CN111709466A - Analytical and statistical method for micro-nano pores of shale - Google Patents
Analytical and statistical method for micro-nano pores of shale Download PDFInfo
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Abstract
The invention relates to an analytical and statistical method for shale micro-nano pores, which utilizes Corel DRAW X4 Image processing software and Image-Pro Plus6.0 Image analysis software to analyze and extract and process shale micro-nano pore pictures acquired by a field emission electron microscope, and then utilizes an EXCEL table to arrange and map the extracted pore data. The invention can realize the automatic identification of the micro-nano pores in the back scattering and secondary electron pictures acquired by the electron microscope, thereby greatly reducing the time cost of manual drawing; the method can realize fine division and quantitative analysis of different types of micro-nano pores of the shale under the condition of low labor cost; the quantitative analysis and statistics of the porosity of the micro-nano pore surface of the shale, the equivalent pore diameter and the like can be realized.
Description
Technical Field
The invention relates to the technical field of unconventional oil and gas exploration of oil fields, in particular to an analytical and statistical method for micro-nano pores of shale.
Background
At present, due to worldwide energy shortage and increasing energy demand, a great deal of research work is carried out in the oil industry in the field of shale oil and gas exploration, and shale reservoirs are paid more and more attention as unconventional oil and gas reservoirs. Particularly, the middle upper part of the gulong sunken mountain group in the central depression region of the Songliaopelvic region is a large section of lime black mudstone, a thin layer of lime black siderite containing the siderophyte mudstone, a gray silty mudstone containing the siderophyte and a argillaceous siltstone, and the middle lower part of the gulblack mudstone, the mudstone containing the siderophyte and the siderophyte layer is a small amount of dark gray siltstone containing the siderophyte. In integral contact with the underburden. The sediment stage of the Qingshan mountain mouth group is in the great lake flooding stage, and a mud (shale) rock stratum with large thickness, pure mud quality and rich organic matters is deposited, so that a good material basis is provided for the development of shale oil. Therefore, the mountainous mouths constitute the most advantageous areas for shale oil exploration. When physical property characteristics of the shale reservoir are evaluated, analysis and evaluation of micro-nano pores are indispensable. Therefore, after the shale micro-nano pore characteristics are shot by a field emission electron microscope, an analytical statistical method for the shale micro-nano pore characteristics needs to be established so as to achieve accurate analysis of the shale micro-nano pore characteristics, and the method has very important significance for evaluating the physical properties of the shale reservoir. The previous people used JMicroVision image processing software to manually depict the shale micro-nano pores obtained by a scanning electron microscope, but the method generates huge manual depicting time cost, is only suitable for depicting individual or a small amount of pores, and cannot realize pore statistics and analysis with large data volume.
Disclosure of Invention
The invention provides an analytical and statistical method for shale micro-nano pores, aiming at overcoming the problems that the existing method in the background technology can not solve the accurate observation of the characteristics of the shale micro-nano pores and the quantitative characterization, analysis and detection of the types and distribution frequencies of the pores, and providing the analytical and statistical method for the shale micro-nano pores. According to the analytical and statistical method for the mud shale micro-nano pores, a mud shale sample with a certain size is observed and analyzed by the effective detection method, the method mainly utilizes Corel DRAW X4 Image processing software and Image-ProPlus6.0 Image analysis software to analyze and extract and process mud shale micro-nano pore images collected by a field emission electron microscope, and then an EXCEL table is utilized to arrange and map the extracted pore data, so that the classified and statistical method for the mud shale micro-nano pores is finally formed.
The invention can solve the problems by the following technical scheme: an analytical and statistical method for micro-nano pores of shale comprises the following steps:
(1) analyzing and processing the pore data of the shale back scattering picture and the secondary electronic picture acquired by the field emission electron microscope by using image processing software:
a) preprocessing a back scattering picture;
b) tracing the preprocessed picture;
c) correcting pores in the traced drawing;
d) classifying micro-nano pores in the traced drawing;
(2) counting the pore data processed in the step (1) by using image analysis software:
a) importing pictures
Importing the pore picture processed by the image processing software in the step (1) into image analysis software;
b) add the scale
After the step a) is finished, a scale is newly built in a 'Measure' pull-down menu, a scale line segment is dragged to be overlapped with a scale line segment in the picture, and scale information in the picture is input;
c) pore identification
After the step b) is finished, selecting colors in the option of 'Count/Size', clicking a color extraction pen, clicking holes to be extracted in the picture, and identifying different types of holes;
d) extraction of pore diameter and pore area
(3) And (3) utilizing an EXCEL table to arrange and map the pore data extracted in the step (2):
and (3) according to the data of the diameters and the areas of the different types of pores extracted in the step (2), counting the distribution frequency of the diameters of the different pores and the face porosity corresponding to the pores with different diameters.
Opening Corel DRAW X4 software, building a new layer in an object manager, introducing the shale backscatter picture into the new layer, selecting the color of a non-pore part to be hidden on the introduced bitmap, and only displaying pore cracks after bitmap processing;
clicking a bitmap after the bitmap is processed, selecting a tracing bitmap (T), and setting three parameters of detail, smoothness and corner smoothness of a tracking control on a setting interface to enable the picture tracing to be closest to a real pore space;
the correction process of the pores in the traced drawing in the step 1) comprises the steps of newly building a layer in an object manager; pasting an object group generated by tracing to a newly-built layer, carrying out contrast correction on the traced pores, deleting redundant noise points formed by automatic tracing, and supplementing a very small amount of unidentified pores by automatic tracing;
classifying the micro-nano pores in the traced drawing in the step 1), namely classifying the micro-nano pores, including inter-granular pores, intra-granular pores, clay mineral inter-granular pores, pyrite inter-granular pores and the like, and filling and distinguishing the pores of different types by using different colors; exporting the classified pictures for further analysis;
the picture importing process in the step 2) a) is to import the pore picture processed by Corel DRAW X4 Image processing software into Image-Pro Plus6.0 Image analysis software;
b) after finishing the step a), newly building a scale in a 'Measure' pull-down menu, dragging a scale line segment to be superposed with a scale line segment in the picture, and inputting scale information in the picture;
after the step 2) c) of the pore identification process is finished, selecting colors in the option of 'Count/Size', clicking a color extraction pen, then clicking pores to be extracted of the picture, and identifying different types of pores;
d) the extraction process of the pore diameter and the pore area comprises the following steps of c), pasting the pore diameter and the pore area data extracted in the option of 'Count/Size' to a shear plate;
the image processing software utilized in the step (1) is Corel DRAW X4; the Image analysis software used in the step (2) is Image-Pro Plus 6.0;
opening Corel DRAW X4 software, building a new layer in an object manager, introducing the shale backscatter picture into the new layer, selecting the color of a non-pore part to be hidden on the introduced bitmap, and only displaying pore cracks after bitmap processing;
clicking a bitmap after the bitmap is processed, selecting a tracing bitmap (T), and setting three parameters of detail, smoothness and corner smoothness of a tracking control on a setting interface to enable the picture tracing to be closest to a real pore space;
the correction process of the pores in the traced drawing in the step 1) comprises the steps of newly building a layer in an object manager; pasting an object group generated by tracing to a newly-built layer, carrying out contrast correction on the traced pores, deleting redundant noise points formed by automatic tracing, and supplementing a very small amount of unidentified pores by automatic tracing;
classifying the micro-nano pores in the traced drawing in the step 1), namely classifying the micro-nano pores, including inter-granular pores, intra-granular pores, clay mineral inter-granular pores, pyrite inter-granular pores and the like, and filling and distinguishing the pores of different types by using different colors; exporting the classified pictures for further analysis;
the picture importing process in the step 2) a) is to import the pore picture processed by Corel DRAW X4 Image processing software into Image-Pro Plus6.0 Image analysis software;
b) after finishing the step a), newly building a scale in a 'Measure' pull-down menu, dragging a scale line segment to be superposed with a scale line segment in the picture, and inputting scale information in the picture;
after the step 2) c) of the pore identification process is finished, selecting colors in the option of 'Count/Size', clicking a color extraction pen, then clicking pores to be extracted of the picture, and identifying different types of pores;
d) the extraction process of the pore diameter and the pore area comprises the step of pasting the pore diameter and the pore area data extracted in the option of 'Count/Size' to the shear plate after the step c) is finished.
Compared with the background technology, the invention has the following beneficial effects: the invention provides an analytical and statistical method for shale micro-nano pores, which can accurately and effectively identify and extract the shale micro-nano pores shot by a field emission electron microscope, divide the types of the pores, count related parameters such as distribution frequency of different types of pores, distribution frequency of pores with different diameters, contribution of the pores with different diameters to surface porosity and the like, and provide data support for evaluation of physical properties of a shale reservoir.
Description of the drawings:
FIG. 1 is a field emission electron microscope backscatter picture of striated lamellar shale;
FIG. 2 is an operation interface screenshot after a new layer is created and a picture is imported by Corel DRAW X4 image processing software;
FIG. 3 is an operator interface screenshot of Corel DRAW X4 image processing software opening a "bitmap color mask", setting a "margin" parameter, and hiding the color of a non-porous portion;
FIG. 4 is a screenshot of three parameter operation interfaces "detail", "smooth", "corner smoothness" for Corel DRAW X4 image processing software setting tracking controls;
FIG. 5 is a photograph of the pore of stratiform shale extracted and processed by Corel DRAW X4 image processing software;
FIG. 6 is a photograph of the lamellar shale intergranular pores isolated and extracted by Corel DRAW X4 image processing software;
FIG. 7 is a photograph of the inner bore of a stratiform shale grain separated and extracted by Corel DRAW X4 image processing software;
FIG. 8 is a photograph of lamellar shale microfractures separated and extracted by Corel DRAW X4 image processing software;
FIG. 9 is a photograph of the intergranular pores of the lamellar shale clay minerals separated and extracted by Corel DRAW X4 image processing software;
FIG. 10 is a screenshot of an Image-Pro Plus6.0 Image analysis software "Add Scale" operating interface;
FIG. 11 is a screenshot of an operation interface of Image-Pro Plus6.0 Image analysis software "extraction of pore diameter and pore area";
FIG. 12 is a frequency histogram of pore distribution of different diameters in the stratiform shale;
FIG. 13 is a histogram of porosity of different diameter pore surfaces in the stratiform shale;
FIG. 14 is a histogram of porosity for different types of pore surfaces in the stratiform shale;
FIG. 15 is a histogram of relative fractional porosities of different types of pore surfaces in the stratiform shale.
Detailed Description
The following detailed description is made by way of example only with reference to the accompanying drawings
The invention provides an analytical and statistical method for shale micro-nano pores, which mainly utilizes Corel DRAW X4 Image processing software and Image-Pro Plus6.0 Image analysis software to analyze, extract and process a shale micro-nano pore picture acquired by a field emission electron microscope, and then utilizes an EXCEL table to arrange and map extracted pore data to finally form a classification and statistical method for the shale micro-nano pores.
The steps and effects of the present invention will be described in detail below with reference to the accompanying drawings and examples.
Examples
First, research background
The experimental sample is selected from a 1-well green section of a gulong concave ancient page in a central concave region of a Songliaopelvic region, and the section mainly develops 5 lithofacies comprising striated shale, oil shale, siltstone, mesochite and argillaceous nephrite. The total porosity of the reservoir at the section is mainly distributed in the range of 4-12%, wherein the total porosity and permeability of the oil shale and the striation layered shale are high, and the total porosity and permeability of the interlayer rock phase are low. The total porosity distribution range of the grained lamellar shale is 3.0-14.9%, the total porosity distribution range of the oil shale is 10.1-14.2%, the total porosity distribution range of the siltstone is 2.6-9.7%, the mesochite limestone is 1.7-3.4%, and the total porosity distribution range of the marbled rock is 0.5-9.5%. Wherein the grained lamellar shale mainly develops inter-granular pores, intra-granular pores, inter-granular pores and a small amount of organic matter pores.
Second, specific detection and analysis method
(1) Analyzing and processing the pore data of the shale backscattering picture and the secondary electronic picture collected by a field emission electron microscope by using Corel DRAW X4 image processing software:
a) pre-processing of back-scattered pictures
Obtaining a shale backscattering picture (taking the striated layered shale as an example) by using a field emission electron microscope, as shown in figure 1, opening CorelDRAW X4 software, clicking a tool, opening an object manager, and newly building a layer on the object manager; clicking the file, selecting import, and importing the shale backscattering picture into a newly-built layer (shown in figure 2); clicking the bitmap, opening a bitmap color mask, selecting a hidden color, clicking a color selection icon, selecting the color of a non-pore part to be hidden on the imported bitmap, setting a tolerance parameter to enable the hidden picture to truly display pore cracks, clicking the application, and only displaying the pore cracks after bitmap processing (figure 3).
b) Tracing of pre-processed pictures
After bitmap processing, clicking a bitmap, selecting a "tracing bitmap (T)", selecting an "outline tracing", selecting a "high-quality picture", if the bitmap is too large, generating a "Power TRACE", if the bitmap is too large, reducing the size of the bitmap, selecting a "reducing bitmap", setting three parameters of detail, smoothness and corner smoothness of a tracking control on a "setting" interface, enabling the picture tracing to be closest to a real pore space, clicking "determining", and automatically generating an editable "object group" (figure 4).
c) Correction of apertures in traced figures
Creating a new layer in the object manager; pasting an object group generated by tracing to a newly-built layer, and clicking a right button to cancel all the groups; and comparing and correcting the traced pores by combining a back scattering picture and a secondary electronic picture acquired by a field emission electron microscope, deleting redundant noise points formed by automatic tracing, deleting the part which is black in color in the back scattering picture and shows no pores in the secondary electronic picture, and supplementing a very small amount of unidentified pores by automatic tracing.
d) Classification treatment of micro-nano pores in traced drawing
The method comprises the steps of combining a back scattering picture and a secondary electron picture of a grained shale sample acquired by a field emission electron microscope, dividing micro-nano pore types of the traced picture in detail, wherein the micro-nano pore types comprise inter-granular pores, intra-granular pores, clay mineral inter-granular pores, pyrite inter-granular pores and the like (the pore identification characteristic is that the inter-granular pores are inter-granular pore spaces formed by mutual support of chip particles, the intra-granular pores are mainly formed by corrosion of feldspar and quartz, the feldspar particles are in residual corrosion and uneven to form a series of pores in a triangular or near-circular shape, the quartz particles are mainly surface etching pits in an irregular or long-strip or circular shape, the clay mineral inter-granular pores are pores between flaky or bundled clay minerals, the chlorite and the illite inter-granular pores are mainly used as the inter-granular pores, the pyrite inter-granular pores are inter-granular pores in a strawberry-shaped pyrite spherical aggregate, filling and distinguishing the pores with different colors (tracing the pores in the picture, click on "property" after right-clicking, click on "fill" icon in "object property"). The class-filled drawing is saved and clicked on the "export in file" picture for further analysis (as shown in figures 5-9).
(2) The pore data after treatment in (1) were counted using Image-Pro plus6.0 Image analysis software:
a) importing pictures
Importing the pore picture processed by Corel DRAW X4 Image processing software into Image-Pro Plus6.0 Image analysis software;
b) add the scale
After the step a) is finished, selecting "Calibration → Spatial" in a "Measure" pull-down menu, clicking "New" to build a New ruler, renaming the ruler in a Name pull-down menu, selecting a Unit (such as um or nm) corresponding to a picture ruler in a Unit pull-down menu, selecting "Image" under a "Pixels/un" dialog box, popping up a ruler line segment and a "Scailing" dialog box at the moment, dragging the ruler line segment to be overlapped with a scale line segment in the picture, filling the ruler information in the picture into the "Scailing" dialog box, and clicking "OK" to confirm the input information (see the attached figure 10);
c) pore identification
After the step b) is completed, selecting a newly-built and renamed ruler option from a drop-down menu of a toolbar "Spatial Calibration" option, clicking an "OK" confirmation option, clicking a "Count/Size" option, selecting and checking a "Manual" option from an "Intensity Range Selection" popped up in a "Count/Size" dialog box, clicking a "Select Colors" to Select the Colors, popping up a "Segmentation" dialog box, establishing pore classification in a "Color Cube Based" option area, selecting corresponding Colors, setting parameters (generally keeping default values) such as "Sensitivity", "Color indexes", "pixels" and the like in an "Options area, then clicking a Color extraction pen, clicking pores to be extracted by the pictures, clicking a Close dialog box after the completion, repeating the operation step to identify different types of pores;
d) extraction of pore diameter and pore area
After the step c) is completed, click "select measures" in the "Measure" option drop-down menu in the "Count/Size" dialog box, select "area (polygon)", "diameter (mean)", click "OK" confirmation information in the pop-up dialog box, click "Count" in the "Count/Size" dialog box, select "Data on clipboard" in the "File" drop-down menu, and paste the extracted pore diameter and pore area Data to the clipboard (fig. 11).
(3) And (3) utilizing an EXCEL table to arrange and map the pore data extracted in the step (2):
according to the pore with different diameters and the pore area data extracted in the step (2), dividing the pore diameters into 5 types such as 0.001um-0.01um, 0.01um-0.05um, 0.05um-0.1um, 0.1um-0.5um, 0.5um-1um and the like; the pore types are divided into 5 types such as intergranular pores, microcracks, intragranular pores, clay mineral intergranular pores and the like according to the pore development characteristics, and then the distribution frequency of pores with different diameters, the surface porosity of pores with different types, the relative occupation ratio of the surface porosities of pores with different types and the like are counted (as shown in attached figures 12 to 15).
Thirdly, detecting the analysis result
Comprehensive analysis shows that the pore diameter range of the striated laminar shale sample is distributed between 0.001um and 1um, wherein the number of pores with the pore diameter within the range of 0.01um to 0.05um is the maximum, and reaches 14330; 809 pores with the diameter of 0.05-0.1 um are counted for several times; the number of pores with a pore diameter in the range of 0.1um to 0.5um is 404; the number of pores with the diameter of 0.5um-1um is 12; the number of pores having a pore diameter in the range of 0.001um to 0.01um is 5 as the minimum. The pore development of the striated laminar shale sample is poor, the surface porosity is 1.76%, wherein the pore surface porosity of the pore with the pore diameter within the range of 0.1-0.5 um is the highest and is 0.96%; the porosity of the pore face with the pore diameter ranging from 0.01um to 0.05um is 0.33 percent, which shows that the number of pores and the pore diameter have important influence on the face porosity.
The grained lamellar shale sample develops 5 types of pores such as interparticle pores, microcracks, intraparticle pores, clay mineral intercrystalline pores and the like, wherein the face porosity occupied by the interparticle pores is the highest and is 1.32%; the surface porosity of the microcracks and the intragranular pores is similar and is 0.33 percent; the surface porosity of the clay mineral intercrystalline pores is the lowest and is less than 0.1 percent.
In the prior art, JMicroVision image processing software is adopted to manually depict shale micro-nano pores obtained by a scanning electron microscope, and different types of pores such as inter-granular pores, micro cracks, intra-granular pores, clay mineral inter-granular pores and the like can be depicted by the technical method, but the method can generate huge manual depicting time cost, is only suitable for individual or small-quantity pore delineation, and cannot realize pore statistics and analysis of large data volume. The effect and the advantage of the technical method of the invention mainly comprise the following three aspects: (1) the micro-nano pores in the back scattering pictures and the secondary electron pictures obtained by the electron microscope can be automatically identified, but the original technical method cannot realize the automatic identification of the pores; (2) the method can realize fine division and quantitative analysis of different types of micro-nano pores of the shale under the condition of less labor time cost, and compared with the prior art, the time consumption is less than 1%; (3) the quantitative analysis and statistics of the porosity of the micro-nano pore surface of the shale, the equivalent pore diameter and the like can be realized.
Claims (10)
1. An analytical and statistical method for micro-nano pores of shale comprises the following steps:
(1) analyzing and processing the pore data of the shale back scattering picture and the secondary electronic picture acquired by the field emission electron microscope by using image processing software:
a) preprocessing a back scattering picture;
b) tracing the preprocessed picture;
c) correcting pores in the traced drawing;
d) classifying micro-nano pores in the traced drawing;
(2) counting the pore data processed in the step (1) by using image analysis software:
a) importing pictures
Importing the pore picture processed by the image processing software into image analysis software;
b) add the scale
After the step a) is finished, a scale is newly built in a 'Measure' pull-down menu, a scale line segment is dragged to be overlapped with a scale line segment in the picture, and scale information in the picture is input;
c) pore identification
After the step b) is finished, selecting colors in the option of 'Count/Size', clicking a color extraction pen, clicking holes to be extracted in the picture, and identifying different types of holes;
d) extraction of pore diameter and pore area
(3) And (3) utilizing an EXCEL table to arrange and map the pore data extracted in the step (2):
and (3) according to the pore diameters and pore area data of different types extracted in the step (2), counting the distribution frequency of the pore diameters and the face porosity corresponding to the pores with different diameters.
2. The analytical statistical method for shale micro-nano pores according to claim 1, wherein the analytical statistical method comprises the following steps: the image processing software utilized in the step (1) is Corel DRAW X4; the Image analysis software used in step (2) is Image-ProPlus 6.0.
3. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: opening Corel DRAW X4 software, building a new layer in an object manager, importing the shale backscatter picture into the new layer, selecting the color of a non-pore part to be hidden on the imported bitmap, and only displaying pore cracks after bitmap processing.
4. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: the tracing process of the preprocessed picture in the step 1) comprises the steps of clicking a bitmap after bitmap processing, selecting a 'tracing bitmap (T'), and setting three parameters of 'detail', 'smoothness' and 'corner smoothness' of a tracking control on a 'setting' interface, so that the picture tracing is closest to a real pore space.
5. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: the correction process of the pores in the traced drawing in the step 1) comprises the steps of newly building a layer in an object manager; pasting the 'object group' generated by tracing to a newly-built layer, carrying out contrast correction on the traced pores, deleting redundant noise points formed by automatic tracing, and supplementing a very small amount of unidentified pores by automatic tracing.
6. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: classifying the micro-nano pores in the traced picture in the step 1), namely classifying the micro-nano pores, including inter-granular pores, intra-granular pores, clay mineral inter-granular pores, pyrite inter-granular pores and the like, filling and distinguishing the pores of different types by using different colors, and exporting the classified picture for next analysis.
7. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: the picture importing process in the step 2) a comprises the steps of importing a pore picture processed by Corel DRAW X4 Image processing software into Image-Pro Plus6.0 Image analysis software;
and 2) b, adding the scale, namely newly building the scale in a 'Measure' pull-down menu after the step a) is finished, dragging the scale line segment to be overlapped with the scale line segment in the picture, and inputting scale information in the picture.
8. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: and c) identifying the pores in the step 2) c, namely, after the step b) is finished, selecting colors in the option of 'Count/Size', clicking a color extraction pen, clicking the pores to be extracted in the picture, and identifying different types of pores.
9. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: and step 2) d, extracting the pore diameter and the pore area, namely pasting the pore diameter and the pore area data extracted from the option of Count/Size to the shear plate after the step c) is finished.
10. The analytical statistical method for shale micro-nano pores according to claim 1 or 2, wherein the analytical statistical method comprises the following steps: the identification characteristics of the types of the micro-nano pores in the step (1) are as follows: the inter-granular pores are inter-granular pore spaces formed by mutual support of the chip particles; the inner pores are mainly formed by etching feldspar and quartz to form pores, the feldspar particles are in a residual etching state and are uneven to form a series of pores in a triangular or nearly circular shape, and the quartz particles are mainly surface etching pits and are irregular in shape and are in a long strip shape or a circular shape; the intercrystalline pores of the clay minerals are pores among the flaky or bundled clay minerals, and are mainly the intercrystalline pores of chlorite and illite; the pyrite intergranular pores are intergranular pores in the strawberry-shaped pyrite spherical aggregate.
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| MX2011001035A (en) * | 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. |
| CN104007049A (en) * | 2014-06-06 | 2014-08-27 | 马存飞 | Classification method of microscopic pores on mud shale |
| CN110766742A (en) * | 2019-09-20 | 2020-02-07 | 中国地质大学（武汉） | Method for accurately measuring dolomite face porosity based on image recognition software |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX2011001035A (en) * | 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. |
| CN104007049A (en) * | 2014-06-06 | 2014-08-27 | 马存飞 | Classification method of microscopic pores on mud shale |
| CN110766742A (en) * | 2019-09-20 | 2020-02-07 | 中国地质大学（武汉） | Method for accurately measuring dolomite face porosity based on image recognition software |
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