Method for quantitatively characterizing tight oil occurrence state and related equipment

Abstract

The invention discloses a method for quantitatively characterizing the occurrence state of tight oil and related equipment, relates to the field of oil and gas exploitation, and mainly aims to solve the problem of lack of a better method for detecting the occurrence state of tight oil. The method comprises the following steps: acquiring a CT in-situ recovery scanning image of a target area sample; representing the basic structure of the rock based on a CT in-situ recovery scanning image; determining a rock pore throat structure based on the rock basic structure in combination with the distribution range and contribution rate of the pore throat radius; based on the CT in-situ recovery scanning image and the gray level change data of the same vision field image before and after oil washing, the gray level range of the tight oil fluid image is determined, and the content of the movable fluid and the content of the bound fluid are calculated according to the proportion data of the communicated pores. The method is used for quantitatively characterizing the tight oil occurrence state process.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction, and in particular to a method and related equipment for quantitatively characterizing the occurrence state of tight oil. Background Technology

[0002] Studies on tight oil reservoirs primarily utilize evaluation indicators such as porosity and permeability, matrix pore type, distribution area and reservoir thickness, formation pressure, fluid properties and flowability, and fracturing capability. Regarding the characterization of basic reservoir physical properties, current techniques include mercury intrusion porosimetry, gas adsorption, scanning electron microscopy, and nuclear magnetic resonance (NMR), but these are limited to quantitative characterization or local two-dimensional feature descriptions, lacking spatial qualitative and quantitative descriptions. Furthermore, the occurrence state of tight oil mainly involves two parameters: oil content and oil saturation. These can be obtained through quantitative fluorescence detection, thin section observation, core analysis, well logging interpretation, and NMR, but like basic physical property characterization, they lack spatial distribution information. Therefore, a more optimal method for detecting the occurrence state of tight oil is still lacking. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method and related equipment for quantitatively characterizing the occurrence state of tight oil, the main purpose of which is to solve the problem of lacking a better method for detecting the occurrence state of tight oil.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides a method for quantitatively characterizing the occurrence state of tight oil, the method comprising:

[0005] Acquire CT in situ reconstruction scan images of the target area sample, wherein the CT in situ reconstruction scan images are after optimized preprocessing;

[0006] Characterizing the basic structure of rocks based on CT in situ reconstruction scan images, wherein the basic structure of rocks includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains;

[0007] The rock pore throat structure is determined based on the basic rock structure combined with the distribution range and contribution rate of the pore throat radius. The rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores.

[0008] Based on CT in situ reconstruction scan images and grayscale change data of the same field of view before and after oil washing, the grayscale range of the tight oil fluid image is determined. The content of movable fluid and bound fluid is calculated based on the proportion of connected pores. The content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

[0009] Optionally, the target area sample is a plunger-shaped sample with a diameter of 25 mm and a length of 20 mm-50 mm. When the target area sample is drilled out, it is sealed by wrapping it in tin foil.

[0010] The method further includes:

[0011] When acquiring CT in situ reconstruction scan images of the target region sample, the center position of the scanning field of view in different directions is controlled to be on the central axis of the target region sample.

[0012] Optionally, the above methods also include:

[0013] Acquire in situ CT reconstruction images of the target area sample before and after oil washing to determine the grayscale change data of the same field of view before and after oil washing;

[0014] The scanning parameters of the scanner are adjusted based on the grayscale change data of the same field of view before and after the oil washing.

[0015] Optionally, the optimized preprocessing includes edge hardening correction and filtering noise reduction, and the method further includes:

[0016] Edge hardening correction is performed on the CT in situ restored scan image to make the internal and external gray values ​​of the CT in situ restored scan image uniform;

[0017] The CT in situ restored scan image is subjected to filtering and noise reduction, wherein the filtering and noise reduction is median filtering and non-local mean filtering.

[0018] Optionally, the characterization of the basic rock structure based on CT in-situ reconstruction scan images is as follows:

[0019] Based on the CT in-situ reconstruction scan image, a three-dimensional model of the pore and fracture space, rock skeleton and mineral grains is extracted, wherein the three-dimensional model of the pore and fracture space, rock skeleton and mineral grains;

[0020] The volume proportions of the pore and fracture space, rock skeleton, and mineral particles are determined based on the comparison between their volume and the total volume of the three-dimensional model.

[0021] Optionally, the pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores therein. The characterization of the basic rock structure based on CT in-situ reconstruction scan images includes:

[0022] Three-dimensional visualization image data of isolated pores, effective pores, and throats were determined;

[0023] Effective porosity is determined based on the three-dimensional visualization image data of the isolated pores, effective pores, and throats;

[0024] Based on the effective porosity, pore connectivity analysis data are determined to construct a pore throat ball-and-stick model;

[0025] Based on the pore throat ball-and-stick model and the pore network model, the pore throat frequency distribution histogram and contribution rate histogram are determined, wherein the pore throat frequency distribution histogram and contribution rate histogram are used to determine the distribution range and contribution rate of the pore throat radius;

[0026] The basic physical properties of rocks are analyzed based on the distribution range and contribution rate of the pore throat radius.

[0027] Optionally, the above methods also include:

[0028] Based on the relationship between grayscale and density, the grayscale changes in the same field of view of CT in situ scans before and after oil washing are compared to determine the range of grayscale values ​​of the fluid in the pore space. The range of grayscale values ​​of the fluid in the pore space is used to determine the occurrence state of all fluids.

[0029] Based on the occurrence state of all the fluids, the percentage of movable fluid and bound fluid voxels in the total pore voxels in the three-dimensional model is calculated to determine the saturation of movable fluid and bound fluid.

[0030] Secondly, embodiments of the present invention also provide a device for quantitatively characterizing the occurrence state of tight oil, comprising:

[0031] The acquisition unit is used to acquire CT in situ reconstruction scan images of the target area sample, wherein the CT in situ reconstruction scan images are after optimized preprocessing;

[0032] The characterization unit is used to characterize the basic structure of rocks based on CT in-situ reconstruction scan images, wherein the basic rock structure includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains.

[0033] The first determining unit is used to determine the rock pore throat structure based on the basic rock structure combined with the distribution range and contribution rate of the pore throat radius, wherein the rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores.

[0034] The second determining unit is used to determine the grayscale range of the tight oil fluid image based on the CT in situ recovery scan image and the grayscale change data of the same field of view before and after oil washing, and to calculate the content of movable fluid and bound fluid according to the proportion data of connected pores, wherein the content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

[0035] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein, when the program is executed by a processor, the steps of the above-described method for quantitatively characterizing the occurrence state of tight oil are implemented.

[0036] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the above-described method for quantitatively characterizing the occurrence state of tight oil.

[0037] By employing the above technical solutions, the present invention provides a method and related equipment for quantitatively characterizing the occurrence state of tight oil. Addressing the lack of a superior method for detecting the occurrence state of tight oil, the present invention acquires CT in-situ reconstruction scan images of a sample from a target area, wherein the CT in-situ reconstruction scan images are pre-processed and optimized. Based on the CT in-situ reconstruction scan images, the basic rock structure is characterized, including a three-dimensional model of pore and fracture spaces, a rock skeleton, and mineral grains. Based on the basic rock structure, the distribution range and contribution rate of pore throat radii are used to determine the rock pore throat structure, which includes the size and connectivity of pore spaces, and the proportion of isolated pores and connected pores. Based on the CT in-situ reconstruction scan images and grayscale change data of the same field of view before and after oil washing, the grayscale range of the tight oil fluid image is determined. The content of movable fluid and bound fluid is calculated based on the proportion of connected pores, wherein the content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of the tight oil fluid. The above scheme proposes using in-situ CT reconstruction scanning to compare grayscale values ​​before and after oil washing to obtain the grayscale range of tight oil images. After oil washing, the tight oil with higher grayscale values ​​disappears, while the pore spaces with lower grayscale values ​​become visible. This method is the most crucial step in characterizing the occurrence state of tight oil and is currently the best method in CT scanning experiments to control human operational variables and compare scanning results in the same field of view to obtain the changes in images of tight sandstone samples before and after oil washing. Simultaneously, it proposes optimizing the entire process of image preprocessing in parameter selection and software calculation to effectively eliminate errors caused by factors such as experimental equipment, environment, sample type, and scanning accuracy in extracting tight oil based on grayscale values, thereby minimizing errors throughout the CT scanning experimental operation. Furthermore, it proposes quantitatively characterizing the fluid occurrence state according to the pore space development characteristic analysis method, making the occurrence state of tight oil visible and deepening the understanding of the mobility of tight oil.

[0038] Accordingly, the apparatus, device, and computer-readable storage medium for quantitative characterization of tight oil occurrence state provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0039] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0041] Figure 1 A flowchart illustrating a method for quantitatively characterizing the occurrence state of tight oil according to an embodiment of the present invention is shown.

[0042] Figure 2 This diagram illustrates a pore space extraction range according to an embodiment of the present invention.

[0043] Figure 3 This diagram illustrates a three-dimensional reconstruction process of a basic rock structure according to an embodiment of the present invention.

[0044] Figure 4 This illustration shows a schematic diagram of two-dimensional images from different directions, the pore space extraction range, and the complete rock composition (including tight oil) provided by an embodiment of the present invention.

[0045] Figure 5 This diagram illustrates a quantitative analysis of isolated pores and interconnected pores provided by an embodiment of the present invention.

[0046] Figure 6 A schematic diagram of a pore network model provided by an embodiment of the present invention is shown;

[0047] Figure 7 This diagram illustrates a pore throat structure analysis process provided by an embodiment of the present invention.

[0048] Figure 8 This diagram illustrates a histogram of the pore throat radius distribution provided in an embodiment of the present invention.

[0049] Figure 9 This diagram illustrates a three-dimensional schematic of a tight oil occurrence state according to an embodiment of the present invention.

[0050] Figure 10 This is a three-dimensional schematic diagram of a tight oil occurrence feature provided by an embodiment of the present invention;

[0051] Figure 11This diagram illustrates a nuclear magnetic resonance analysis of a No. 1 dense sandstone sample provided in an embodiment of the present invention.

[0052] Figure 12 This diagram illustrates a computational flow diagram of Avizo software provided in an embodiment of the present invention.

[0053] Figure 13 This diagram illustrates a schematic block diagram of a device for quantitatively characterizing the occurrence state of tight oil according to an embodiment of the present invention.

[0054] Figure 14 This diagram illustrates the composition of an electronic device for quantitatively characterizing the occurrence state of tight oil, as provided in an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0056] To address the lack of a superior method for detecting the occurrence state of tight oil, embodiments of the present invention provide a method for quantitatively characterizing the occurrence state of tight oil.

[0057] Understandably, with the advancement of imaging principles and computer algorithms, digital 3D image analysis has been widely applied in various disciplines. Digital core technology has developed in this context, and based on CT scanning imaging and FIB-SEM analysis technology, the qualitative and quantitative description of reservoirs is no longer limited to the above methods.

[0058] Currently, CT has been widely applied in oilfield geology research. Originally used for reservoir property studies in conventional oil and gas reservoirs, its application has expanded significantly in recent years with the deepening research in unconventional oil and gas fields, particularly focusing on tight reservoirs. Driven by increasing demands for precision, image analysis at the micrometer, submicrometer, and even nanometer scales has become increasingly prevalent. This technology has made progress in research on coal and rock properties, bituminous material identification, sandstone microstructure classification, shale pore structure, and quantitative microscopic characterization of rocks. In characterizing the occurrence characteristics of tight oil, this application references the existing technology of quantitative CT and three-dimensional visualization testing of the occurrence state of tight oil in reservoirs, which classifies the gray values ​​of rock particles, pores, tight oil, and water. However, when characterizing the occurrence state of tight oil based on this technology, this application found that due to the influence of parameters such as voltage, current, and distance between the X-ray source and the sample during CT scanning, the gray values ​​of various elements vary greatly under different scanning conditions for the same batch of samples, and even for the same sample. Therefore, a single, definitive gray value cannot be used as an absolute standard for threshold segmentation; only the approximate gray value distribution range of different components under a certain scanning condition for a particular sample can be determined, approximately air = pores and fractures < fluid < interstitial material < mineral particles. Furthermore, in petroleum geological experiments, the same sample often requires CT image comparison before and after different experiments. Achieving in-situ image comparison via CT scanning requires optimization in both the manual operation stage and the instrument parameter selection. Therefore, how to optimize these two stages to minimize scanning errors is an urgent issue to be explored in the field of CT imaging. This application also takes into account the issue of CT image accuracy, and finds that the gray values ​​of dense oil and pores are very close. At this level of accuracy, there is currently no relevant standard that can select a specific range of gray values ​​to accurately identify the two. Therefore, this technology requires a large number of horizontal comparisons and innovation in experimental methods.

[0059] like Figure 1 As shown, the method of this application includes:

[0060] S101. Obtain the CT in situ reconstruction scan image of the sample in the target area, wherein the CT in situ reconstruction scan image is after optimized preprocessing;

[0061] Specifically, the target area sample is a plunger-shaped sample with a diameter of 25 mm and a length of 20 mm-50 mm. When the target area sample is drilled out, it is sealed by wrapping it in tin foil.

[0062] The steps in S101 above also include S1011, S1012, and S1013:

[0063] S1011. When acquiring the CT in-situ recovery scan image of the target area sample, control the center position of the scanning field of view in different directions to be on the central axis of the target area sample.

[0064] For example, a plunger sample with a diameter of 25 mm and a length of 20 mm to 50 mm was uniformly selected for the experimental sample, and fresh samples were used after drilling.

[0065] Furthermore, when performing a full-diameter CT scan, it is necessary to mark the top and bottom surfaces of the sample, keep the sample perpendicular to the horizontal plane when clamping it, and use modeling clay to fix it to ensure the stability of the sample during scanning; record the initial scanning position, including the scanning direction, resolution, and the scanning height of the top surface at that resolution, and ensure that the center of the scanning field of view in different directions is on the central axis of the cylinder when adjusting the rotation center of the sample stage.

[0066] S1012. Obtain CT in-situ reconstruction scan images of the target area sample before and after oil washing to determine the grayscale change data of the same field of view before and after oil washing; adjust the scanning parameters of the scanning instrument based on the grayscale change data of the same field of view before and after oil washing.

[0067] It is important to note that transmittance is a key parameter affecting imaging quality, and it is controlled by voltage. Higher voltage results in higher transmittance, and samples of different densities will exhibit significantly different transmittance under the same voltage conditions. Transmittance is equal to the ratio of the grayscale value of the sample area to the grayscale value of the non-sample area during scanning. For conventional sandstone plunger samples (25mm in diameter), a transmittance of 30%-50% is sufficient for good imaging. If the maximum voltage is insufficient to meet the transmittance requirements, then scanning should be performed at the maximum voltage.

[0068] During scanning, the grayscale value of the non-sample area (air) needs to be maintained between 30,000 and 40,000 to increase the grayscale range of the CT scan and improve the discrimination of each component extraction. The grayscale value is directly proportional to the current, voltage, and exposure time, and is most affected by the voltage. In addition, the resolution for full-diameter scanning is generally 10 to 16 μm, and the scanning power should be 1 to 2 times the scanning resolution. Exceeding 2 times may result in image distortion and unclear particle edges. Therefore, the current and voltage are adjusted to 100kV to 150kV, the current to 160μA to 300μA, the power to 16 to 45W, and the exposure time is dynamically adjusted according to the scanning conditions, which is generally 0.8s to 1.5s at this scale.

[0069] Scanning time is positively correlated with exposure time, the number of image merges, and the number of scan frames. Increasing these three parameters is an effective way to eliminate the effects of noise, but it also increases costs significantly with little improvement. Furthermore, because exposure time is related to image grayscale, excessively long exposure times can lead to unclear edges on rock particles and ghosting.

[0070] In this embodiment of the application, an in-situ image comparison is performed before and after the oil washing process, based on the above procedure.

[0071] S1013. The optimized preprocessing includes edge hardening correction and filtering noise reduction. The method further includes: performing edge hardening correction on the CT in situ recovery scan image to make the internal and external grayscale values ​​of the CT in situ recovery scan image uniform; and performing filtering noise reduction on the CT in situ recovery scan image, wherein the filtering noise reduction is median filtering and non-local mean filtering.

[0072] Furthermore, the CT in situ restored scan images are optimized and preprocessed:

[0073] Considering that the software automatically compresses the acquired CT images into a uniform file when storing and exporting them on the computer, the overall image will be dark when using the reconstruction software that comes with the instrument to process them because the sample edges are easily penetrated, the density information of each component is very obvious, and the penetration rate at the center is limited. Therefore, it is necessary to harden and correct the image edges to make the gray values ​​inside and outside the image uniform before reconstruction.

[0074] Since CT image reconstruction yields a raw format 3D data volume, some information on the top and bottom surfaces of this data volume is affected by the projection range of the X-ray source and cannot be used. It is necessary to use the Volume Edit command on Avizo to cut the data, retain useful information, and use filtering tools to reduce noise before quantitative calculation and extraction of each component can be performed. For dense sandstone, the median filter and non-local mean filter are generally selected as filtering tools. When extracting each component, most of the useless information can be filtered out.

[0075] S102. Characterize the basic structure of rocks based on CT in-situ reconstruction scan images, wherein the basic structure of rocks includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains.

[0076] The steps in S102 above also include S1021 and S1022:

[0077] S1021. Extract a three-dimensional model of the pore and fracture space, rock skeleton, and mineral particles based on the CT in-situ recovery scan image, wherein the three-dimensional model of the pore and fracture space, rock skeleton, and mineral particles; determine the volume ratio of the pore and fracture space, rock skeleton, and mineral particles based on the comparison result between the volume of the pore and fracture space, rock skeleton, and mineral particles and the total volume of the three-dimensional model.

[0078] For example, this application uses CT images to characterize the basic structure of rocks, mainly extracting three-dimensional models of pore and fracture spaces, rock skeleton, and mineral grains. Based on this, spatial visualization and quantitative analysis of the basic structure of rocks can be performed. By comparing the volume of each part with the total volume of the reconstructed model using a computer to obtain the volume percentage, the proportion of each component can be quantitatively calculated.

[0079] After opening the software, import the volume data in raw format. Input the corresponding resolution and reconstructed data size in the x, y, and z directions. The default size is 1920×1920×1536, in micrometers. The reconstructed data type is 16-bit no-signal integer. After importing the data, first use Median Filter to reduce image noise, then select Interactive Thresholding for threshold segmentation. During threshold segmentation, follow the grayscale distinction range of air = pores and fissures < fluid < rock skeleton. First, increase the image contrast by dragging the contrast selection range to both sides of the grayscale distribution histogram to make the edges of the rock skeleton particles clear.

[0080] This application takes pore space extraction as an example. Based on this, the grayscale range is thresholded to the left part of the histogram, and the range of the right line is adjusted until the edge of the pore space is filled (see...). Figure 2 At this point, the extracted pore range is defined as the tight oil storage space. Based on this, a file with the .threshold extension can be obtained to represent the pores, rock skeleton, and metallic minerals as the basic rock structure (see...). Figure 3 , Figure 4 Select the file corresponding to the pore size and input the Volume Fraction command to calculate the voxel percentage, obtaining the total volume and porosity.

[0081] Specifically, Figure 3 a, b, c, and d are three-dimensional reconstruction images of clay, quartz, feldspar + carbonate minerals, and metallic minerals, respectively; e: three-dimensional reconstruction image of pore space; f: three-dimensional reconstruction image of rock skeleton. Figure 4 Images showing the extraction range of two-dimensional pore space and the complete rock composition in different directions, including tight oil: a: CT grayscale image in the xy direction; b: pore space in the xy direction; c: complete rock composition in the xy direction; d: CT grayscale image in the xz direction; e: pore space in the xz direction; f: complete rock composition in the xz direction; g: CT grayscale image in the yz direction; h: pore space in the yz direction; i: complete rock composition in the yz direction.

[0082] S1022. The pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores within it. The characterization of the basic rock structure based on CT in-situ reconstruction scan images includes: determining three-dimensional visualization image data of isolated pores, effective pores, and throats; determining effective porosity based on the three-dimensional visualization image data of isolated pores, effective pores, and throats; determining pore connectivity analysis data based on the effective porosity to construct a pore throat ball-and-stick model; determining a pore throat frequency distribution histogram and a contribution rate histogram based on the pore throat ball-and-stick model and the pore network model, wherein the pore throat frequency distribution histogram and contribution rate histogram are used to determine the distribution range and contribution rate of the pore throat radius; and analyzing the basic physical properties of the rock based on the distribution range and contribution rate of the pore throat radius.

[0083] For example, tight reservoirs are characterized by complex physical properties and low porosity and permeability. Therefore, the primary focus of analyzing the occurrence characteristics of tight oil is on the size and connectivity of the pore space, as well as the proportion of isolated and connected pores. The analysis method is as follows:

[0084] Connectivity analysis is performed using Avizo's built-in Axis Connectivity algorithm. Clicking "Apply" yields connected pore data. Using the "Arithmetic" command, output object A is selected as total pores, and output object B as connected pores. The formula (AB) is entered in the output type, and clicking "Apply" yields isolated pore data. This provides a 3D visualization of isolated pores, effective pores, and throats, along with relevant data. Effective porosity can then be calculated (see...). Figure 5 , Figure 6 In addition, after obtaining the pore data, you need to input the "separate objects" command to separate the objects, select "Connected Object" as the data type, obtain the pore connectivity analysis data, and then select the connectivity data to input "Generate Pore Network Model" to generate the pore throat ball-and-stick model (see...). Figure 7 ).

[0085] Specifically, Figure 5 a: isolated pores and connected pores; b: isolated pores; c: connected pores. Figure 7 a: 3D reconstructed image of pores; b: pore connectivity analysis; c: pore throat ball-and-stick model.

[0086] Furthermore, the data was processed to generate a pore network model, outputting an XML format table with millimeters as the default unit. During pore throat data statistics, the units were uniformly converted to micrometers, thereby obtaining a histogram of pore throat frequency distribution and a histogram of contribution rate. The distribution range and contribution rate of pore throat radii were used to analyze the basic physical properties of the rock (see...). Figure 8 The complete software calculation process can be found here. Figure 12 ).

[0087] Specifically, Figure 8 a: Aperture distribution histogram; b: Pore contribution rate histogram; c: Throat radius distribution histogram; d: Throat radius frequency histogram.

[0088] S103. Based on the basic rock structure and the distribution range and contribution rate of the pore throat radius, the rock pore throat structure is determined, wherein the rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores.

[0089] S104. Based on the CT in-situ recovery scan image and the grayscale change data of the same field of view before and after oil washing, determine the grayscale range of the tight oil fluid image, and calculate the content of movable fluid and bound fluid according to the proportion data of connected pores, wherein the content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

[0090] The above-mentioned step S104 further includes S1041: based on the relationship between grayscale and density, compare the grayscale changes in the same field of view of CT in situ scanning before and after oil washing to determine the range of grayscale values ​​of the fluid in the pore space, wherein the range of grayscale values ​​of the fluid in the pore space is used to determine the occurrence state of all fluids; based on the occurrence state of all fluids, calculate the percentage of movable fluid and bound fluid voxels in the three-dimensional model to the total pore voxels to determine the saturation of movable fluid and bound fluid.

[0091] For example, when performing quantitative analysis of fluids, it is only necessary to extract the fluid storage space. Therefore, after comprehensively considering the grayscale changes of the same field of view before and after washing the oil, fine data adjustments are made within the threshold segmentation range of the pore throat structure. This mainly involves the following three steps.

[0092] First, based on the relationship between grayscale and density, a simple distinction can be made between fluid and pore space. To control variables, all samples used for the analysis of the occurrence state of tight oil are scanned under the same conditions and have the same sample preparation specifications.

[0093] Then, based on the relationship between grayscale and density, it was found that crude oil has a significantly higher density than air, therefore its grayscale value is significantly greater than that of pores. Considering the influence of wettability, the imaging shows obvious edge blurring, with brightness between that of the rock matrix and the pore space. By comparing the grayscale changes in the same field of view of CT in situ scans before and after oil washing, the range of grayscale values ​​of the fluids extracted in the second step of pore space was determined. Based on this, the occurrence state of all fluids can be obtained (see...). Figure 9 ).

[0094] Specifically, Figure 9a: Three-dimensional reconstruction image of the fluid; b: Distribution of fluid in pore space; c: Existence state of fluid in pore space and rock skeleton.

[0095] Finally, similar to the quantitative analysis of pore throat structure, distinguishing between movable and bound fluids also involves separating connected and isolated objects. First, the `axis connectivity` command is used for connectivity analysis to obtain movable fluid data. Then, using the `Arithmetic` command, output object A is selected as the total fluid data, and output object B as the movable fluid. The formula (AB) is entered in the output type, and the application is clicked to obtain bound fluid data. Voxel sizes are calculated, and a 3D image model is displayed for comparison with the pore space model. The percentages of movable and bound fluid voxels in the pore voxels are calculated to obtain the movable fluid saturation and bound fluid saturation (see...). Figure 10 ).

[0096] Specifically, Figure 10 a: movable fluid and bound fluid; b: movable fluid; c: bound fluid.

[0097] By employing the aforementioned technical solution, an innovative CT in-situ reconstruction scanning technique is used to obtain the grayscale range of tight oil images by comparing grayscale images before and after oil washing. After oil washing, the tight oil with higher grayscale values ​​disappears, while the pore space with lower grayscale values ​​becomes apparent. This method is the core step in characterizing the occurrence state of tight oil and is currently the optimal method in CT scanning experiments for controlling human operational variables and comparing scan results from the same field of view to obtain the changes in images of tight sandstone samples before and after oil washing. This invention also emphasizes the optimization of the entire process of image preprocessing, including parameter selection and software calculation, effectively eliminating errors caused by factors such as experimental equipment, environment, sample type, and scanning accuracy when extracting tight oil based on grayscale values, thus minimizing errors throughout the entire CT scanning experimental operation. Furthermore, this invention quantitatively characterizes the fluid occurrence state based on pore space development characteristic analysis methods, making the occurrence state of tight oil visible and deepening the understanding of the mobility of tight oil. This innovation is of great significance for improving the recovery rate of tight reservoirs.

[0098] Furthermore, as a response to the above Figure 1 In addition to the method shown, this embodiment of the invention also provides a device for quantitatively characterizing the occurrence state of tight oil, used for the above-mentioned... Figure 1 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 13As shown, the device includes: an acquisition unit 21, a characterization unit 22, a first determination unit 23, and a second determination unit 24, wherein...

[0099] The acquisition unit 21 is used to acquire the CT in situ reconstruction scan image of the target area sample, wherein the CT in situ reconstruction scan image is after optimized preprocessing;

[0100] Characterization unit 22 is used to characterize the basic structure of rocks based on CT in-situ reconstruction scan images, wherein the basic structure of rocks includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains.

[0101] The first determining unit 23 is used to determine the rock pore throat structure based on the basic rock structure combined with the distribution range and contribution rate of the pore throat radius, wherein the rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores.

[0102] The second determining unit 24 is used to determine the grayscale range of the tight oil fluid image based on the CT in situ recovery scan image and the grayscale change data of the same field of view before and after oil washing, and to calculate the content of movable fluid and bound fluid according to the proportion data of connected pores, wherein the content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

[0103] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, a quantitative method for characterizing the occurrence state of tight oil can be implemented, addressing the lack of a superior method for detecting the occurrence state of tight oil.

[0104] This invention provides a computer-readable storage medium including a stored program that, when executed by a processor, implements the method for quantitatively characterizing the occurrence state of tight oil.

[0105] This invention provides a processor for running a program, wherein the program executes the method for quantitatively characterizing the occurrence state of tight oil.

[0106] This invention provides an electronic device, which includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the method for quantitatively characterizing the occurrence state of tight oil as described above.

[0107] This invention provides an electronic device 30, such as... Figure 14As shown, the electronic device includes at least one processor 301, and at least one memory 302 and bus 303 connected to the processor; wherein, the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call program instructions in the memory to execute the above-mentioned method for quantitatively characterizing the occurrence state of tight oil.

[0108] The smart electronic devices mentioned in this article can be PCs, tablets, mobile phones, etc.

[0109] This application also provides a computer program product that, when executed on a process management electronic device, is suitable for executing a program that initializes the above-described method steps for quantitatively characterizing the occurrence state of tight oil.

[0110] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The control flow of the memory in the corresponding embodiment.

[0116] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0119] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for quantitatively characterizing the occurrence state of tight oil, characterized in that, include Acquire CT in situ reconstruction scan images of the target area sample, wherein the CT in situ reconstruction scan images are after optimized preprocessing; Characterizing the basic structure of rocks based on CT in situ reconstruction scan images, wherein the basic structure of rocks includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains; The rock pore throat structure is determined based on the basic rock structure combined with the distribution range and contribution rate of the pore throat radius. The rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores. Based on CT in situ reconstruction scan images and grayscale change data of the same field of view before and after oil washing, the grayscale range of the tight oil fluid image is determined. The content of movable fluid and bound fluid is calculated based on the proportion of connected pores. The content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

2. The method according to claim 1, characterized in that, The target area sample is a plunger-shaped sample with a diameter of 25 mm and a length of 20 mm-50 mm. When the target area sample is drilled out, it is sealed by wrapping it in tin foil. The method further includes: When acquiring CT in situ reconstruction scan images of the target region sample, the center position of the scanning field of view in different directions is controlled to be on the central axis of the target region sample.

3. The method according to claim 1, characterized in that, Also includes: Acquire in situ CT reconstruction images of the target area sample before and after oil washing to determine the grayscale change data of the same field of view before and after oil washing; The scanning parameters of the scanner are adjusted based on the grayscale change data of the same field of view before and after the oil washing.

4. The method according to claim 1, characterized in that, The optimized preprocessing includes edge hardening correction and filtering noise reduction, and the method further includes: Edge hardening correction is performed on the CT in situ restored scan image to make the internal and external gray values ​​of the CT in situ restored scan image uniform; The CT in situ restored scan image is subjected to filtering and noise reduction, wherein the filtering and noise reduction is median filtering and non-local mean filtering.

5. The method according to claim 1, characterized in that, The characterization of basic rock structure based on CT in-situ reconstruction scan images: Based on the CT in-situ reconstruction scan image, a three-dimensional model of the pore and fracture space, rock skeleton and mineral grains is extracted, wherein the three-dimensional model of the pore and fracture space, rock skeleton and mineral grains; The volume proportions of the pore and fracture space, rock skeleton, and mineral particles are determined based on the comparison between their volume and the total volume of the three-dimensional model.

6. The method according to claim 1, characterized in that, The pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores within it. The characterization of the basic rock structure based on CT in-situ reconstruction scan images includes: Three-dimensional visualization image data of isolated pores, effective pores, and throats were determined; Effective porosity is determined based on the three-dimensional visualization image data of the isolated pores, effective pores, and throats; Based on the effective porosity, pore connectivity analysis data are determined to construct a pore throat ball-and-stick model; Based on the pore throat ball-and-stick model and the pore network model, the pore throat frequency distribution histogram and contribution rate histogram are determined, wherein the pore throat frequency distribution histogram and contribution rate histogram are used to determine the distribution range and contribution rate of the pore throat radius; The basic physical properties of rocks are analyzed based on the distribution range and contribution rate of the pore throat radius.

7. The method according to claim 1, characterized in that, Also includes: Based on the relationship between grayscale and density, the grayscale changes in the same field of view of CT in situ scans before and after oil washing are compared to determine the range of grayscale values ​​of the fluid in the pore space. The range of grayscale values ​​of the fluid in the pore space is used to determine the occurrence state of all fluids. Based on the occurrence state of all the fluids, the percentage of movable fluid and bound fluid voxels in the total pore voxels in the three-dimensional model is calculated to determine the saturation of movable fluid and bound fluid.

8. A device for quantitatively characterizing the occurrence state of tight oil, characterized in that, The acquisition unit is used to acquire CT in situ reconstruction scan images of the target area sample, wherein the CT in situ reconstruction scan images are after optimized preprocessing; The characterization unit is used to characterize the basic structure of rocks based on CT in-situ reconstruction scan images, wherein the basic rock structure includes a three-dimensional model of pore and fracture space, rock skeleton and mineral grains. The first determining unit is used to determine the rock pore throat structure based on the basic rock structure combined with the distribution range and contribution rate of the pore throat radius, wherein the rock pore throat structure includes the size and connectivity of the pore space, and the proportion of isolated pores and connected pores. The second determining unit is used to determine the grayscale range of the tight oil fluid image based on the CT in situ recovery scan image and the grayscale change data of the same field of view before and after oil washing, and to calculate the content of movable fluid and bound fluid according to the proportion data of connected pores, wherein the content of movable fluid and bound fluid is used to quantitatively characterize the occurrence state of tight oil fluid.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, it implements the steps of the method for quantitatively characterizing the occurrence state of tight oil as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the method for quantitatively characterizing the occurrence state of tight oil as described in any one of claims 1 to 7.

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