• 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:
    DIGITAL ROCK ANALYSIS SYSTEMS AND METHODS WITH DETERMINATION OF SAFE MULTIPHASE PERMEABILITY. The structure of rocks and other materials can be determined through microscopy and subjected to digital simulation to determine the properties of multiphase fluid flows through the material. To ensure reliable results, the digital rock model is first analyzed through a series of operations that, in some modalities, include: obtaining a three-dimensional pore / matrix model from a sample; determine a flow axis; verify that the dimension of the model along the flow axis exceeds that of a representative elementary volume (REV); select a flow direction; extend model for mirroring if pore statistics at a given saturation are mismatched for different percolation phases; and increase resolution if the smallest non-percolating sphere dimension is below a predetermined threshold. This sequence of operations increases the reliability of results by measuring relative permeability using the model and displaying relative permeability measurements to the user.
    公开号:BR112015000697B1
    申请号:R112015000697-3
    申请日:2013-07-02
    公开日:2020-11-10
    发明作者:Giuseppe DE PRISCO；Jonas Toelke
    申请人:Ingrain, Inc；
    IPC主号:
    专利说明:

    REMISSIVE REFERENCE TO RELATED ORDERS
    [0001] This application claims priority for non-provisional US application serial number 13 / 549,354, entitled “Digital rock analysis systems and methods with reliable multiphase permeability determination” and filed on July 13, 2012 by Giuseppe de Prisco and Jonas Toeke, which is incorporated here as a reference. BACKGROUND
    [0002] Microscopy offers scientists and engineers a way to gain a better understanding of the materials they work with. Under high magnification, it becomes evident that many materials (including rock and bone) have a porous microstructure that allows fluid flows. Such fluid flows are often of great interest, for example, in underground hydrocarbon reservoirs. Consequently, significant efforts have been expended to characterize materials in terms of their flow-related properties including porosity, permeability and saturation.
    [0003] Scientists typically characterize materials in the laboratory by applying selected fluids with a range of pressure differentials across the sample. Such tests often take weeks and are filled with difficulties, including requirements for high temperatures, pressures and fluid volumes, risks of leakage and equipment failures, and imprecise initial conditions. (Flow-related measurements are generally dependent not only on the applied fluids and pressures, but also on the sample history. The experiment must start with the sample in a native state, but this state is difficult to obtain after the sample has been removed from its original environment).
    [0004] Therefore, the industry turned to digital rock analysis to characterize the properties related to material flow in a fast, safe and repeatable way. A digital representation of the material's pore structure is obtained and used to characterize the material flow-related properties. However, the quality of the characterization depends on the degree to which the digital representation represents precisely the physical material. A sample that is too small is unlikely to be representative of the general material due to anisotropy and / or heterogeneities, whereas a sample that is too large will impose excessive computational demands without providing any additional benefits. It would be desirable to have a procedure that efficiently ensures the accuracy of measurements related to multiphase flow derived from a digital rock model. BRIEF DESCRIPTION OF THE DRAWINGS
    [0005] Therefore, systems and methods for digital rock analysis with reliable determinations of multiphase permeability are revealed here. In the drawings:
    [0006] Figure 1 shows a high-resolution scanning electron microscope and focused ion beam.
    [0007] Figure 2 shows an illustrative high performance computer network.
    [0008] Figure 3A shows an illustrative volumetric representation of a sample.
    [0009] Figure 3B shows an illustrative coordinate system for performing sample analysis.
    [0010] Figure 4 shows an illustrative division of a sliced model region.
    [0011] Figures 5A-5B show illustrative sample parameter distributions.
    [0012] Figure 6 illustrates a dependence on moments of distribution in size of subvolume.
    [0013] Figures 7A-7D illustrate a phase-based porosity division.
    [0014] Figures 8A-8B show a permeability / saturation relationship with parameter distributions.
    [0015] Figure 9 is a flow chart of an illustrative analysis method.
    [0016] It should be understood, however, that the specific modalities given in the drawings and detailed description below do not limit the disclosure. On the contrary, they provide the basis for a person with common knowledge to discern the alternative, equivalent forms and other modifications that fall within the scope of the attached claims. DETAILED DESCRIPTION
    [0017] For context, figure 1 provides an illustration of a scanning electron microscope and focused high-resolution ion beam, 100, having an observation chamber 102 in which a sample of material is placed. A computer 104 is coupled to the observation camera instrumentation to control the measurement process. Software on computer 104 interacts with a user through a user interface having one or more input devices 106 (such as a keyboard, mouse, joystick, light pen, touchpad or touchscreen) and one or more output devices 108 (such as a display or printer).
    [0018] For high resolution imaging, observation chamber 102 is typically evacuated from air and other gases. An electron or ion beam can be rasterized across the sample surface to obtain a high resolution image. In addition, ion beam energy can be increased to laminate thin layers of the sample, thereby allowing sample images to be taken at multiple depths. When stacked, these images provide a three-dimensional image of the sample to be acquired. As an illustrative example of the possibilities, some systems enable such imaging of a 40x40x40 micrometer cube at a resolution of 10 nanometers.
    [0019] The system described above is just an example of the technologies available for imaging a sample. Transmission electron microscopes (TEM) and three-dimensional tomographic x-ray transmission microscopes are two other technologies that can be employed to obtain a digital model of the sample. Regardless of how images are acquired, the following development applies as long as the resolution is sufficient to reveal the sample's porosity structure.
    [0020] The source of the sample, as in the case of a rock formation sample, is not particularly limited. For rock formation samples, for example, the sample can be side cores, whole cores, drilling cuts, outcrop extraction samples, or other sample sources that can provide samples suitable for analysis using methods according to the present disclosure .
    [0021] Figure 2 is an example of a larger system 200 in which the scanning microscope 100 can be used. In the larger system 200, a personal workstation 202 is coupled to the scanning microscope 100 by a local area network (LAN) 204. LAN 204 also allows intercommunication between scanning microscope 100, personal workstation 202, one or more high-performance computing platforms 206, and one or more shared storage devices 208 (such as RAID, NAS, SAN or similar). The high-performance computing platform 206 generally employs multiple processors 212 each coupled to a local memory 214. An internal bus 216 provides high-bandwidth communication between multiple processors (via local memories) and a network interface 220. Memory-resident parallel processing software 214 allows multiple processors to cooperatively divide and perform tasks to be performed in a convenient manner, accessing shared storage device 208 as needed to provide results and / or obtain input and output data intermediate results.
    [0022] Typically, a user would employ a personal workstation 202 (such as a desktop computer or laptop) to interact with the larger system 200. Software in the memory of personal workstation 202 causes one or more processors to interact with the user through a user interface, allowing the user, for example, to create and run software to process the images acquired by the scanning microscope. For tasks having small computational demands, the software can be run on the personal workstation 202, while the computational demand tasks can preferably be run on the high-performance computing platform 206.
    [0023] Figure 3A is an illustrative image 302 that could be acquired by scanning microscope 100. This three-dimensional image is composed of three-dimensional volume elements ("voxels") each having a value indicative of the sample composition at that point.
    [0024] Figure 3B provides a coordinate system for a data volume 402, with the x-, y- and z- axes intersecting at one corner of the volume. In the data volume, a 404 subvolume is defined. The illustrated subvolume 404 is a cube having sides a length, but other shapes of the subvolume may alternatively be used, for example, a parallelogram having the same shape as the general data volume, a sphere, or a tetrahedron. It is desirable, although not necessary, that the chosen sub-volume format be scalable through a characteristic dimension such as diameter or length on one side. Subvolume 404 can be defined at any position 406 in data volume 402 using an offset vector 408 from the origin to a fixed point in the subvolume. Similarly, sub-volumes can be defined and positioned in each sub-volume. For example, figure 4 shows a subvolume divided into slices 502 perpendicular to the flow direction (in this case, the z axis).
    [0025] One way to characterize the porosity structure of a sample is to determine a general parameter value, for example, porosity. The image is processed to categorize each voxel as representing a pore or portion of the matrix, thereby obtaining a pore / matrix model in which each voxel is represented by a single bit indicating whether the model at that point is matrix material or space. pore. The total porosity of the sample can then be determined with a direct counting procedure. However, the resulting number reveals little about the structure, heterogeneity and isotropy of the sample. Therefore, a more sophisticated measurement may be preferred.
    [0026] An example of a more sophisticated measurement is the standard deviation of porosity along a specific direction. As shown in figure 4, a volume (or subvolume) can be divided into slices perpendicular to the flow direction. The pore structure can cause the porosity to vary from slice to slice, from which a standard deviation of porosity (in relation to the average porosity) can be determined. Although this measurement provides a useful indication of the pore structure, it can be extended. If the sample volume is divided into subvolumes (see, for example, figure 3b) and the standard porosity deviation measured (in relation to the average porosity of the entire sample and normalized by that same mediated porosity) for each subvolume provides a histogram as the one shown in figure 5A. Note, however, that this histogram is a function of the subvolume size. As the subvolume size increases from almost zero to a representative elementary volume (“SEE”), the histogram converges and becomes almost Gaussian in shape. (By way of comparison, when the subvolume dimension in a perfectly periodic “ideal” sample has a size that is an integer multiple of the VER size, the histogram will have zero mean and zero variance, in other words a Dirac delta function centered on zero).
    [0027] The VER size depends on the statistical measurement used to define it. The above approach provides an appropriate VER for Darcian analysis, and consequently that VER size (for example, diameter, length or other dimension) is referred to here as the "full scale" or "Darcian scale". Other length scales may also be important for the analysis. For example, the percolation scale, defined here as the size of the subvolume in which the difference between total porosity and connected porosity (porosity connected in some way to the input face) is below a limit, for example, 2%. The percolation scale can be greater than, or less than, the integral scale.
    [0028] Another measurement of porosity structure is the standard deviation from surface to volume ratio. If the surface area (or in a two-dimensional image, the perimeter) of the pores in each 502 slice (figure 4) is divided by the volume (or in 2D, the surface area) of the corresponding pores, the resulting ratio shows some variation from slice to slice, which can be measured in terms of standard deviation. Since the standard deviation of the surface to volume ratio is determined for each subvolume in a model, a histogram like the one in figure 5 results. As before, the histogram must converge and approximate a Gaussian distribution when the size of the subvolume reaches or exceeds the full scale.
    [0029] Figure 6 compares the moments of the two histograms (standard deviation of porosity and standard deviation of surface ratio to volume (SVR)) for two different samples as a function of subvolume size. The first four moments (mean, standard deviation, slope and kurtosis) are shown for subvolume sizes as measured by the subvolume's edge length (which is a cube) in the range of 60 to 480 units. The first moment for the two samples approaches zero, that is, the center of the standard deviation of porosity and SVR distributions approaches that of the entire sample, in approximately 200 units, and the width of the peak distribution also approaches zero close to that limit. The second moment for the two samples is similarly close to zero at this point, that is, the probability of a subvolume having the same standard deviation of porosity and SVR as the whole sample is quite high. The distribution asymmetry (as indicated by the slope value) and kurtosis also become small at and above this limit, suggesting that the VER size, to define a full-length scale according to Darcy analysis, is not greater than 200 units . As explained in provisional order US 61 / 618,265 entitled “An efficient method for selecting representative elementary volume in digital representations of porous media” and filed on March 30, 2012 by inventors Giuseppe De Prisco and Jonas Toelke (and requests for continuation thereof) , either or both of these measurements can be used to determine whether small portions of the original data volume adequately represent everything for analyzes related to permeability and porosity.
    [0030] A potential difficulty arises, however, in analyzes in relation to multiple fluid phases effectively occupying different parts of the pore space. To understand why this occurs, please consider figures 7A-7D. Figure 7A shows an illustrative sample image having pore space between circularly shaped grains of matrix material. Taking the white pore space as being filled with a wetting fluid phase, consider invasion by a second non-wetting phase. According to the Young-Laplace equation, a positive capillary pressure produces an interface having a constant average curvature and radii of curvature that shrink with increased pressure, providing a pressure-related degree of pore invasion.
    [0031] Figure 7B shows the sample in figure 7A with the addition of an invasive fluid (not wetting) phase shown as black. It can be seen that the pore space has been divided. Figure 7C shows the pore space filled by the non-wetting phase (in black) while figure 7D shows the pore space filled by the wetting phase (in black). The matrix / pore model is thus divided into two phase-based matrix / pore models, hereinafter called matrix / phase models. One model considers only the wetting phase and the rest is considered a matrix, while the other model considers only the non-wetting phase and treats the rest as a matrix. This process can be repeated for different radii of curvature to generate the phase / matrix models as a function of relative saturation. More information in an illustrative division process can be found in Hilpert and Miller, “Pore-morphology-based simulation of drainage in totally wetting porous media”, Advances in water resources 24 (2001) 243-255.
    [0032] Division is a function of the mode (injection, drainage, imbibition), history and degree of movement of the simulated fluid. In an illustrative implementation, spheres of gradually decreasing diameter, which here represent a perfect non-wetting fluid having negligible viscous coupling with the other phase, are used to invade the pore space of one or more edges of the data model. The gradually decreasing diameter allows the invasion fluid to reach more of the pore space, depending on the size and connectivity. (This approach is hereinafter referred to as the mercury injection capillary pressure (“MICP”) approach because it precisely models the physical process of the same name.) In other implementations, connectivity may not be required, and fluid invasion allowed anywhere where the required balls fit, the ball diameters gradually increase to provide less and less pore space to be occupied by the non-wetting fluid. (This approach is hereinafter referred to as the Open Map approach due to its relation to the concept of the same name taken from the field of mathematical morphology. An illustrative physical analogue of this process occurs when gas leaves the solution and occupies a little of the space of pore.)
    [0033] At each stage (that is, each sphere diameter), saturations are determined and the corresponding matrix / phase models are determined and subjected to separate analyzes. For example, each matrix / phase model can be subjected to a separate determination of porosity, permeability, surface to volume ratio, porosity standard deviation histogram, surface ratio to volume standard deviation histogram, and / or characteristic dimension of a REV. For example, absolute permeability
    can be computed from each matrix / phase model, where p is the phase (for example, wetting or not wetting) and s is the saturation of that phase as measured in the current invasion step in the original matrix / pore model. See, for example, Papatzacos “Cellular automation model for fluid flow in porous media”, Complex systems 3 (1989) 383-405. From the computed absolute permeability values, it is possible to obtain a relative (almost static) permeability
    where the denominator is the absolute permeability kabs of the original matrix / pore model. This calculation assumes a strong uniform wetting capacity and negligible viscous coupling between the two phases, that is, it represents the so-called almost static relative permeability.
    [0034] The carbonate rock sample that was used to determine the histograms in figures 5A-5B was subjected to this analysis, providing relative vs. static permeability. Saturation curves shown in figures 8A-8B. In both figures, curve 802 shows quasi-static relative saturation permeability for the wetting phase, while curve 804 shows quasi-static relative saturation permeability for the non-wetting phase. Notches in figure 8A show histograms of the standard deviation for subvolume porosity, while indentations in figure 8B show histograms of the standard deviation for subvolume / volume surface ratio. The indentations in high relative permeability values can be compared with figures 5A-5B to confirm that the distributions match when the pores are widely occupied by any single phase.
    [0035] As the non-wetting phase saturation drops from almost 1 to approximately 0.5 (shown in the figure as wetting saturation close to 0 and 0.5, respectively), the histogram of the porosity standard deviation increases and moves upwards, indicating much increased heterogeneity. In other words, the porosity network that the non-wetting fluid is forming for lower non-wetting saturation invades only parts of the pore space, and this leads to heterogeneous distribution. The histogram of standard deviation from surface ratio to volume and porosity shows an increasing peak on the right side of the graph for the non-wetting phase curve, probably indicating the presence of large heterogeneities.
    [0036] As the wetting phase saturation drops from almost 1 to approximately 0.5, the porosity standard deviation histogram for the wetting phase remains a match consistent with the original distribution. This observation suggests that once the wetting phase is flowing, you can access almost any part of the pore space. The standard deviation histogram for the surface to volume ratio, shown in figure 8B, acquires additional peaks, revealing increased heterogeneity from the reduced amount of wetting phase.
    [0037] Given the above principles and practices, we now turn to a discussion of certain workflows that greatly increase the reliability of relative permeability measurements derived from a digital rock model. Figure 9 is an illustrative flow chart to support this discussion.
    [0038] The illustrative workflow starts at block 902, where the system takes one or more images of the sample, for example, with a scanning microscope or tomographic X-ray transmission microscope. Of course, the images can alternatively be provided as data files in an information storage medium. In block 904, the system processes the images to derive a matrix / pore model. Such processing may involve sophisticated filtration as exposed in existing literature to classify each image voxel as representing a pore or portion of the matrix. In block 906, the system analyzes the matrix / total pore model to obtain single-phase statistics such as porosity, surface to volume ratio absolute permeability along each axis and standard deviations of porosity and surface to volume ratio along each axis of the entire sample.
    [0039] In block 908, the system determines a flow axis. This determination can be based on the preceding analysis or on external factors (for example, the orientation of the material sample in relation to the well, formation pressure gradients, customer specifications). When the axis is not based on external factors, it can be selected based on the standard deviation of porosity: the axis having the lowest standard deviation can be preferred. Alternative bases exist and can be used.
    [0040] In block 910, the system verifies that the digital model is Darcian, that is, that the matrix / pore model is substantially larger than the Darcian length scale (the matrix / pore model is preferably at least 2-4 times greater than REV, but at a minimum it should be at least as large as REV). The size of the REV can be determined using the porosity standard deviation histograms and / or SVR for subvolumes of different dimensions and determine in which dimension the histograms converge to a Gaussian distribution with a small enough variance (moment analysis). If the digital model is too small, it must be rejected. In some cases, it may be possible to use the scanning microscope to process a new sample with an increased field of view.
    [0041] In block 912, the system determines a flow direction along the flow axis, that is, which of the opposite sides perpendicular to the axis will be the entrance and which will be the exit. A major consideration is that the entry face should not have any large pores (relative to the average pore size) close to the sample limit. In addition, the face with the most homogeneous pore distribution should be preferred. The system can employ an algorithmic measurement of pore homogeneity or be based on visual inspection by the operator. In particular, the same moment analysis can be applied to 2D slices and quantitatively select the most homogeneous face.
    [0042] In block 914, the system performs a MICP analysis with gradually decreasing sphere size to determine situation A at the percolation point (that is, the point at which the non-wetting phase is becoming connected to enable flow from from entrance to exit). Next, the same analysis is used for the reverse flow direction to determine saturation B at the percolation point. In block 916, the system determines whether the difference | A-B | is too large (for example, greater than 0.2). A strong directional dependence indicates that the sample is not suitable for a calculation of relative permeability and should be rejected.
    [0043] In block 920, the system determines the phase / matrix model for each phase in each saturation using a MICP approach. For those phase / matrix models that are connected (percolation), the system determines the standard deviation histograms for porosity and / or SVR. (See, for example, figure 8A showing histograms of standard porosity deviation for each phase at different saturation values, and figure 8B showing histograms of SVR standard deviation for each phase at different saturation values.) In the block 922, the system checks for each saturation value that the histograms for the different percolation phases look mutually (for example, having comparable first, second and possibly higher order moments). Absent some unique characteristics of the sample that would explain mismatch between distributions (for example, such as unusual grain structure or heterogeneity of the sample), such mismatches could be due to boundary effects artificially introduced by the walls. The mismatch in figures 8A and 8B at intermediate saturation values (S = 0.5 - 0.7) indicates that the relative permeability measurements here are likely to be inaccurate because the non-wetting phase network does not meet the REV requirements .
    [0044] Therefore, when such mismatch is detected, the system extends the phase / matrix model (s) by mirroring in the X and / or Y directions in block 925. Such mirroring doubles the model dimension in dimension X and / or Y (for a total size that is up to 4 times the original size), while providing more pore connectivity along the mirror faces.
    [0045] In block 926, the system again measures the percolation limit using a MICP approach. As an example of how this can be done: spheres of gradually decreasing diameter are used to invade the pore space from the entrance face of the matrix / pore model. In some diameter, the spheres are able to pass from the entry face through the model and reach the exit face. The largest diameter (or some other sphere size measurement) that provides percolation is hereinafter referred to as the percolation size, and the next largest diameter (that is, the smallest non-percolating sphere diameter) is referred to as the connectivity limit. The connectivity limit is preferably at least a sphere diameter of eight or more voxels, but in any case it must be at least 3 voxels.
    [0046] In block 928, the system determines whether the connectivity limit is high enough, and if not, the system increases the sample resolution in block 930 and repeats the operations represented by blocks 904-928. The sample resolution can be increased in several ways. For example, if as part of deriving the pore / matrix model in block 904, the system has reduced high resolution images from block 902, the system can reduce the reduction factor. In some cases, it may be possible for the system to employ image processing to increase the resolution of the images before deriving the matrix / pore model. As another option, the system can acquire new microscopy images with a reduced field of view and correspondingly increased resolution. Where it is not possible to improve the resolution, the system should reject the sample.
    [0047] If the connectivity limit is sufficient, the system can then engage in a calculation of relative permeability in block 932 and expect to obtain safe measurements for display to a user in block 934. In some implementations, the system uses one of the exposed methods in US patent application 13 / 539,543, “Method for simulating fractional multi-phase / multi-component flow through porous media,” filed on July 2, 2012 by inventors Giuseppe De Prisco, Jonas Toelke, and Yaoming Mu (No. dossier of the prosecutor 3091-015-01). Other relative permeability measurement techniques are known and can be employed. The results can be shown illustratively in a format similar to figure 8A.
    [0048] For explanatory purposes, the operations of the method above have been described as occurring in a sequential, orderly mode, but it must be understood that at least some of the operations can occur in a different order, in parallel and / or in an asynchronous mode.
    [0049] Numerous variations and modifications will become evident to those skilled in the art after the above disclosure is fully appreciated. For example, the above disclosure describes illustrative statistics for determining a REV size, but other suitable statistics exist and can be employed. The following claims are intended to be interpreted to cover all such variations and modifications.
    权利要求:
    Claims (9)
    [0001]
    1. Method for determining multiphase permeability, the method characterized by the fact that it comprises the following steps: i. obtaining a three-dimensional matrix / pore model of a sample (902) by scanning a physical rock sample to obtain a three-dimensional digital image; and deriving the pore / matrix model from the three-dimensional image; the three-dimensional image being composed of three-dimensional volume elements each having a value indicative of the sample composition at that point; ii. determine a flow axis (908), the said determination being based on a standard deviation of porosity; iii. verify that a dimension of the model along the flow axis (908) exceeds a dimension of a representative elementary volume (VER) of the model (910), and the VER is determined using a standard deviation of porosity determined for each subvolume of the model ; iv. selecting a flow direction along the flow axis (908) of the model (912); v. extend the model by mirroring the model in an axial direction of the model (914) when model pore statistics, at a given model saturation, are mismatched for different percolation phases; saw. increase the resolution (930) of the three-dimensional image of the sample and repeat the previous steps i-v when a dimension of a smaller non-percolating sphere in determining a percolation limit of the model (926) is below a predetermined limit (328); vii. measure the relative permeability (932) using the model; and viii. display the relative permeability measurements (934) to the user.
    [0002]
    2. Method according to claim 1, characterized in that it additionally comprises: determining the absolute permeability along each axis before the aforementioned flow axis determination (908).
    [0003]
    3. Method according to claim 1, characterized in that the selection of a flow direction includes: - examining opposite faces perpendicular to the flow axis (908) to eliminate any face having large pores close to a limit; and - if two faces remain, select the face having better pore homogeneity as a preferred entry face.
    [0004]
    4. Method, according to claim 1, characterized by the fact that the aforementioned selection of a flow direction includes: - determining a mercury injection capillary pressure saturation (MICP) in which connectivity occurs for each direction along said axis flow (908); and - verify that a strong directional dependency does not exist.
    [0005]
    5. Method according to claim 1, characterized by the fact that the predetermined limit is a diameter of 3 voxels.
    [0006]
    6. Method, according to claim 1, characterized by the fact that the aforementioned increase in resolution includes the repetition of the operations of obtaining, determining, verifying, selecting, extending and increasing.
    [0007]
    7. Method according to claim 1, characterized by the fact that the aforementioned pore statistics include at least one of the porosity standard deviation distribution and a pore-to-volume surface ratio standard deviation distribution.
    [0008]
    8. Method, according to claim 1, characterized by the fact that the three-dimensional image is obtained by means of a beam of electrons or ions rasterized through the surface of the sample to obtain a high resolution image, increasing the beam energy of ions to laminate thin layers of the sample allowing sample images to be taken at multiple depths.
    [0009]
    9. System for determining multiphase permeability, characterized by the fact that it comprises: - a memory with software; and - one or more processors coupled to the memory to run the software, the software causing one or more processors to effect the method for determining multiphase permeability, as defined in any of claims 1 to 8.
    类似技术:
    公开号 | 公开日 | 专利标题
    BR112015000697B1|2020-11-10|method for determining multiphase permeability and system for determining multiphase permeability
    AlRatrout et al.2017|Automatic measurement of contact angle in pore-space images
    RU2639727C2|2017-12-22|Systems and methods of digital analyzing rocks with definition of sve multiphase flow
    AU2017239499B2|2018-09-27|Digital rock analysis systems and methods that reliably predict a porosity-permeability trend
    Joekar Niasar et al.2009|Simulating drainage and imbibition experiments in a high‐porosity micromodel using an unstructured pore network model
    Saxena et al.2018|Imaging and computational considerations for image computed permeability: operating envelope of digital rock physics
    Chung et al.2019|Approximating permeability of microcomputed-tomography images using elliptic flow equations
    EA032063B1|2019-04-30|Systems and methods for improving direct numerical simulation of material properties from rock samples and determining uncertainty in the material properties
    Garfi et al.2020|The sensitivity of estimates of multiphase fluid and solid properties of porous rocks to image processing
    US10115188B2|2018-10-30|Porous material analysis system and method for hydrology, petroleum and environment engineering
    Verri et al.2017|Development of a digital rock physics workflow for the analysis of sandstones and tight rocks
    Jing et al.2017|Topological characterization of fractured coal
    Jin et al.2016|Microstructural modeling method for asphalt specimens supporting 3D adaptive and automatic mesh generation
    US20140052420A1|2014-02-20|Digital Rock Analysis Systems and Methods that Estimate a Maturity Level
    BR112016002538B1|2020-10-13|method for generating forming properties of a pressurized downhole environment and system for generating forming properties of a pressurized downhole environment
    Fu et al.2020|Resolution effect: An error correction model for intrinsic permeability of porous media estimated from lattice boltzmann method
    Burian et al.2015|Considerations on the model-free shape retrieval of inorganic nanocrystals from small-angle scattering data
    Zhu et al.2018|Study of the numerical simulation of tight sandstone gas molecular diffusion based on digital core technology
    Schena et al.2008|Pore space characterisation and permeability prediction using fast network extraction and pore throat conductance calculation
    Oster et al.2021|The Thermal Conductivity of Polar Firn
    CN112734143A|2021-04-30|Method, device and system for fractal characterization of pore structure
    Riasi2019|Image-Based Micro-Scale Modeling of Flow in Porous Media
    Alizadeh et al.2013|SMALL SCALE PERMEABILITY MEASUREMENTS AT EVELATED PRESSURES: ANALYSIS AND CORRECTION OF EXPERIMENTAL ERROR USING MICRO XRAY-CT
    Krassakopoulos2013|Petrophysical Characterization of Gas Reservoir Rock Samples using High Resolution Micro-CT Images
    Arns0|This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1029/2020WR027978
    同族专利:
    公开号 | 公开日
    EP2872741A2|2015-05-20|
    US9285301B2|2016-03-15|
    ES2720961T3|2019-07-25|
    CN104619952A|2015-05-13|
    CA2917494A1|2014-01-16|
    WO2014011448A2|2014-01-16|
    BR112015000697A2|2017-06-27|
    EP2872741A4|2016-04-06|
    AU2013289017B2|2017-09-21|
    WO2014011448A3|2014-02-27|
    CO7200290A2|2015-02-27|
    US20140019054A1|2014-01-16|
    EP2872741B1|2019-03-13|
    CN104619952B|2019-01-08|
    AU2013289017A1|2015-03-05|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    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|
    US8331626B2|2008-05-21|2012-12-11|Ingrain, Inc.|Method for estimating material properties of porous media using computer tomographic images thereof|
    EP2289046A4|2008-05-23|2016-03-30|Fei Co|Image data processing|
    US8155377B2|2008-11-24|2012-04-10|Ingrain, Inc.|Method for determining rock physics relationships using computer tomographic images thereof|
    US8081802B2|2008-11-29|2011-12-20|Ingrain, Inc.|Method for determining permeability of rock formation using computer tomograpic images thereof|
    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|
    US8583411B2|2011-01-10|2013-11-12|Saudi Arabian Oil Company|Scalable simulation of multiphase flow in a fractured subterranean reservoir as multiple interacting continua|
    US9080946B2|2012-06-15|2015-07-14|Ingrain, Inc.|Digital rock analysis systems and methods with multiphase flow REV determination|
    US9285301B2|2012-07-13|2016-03-15|Ingrain, Inc.|Digital rock analysis systems and methods with reliable multiphase permeability determination|MX2011001035A|2011-01-27|2012-07-27|Mexicano Inst Petrol|Procedure for the determination of effective and total porosity of carbonated sedimentary rocks, and morphology characterization of their micro and nanopores.|
    US9285301B2|2012-07-13|2016-03-15|Ingrain, Inc.|Digital rock analysis systems and methods with reliable multiphase permeability determination|
    RU2015104162A|2012-08-10|2016-09-27|Ингрейн, Инк.|METHOD FOR INCREASING THE ACCURACY OF VALUES OF ROCK PROPERTIES OBTAINED FROM DIGITAL IMAGES|
    US10514372B2|2012-08-23|2019-12-24|Halliburton Energy Services, Inc.|Digital rock analysis systems and methods that reliably predict a porosity-permeability trend|
    CN104198351A|2014-07-11|2014-12-10|中国石油天然气股份有限公司|Three-phase relative permeability curve test method|
    US10552553B2|2015-08-17|2020-02-04|Saudi Arabian Oil Company|Capillary pressure analysis for petrophysical statistical modeling|
    DE112016005364T5|2016-02-16|2018-08-02|Halliburton Energy Services, Inc.|A method of selecting an earth model from a plurality of earth models|
    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|
    MX2019005110A|2016-10-31|2019-08-05|Bp Corp North America Inc|Direct numerical simulation of petrophysical properties of rocks with two or more immicible phases.|
    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|
    CN107784169B|2017-10-11|2020-10-13|上海电力学院|Porous medium characterization unit body determination method based on variation function and porosity|
    US10451571B2|2017-12-11|2019-10-22|Saudi Arabian Oil Company|Measuring the wettability of porous media based on the temperature sensitivity of nuclear magnetic resonance relaxation time|
    US10495589B2|2018-04-17|2019-12-03|Saudi Arabian Oil Company|Determining permeability of porous media based on nuclear magnetic resonance measurement|
    CN109458175B|2018-11-14|2021-09-28|中国石油化工股份有限公司|Method for predicting oil saturation of reservoir in overpressure environment|
    US11249002B2|2019-03-28|2022-02-15|Halliburton Energy Services, Inc.|Measuring size and shape of pore throat using digital porous plate experiments|
    法律状态:
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|
    2020-02-11| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|
    2020-09-15| B09A| Decision: intention to grant|
    2020-11-10| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 02/07/2013, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/549,354|2012-07-13|
    US13/549,354|US9285301B2|2012-07-13|2012-07-13|Digital rock analysis systems and methods with reliable multiphase permeability determination|
    PCT/US2013/049113|WO2014011448A2|2012-07-13|2013-07-02|Digital rock analysis systems and methods with reliable multiphase permeability determination|
    [返回顶部]