1976, “Digital processing of biomedical images:" ”Plenum Press, p. 187-203) is a non-linear noise reduction filter that tries to preserve edges. The filter calculates the variance and average intensity of 4 sub-regions of overlap for each pixel. The average of the part with the minimum variance is returned to the central pixel. , using the same theme, have been introduced since it was first developed. See Papari, G., Petokov, N., and Campisi, P., 2007, “Artistic edge and corner enhancing smoothing:” IEEE Transactions on image Processing, v. 16, no. 10, pages 2449-2462 and Kyprianidis, JE, Kang, H. and Dollner, J., 2009, “Image and video 5 abstraction by anisotropic Kuwahara filtering:" ”Computer. 'graphics Forum, v.28, no. 7, p. 1955-1963.
    Logical operators are operators that can be applied using two images. For example, a logical AND operator would produce a white pixel if and only if
    * input images have white pixels in the same location. . A logical OR operator would produce a white pixel if a white pixel exists in any of the input images at that location.
    The watershed transformation is an operation that tries to find and separate different elements in the image. This transformation works best with elliptical and circular objects. Many algorithms exist to perform such operations and most can be classified into one of two very broad categories: (1) immersion, and (2) topographic distance algorithms. Roerdink, J. B.T.M. and Meijster, A., 2000, “The watershed transform:" Definitions, algorithms and parallelization strategies: fundamenta informaticae, v. 41, pages 187-228 wrote a comprehensive examination of Wwatershedding. Immersion algorithms as in Vincent, L., and Soille, P., 1991, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations:" IEEE transactions on pattern analysis and machine intelligence, v. 13, no. 6., p. 583-598, simulate flooding of basins from the distance map of the binary image. A distance map assigns numerical values to each pixel based on the - minimum distance for background pixels. One pixel in. : center of a pore has a relatively high value. If a person takes the inverse of the distance map, 'basins' are created instead of 'mountains.' Water flooding is then simulated by a region that grows until two
    "basins begin to overlap. At that point, the algorithm for e. a watershed line is created. Topographic algorithms try to minimize the distance between the object center and the watershed lines.
    Figure 2 represents a watershed example using the output image of figure 1, according to some modalities. Input image 120 is shown, in this case after limitation. The immersion-based watershed transformation separates different retention basins in the image. This flood is simulated on distance map 210 to provide output image 212 which has separations as shown.
    Figure 3 represents a flow chart of a selective Kuwahara filter procedure to “cure” large pores, according to some modalities. In block 310, the watershed lines are identified by subtracting the watershedded image from the binary image (for example, subtracting image 120 from image 212). In block 312, the macropore image is obtained by separating the watershedded image into two images by an area cut. In block 314, the Kuwahara filter is implemented in a binary image - to try to recover large segmented pores in your. 'original state, that is, before segmentation and after watershedding. As the watershed line is relatively small in width (1 or 2 pixels at most), the Kuwahara 5x5 filter will fill the watershed line with a gray tone. At the
    At block 316, the gray tones can then be converted from "back to porosity. Note that the watershed lines in microporosity will not be recovered because the image used is the macropore image. The Kuwahara filter is applied to the pixels of the watershed line.
    The cut for what is considered to be a large pore originates from the procedure shown in figure 3. This cut is related to the watershedding algorithm used as well as the definition of micro- and macroporosity.
    A smaller cut should be used for algorithms that segment excess pores. It was found that a cut of 50 microns in pore body diameter is appropriate for the watershed algorithm used in ImageJ. Finding the most appropriate cut, if necessary, should be part of the system calibration process.
    Figure 4 represents an example of a clustering algorithm, according to some modalities. A grouping algorithm labels each element in the image by a unique identifier. As a result, masking, measurements and other types of operations can be applied to. each element. In figure 4, input image 212 and image - output 410 are shown for a grouping algorithm: '400. Two-dimensional grouping can be connected in 4 or connected in 8. Grouping connected in 4 considers pixels that are connected diagonally as two elements separate, while grouping connected in 8
    ... considers them as an element. A simple & algorithm would scan the image pixel by pixel and assign a label to each pixel depending on its neighbors. If no neighbors are found, a new label is created. See Hoshen, J. and Kopelman, R., 1976, “Percolation and cluster distribution - IL. Cluster multiple labeling technique and critical concentration algorithm: "Physical Review B., v. 14, no. 8, pp. 3438-3445. Another scan is used to merge connected labels.
    Finally, different types of measurements can be made on binary images. Masking of individual elements can be applied and area, perimeter, better elliptical fit, orientation and other measurements can be calculated.
    Figure 5 represents an expanding flow model (EFM) used to understand capillary pressure, according to some modalities. Capillary pressure is shown in white arrows, like arrows 520, 522 and 524, and the opposite water pressure is shown in solid lines like arrows 530 and 532 for an individual pore 500. The pore 500 in this example has three throats 510, 512 and 514. Note that the larger throat r 510 has the lowest capillary pressure 520, therefore fluid. Ú tends to come out of the 510 throat. EFM treats each pore and its throats as individual elements. From this view, the centroid of each pore is the source of the fluid, as source 502 of the pore 500.
    The. Pressure, that is, capillary pressure, comes out in throats and is - proportional to the inverse of its diameter. For fluid to escape from a pore, a certain pressure, equal to capillary pressure or greater, within the pore must be achieved. Note that the minimum capillary pressure (520) comes out around the larger throat (510) and that way the fluid starts to escape from that throat first. As the fluid escapes, the pressure does not increase in the pore. According to some modalities, using EFM the largest throat size for each pore is considered when calculating capillary pressure. Smaller throats can be assumed not to transmit fluid unless the pressure has instantly increased to your capillary pressure or other larger throat paths have been filled with fluid. It has been found that for a well-connected pore network, this does not happen often.
    Given two pores with two different throat sizes, fluid will enter the pore with the largest throat first. This assumption is not valid if the fluid has to pass through the pore with the smaller throat first to reach the pore with the larger throat.
    "Figures 6 and 7 represent two views of pores and throats, according to some modalities. Figure 6 represents a more conventional rod and ball view, in which pores such as pores 610 and 612 are connected by throats like throat 620. The model in figure 7, in contrast
    "the. considers throats as lines of local minimum of diameter to connecting pores.
    As can be seen pores 610 and 612 are connected by a smaller pore 710, and there is a throat 720 connecting pore 610 and pore 710, and a throat 722 s connecting pore 710 to pore 612. Thus, in the EFM model, a throat in traditional view translates into two throats that are connected to a small pore.
    The EFM view shown in Figure 7 is more realistic in terms of physics, because a pore and a throat are fundamentally two different entities.
    In contrast to the view in figure 6, a throat in the EFM view can never be a pore because it is a 2D line or a 3D plane.
    Figure 8 represents a numerical capillary pressure calculation workflow based on the EFM model, according to some modalities.
    In block 810, the pores are grouped by the size of your largest throat.
    The larger pore throat attached to each pore body can be determined using image analysis to determine throat-orifice sizes.
    This value is used to characterize that pore body in the simulated capillary pressure curves made for pore throats.
    In block 812, cumulative porosity is calculated using total porosity for each cluster.
    In block 814, capillary pressure is calculated using the Washburn equation (equation 1) and the cluster size.
    Graph 820 shows an example of the capillary pressure curve
    . simulated.
    E Properties obtained from Special Core Analysis (SCAL) provide an input for reservoir simulators. Such properties include pore throat and pore body size distributions, and capillary pressure curves. Pore throat size distributions can be computed from laboratory mercury injection capillary pressure (MICP) experiments.
    Petrographic image analysis provides another means to obtain SCAL measurements. With advances in computers over the past decade, hundreds of millions of pixels can be analyzed in minutes to hours. Advances in microscopy have enabled us to acquire high-resolution, fast images over large areas (many mm2) of a thin section. Combined with new techniques described here, accurate estimation of SCAL measurements is obtained, according to some modalities. The example approach described here is to estimate numerical rock properties based on calculations made directly from petrographic images, which are used to compute pore throat and pore body size distributions and simulated capillary pressure curves.
    ft Figure 9 shows systems for determining pore throat and boron body size distributions and simulated capillary pressure curves from petrographic data, according to some modalities. Dice
    - acquired petrographic 910 (as digital images of rocks) are transmitted to a processing center 950 which includes one or more central processing units 944 to perform the data processing procedures as described here, as well as other processing.
    The processing center includes a storage system 942, communications and input / output modules 940, a user display 946 and a user input system 948. According to some modalities, the processing center 950 can be located in a remote location from the place of acquisition of petrographic data.
    The processing center receives many other types of 912 data used in digital rock modeling, such as core analysis data and well profile data.
    In figure 9, data and / or samples from an underground porous formation 902 are being collected at the location of well 900 through a cable trick 920 using a cable tool 924 in well 922. According to some modalities, the tool a cable 924 includes a core sampling tool for collecting one or more core samples from porous formation 902. One of the outputs of the processing center is capillary pressure 914 as shown.
    Although. : the system in figure 9 is shown applied to the example of digital rock images of an underground porous formation, in general the described techniques can be applied in any porous medium.
    ss
    “E Figure 10 represents a workflow for. methods described to determine pore body distribution, pore throat distribution and capillary pressure, according to some modalities.
    From a core plug 1010, thin sections 1012 are prepared, which are then imaged using a high resolution microscope (such as 1014 confocal microscope). Porosity and permeability (p & p) can be used in core buffer 1010 to segment the image into a binary image of grains and pores.
    The image is analyzed and a representative element area (or volume) 1020 is calculated.
    The image is segmented into pores and grains using the porosity value measured in the laboratory (segmented image 1022). Using methods of the present disclosure, pore body size distribution, pore throat size distribution (numerical SCAL 1030) and capillary pressure are obtained, for example, using Matlab code.
    According to some modalities, procedures are revealed to determine pore size distributions of body and pore throat, and simulated capillary pressure curves.
    The procedures can be applied to any porous medium, although in some examples described here it is applied to digital rock images.
    Figure 11 is a flow chart that illustrates a 2D workflow according to some modalities.
    In block 1110, a sample is collected and prepared.
    The purpose of this
    “The stage is to prepare the sample for imaging.
    The system * described here uses high resolution microscopic images, any high resolution gray scale images are sufficient.
    Sample preparation depends on the type of microscope used.
    According to some modalities, a confocal microscope is used with standard thin sections (thickness of 30 microns) or thick sections (for example, thickness of 5,000 microns). According to some modalities, the sample preparation comprises vacuum pressure impregnation using a fluorescent dyed epoxy.
    Normally, the sample is not tensioned, although it is possible to apply external tension before the epoxy injection, and maintain tension until the epoxy is cured.
    Standard thin sections have been found to produce quite high Oo resolution with low signal-to-noise ratios.
    In block 1112, the rock sample is imaged.
    According to one modality, a Zeiss LSM 710 Vertical Confocal Microscope is used to acquire sample images.
    It has been found that such a system is capable of providing appropriate high-resolution images (up to approximately 0.25 microns) with adequate sample coverage within a reasonable time frame.
    According to some modalities = It is alternatives, an environmental scanning electron microscope (in the acronym in English for environmental scanning electron microscope, ESEM) can be used to provide higher resolution (nm scale), however it was found that
    * in many applications the work is more time consuming and expensive. 'In addition, ESEM images were found to be irregular in lighting, due to the surface load of uncoated samples. That being said, the workflow presented here applies to images obtained by other means with little or no modification, since a good quality image is obtained. According to some modalities, imaging is automated using a computer. The confocal microscope scans the sample in a grid pattern and records the image point by point. The output of this are image tiles constituting a large 8 bit image. According to one modality, 8 bits were chosen instead of 16 bits to reduce the file size. However, it is generally better to choose the most accurate file type. Scale, in terms of micron per pixel, is also recorded. 30% overlap was used in the imaging process to ensure accurate seamless stitching. However, according to some modalities, an overlap of 10% or even 5% can be used to speed up the process.
    - In block 1114, the image is pre-processed and:. increased. The purpose of this phase is to prepare the raw image for image analysis. Tiles of raw image of 512 by 512 pixels in size, for example, are sewn to produce the complete image. According to some
    . modalities, an algorithm used for sewing is a + developed by Preibisch, S., Saalfeld, S. and Tomancak, P., 2009, “Globally optimal stitching of tiled 3D microscopic image acquisitions:" ”Bioinformatics, v. 25, n. 11, pages 1463-1465 (hereinafter “Preibisch 2009”) .The algorithm uses the correlation based on fast Fourier transform (F) to calculate the translation displacements between each tile. See Kuglin, CD, and Hines, DC, 1975, “The phase correlation image alignment method:" ”Proceedings of the IEEE, International conference on Cybernetics and society, p. 163-165 (hereinafter “Kuglin 1975”). In addition, the algorithm incorporates global logging to prevent error propagation due to tile and mix alignment (linear and non-linear) to provide the most seamless transition.
    The images are taken through a number of steps. Clipping and rotation are applied to produce square images, according to some modalities. Manual adjustments using applications such as Photoshop or Paint.net can be applied to remove some irregularities.
    In block 1116, the image is segmented into grains and pores. According to some modalities, porosity. 'laboratory measurement is used to limit the image and convert it to a binary image. The porosity value measured in the laboratory is used to manually determine the best limit value. Like oO
    “. The search range is relatively small (between 1 and 254 - for 8-bit images), a bi-section method can be implemented and used. The bi-section method is a root find algorithm that iteratively calculates the midpoint and selects the interval that contains the root. According to some modalities, a more developed optimization algorithm can be applied if dealing with larger intervals, as is the case with 16-bit images.
    In block 1118, the representative element area is compared to the image size. The phase involves measuring porosity for random non-overlapping tiles of different sizes. The standard deviation (for standard deviation, STD) is calculated for each tile size. The interpolation is done to reach the average sample value and the REA is determined as the intersection of that interpolation.
    Figure 12 is a flow chart that illustrates the basic workflow for determining the representative element area (OER), according to some modalities. Given an image composed of two constituents, for example, grains and pores, one can measure porosity for 0 different parts (or tiles) of the image. By measuring porosity for different non-overlapping tiles of the same size, a standard deviation (STD) can be calculated. As the tile size increases, it is observed that the STD
    . decreases.
    OER is determined using an 'iterative process, whereby variance in a given parameter, such as porosity, is measured for successively larger sample areas.
    OER is determined as the area where the standard deviation of the variance from the sample mean is zero, or an acceptable low value.
    Sample mean is lab-derived core analysis porosity.
    In block 1210, a large area is modeled or measured with the rock properties of interest.
    In block 1212, a subsample of a given size in the large area is randomly selected.
    In block 1216, other non-overlapping subsamples of the same size are randomly selected.
    This is repeated many times.
    In block 1218, the subsample size is increased by an increment and many similar areas are sampled.
    Blocks 1212, 1216 and 1218 are repeated, according to some “modalities, until it is not possible to have a statistically large sample representation.
    In block 1222, the variance in measured property versus subsample size for each defined element area is crossed out.
    The best fit is determined.
    The REA at the intersection of the variance interpolation and the rock property measured in the laboratory are read.
    With reference again to figure 11, in block 1120, image quality tests are performed, according to some modalities.
    CLAHE is applied to some
    - images in an attempt to remove irregular - artificial lighting, which is due to the inclination of the sample in the imaging stage. Unclear masking can be applied in some cases to assist the watershed transformation in the separation of micropores. As not all images require CLAHE or an unclear mask, two tests were designed to automatically determine your need. A CLAHE process tries to smooth out artificial lighting problems. To test whether it is necessary or not, a correlation test was performed using the equation
    5. Note that this test assumes that the image is twice the size of the OER. The equation is: 2010 x Mean [CorrCoef (H], H2) + CorrCoef (H3, H4)]) Ch = —A — A € A — A — Ê € ——e — c € ODM 0 <C, <1 (5) where the correlation coefficients (CorrCoef) are calculated for two histogranges of non-overlapping parts as an image as follows: H1.2 = horizontal halves of the image (6) H3z, a = vertical halves of the image (7) If the result of this test is less than 0.85,. ] 20 CLAHE is performed in the image. This value is chosen by visual inspection of the images. Corresponding results can be changed depending on the circumstances. To determine whether or not to apply the mask
    ”Not clear, the correlation is again used to get the 'best image.
    The sharp and original images are limited (see below) and a watershed transformation is applied.
    The resulting two images are then correlated with the original image, and the image with the highest correlation factor is taken.
    Note that it is generally better to increase the laser gain during acquisition of confocal microscopy instead of relying on this filter.
    In block 1122 morphological operations are performed.
    The purpose of this phase is to produce an accurate, segmented binary image that is easy to analyze.
    Such an objective is generally difficult to achieve.
    Figure 13 represents an image analysis procedure for throat and pore, according to some modalities.
    Pores and throats are separated or segmented using a watershed transformation (1310 and 1312). Group analysis (1314) is used to label each pore and throat.
    A neighborhood search (1316) is used to relate throats and pores.
    By experiment and error, it was found that the region growth watershedding algorithm implemented in ImageJl provides appropriate results for
    : avoid over-targeting. : Figure 14 illustrates a procedure for healing “large pores, according to some modalities.
    It has been found that the watershed transformation sometimes produces undesirable artifacts, in which some of the largest pores are. targeted at minors. To remedy this, according to some - modalities, the image “watershedded binary | The resulting 1410 is separated into two images: micro-porosity image 1412 and macro-porosity image 1414. The micro-porosity image is treated with a selective, specialized Kuwahara edge preservation filter, applied to the removed pixels encoded for this purpose . Unlike the use of a medium filter, this filter resulted in the recovery of the large pores to their original state before the watershed transformation, while maintaining the original pore shape (image 1416). The two images (micro- and macro-porosity images) are then combined to continue the analysis on the cured image 1420.
    Figures 15 and 16 represent a typical pore image 1510 and throat image 1610 after processing the binary image. In this example, no large pore cure was applied to produce these two images. As used in this description, “pore image” refers to the watershedded image (whether cured or not cured). To produce the “canyon image”, a simple binary logical operation was used to find the difference between the “pore image” and the original binary image.
    '' Referring again to figure 1, the next stage involves grouping both pore and canyon images. According to some modalities, a 4-connected implementation is applied to ensure that
    4a * the diagonally connected groups are separated. On the block. 1124, pore and throat measurements are performed. Calculations i of the pore area and the maximum length of throats (for approximate diameter) are made. The relationship of throats and pores together is obtained by examining the pixel neighborhood of each throat. Each pore is assigned the diameter of the largest throat connected to it for reasons described in the Expanding Flow Model.
    In block 1126, data analysis is performed. According to some modalities, data analysis involves calculation steps that lead to numerical SCAL for the sample. The main entrances are the pore areas, and the diameter (length) of the largest throat for each pore. Graphs of pore size distribution, cumulative pore size distribution, and fractional pore volume are produced using the measured pore areas. To produce a simulated capillary pressure curve, data is filtered to remove isolated pores. This is done by excluding pores without a throat. The remaining pores are then grouped according to their largest throat size. The total pore size for each cluster is calculated and a percentage of. 'porosity is given. Cumulative porosity is calculated and used as a cumulative percentage. From these clusters, a simulated capillary pressure value is calculated using equation 1. These values, with the
    “. cumulative porosity, are plotted on a profile-profile scale to produce the well-known capillary pressure curve. Figure 17 represents an EFM model for calculating capillary pressure. The pores are approximated by tubes and separated in decreasing throat size (ie, diameter). As shown in diagram 1710, each pore will act as a throat for the next larger pore.
    With reference again to figure 11, in block 1128, confidence factor calculations are made. A confidence factor is calculated to give an idea of the error associated with the results. This is calculated by examining three parts: the image size compared to the representative element area (OER), the gray-scale image histogram, and an optional user-defined quality control factor for artifacts. Equation 8 provides the relationship used. The factor is a positive non-limited number. Generally, the higher the value, the greater the confidence, with 1 being a limit between acceptable and unacceptable results. The equation is:. G = C0, xXCXC, (8): S where 6 is the confidence factor, Cs, CC and C, are defined in equations 9, 5 and 10 respectively. C, is defined as being equal to 1 when the analyzed image is O twice the size of the OER. This is done to ensure
    "And adequate sample representation. C, can be imagined 'as the residual irregular lighting.] Size (Image) C, = FR TS C; 20 (9) Size (REA) Cy = User-defined input reflecting image quality 0 <C, E1 (10) It will be recognized that a low Cr is likely to mean a low value in C '., However the opposite is not true. C; it is a measurement of the representative element area that assumes a perfect image. Like most of the images are not perfect, Ch was included in the calculation.
    This procedure is illustrated by a sample composed of micrite and dolomite. Helium injection porosity was measured to be 20.7%. The pores are micropores. Applying the described workflow, pore size distribution and capillary pressure curves were obtained.
    Figures 18-25 represent results of petrographic image analysis for a sample, according to some modalities. The results shown are for images of a studied sample, taken by a camera & handheld, a standard microscope and a confocal microscope.
    i 20 The contrast test in the close-up confocal image only returned a value of 0.97. This is considered a very good value and the contrast-limited adoptive histogram equalization (HPLC) was not necessary. Limitation produced a binary image similar to the confocal image. Watershedding
    It is separated the individual pores. The throat image shows. throats extracted. The effective porosity for this sample was determined to be 18.60%, which is close to the maximum cumulative volume percentage porosity of 19.47%. Using a statistical approach and non-overlapping sub-sample areas of various sizes, OER was determined to be approximately 6 mmº, much smaller than the 16 mmº image size. Figures 18-25 show the pore size distribution graphs produced for this sample. Throat and pore size distribution data are plotted as absolute frequency (graphs 1810 and 1910), cumulative frequency (2010 and 211), and percentage of pore volume (2210 and 2310). Capillary mercury-air pressure curves derived from the throat and derived from the pore are also recorded (2410 and 2510). It can be seen that the sample is mono-modal with microporosity. The modal and median pore body size is approximately 2 microns in diameter.
    An example for 3D images will now be described in more detail. Laser scanning fluorescence microscopy allows us to acquire image slices stacked from the same rock, which results in 3D images of "pore systems. Image registration is applied to fit slices on top of each other and generate 3D images. figure 26 is a flow chart that illustrates a 3D workflow according to some modalities.
    S. 2610 a sample is collected and prepared.
    The purpose of this' phase is to prepare the sample for imaging.
    The system presented here uses high resolution microscopic images.
    Any high resolution gray scale images are sufficient.
    Sample preparation depends on the type of microscope used.
    According to some modalities, a confocal microscope was used with standard thin sections (thickness of 30 microns) or thick sections (for example, thickness of 5000 microns). According to some modalities, sample preparation encompasses vacuum pressure impregnation using a fluorescent dyed epoxy.
    Normally, the sample is not tensioned, although it is possible to apply external tension before the epoxy injection, and maintain tension until the epoxy is cured.
    It was found that standard thin sections produce sufficient high Oo resolution with low signal-to-noise ratios, so that they can be used in this analysis.
    In block 2612, the rock sample is imaged.
    According to one modality, a Zeiss LSM 710 Vertical Confocal Microscope was used to acquire images of. sample.
    It has been found that such a system is capable of providing "E appropriate high-resolution images (up to approximately 0.25 microns) with adequate sample coverage within a reasonable time frame.
    Vertical stacks with 0.4 micron spacing, for example, are acquired through the sample. THE
    . The workflow described here applies to 3D images by 'any other means with little or no modification, as a good quality image is obtained. According to some modalities, imaging is automated using a computer. For each vertical step (z-step), the confocal microscope scans the sample in a grid pattern and records the image step by step. The output of this step are image tiles constituting a large 8-bit image. According to some modalities, 8 bits were chosen instead of 16 bits to reduce the file size. However, it is generally better to choose the most accurate file type. Scale, in terms of micron per pixel, is also recorded. 30% overlap was used in the imaging process to ensure accurate seamless stitching. However, a 10% or perhaps 5% overlap can be used to speed up the process.
    In block 2614, the image is pre-processed and enlarged. The purpose of this phase is to prepare the raw image for image analysis. For each vertical step (z-step), the raw image tiles of 512 by 512 pixels in size, for example, were sewn to produce the 'complete' image. As in the 2D example, the algorithm used for sewing can be as discussed in Preibisch 2009. This algorithm uses the correlation based on fast Fourier transformation (F) to calculate the displacements of
    "the translation between each tile (Kuglin 1975). In addition, the - algorithm incorporates global logging to prevent error propagation due to tile and mix alignment (linear and non-linear) to provide the most seamless transition.
    The images produced were taken through a number of steps. Clipping and rotation were applied to produce square images. Manual adjustments in Photoshop or Paint .Net were applied to remove some irregularities. “Image normalization is commonly needed to adjust the brightness of individual z-steps to match a chosen pattern.
    In block 2616, the image is segmented into grains and pores. According to some modalities, the porosity measured in the laboratory is used to limit the image and convert it into a binary image. Since automatic limitation methods provide results that are highly dependent on image quality and histogram distribution, they cannot be used. The porosity value measured in the laboratory is used to manually determine the best limit value. As the search interval is relatively small (between 1 and 254 for 8: bit images), the bi-section method was implemented and used. THE . . bi-section method is a root find algorithm that iteratively calculates the midpoint and selects the range that contains the root. According to some modalities, a more developed and improved optimization algorithm. can be applied if dealing with longer intervals, as is the case with 16-bit images. In block 2618, the volume of the representative element is compared with the image size. The phase involves measuring porosity for random non-overlapping cubes of different sizes. The standard deviation (STD) is calculated for each cube size. Interpolation is done to reach the average sample value and the REV is determined as the intersection of that interpolation.
    Figure 27 is a flow chart that illustrates the basic workflow for determining the volume of representative element (REV), according to some modalities. Given an image composed of two constituents, for example, grains and pores, porosity can be measured for different volumes of the image. By measuring porosity for different non-overlapping volumes of the same size, a standard deviation (STD) can be calculated. As the volume of the subsample increases, it is observed that Oo STD decreases. REV is determined using an iterative process, so that variance in a given parameter, such as porosity, is measured for successively larger sample volumes. REV - is determined as the area where the standard deviation of the variance from the sample mean is zero, or an acceptable low value. Sample mean is porosity of laboratory-derived core analysis. In block 2710, a large volume is modeled or measured with the properties of
    - .. rock of interest.
    In block 2712, a subsample of a 'given size in the large volume is randomly selected.
    In block 2716, other non-overlapping subsamples of the same size are randomly selected.
    This is repeated many times.
    In block 2718, the subsample size is increased by an increment and many similar objects are sampled.
    Blocks 2712, 2716 and 2718 are repeated, according to some modalities, until it is not possible to have a statistically large sample representation.
    In block 2722, the variance in measured property versus subsample size for each defined elementary volume is crossed out.
    The best fit is determined.
    The REV at the intersection of the variance interpolation and the rock property measured in the laboratory are read.
    With reference again to figure 26, in block 2620, image quality tests are performed, according to some modalities.
    CLAHE is applied to some images in an attempt to remove uneven artificial lighting, which is due to the sample tilt in the imaging stage.
    Unclear masking can be applied in rare cases to assist the watershed transformation in the separation of micropores.
    As not all images require "E CLAHE or an unclear mask, two tests were designed to automatically determine your need.
    A CLAHE process tries to smooth out artificial lighting problems.
    To test whether it is necessary or not,
    - a correlation test was performed using the equation
    5. Note that this test assumes that the image is twice the size of the REV. Use equations 5 through 7, as illustrated in the 2D workflow. If the result of this test is less than 0.85, HPLC is performed on the image. This value is chosen by visual inspection of the images. Corresponding results can be changed depending on the circumstances. To determine whether or not to apply the unclear mask, correlation is again used to get the best image. The sharp and original images are limited (see below) and a watershed transformation is applied. The resulting two images are then correlated with the original image, and the image with the highest correlation factor is taken. Note that it is generally better to increase the laser gain during acquisition of confocal microscopy instead of relying on this filter. In block 2622 morphological operations are performed. The purpose of this phase is to produce an accurate, segmented binary image that is easy to analyze. Such an objective is generally difficult to achieve. The pores in the. binary image are segmented and separated using the watershed transformation. The watershed transformation sometimes produces unwanted artifacts, in which some of the larger pores are segmented into smaller ones. To remedy this, the image
    The resulting watershedded binary is separated into two images: micro-porosity image and macro-porosity image. The macro-porosity image is treated with a specialized selective Kuwahara edge preservation filter, applied to the removed pixels encoded for this purpose. Unlike the use of a medium filter, this filter resulted in recovering the large pores to their original state before the watershed transformation, while maintaining the original pore shape. The two images (micro- and macro-porosity images) were then combined to continue the analysis.
    The next step involved grouping both pores and throats. A connected-6 implementation has been applied to ensure that the diagonally connected groups are separated. In block 2624, pore and throat measurements are performed. Calculations of pore volumes and throats area were performed. The relationship of throats and pores together is made by examining the pixel neighborhood of each throat. Each pore is assigned the diameter of the largest throat connected to it for reasons described in the Expanding Flow Model.
    In block 2626, data analysis is performed. According to some modalities, data analysis involves calculation steps that lead to numerical SCAL for the sample.
    The main inlets are the pore volumes, and the largest throat diameter for each pore. Distribution charts
    - pore size, cumulative pore size distribution Ú, and fractional pore volume were produced using the measured pore areas. To produce a simulated capillary pressure curve, the data was filtered to remove isolated pores. This is done by excluding pores without any throats. The remaining pores are then grouped according to their largest throat size. The total pore size for each cluster is calculated and a percentage of porosity is given. Cumulative porosity is calculated and used as a cumulative percentage. From these clusters, a simulated capillary pressure value is calculated using the equation
    1. These values, with cumulative porosity, are plotted on a profile-profile scale to produce the well-known capillary pressure curve. In block 2628, confidence factor calculations are made. A confidence factor is calculated to give an idea of the error associated with the results. This is calculated by examining three parts: the image size compared to the representative element volume (REV), the gray-scale image histogram, and a factor of: optional user-defined quality control for 'artifacts (see equations 8, 9 and 10, and the 2D workflow in figure 11). The factor is a positive non-limited number. Generally, the higher the value, the greater the confidence, with 1 being a limit between results
    “Acceptable and not acceptable.
    Although the present disclosure is described through the above modalities, it will be understood by those of ordinary skill in the art that modification in and variation of the illustrated modalities can be made without departing from the inventive concepts disclosed herein. In addition, although preferred embodiments are described with respect to various illustrative structures, a person skilled in the art will recognize that The system can be incorporated using a variety of specific structures. Therefore, the present disclosure should not be seen as limited except for the scope and spirit of the attached claims.
    权利要求:
    Claims (17)
    [1]
    1. METHOD FOR CHARACTERIZING A MEDIA SAMPLE
    POROUS INCLUDING PLURALITY OF PORE BODIES AND PLURALITY OF PORE THROATS, the method characterized by the fact that it comprises: preparing the sample of the porous medium in such a way that a single plane of the sample can be imaged; generate a two-dimensional high-resolution image of the single plane of the sample prepared from the porous medium; process the high resolution image in part by performing a watershed image processing technique; identifying a plurality of pore throats based at least in part on the watershed technique; and determining a dimension associated with each of the identified plurality of pore throats.
    [2]
    2. Method, according to claim 1, characterized by the fact that the high resolution image is made using confocal microscopy.
    [3]
    Method according to claim 1, characterized in that the preparation includes subjecting the sample of porous medium to vacuum pressure impregnation with fluorescent epoxy.
    [4]
    4. Method, according to claim 1, characterized by the fact that the watershed technique includes simulated flooding.
    [5]
    . 5. Method according to claim 1,. characterized by the fact that it further comprises determining pore body and pore throat distributions and capillary pressure curves in the porous medium based at least in part on the watershed technique.
    [6]
    6. Method, according to claim 1, characterized by the fact that the processing includes one or more pre-processing and augmentation techniques selected from a group consisting of: sewing, registration, mixing, clipping and rotation.
    [7]
    7. Method, according to claim 1, characterized by the fact that the processing includes segmenting the high resolution image into grains and pores, thereby generating a binary image.
    [8]
    8. Method, according to claim 7, characterized by the fact that a porosity value from an analysis of the porous medium is used to control a limit in the segmentation.
    [9]
    9. Method according to claim 7, characterized by the fact that a binary image is separated into a pore image and an image of throats Ls using one or more binary logic operations.
    [10]
    10. Method, according to claim 9, characterized by the fact that the image of pores and the image of throats are differentiated using one or more clustering algorithms thereby generating an image of
    . clustered pores and a clustered throats image, and pore body size and pore throat distributions are computed based on clustered images.
    [11]
    11. Method, according to claim 10, characterized by the fact that the grouped pore image and throat image are subjected to data analysis characterized by the fact that each pore body is assigned a larger pore throat diameter connected to the pore body, and each pore body has a known pore area, and pores having at least one connected throat are grouped according to the largest throat size, and pore body size for each cluster is computed and used to generate simulated capillary pressure curves using a Washburn equation.
    [12]
    12. SYSTEM FOR CHARACTERIZING A MEDIA SAMPLE
    POROUS INCLUDING PLURALITY OF PORE BODIES AND PLURALITY OF PORE THROATS, the system characterized by the fact that it comprises: a sample preparation system adapted to prepare a sample of the porous medium in such a way that a single sample plane can be imagined; . an imaging system adapted to generate a two-dimensional high-resolution image of a single plane of a sample prepared from the porous medium; and a processing system adapted and programmed to process a two-dimensional high-resolution image
    : generated in part by performing a watershedy image processing technique to identify a plurality of pore throats based at least in part on the watershed technique; and determining a dimension associated with each of the identified plurality of pore throats.
    [13]
    13. System, according to claim 12, characterized by the fact that it also comprises a sample collection system adapted to collect a sample of the porous medium.
    [14]
    14. System according to claim 12, characterized by the fact that the imaging system uses laser scanning fluorescence microscopy.
    [15]
    15. System according to claim 12, characterized in that the processing system is additionally adapted and programmed to determine pore body and pore throat size distributions and capillary pressure curves in the porous medium based on at least partly in the watershed technique.
    [16]
    16. METHOD FOR CHARACTERIZING A SAMPLE OF
    POROUS UNDERGROUND ROCK FORMATION INCLUDING A
    PLURALITY OF PORE BODIES AND A PLURALITY OF
    4. PORO GARGANTAS, the method characterized by the fact that it comprises: preparing the sample of porous underground rock formation in such a way that a single plane of the sample can be imaged;
    : generate a two-dimensional high-resolution image of the single plane of the prepared rock sample using laser scanning fluorescence microscopy; segment the high resolution image into grains and 5 pores, thereby generating a binary image processing the binary image by performing a watershed image processing technique; identifying a plurality of pore throats based at least in part on the watershed technique; and determining a throat length dimension associated with each of the identified plurality of pore throats, and determining pore body and pore throat size distributions and capillary pressure curves in the porous medium based at least in part on the watershed technique.
    [17]
    17. Method, according to claim 16, characterized by the fact that a porosity value of an analysis of the porous rock is used to control a limit in the segmentation.
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    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:
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    US201161447434P| true| 2011-02-28|2011-02-28|
    US61/447,434|2011-02-28|
    US13/407,561|2012-02-28|
    PCT/US2012/027041|WO2012118868A2|2011-02-28|2012-02-28|Petrographic image analysis for determining capillary pressure in porous media|
    US13/407,561|US8909508B2|2011-02-28|2012-02-28|Petrographic image analysis for determining capillary pressure in porous media|
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