Determination method for porosity of sedimentary rock

Abstract

The invention belongs to the field of sedimentary basin rock permeability evaluation, and particularly relates to a sedimentary rock porosity determination method which comprises the following steps: step 1, collecting a sample of a research target layer; 2, treating a sample, and selecting an observation surface; 3, under-mirror observation, description and photo collection are carried out; step 4, preprocessing the photo; 5, improving the contrast ratio of the photo; 6, extracting pores and calculating the pore proportion; and 7, calculating the porosity of the sample. The method provided by the invention is suitable for rapid evaluation work of sandstone porosity on a field site, and can rapidly evaluate sedimentary rock media and pore development characteristics and statistically calculate the porosity.

Description

Technical Field

[0001] The invention belongs to the field of sedimentary basin rock permeability evaluation, and in particular relates to a sedimentary rock porosity determination method. Background Art

[0002] Sandstone-type uranium deposits are exogenous epigenetic uranium deposits produced in medium-coarse-grained clastic rocks such as sandstone or conglomerate. Since the successful mining of in-situ leaching technology in the 1960s, this type of uranium deposit has become one of the most important uranium deposit types in the world and the preferred target type for uranium exploration and development in China. Sandstone-type uranium deposits can be mainly divided into subtypes such as phreatic oxidation zone type, interlayer oxidation zone type, and ancient river valley type. The mineralization process of the above-mentioned types of uranium deposits is that uranium-carrying phreatic water or groundwater seeps along permeable sandstone, and is formed by precipitation and enrichment due to changes in the geochemical environment. It is affected by many factors such as regional structure, basin structure, uranium source, paleoclimate, lithology, lithofacies, and hydrogeology, and the permeability of sandstone is one of the main controlling factors of groundwater seepage. Sandstone-type uranium deposits are mainly mined by in-situ leaching. The basic principle is to drill holes in a certain grid arrangement for in-situ leaching sandstone-type uranium deposits, inject in-situ leaching liquid from the injection holes, allow the in-situ leaching liquid to fully react with the uranium ore, allow uranium to enter the in-situ leaching liquid, and extract it from the surface through the extraction holes. Uranium is extracted in the surface factory. The permeability of ore and rock to chemical solvents is a necessary condition for in-situ leaching uranium mining. In summary, in the mineralization or mining process of sandstone-type uranium deposits, the permeability of sandstone is particularly important, and this parameter is mainly affected by factors such as particle size grading, porosity, and mineral composition. In general, the larger the porosity, the higher the permeability, and it is roughly logarithmic. Therefore, the determination of porosity plays an important role in evaluating the prospecting target layer or in-situ leaching conditions, and is also extremely important in other mineral fields such as oil and gas.

[0003] Porosity refers to the ratio of the pore volume in a rock to the volume of the rock including the pore volume. The porosity of sandstone is generally between 5% and 35%. At present, in addition to the conventional test methods such as weight method, volume method, water immersion method, and water drainage method in the laboratory, there are several quantitative or qualitative methods for measuring porosity, including electrical logging, radioactive logging, nuclear magnetic resonance (NMR), mercury injection method, adsorption method, etc. The above methods have their own advantages and disadvantages, but in general, they are not suitable for rapid evaluation of sandstone porosity in the field. In theory, the plane porosity of sandstone is consistent with the volume porosity, so the porosity of sandstone can be measured by counting the proportion of pores on the sandstone plane under a microscope. Summary of the invention

[0004] The purpose of the present invention is to provide a sedimentary rock porosity determination method, which can quickly evaluate sedimentary rock media and pore development characteristics and statistically calculate porosity.

[0005] The technical solution to achieve the purpose of the present invention is:

[0006] A method for measuring the porosity of sedimentary rocks, the method comprising:

[0007] Step 1: Collect samples of target layer;

[0008] Step 2: Sample processing and selection of observation surface;

[0009] Step 3: Microscopic observation, description and photo collection;

[0010] Step 4: Photo preprocessing;

[0011] Step 5: Increase the contrast of the photo;

[0012] Step 6: Pore extraction and pore ratio calculation;

[0013] Step 7: Calculate the porosity of the sample.

[0014] The sample collected in step 1 should be fresh, have a low degree of weathering, be free of mud wrapping, and have multiple relatively flat cross sections.

[0015] The step 2 comprises: selecting a relatively flat surface and grinding it on sandpaper until there are no obvious raised particles when observing the cross section with the naked eye, and using the ground surface as the observation surface.

[0016] In the step 2, the sand type is selected to be 60 mesh to 100 mesh.

[0017] The contents of microscopic observation and description in step three include: the composition, shape, particle size, sorting, and arrangement of sedimentary rock fragments under a microscope; the structure, cementation type, and particle support properties of the matrix and cement; and observation of all pore characteristics and their composition.

[0018] The pore characteristics include pore size (pore radius), shape, connectivity, and type.

[0019] The step four comprises: distinguishing the main color difference between pores and other components according to the main characteristics of the debris, matrix and cement observed in step three; if the colors of the pores and other components are difficult to distinguish, circle the dark components of other components and replace them with light colors using photo processing software.

[0020] The step five includes: using Python software to call the ImageEnhance module in the image processing library PIL, using the Contrast class in the module to adjust the photo preprocessed in step four, and setting the function parameter to 2.0 to increase the contrast of the photo by 2 times.

[0021] The step six includes: using Python software, calling the cv2 library and the NumPy library, setting the RGB color range value of the pores in the image after processing in step five, using the inRange function in the cv2 library to limit the RGB color range value of the pores in the image, and replacing them all with colors that have a large contrast with the mineral colors in the photo, using the np.sum() function in the NumPy library to count the number of pixels of the replaced colors, and calculating the ratio to the total pixels of the image, which is the pore ratio.

[0022] The step seven includes: repeating the operations from step two to step six, selecting different surfaces of the sample and repeating the operations three times, and calculating the average value of the plane pore proportions obtained three times, which is the porosity of the sample.

[0023] The beneficial technical effects of the present invention are:

[0024] 1. A sedimentary rock porosity determination method provided by the present invention is based on the microscopic characteristics of sedimentary rocks, systematically observes and describes the composition, size, contact relationship, sorting of mineral particles under the microscope, and the shape and size of pores, uses Python software to increase the contrast of microscopic photos, and then statistically calculates the proportion of planar pores. It is suitable for rapid evaluation of sandstone porosity in the field.

[0025] 2. The present invention provides a sedimentary rock porosity determination method, which takes sedimentary rock as the research object, does not consider the size of the sample block, can quickly evaluate the sedimentary rock medium and pore development characteristics, statistically calculate the porosity, and provide data support for the evaluation of the target layer of mineral exploration or in-situ leaching conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a sedimentary rock porosity determination method provided by the present invention;

[0027] Figure 2 This is an original photo of a sedimentary rock under a portable microscope in an embodiment of the present invention;

[0028] Figure 3 for Figure 2 The image after increasing the contrast of the original image;

[0029] Figure 4 for Figure 3 The middle picture shows the image after pores (green) are extracted. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0031] like Figure 1 As shown, the present invention provides a sedimentary rock porosity determination method, which specifically includes the following steps:

[0032] Step 1: Sample collection of target layer

[0033] Collect rock samples of the target layer on the surface or in boreholes. The samples should be fresh, have a low degree of weathering, be free of mud, and have multiple relatively flat sections.

[0034] Step 2: Sample processing, selection of observation surface

[0035] Pre-treat the samples collected in step 1, select a relatively flat surface and polish it on sandpaper with a sand quality of 60 to 100 mesh. Polish until there are no obvious protruding particles when observing the cross section with the naked eye. Use the polished surface as the observation surface.

[0036] Step 3: Microscopic observation, description and photo collection

[0037] Use a portable handheld microscope to observe and photograph the observation surface selected in step 2, and describe in detail the composition, shape, particle size, sorting, arrangement, etc. of the sedimentary rock debris under the microscope; describe the structure, cementation type and particle support properties of the matrix and cement; observe the size (pore radius), shape, connectivity, type and other pore characteristics and its composition. Select representative observation points under the microscope for photo collection.

[0038] Step 4: Photo preprocessing

[0039] According to the main characteristics of debris, matrix and cement observed in step 3, distinguish the main color difference between pores and other components. Pores are mainly black or gray-black under the microscope. If the debris and filling materials do not contain dark components and are easy to distinguish from pores in color, go directly to the next step; if the debris and filling materials contain dark components and are difficult to distinguish from pores in color, the dark components should be circled with the help of photo processing software and replaced with light colors.

[0040] Step 5: Increase the contrast of your photo

[0041] Use Python software to call the ImageEnhance module in the image processing library PIL, and use the Contrast class in the module to adjust the photo preprocessed in step 4. Set the function parameter to 2.0 to increase the contrast of the photo by 2 times, and save the processed image as a new image.

[0042] Step 6: Pore extraction and pore ratio calculation

[0043] Use Python software to call the cv2 library and the NumPy library, set the RGB color range value of the pores in the image after processing in step five, use the inRange function in the cv2 library to limit the RGB color range value of the pores in the image, and replace them all with colors that have a large contrast with the mineral colors in the photo, such as green (if the photo contains green minerals, replace them with other colors); use the np.sum() function in the NumPy library to count the number of pixels with replaced colors, and calculate the ratio to the total pixels of the image, which is the pore ratio; save the processed image as a new image.

[0044] Step 7: Calculate the porosity of the sample

[0045] Repeat steps 2 to 6, select different surfaces of the sample and repeat the operation three times, and calculate the average of the plane pore proportions obtained three times, which is the porosity of the sample.

[0046] Example

[0047] Taking a sandstone sample as an example, the porosity determination includes the following steps:

[0048] Step 1: Sample collection of target layer

[0049] A light red medium-coarse sandstone sample was collected from the surface outcrop. The sample was relatively fresh, less weathered, and had multiple relatively flat sections.

[0050] Step 2: Sample processing, selection of observation surface

[0051] Pre-treat the sample collected in step 1, select a relatively flat surface and polish it on 60-mesh sandpaper until there are no obvious raised particles on the cross section when observed with the naked eye, and use the polished surface as the observation surface.

[0052] Step 3: Microscopic observation, description and photo collection

[0053] Using a portable handheld microscope, observe and photograph the observation surface selected in step 2. The debris of this sample is mainly quartz, containing a small amount of rock debris, with a particle size of 0.3-0.6mm, round, well sorted, linear contact, less impurities, iron cementation, and a cement content of less than 1%; the pores are mainly intergranular pores, in the form of long strips, with good connectivity, and generally about 0.1-0.3mm wide. Figure 2 As shown, select representative observation points under the microscope for photo collection.

[0054] Step 4: Photo preprocessing

[0055] According to the main characteristics of the debris, matrix and cement observed in step three, the pores are mainly black or gray-black under the microscope, and the debris particles and fillings have no dark components and are easy to distinguish from the pores in color, so go directly to the next step.

[0056] Step 5: Increase the contrast of your photo

[0057] Open the Python software, call the ImageEnhance module in the image processing library PIL, use the Contrast class in the module to adjust the photo preprocessed in step 4, set the function parameter to 2.0 to increase the contrast of the photo by 2 times, and save the processed image as a new image.

[0058] like Figure 3 The photo shown here is a photo with increased contrast, which makes the pores more clear and prominent.

[0059] Step 6: Pore extraction and pore ratio calculation

[0060] Open Python software, call cv2 library and NumPy library, set the RGB color range value of the pores in the image after step 5, use the inRange function in the cv2 library to limit the RGB color range value of the pores in the image, and replace them all with green (if the photo contains green minerals, replace them with other colors); use the np.sum() function in the NumPy library to count the number of green pixels and calculate its ratio to the total pixels of the image, which is the pore ratio; save the processed image as a new image.

[0061] like Figure 4 The photo shown here shows the extracted pores replaced with green. The final calculated pore ratio is 14.68%.

[0062] Step 7: Calculate the porosity of the sample

[0063] Repeat steps 2 to 6, select different surfaces of the sample and repeat the operation three times. The porosity percentages obtained three times are 14.68%, 17.31%, and 15.49%, respectively. The average value of the planar porosity obtained three times is 15.83%, which is the porosity of the sample.

[0064] The present invention is described in detail above with reference to the accompanying drawings and embodiments, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. The contents not described in detail in the present invention can adopt the existing technology.

Claims

1. A method for measuring the porosity of sedimentary rocks, characterized in that: The method comprises: Step 1: Collect samples of target layer; Step 2: Sample processing and selection of observation surface; Step 3: Observation, description and photo collection under the microscope; Step 4: Photo preprocessing; Step 5: Increase the contrast of the photo; Step 6: Pore extraction and pore ratio calculation; Step 7: Calculate the porosity of the sample.

2. A sedimentary rock porosity determination method according to claim 1, characterized in that: The sample collected in step 1 should be fresh, have a low degree of weathering, be free of mud wrapping, and have multiple relatively flat cross sections.

3. A sedimentary rock porosity determination method according to claim 1, characterized in that: The step 2 comprises: selecting a relatively flat surface and grinding it on sandpaper until there are no obvious raised particles when observing the cross section with the naked eye, and using the ground surface as the observation surface.

4. A sedimentary rock porosity determination method according to claim 3, characterized in that: In the step 2, the sand type is selected to be 60 mesh to 100 mesh.

5. A sedimentary rock porosity determination method according to claim 1, characterized in that: The contents of microscopic observation and description in step three include: the composition, shape, particle size, sorting, and arrangement of sedimentary rock fragments under a microscope; the structure, cementation type, and particle support properties of the matrix and cement; and observation of all pore characteristics and their composition.

6. A sedimentary rock porosity determination method according to claim 5, characterized in that: The pore characteristics include pore size (pore radius), shape, connectivity, and type.

7. A sedimentary rock porosity determination method according to claim 5, characterized in that: The step four comprises: distinguishing the main color difference between pores and other components according to the main characteristics of the debris, matrix and cement observed in step three; if the colors of the pores and other components are difficult to distinguish, circle the dark components of other components and replace them with light colors using photo processing software.

8. A sedimentary rock porosity determination method according to claim 1, characterized in that: The step five includes: using Python software to call the ImageEnhance module in the image processing library PIL, using the Contrast class in the module to adjust the photo preprocessed in step four, and setting the function parameter to 2.0 to increase the contrast of the photo by 2 times.

9. A sedimentary rock porosity determination method according to claim 1, characterized in that: The step six includes: using Python software, calling the cv2 library and the NumPy library, setting the RGB color range value of the pores in the image after processing in step five, using the inRange function in the cv2 library to limit the RGB color range value of the pores in the image, and replacing them all with colors that have a large contrast with the mineral colors in the photo, using the np.sum() function in the NumPy library to count the number of pixels of the replaced colors, and calculating the ratio to the total pixels of the image, which is the pore ratio.

10. A sedimentary rock porosity determination method according to claim 1, characterized in that: The step seven includes: repeating the operations from step two to step six, selecting different surfaces of the sample and repeating the operations three times, and calculating the average value of the plane pore proportions obtained three times, which is the porosity of the sample.

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