CN116168073A - REV determination method and device for digital core seepage characteristic analysis - Google Patents
REV determination method and device for digital core seepage characteristic analysis Download PDFInfo
- Publication number
- CN116168073A CN116168073A CN202111403890.7A CN202111403890A CN116168073A CN 116168073 A CN116168073 A CN 116168073A CN 202111403890 A CN202111403890 A CN 202111403890A CN 116168073 A CN116168073 A CN 116168073A
- Authority
- CN
- China
- Prior art keywords
- pore
- radius
- average
- core
- throat
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000004458 analytical method Methods 0.000 title claims abstract description 26
- 239000011148 porous material Substances 0.000 claims abstract description 252
- 238000004364 calculation method Methods 0.000 claims abstract description 191
- 230000035699 permeability Effects 0.000 claims abstract description 100
- 239000011435 rock Substances 0.000 claims abstract description 60
- 238000002474 experimental method Methods 0.000 claims abstract description 38
- 230000011218 segmentation Effects 0.000 claims description 30
- 238000004088 simulation Methods 0.000 claims description 29
- 239000012530 fluid Substances 0.000 claims description 25
- 230000015654 memory Effects 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 9
- 230000009467 reduction Effects 0.000 claims description 7
- 238000012795 verification Methods 0.000 claims description 6
- 238000005325 percolation Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 description 9
- 238000011160 research Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 4
- 229910052753 mercury Inorganic materials 0.000 description 4
- 238000003825 pressing Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000010603 microCT Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geometry (AREA)
- Chemical & Material Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Fluid Mechanics (AREA)
- Dispersion Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a REV determination method and device for digital core seepage characteristic analysis. The method comprises the following steps: scanning a rock core to obtain a rock core image, and dividing a pore structure and a skeleton structure in the rock core image; selecting a plurality of computing units with pore-throat structure connectivity meeting requirements from the core image; determining an average pore radius and an average throat radius of each computing unit respectively, and determining an average pore throat radius based on the average pore radius and the average throat radius; and respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as REV. REV can be accurately and quickly selected, and unnecessary analog calculation is not needed.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a REV determination method and device for digital core seepage characteristic analysis.
Background
In the technical field of oil and gas exploration, research on rock pore structures is a research hotspot, in order to accurately describe the rock pore structures, a three-dimensional digital rock core can be established by using a physical imaging technology, and characteristics such as permeability of the rock can be simulated and researched through the digital rock core.
When researching the rock-related characteristics by using the digital core, it is necessary to identify a representative unit volume (Representative Element Volume, REV, also referred to as representative basic volume or representative unit volume), which is the minimum volume of the soil body when the rock-related characteristics tend to be substantially stable, and the pore structure of the rock can be completely represented by the REV of the rock with stronger non-uniformity.
At present, in the research of the permeability characteristics of the digital core, a plurality of calculation units are randomly selected to perform single-phase seepage characteristic calculation, and finally, units close to experimental permeability results are selected from a plurality of groups of calculation results to serve as representative calculation units.
Disclosure of Invention
The present invention has been made in view of the above problems, and it is an object of the present invention to provide a REV determination method and apparatus for digital core percolation characteristics analysis that overcomes or at least partially solves the above problems.
The embodiment of the invention provides a REV determination method for digital core seepage characteristic analysis, which comprises the following steps:
scanning a rock core to obtain a rock core image, and dividing a pore structure and a skeleton structure in the rock core image;
selecting a plurality of computing units with pore-throat structure connectivity meeting requirements from the core image;
determining an average pore radius and an average throat radius of each computing unit respectively, and determining an average pore throat radius based on the average pore radius and the average throat radius;
and respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as a representative volume unit REV.
In some optional embodiments, the scanning the core to obtain a core image and dividing a pore structure and a skeleton structure in the core image includes:
scanning the rock core to obtain a group of rock core images with voxels of a first set size;
adopting a median filter to perform noise reduction treatment on the core image;
and dividing the core image based on a set dividing threshold value to divide a pore structure and a skeleton structure in the core image.
In some optional embodiments, the segmenting the core image based on the set segmentation threshold value, to segment the pore structure and the skeleton structure in the core image, includes:
based on a set segmentation threshold, identifying a region with a pixel value smaller than the segmentation threshold in the core image as a pore structure, identifying a region with a pixel value not smaller than the segmentation threshold as a skeleton structure, setting the gray value of the pore structure as a first value, and setting the gray value of the skeleton structure as a second value to obtain a binarized core image.
In some optional embodiments, the selecting a plurality of computing units from the core image that have a pore throat structure connectivity meeting requirements includes:
carrying out continuous treatment on the pore structure;
and selecting a plurality of calculation units with voxels of a second set size from the core image, and screening out the calculation units with pore-throat structure connectivity meeting the requirements.
In some optional embodiments, selecting voxels from the core image as the plurality of computing units of the second set size includes:
and randomly selecting a plurality of calculation units with voxels of a second set size from the rock core image, calculating the porosity based on the gray values of the pore structure and the skeleton structure in the calculation units, comparing the calculated porosity with the experimentally measured porosity, and selecting a plurality of calculation units with the porosity errors obtained by comparison within a set error range.
In some alternative embodiments, determining an average pore radius and an average throat radius for each computing unit, respectively, based on the average pore radius and the average throat radius, comprises:
determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius;
and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
In some optional embodiments, determining the error of the average pore throat radius of each computing unit to the pore throat radius corresponding to the experimentally measured peak permeability contribution of the core, respectively, comprises:
comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments;
and when the determined average pore radius, average throat radius and distribution interval dimension of the corresponding pore throat radius are the same, calculating a radius difference value of the average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments according to each calculation unit, and taking the ratio of the radius difference value to the corresponding pore throat radius as the error.
In some alternative embodiments, the foregoing further comprises: and performing simulation calculation on the seepage characteristics of each calculation unit by adopting the selected fluid medium, and verifying whether the selected REV is representative or not based on simulation calculation results.
In some alternative embodiments, the simulating calculation of the seepage characteristics of each calculation unit using the selected fluid medium, verifying whether the selected REV is representative based on the simulation calculation results, includes:
simulating and calculating the seepage characteristics of each calculation unit by adopting the selected fluid medium to respectively obtain the seepage rate of each calculation unit in each direction and/or the average value of the seepage rates in all directions;
comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting the fluid medium experimental test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction;
and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit.
The embodiment of the invention also provides a REV determining device for analyzing the seepage characteristics of the digital core, which comprises the following components:
the image acquisition module is used for scanning the rock core to obtain a rock core image and dividing a pore structure and a skeleton structure in the rock core image;
the unit selection module is used for selecting a plurality of calculation units with the pore-throat structure connectivity meeting the requirements from the core image;
the parameter calculation module is used for respectively determining the average pore radius and the average throat radius of each calculation unit, and determining the average pore throat radius based on the average pore radius and the average throat radius;
and the analysis and selection module is used for respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as a representative volume unit REV.
In some optional embodiments, the image acquisition module is configured to scan a core to obtain a core image, and segment a pore structure and a skeleton structure in the core image, including:
scanning the rock core to obtain a group of rock core images with voxels of a first set size;
Adopting a median filter to perform noise reduction treatment on the core image;
and dividing the core image based on a set dividing threshold value to divide a pore structure and a skeleton structure in the core image.
In some optional embodiments, the image acquisition module is configured to segment a core image based on a set segmentation threshold, and segment a pore structure and a skeleton structure in the core image, including:
based on a set segmentation threshold, identifying a region with a pixel value smaller than the segmentation threshold in the core image as a pore structure, identifying a region with a pixel value not smaller than the segmentation threshold as a skeleton structure, setting the gray value of the pore structure as a first value, and setting the gray value of the skeleton structure as a second value to obtain a binarized core image.
In some optional embodiments, the unit selecting module is configured to select a plurality of computing units with pore throat structure connectivity meeting requirements from the core image, including:
carrying out continuous treatment on the pore structure;
and selecting a plurality of calculation units with voxels of a second set size from the core image, and screening out the calculation units with pore-throat structure connectivity meeting the requirements.
In some optional embodiments, the unit selection module is configured to select a plurality of computing units with voxels of a second set size from the core image, including:
and randomly selecting a plurality of calculation units with voxels of a second set size from the rock core image, calculating the porosity based on the gray values of the pore structure and the skeleton structure in the calculation units, comparing the calculated porosity with the experimentally measured porosity, and selecting a plurality of calculation units with the porosity errors obtained by comparison within a set error range.
In some alternative embodiments, the parameter calculation module is configured to determine an average pore radius and an average throat radius for each calculation unit, respectively, and determine an average pore throat radius based on the average pore radius and the average throat radius, including:
determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius;
and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
In some optional embodiments, the analysis selection module is configured to determine an error of the average pore throat radius of each calculation unit corresponding to an experimentally measured permeability contribution peak of the core, including:
Comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments;
and when the determined average pore radius, average throat radius and distribution interval dimension of the corresponding pore throat radius are the same, calculating a radius difference value of the average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments according to each calculation unit, and taking the ratio of the radius difference value to the corresponding pore throat radius as the error.
In some alternative embodiments, the foregoing further comprises:
and the simulation verification module is used for performing simulation calculation on the seepage characteristics of each calculation unit by adopting the selected fluid medium and verifying whether the selected REV is representative or not based on simulation calculation results.
In some alternative embodiments, the analog verification module is specifically configured to:
simulating and calculating the seepage characteristics of each calculation unit by adopting the selected fluid medium to respectively obtain the seepage rate of each calculation unit in each direction and/or the average value of the seepage rates in all directions;
comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting the fluid medium experimental test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction; and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit.
The embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the REV determination method for analyzing the seepage characteristics of the digital core is realized when the computer executable instructions are executed by a processor.
The embodiment of the invention also provides a terminal device, which comprises: the REV determination method for the digital core seepage characteristic analysis is realized when the processor executes the program.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the method comprises the steps of identifying pores and a skeleton structure in a rock core image, and calculating the average pore radius and the average throat radius of a selected calculation unit to obtain the average pore throat radius; REV is selected according to the calculated average pore throat radius and the error of the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, representative REV can be selected in advance, multiple unnecessary simulation calculations do not need to be carried out on a calculation unit which is not provided with representativeness, the simulation calculation amount is greatly reduced, time and labor are saved, the selected REV can well meet the requirements of core seepage characteristics and mechanism analysis, the seepage characteristics research can be carried out by using a digital core technology more effectively and rapidly, the seepage characteristics and mechanism of the core can be accurately obtained, and the core seepage characteristics and mechanism can be better analyzed from a microscopic scale.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a REV determination method for analyzing seepage characteristics of a digital core according to an embodiment of the invention;
FIG. 2 is a flow chart of a REV determination method for analyzing seepage characteristics of a digital core according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of an original core image obtained by scanning in the second embodiment of the present invention;
fig. 4 is a schematic diagram of a binarized core image after threshold segmentation in the second embodiment of the present invention;
fig. 5 is an exemplary diagram of selecting a computing unit on a core image according to a second embodiment of the present invention;
FIG. 6 is a graph showing probability distribution of pore radius in a second embodiment of the present invention;
FIG. 7 is a graph showing the probability distribution of throat radius in a second embodiment of the present invention;
FIG. 8 is a graph showing an example of the measurement results of the mercury injection experiment in the second embodiment of the present invention;
fig. 9 is a schematic structural diagram of an REV determining device for analyzing seepage characteristics of a digital core according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems that in the prior art, REV selection can be completed by carrying out multiple unnecessary simulation calculations on a non-representative calculation unit, which is time-consuming and labor-consuming and is difficult to meet the actual requirements in the aspect of analysis and research of core seepage mechanism, the embodiment of the invention provides a REV determination method for digital core seepage characteristic analysis, which selects REV based on the average pore throat radius of the calculation unit in the digital core and the error of the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments, and can accurately and quickly select REV without carrying out unnecessary simulation calculations. The following is a detailed description of specific embodiments.
Example 1
An embodiment of the present invention provides a REV determining method for analyzing seepage characteristics of a digital core, the flow of which is shown in fig. 1, and the method includes the following steps:
step S101: and scanning the core to obtain a core image, and dividing a pore structure and a skeleton structure in the core image.
In the step, a rock core is scanned to obtain a group of rock core images with voxels of a first set size, the rock core images are subjected to noise reduction treatment by adopting a median filter, and then the rock core images are segmented based on a set segmentation threshold value to segment pore structures and skeleton structures in the rock core images. After the original core image obtained by scanning is segmented, different gray values can be given to the pore structure and the skeleton structure, and the original core image is converted into a binarized core image.
Step S102: and selecting a plurality of computing units with the pore-throat structure connectivity meeting the requirements from the core image.
In the step, after the binarized core image is obtained, a plurality of calculation units can be selected to execute the subsequent steps after the tiny flaws of the continuity of the pore mechanism are processed. And continuously processing the pore structure, selecting a plurality of calculation units with voxels of a second set size from the rock core image, and screening the calculation units with pore-throat structure connectivity meeting the requirements.
Step S103: the average pore radius and average throat radius of each computing unit are determined separately, and the average pore throat radius is determined based on the average pore radius and the average throat radius.
In the step, the average pore radius and the average throat radius of each selected computing unit are respectively determined, and can be determined by using a maximum sphere algorithm, so that the average pore throat radius is determined, namely, for each computing unit:
determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius; and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
Step S104: and respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as REV.
In the step, comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments; when the average pore radius, the average throat radius and the distribution interval dimension of the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments are the same, calculating the radius difference between the average pore-throat radius and the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments according to each calculation unit, and taking the ratio of the obtained radius difference to the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments as an error. And selecting based on the determined error value, and selecting a calculation unit with a less error value and a set threshold value as a representative calculation unit.
In the method of the embodiment, the average pore radius and the average throat radius of the selected calculation unit are calculated by identifying the pores and the skeleton structure in the core image, so that the average pore throat radius is obtained; REV is selected according to the calculated average pore throat radius and the error of the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, representative REV can be selected in advance, multiple unnecessary simulation calculations do not need to be carried out on a calculation unit which is not provided with representativeness, the simulation calculation amount is greatly reduced, time and labor are saved, the selected REV can well meet the requirements of core seepage characteristics and mechanism analysis, the seepage characteristics research can be carried out by using a digital core technology more effectively and rapidly, the seepage characteristics and mechanism of the core can be accurately obtained, and the core seepage characteristics and mechanism can be better analyzed from a microscopic scale.
Example two
The second embodiment of the present invention provides a specific implementation process of a REV determination method for analyzing seepage characteristics of a digital core, where a flow is shown in fig. 2, and the method includes the following steps:
step S201: and scanning the core to obtain a group of core images with voxels of a first set size.
The core may be scanned using a high precision Micro-CT scanner or other scanning instrument, resulting in a set of volume stack images with voxels 1024 x 1024, of course the voxel size of the core image may be other sizes, such as 2048 x 2048, the voxel size may be specifically selected as desired.
Taking a hypotonic bailey core (5.6 mD) as an example, bailey sandstone is a sandstone recommended by an application program interface (Application Programming Interface, API) for checking perforation ejection depth and flow efficiency, the porosity of the bailey core is 19-21%, and the core of the sandstone is called as the bailey core. An example view of an original core image based on a high-precision micro-CT scan is shown in fig. 3.
Step S202: and adopting a median filter to perform noise reduction treatment on the core image.
The image can be imported
And the software or other processing software is adopted, and the median filter is utilized to reduce noise so as to ensure the definition of the core image.
Step S203: and dividing the core image based on the set dividing threshold value to divide the pore structure and the skeleton structure in the core image.
In the step, based on a set segmentation threshold, a region with a pixel value smaller than the segmentation threshold in the core image is identified as a pore structure, a region with a pixel value not smaller than the segmentation threshold is identified as a skeleton structure, the gray value of the pore structure is set as a first value, the gray value of the skeleton structure is set as a second value, and the binarized core image is obtained.
Specifically, a manual threshold segmentation method can be adopted to distinguish pores in a rock core from a framework, the reasonable selection of a segmentation threshold can be adopted to ensure that the rock core porosity is consistent with an experimental porosity result during segmentation, an original gray level image is processed into a binarized rock core image, for example, a gray level value '0' represents the pores, a gray level value '255' represents the framework, the original gray level image is reconstructed, the value of a pixel with the gray level value smaller than the segmentation threshold in the original gray level image is reconstructed to be 0, and the value of a pixel with the gray level value not smaller than the segmentation threshold in the original gray level image is reconstructed to be 255, so that a reconstructed binarized image is obtained. An exemplary view of the binarized core image after thresholding is shown in fig. 4.
Step S204: the pore structure is continuously processed.
Importing binarized core images
Software or other processing software reprocesses minor imperfections in the continuity of the pore structure, making the image features more accurate.
Step S205: and selecting a plurality of calculation units with voxels of a second set size from the core image.
In the step, a plurality of calculation units with voxels of a second set size are randomly selected from the core image, the porosity is calculated based on the gray values of the pore structure and the skeleton structure in the calculation units, the calculated porosity is compared with the porosity measured by experiments, and a plurality of calculation units with the porosity errors obtained by comparison within a set error range are selected.
Optionally, the processed binarized core image is imported
Software or other processing software having similar functions, a calculation unit with the voxel size of 200 multiplied by 200 is randomly selected on the binarized image of the core, the voxel size can be selected according to the requirement, the porosity of the calculation unit is primarily calculated by using the gray value of the pixel in the binarized image, so that the porosity value of the calculation unit and the porosity error obtained by experimental measurement are ensured to be within a set range, for example, in the range of 10%, that is, the absolute value of the difference between the calculated porosity value of the calculation unit and the experimentally measured porosity value divided by the experimentally measured porosity value, the obtained ratio is in the range of 10%, so that the calculation unit with the porosity meeting the requirement is selected to continue the subsequent steps.
An example of selecting a computational unit on a core image is shown in fig. 5, where three white boxes represent the three computational units selected.
Step S206: and screening out the computing units with the pore-throat structure connectivity meeting the requirements.
And the three-dimensional structure of the rock core is presented by using software, and a computing unit with the connectivity meeting the requirements is selected through observation and output in a specified file type. Alternatively, can utilize 
The 3D viewer function of the software or other processing software with similar functions presents a three-dimensional structure of the core, whether the pore and throat of the selected computing unit can penetrate through the volume covered by the selected computing unit in all directions is observed, the computing unit with better connectivity is screened out and output in a raw file form, and the raw file is an image file in an unprocessed original data form.
Step S207: the average pore radius and average throat radius for each calculation unit were determined separately.
In the step, the pore radius of each pore and the throat radius of each throat can be determined by using a maximum sphere algorithm; then, weighting calculation is carried out on each obtained pore radius to obtain an average pore radius; and carrying out weighted calculation on the obtained throat radius to obtain the average throat radius. Because each calculation unit is provided with a plurality of pores and throats, when calculating the average radius value, the weighting coefficients can be set according to practical situations, for example, the calculation of the average pore radius can set different weighting coefficients for pores with different radii, so that the weighted average is carried out on the different pore radii to obtain the average pore radius. Calculating the average throat radius is also similar.
Alternatively, each selected computing element may be imported into
The software or other software with related functions clicks a built-in 'apply maximum sphere algorithm' button of the software and generates an analysis report to obtain the average pore radius and average throat radius of the selected calculation unit, and the pore and throat size parameters are decisive parameters for determining the single-phase seepage characteristics of the core.
For example, as shown in fig. 6, the probability distribution of pore radius obtained by analysis is shown on the abscissa indicating the size of pore radius in μm, and the probability distribution of pores of different pore radius is shown on the ordinate, and it can be seen from fig. 6 that the probability distribution of pores of radius 2 μm is maximum. The throat radius probability distribution obtained by analysis is shown in fig. 7, the abscissa represents the size of the throat radius, the unit is μm, the ordinate represents the distribution probability of the throats with different throat radii, and the maximum throat distribution probability with the radius of 1 μm can be seen from fig. 7.
Taking the above 3 selected calculation units as an example, as shown in table 1 below, the calculation results of the average pore radius, the average throat radius, and the average pore throat radius inside the core of each calculation unit are illustrated.
TABLE 1
Step S208: the average pore throat radius is determined based on the average pore radius and the average throat radius.
For the selected calculation unit, the average pore radius and the average throat radius are weighted and averaged to obtain the average pore throat radius.
Step S209: and respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as REV.
In the step, comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments; and when the determined average pore radius, average throat radius and the distribution interval dimension of the pore-throat radius corresponding to the permeability contribution peak value of the core measured by the experiment are the same, calculating a radius difference value of the pore-throat radius corresponding to the permeability contribution peak value of the core measured by the experiment according to each calculation unit, and taking the ratio of the radius difference value to the pore-throat radius corresponding to the permeability contribution peak value of the core measured by the experiment as an error.
As shown in fig. 8, the measured results obtained by performing mercury intrusion test on the core are shown, the abscissa represents different pore intervals (μm), the left ordinate represents the permeability contribution, the right ordinate represents the pore throat distribution frequency, the bar-shaped columnar graph in fig. 8 represents the permeability contribution corresponding to the different pore intervals, and the line with dots represents the pore throat distribution frequency of the different pore intervals. As can be seen from fig. 8, the permeability contribution and pore throat distribution frequency peak when the pore space is around 1.7400.
Along the above example, when screening and analyzing each calculation unit, firstly, the pore and throat radius range obtained by processing the core image is primarily compared with the pore and throat size interval of the permeability contribution size obtained by the core mercury intrusion test, and the pore and throat size distribution interval dimension is determined to be the same. Examples of comparison of the pore radius, throat radius size range and the size interval in which the permeability contribution size pore throats are located in the mercury intrusion test results are shown in table 2 for each calculation unit. It can be seen from the table that the three sections have the same dimension.
TABLE 2
And further, calculating the average pore radius and the arithmetic average of the average throat radius to obtain the average pore throat radius, comparing the average pore throat radius with the pore throat radius of the peak value of the permeability contribution measured by the mercury-pressing experiment, and determining the error range of each calculation unit, wherein the comparison of the average pore throat radius obtained by calculation and the pore throat radius corresponding to the peak value of the pore throat permeability contribution obtained by the mercury-pressing experiment and the corresponding error range are shown in the table 3. From which the closest computing unit 2 and computing unit 3 with an error in the range of 5% can be chosen as representative volumes (REVs).
TABLE 3 Table 3
Step S210: and performing simulation calculation on the seepage characteristics of each calculation unit by adopting the selected fluid medium, and verifying whether the selected REV is representative or not based on simulation calculation results.
In the step, the seepage characteristics of each calculation unit are simulated and calculated by adopting the selected fluid medium, so that the seepage rate of each calculation unit in each direction and/or the average value of the seepage rates in all directions are respectively obtained; comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting a fluid medium experiment test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction; and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit. The fluid medium may be water, gas or other fluid medium.
In a specific embodiment, the single-phase flow percolation simulation calculation using the digital core technique may include the steps of: reconstructing a voxel with gray value of 0' in a computing unit, namely a pore and a throat through which fluid flows in a core, to obtain a fluid domain model, and then utilizing
Software or other similar software processes the continuity of the fluid flow areas in different directions of the model, removes isolated areas, and introduces the reconstructed model +. >
Or other similar software, and then using +.>
Or other similar software sets the inlet-outlet boundary condition as the pressure boundary condition, uses the selected gas as the flowing working medium, selects the SIMPLE calculation method, and develops the simulation calculation of the single-phase seepage characteristic in the core.
Along the above examples, the obtained three-way permeability simulation calculation average value and experimental gas permeability of each calculation unit and the corresponding error range thereof are shown in table 4, and the error range of each calculation unit can be seen to be within 30%, and the calculation unit 2 can be seen to be closer to the experimental gas permeability than the calculation unit 3 by the error range, which indicates that the calculation unit 2 is more representative.
TABLE 4 Table 4
The embodiment provides a feasible new method for selecting the single-phase seepage characteristic calculation representative volume unit (REV) of the digital rock core, provides a screening method for calculating the representative volume of the digital rock core based on the positive correlation of the single-phase permeability and the pore size of the rock core, obtains the radius parameter of the rock core pore throat by using image processing, compares the radius parameter with a mercury-pressing test result, selects the REV based on the comparison result, can accurately select the representative calculation unit for single-phase seepage characteristic research from a plurality of calculation units, reduces multiple unnecessary simulation calculations carried out on the calculation unit without the representativeness, thereby more conveniently, effectively and quickly carrying out calculation research by using the digital rock core technology, accurately obtaining the single-phase seepage characteristic of the rock core, analyzing the single-phase seepage characteristic and the seepage mechanism on a microscopic scale, and having important practical significance in the aspects of geological exploration, petroleum exploitation and the like.
Compared with the method for finally determining REV by carrying out single-phase permeability calculation based on the random selection calculation units, the REV selection efficiency can be improved by 60% under the condition that the number of the random selection calculation units is 3; as the number of the calculation units increases and the number of the randomly selected calculation units increases, the efficiency improvement effect is more obvious.
Based on the same inventive concept, the embodiment of the invention also provides a REV determining device for analyzing the seepage characteristics of the digital core, which can be arranged in a computer or a server with a calculation and analysis function, and the structure of the device is shown in fig. 9, and comprises: an image acquisition module 11, a unit selection module 12, a parameter calculation module 13 and an analysis selection module 14.
The image acquisition module 11 is used for scanning the rock core to obtain a rock core image and dividing a pore structure and a skeleton structure in the rock core image;
the unit selection module 12 is used for selecting a plurality of calculation units with the pore-throat structure connectivity meeting the requirements from the core image;
a parameter calculation module 13, configured to determine an average pore radius and an average throat radius of each calculation unit, and determine an average pore throat radius based on the average pore radius and the average throat radius;
The analysis and selection module 14 is configured to determine an error of the average pore throat radius of each calculation unit corresponding to the permeability contribution peak of the core measured through experiments, and select a calculation unit with an error not greater than a set threshold as REV.
Optionally, the image obtaining module 11 is configured to scan a core to obtain a core image, and segment a pore structure and a skeleton structure in the core image, including: scanning the rock core to obtain a group of rock core images with voxels of a first set size; adopting a median filter to perform noise reduction treatment on the core image; and dividing the core image based on a set dividing threshold value to divide a pore structure and a skeleton structure in the core image.
Optionally, the image obtaining module 11 is configured to segment the core image based on a set segmentation threshold, segment a pore structure and a skeleton structure in the core image, and include: based on a set segmentation threshold, identifying a region with a pixel value smaller than the segmentation threshold in the core image as a pore structure, identifying a region with a pixel value not smaller than the segmentation threshold as a skeleton structure, setting the gray value of the pore structure as a first value, and setting the gray value of the skeleton structure as a second value to obtain a binarized core image.
Optionally, the unit selecting module 12 is configured to select a plurality of computing units from the core image, where connectivity of the pore throat structure meets the requirement, and includes: carrying out continuous treatment on the pore structure; and selecting a plurality of calculation units with voxels of a second set size from the rock core image, and screening out the calculation units with the pore-throat structure connectivity meeting the requirements.
Optionally, the unit selecting module 12 is configured to select voxels from the core image as a plurality of computing units with a second set size, including: and randomly selecting a plurality of calculation units with voxels of a second set size from the rock core image, calculating the porosity based on the gray values of the pore structure and the skeleton structure in the calculation units, comparing the calculated porosity with the experimentally measured porosity, and selecting a plurality of calculation units with the porosity errors obtained by comparison within a set error range.
Optionally, the parameter calculation module 13 is configured to determine an average pore radius and an average throat radius of each calculation unit, and determine an average pore throat radius based on the average pore radius and the average throat radius, where the determining includes: determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius; and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
Optionally, the analysis selection module 14 is configured to determine an error of the average pore throat radius of each calculation unit corresponding to the pore throat radius of the core measured by the experiment, and the error includes: comparing the determined average pore radius and average throat radius of each calculation unit with the throat radius corresponding to the permeability contribution peak value of the core measured through experiments; and when the determined average pore radius, average throat radius and distribution interval dimension of the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments are the same, calculating a radius difference value of the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments according to each calculation unit, and taking the ratio of the radius difference value to the pore-throat radius corresponding to the permeability contribution peak value of the core measured through experiments as an error.
Optionally, the apparatus further includes a simulation verification module 15 for performing simulation calculation on the seepage characteristics of each calculation unit using the selected fluid medium, and verifying whether the selected REV is representative or not based on the simulation calculation result.
The simulation verification module 15 is specifically configured to perform simulation calculation on the seepage characteristic of each calculation unit by using the selected fluid medium, so as to obtain the permeability of each calculation unit in each direction and/or the average value of the permeability in each direction; comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting a fluid medium experiment test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction; and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit.
The embodiment of the invention also provides a computer storage medium, wherein computer executable instructions are stored in the computer storage medium, and the REV determination method for analyzing the seepage characteristics of the digital core is realized when the computer executable instructions are executed by a processor.
The embodiment of the invention also provides a terminal device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the REV determination method for analyzing the seepage characteristics of the digital core is realized when the processor executes the program.
The specific manner in which the various modules perform operations in the REV determination device for digital core percolation characteristics analysis in the above embodiments has been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, or the like, may refer to an action and/or process of one or more processing or computing systems, or similar devices, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the processing system's registers or memories into other data similarly represented as physical quantities within the processing system's memories, registers or other such information storage, transmission or display devices. Information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
It should be understood that the specific order or hierarchy of steps in the processes disclosed are examples of exemplary approaches. Based on design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of this invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. The processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. These software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
The foregoing description includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, as used in the specification or claims, the term "comprising" is intended to be inclusive in a manner similar to the term "comprising," as interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean "non-exclusive or".
Claims (20)
1. A REV determination method for analysis of seepage characteristics of a digital core, comprising:
scanning a rock core to obtain a rock core image, and dividing a pore structure and a skeleton structure in the rock core image;
selecting a plurality of computing units with pore-throat structure connectivity meeting requirements from the core image;
determining an average pore radius and an average throat radius of each computing unit respectively, and determining an average pore throat radius based on the average pore radius and the average throat radius;
And respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as a representative volume unit REV.
2. The method of claim 1, wherein scanning the core to obtain a core image and segmenting the pore structure and the skeleton structure in the core image comprises:
scanning the rock core to obtain a group of rock core images with voxels of a first set size;
adopting a median filter to perform noise reduction treatment on the core image;
and dividing the core image based on a set dividing threshold value to divide a pore structure and a skeleton structure in the core image.
3. The method of claim 2, wherein segmenting the core image based on the set segmentation threshold value segments a pore structure and a skeleton structure in the core image, comprising:
based on a set segmentation threshold, identifying a region with a pixel value smaller than the segmentation threshold in the core image as a pore structure, identifying a region with a pixel value not smaller than the segmentation threshold as a skeleton structure, setting the gray value of the pore structure as a first value, and setting the gray value of the skeleton structure as a second value to obtain a binarized core image.
4. The method of claim 1, wherein the selecting from the core image a plurality of computing units for which pore throat structure connectivity is satisfactory comprises:
carrying out continuous treatment on the pore structure;
and selecting a plurality of calculation units with voxels of a second set size from the core image, and screening out the calculation units with pore-throat structure connectivity meeting the requirements.
5. The method of claim 4, wherein selecting voxels from the core image as a plurality of computing units of a second set size comprises:
and randomly selecting a plurality of calculation units with voxels of a second set size from the rock core image, calculating the porosity based on the gray values of the pore structure and the skeleton structure in the calculation units, comparing the calculated porosity with the experimentally measured porosity, and selecting a plurality of calculation units with the porosity errors obtained by comparison within a set error range.
6. The method of claim 1, wherein determining an average pore radius and an average throat radius for each computing unit, respectively, and determining an average pore throat radius based on the average pore radius and the average throat radius comprises:
Determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius;
and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
7. The method of claim 1, wherein determining the error in the average pore throat radius for each computing unit corresponding to the experimentally measured peak permeability contribution of the core, respectively, comprises:
comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments;
and when the determined average pore radius, average throat radius and distribution interval dimension of the corresponding pore throat radius are the same, calculating a radius difference value of the average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments according to each calculation unit, and taking the ratio of the radius difference value to the corresponding pore throat radius as the error.
8. The method of any one of claims 1-7, further comprising: and performing simulation calculation on the seepage characteristics of each calculation unit by adopting the selected fluid medium, and verifying whether the selected REV is representative or not based on simulation calculation results.
9. The method of claim 8, wherein simulating the percolation characteristics of each computing unit using the selected fluid medium, verifying whether the selected REV is representative based on the simulation results, comprises:
simulating and calculating the seepage characteristics of each calculation unit by adopting the selected fluid medium to respectively obtain the seepage rate of each calculation unit in each direction and/or the average value of the seepage rates in all directions;
comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting the fluid medium experimental test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction;
and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit.
10. A REV determination device for analysis of seepage characteristics of a digital core, comprising:
the image acquisition module is used for scanning the rock core to obtain a rock core image and dividing a pore structure and a skeleton structure in the rock core image;
the unit selection module is used for selecting a plurality of calculation units with the pore-throat structure connectivity meeting the requirements from the core image;
the parameter calculation module is used for respectively determining the average pore radius and the average throat radius of each calculation unit, and determining the average pore throat radius based on the average pore radius and the average throat radius;
and the analysis and selection module is used for respectively determining the error of the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments, and selecting the calculation unit with the error not larger than a set threshold value as a representative volume unit REV.
11. The apparatus of claim 10, wherein the image acquisition module is configured to scan a core to obtain a core image and segment a pore structure and a skeleton structure in the core image, and comprises:
scanning the rock core to obtain a group of rock core images with voxels of a first set size;
Adopting a median filter to perform noise reduction treatment on the core image;
and dividing the core image based on a set dividing threshold value to divide a pore structure and a skeleton structure in the core image.
12. The apparatus of claim 11, wherein the image acquisition module is configured to segment a core image based on a set segmentation threshold, segment a pore structure and a skeleton structure in the core image, comprising:
based on a set segmentation threshold, identifying a region with a pixel value smaller than the segmentation threshold in the core image as a pore structure, identifying a region with a pixel value not smaller than the segmentation threshold as a skeleton structure, setting the gray value of the pore structure as a first value, and setting the gray value of the skeleton structure as a second value to obtain a binarized core image.
13. The apparatus of claim 10, wherein the unit selection module is configured to select from the core image a plurality of computing units for which pore throat structure connectivity is satisfactory, comprising:
carrying out continuous treatment on the pore structure;
and selecting a plurality of calculation units with voxels of a second set size from the core image, and screening out the calculation units with pore-throat structure connectivity meeting the requirements.
14. The apparatus of claim 13, wherein the means for selecting a plurality of computing means of a second set size for voxels from the core image comprises:
and randomly selecting a plurality of calculation units with voxels of a second set size from the rock core image, calculating the porosity based on the gray values of the pore structure and the skeleton structure in the calculation units, comparing the calculated porosity with the experimentally measured porosity, and selecting a plurality of calculation units with the porosity errors obtained by comparison within a set error range.
15. The apparatus of claim 10, wherein the parameter calculation module to determine an average pore radius and an average throat radius for each calculation unit, respectively, determines an average pore throat radius based on the average pore radius and the average throat radius, comprises:
determining the pore radius of each pore and the throat radius of each throat by using a maximum sphere algorithm, carrying out weighted calculation on each obtained pore radius to obtain an average pore radius, and carrying out weighted calculation on the obtained throat radius to obtain an average throat radius;
and carrying out weighted average on the average pore radius and the average throat radius to obtain the average pore throat radius.
16. The apparatus of claim 10, wherein the analysis selection module to determine an error in the average pore throat radius for each computing unit corresponding to an experimentally measured peak permeability contribution of the core, respectively, comprises:
comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the core measured through experiments;
and when the determined average pore radius, average throat radius and distribution interval dimension of the corresponding pore throat radius are the same, calculating a radius difference value of the average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured by experiments according to each calculation unit, and taking the ratio of the radius difference value to the corresponding pore throat radius as the error.
17. The apparatus of any one of claims 10-16, further comprising:
and the simulation verification module is used for performing simulation calculation on the seepage characteristics of each calculation unit by adopting the selected fluid medium and verifying whether the selected REV is representative or not based on simulation calculation results.
18. The apparatus of claim 17, wherein the analog verification module is configured to:
Simulating and calculating the seepage characteristics of each calculation unit by adopting the selected fluid medium to respectively obtain the seepage rate of each calculation unit in each direction and/or the average value of the seepage rates in all directions;
comparing the obtained permeability in each direction and/or the average value of the permeability in each direction with the permeability in each direction and/or the average value of the permeability in each direction obtained by adopting the fluid medium experimental test to obtain the permeability error in each direction and/or the average value error of the permeability in each direction; and verifying whether the selected REV is representative or not according to the permeability error of each direction and/or the average permeability error of each direction of each calculation unit.
19. A computer storage medium, wherein computer executable instructions are stored in the computer storage medium, and when the computer executable instructions are executed by a processor, the REV determination method for analyzing the seepage characteristics of the digital core is realized according to any one of claims 1 to 9.
20. A terminal device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the REV determination method of digital core percolation characteristics analysis according to any one of claims 1 to 9 when the program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111403890.7A CN116168073A (en) | 2021-11-24 | 2021-11-24 | REV determination method and device for digital core seepage characteristic analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111403890.7A CN116168073A (en) | 2021-11-24 | 2021-11-24 | REV determination method and device for digital core seepage characteristic analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116168073A true CN116168073A (en) | 2023-05-26 |
Family
ID=86416824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111403890.7A Pending CN116168073A (en) | 2021-11-24 | 2021-11-24 | REV determination method and device for digital core seepage characteristic analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116168073A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118095021A (en) * | 2024-04-28 | 2024-05-28 | 中国石油大学(华东) | Efficient calculation method for permeability of large-size digital rock core |
-
2021
- 2021-11-24 CN CN202111403890.7A patent/CN116168073A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118095021A (en) * | 2024-04-28 | 2024-05-28 | 中国石油大学(华东) | Efficient calculation method for permeability of large-size digital rock core |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107449707B (en) | Three-dimensional characterization determination method and device for quantification of pores with different scales in shale reservoir | |
| RU2543698C1 (en) | Analysis of petrographic images for detection of capillary pressure in porous media | |
| CN103513285B (en) | A kind of method and device determining horizontal surface relaxation rate | |
| CN103698803A (en) | Blowhole structural characterization method and device | |
| EA032063B1 (en) | Systems and methods for improving direct numerical simulation of material properties from rock samples and determining uncertainty in the material properties | |
| Youssef et al. | Quantitative 3D characterisation of the pore space of real rocks: improved µ-CT resolution and pore extraction methodology | |
| US11885757B2 (en) | Material properties from two-dimensional image | |
| Ar Rushood et al. | Segmentation of x-ray images of rocks using deep learning | |
| CN111239821B (en) | Carbonate reservoir pore structure prediction method, device, equipment and storage medium | |
| CN116168073A (en) | REV determination method and device for digital core seepage characteristic analysis | |
| CN111426616A (en) | Method and device for acquiring elastic property and pore structure of carbonate rock and storage medium | |
| Sun et al. | EPCI: A new tool for predicting absolute permeability from computed tomography images | |
| CN111738978B (en) | Reservoir pore throat connectivity evaluation method and device, electronic equipment and storage medium | |
| CN111539286A (en) | Lining line identification method and device and readable storage medium | |
| CN112147680A (en) | Rock internal micro-crack extraction and characterization method and system | |
| CN116413183A (en) | Pore connectivity evaluation method and device | |
| Wang et al. | 3D reconstruction and characterization of reef limestone pores based on optical and acoustic microscopic images | |
| CN115147539A (en) | Indication-kriging-based pore structure residual oil characterization method | |
| Rahimov et al. | Use of local binary pattern in texture classification of carbonate rock micro-CT images | |
| RU2774959C1 (en) | Method for determining filtration properties of non-homogeneous porous samples | |
| CN116930219A (en) | Rock pore throat parameter analysis method, throat length prediction method, device and equipment | |
| CN117369007A (en) | Electromagnetic inversion data weak information extraction method and device | |
| CN113160107A (en) | Rock core micro-crack extraction method and device, electronic equipment and storage medium | |
| CN115901834A (en) | Rock pore tortuosity fractal dimension prediction method based on nuclear magnetic resonance image | |
| CN114722648A (en) | Method and device for acquiring core acoustic response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |