Rock type identification method, device and storage medium

Abstract

This disclosure provides a rock type identification method, device, and storage medium, belonging to the field of oil and gas exploration technology. The method comprises: obtaining a first image and a second image obtained by scanning a rock sample using X-rays of different energy values; obtaining a characteristic curve of the rock sample based on the first and second images; wherein the characteristic curve includes a density curve and / or an effective atomic number curve; and determining the target type of the rock sample based on the characteristic curve. Embodiments of this disclosure enable rapid and accurate rock sample type identification.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil and gas exploration, and in particular to a rock type identification method, device, and storage medium. Background Art

[0002] During the oil and gas drilling process, rock type identification plays a crucial role in determining geological conditions. During actual field drilling, rock type can be used to understand rock properties, formation changes, and the status of oil and gas reservoirs.

[0003] Currently, well logging analysis, core analysis, and rock cuttings analysis are the main methods used to identify the type of rock samples. Well logging analysis primarily uses acoustic, electrical, and radioactive signals from the formation received by underground instruments to indirectly determine the rock type. Core analysis analyzes rock samples drilled underground to determine rock type. This method has the advantages of low cost and intuitive results, but its disadvantages are limited sample representativeness. Furthermore, rock type identification is primarily based on visual observation by geologists, and the results of observations by different individuals may vary significantly. Rock cuttings analysis analyzes rock cuttings samples generated during oil and gas well drilling, primarily through visual observation by geologists or the use of handheld instruments (such as handheld fluorescence spectrometers) to qualitatively determine rock type. This method has the advantages of low cost and high speed, but its disadvantages are that its accuracy is highly dependent on the experience of geologists, and rock cuttings samples are generally less representative. Summary of the Invention

[0004] Embodiments of the present disclosure provide a rock type identification method, device, and storage medium.

[0005] The technical solution of the present disclosure is achieved as follows:

[0006] In a first aspect, a rock type identification method is provided, the method comprising:

[0007] Acquire a first image and a second image obtained by scanning a rock sample using X-rays of different energy values;

[0008] Acquire a characteristic curve of the rock sample according to the first image and the second image; wherein the characteristic curve includes: a density curve and / or an effective atomic number curve;

[0009] The target type of the rock sample is determined based on the characteristic curve.

[0010] In the above solution, obtaining the characteristic curve of the rock sample based on the first image and the second image includes:

[0011] Analyzing the first image to obtain a first three-dimensional data volume of the rock sample;

[0012] Analyzing the second image to obtain a second three-dimensional data volume of the rock sample;

[0013] Acquire a density data volume and an effective atomic number data volume of the rock sample based on the first three-dimensional data volume and the second three-dimensional data volume;

[0014] A characteristic curve of the rock sample is obtained based on the density data volume and the effective atomic number data volume of the rock sample.

[0015] In the above solution, obtaining the characteristic curve of the rock sample based on the density data volume and the effective atomic number data volume of the rock sample includes:

[0016] generating a density curve of the rock sample based on the density data volume; and / or

[0017] An effective atomic number curve of the rock sample is generated based on the effective atomic number data body.

[0018] In the above solution, before the step of determining the target type of the rock sample according to the characteristic curve, the method further includes:

[0019] The density curve and / or the effective atomic number curve are calibrated.

[0020] In the above solution, determining the target type of the rock sample according to the characteristic curve includes:

[0021] If the characteristic curve includes the density curve, determining a first range interval in which a value range of the density curve lies;

[0022] Determining the rock type corresponding to the first range interval as the target type of the rock sample;

[0023] or,

[0024] If the characteristic curve includes the effective atomic number curve, determining a second range interval in which the value range of the effective atomic number curve lies;

[0025] determining the rock type corresponding to the second range interval as the target type of the rock sample;

[0026] or,

[0027] If the characteristic curve includes the density curve and the effective atomic number curve, the rock type corresponding to both the first range interval and the second range interval is determined as the target type of the rock sample.

[0028] In the above solution, if the characteristic curve includes: the density curve and the effective atomic number curve, the method further includes:

[0029] In the same rectangular coordinate system, aligning the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located;

[0030] According to the distance range between the aligned density curve and the effective atomic number curve, the subtype of the rock sample is determined among the multiple subtypes included in the target type; wherein different subtypes correspond to different distance ranges.

[0031] In the above solution, the method further includes:

[0032] obtaining a well logging curve of the rock sample;

[0033] Determining the target type of the rock sample according to the characteristic curve includes:

[0034] The target type of the rock sample is determined according to the well logging curve of the rock sample and the characteristic curve.

[0035] In a second aspect, a rock type identification device is provided, the device comprising:

[0036] A first acquisition module is used to acquire a first image and a second image obtained by scanning a rock sample with X-rays of different energy values;

[0037] a second acquisition module, configured to acquire a characteristic curve of the rock sample based on the first image and the second image; wherein the characteristic curve includes: a density curve and / or an effective atomic number curve;

[0038] A determination module is used to determine the target type of the rock sample based on the characteristic curve.

[0039] In the above solution, the second acquisition module includes:

[0040] an analysis submodule, configured to analyze the first image to obtain a first three-dimensional data volume of the rock sample, and to analyze the second image to obtain a second three-dimensional data volume of the rock sample;

[0041] A first acquisition submodule is configured to acquire a density data volume and an effective atomic number data volume of the rock sample based on the first three-dimensional data volume and the second three-dimensional data volume;

[0042] The second acquisition submodule is used to acquire the characteristic curve of the rock sample according to the density data volume and the effective atomic number data volume of the rock sample.

[0043] In the above solution, the second acquisition submodule is specifically used to:

[0044] generating a density curve of the rock sample based on the density data volume; and / or

[0045] An effective atomic number curve of the rock sample is generated based on the effective atomic number data body.

[0046] In the above solution, the device further comprises:

[0047] A correction module is used to correct the density curve and / or the effective atomic number curve.

[0048] In the above solution, the determining module is specifically used to:

[0049] If the characteristic curve includes the density curve, determining a first range interval in which a value range of the density curve lies;

[0050] Determining the rock type corresponding to the first range interval as the target type of the rock sample;

[0051] or,

[0052] If the characteristic curve includes the effective atomic number curve, determining a second range interval in which the value range of the effective atomic number curve lies;

[0053] determining the rock type corresponding to the second range interval as the target type of the rock sample;

[0054] or,

[0055] If the characteristic curve includes the density curve and the effective atomic number curve, the rock type corresponding to both the first range interval and the second range interval is determined as the target type of the rock sample.

[0056] In the above solution, if the characteristic curve includes: the density curve and the effective atomic number curve, the determination module is further configured to:

[0057] In the same rectangular coordinate system, aligning the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located;

[0058] According to the distance range between the aligned density curve and the effective atomic number curve, the subtype of the rock sample is determined among the multiple subtypes included in the target type; wherein different subtypes correspond to different distance ranges.

[0059] In the above solution, the device further includes a third acquisition module;

[0060] The third acquisition module is used to obtain the well logging curve of the rock sample;

[0061] The determination module is further configured to determine the target type of the rock sample based on the well logging curve of the rock sample and the characteristic curve.

[0062] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the rock type identification method described in any one of the first aspects are implemented.

[0063] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the rock type identification method described in any one of the first aspects are implemented.

[0064] The present disclosure provides a rock type identification method, device, and storage medium. The method includes: obtaining a first image and a second image obtained by scanning a rock sample using X-rays of different energy values; obtaining a characteristic curve of the rock sample based on the first image and the second image; wherein the characteristic curve includes a density curve and / or an effective atomic number curve; and determining the target type of the rock sample based on the characteristic curve. Since the first image and the second image obtained by scanning the rock sample using X-rays of different energy values ​​can obtain a characteristic curve of the rock sample, the characteristic curve includes a density curve and / or an effective atomic number curve. Both the density curve and the effective atomic number curve can be used as quantitative parameters to determine the target type of the rock sample, thereby greatly improving the reliability of rock type identification. Furthermore, compared with traditional rock type identification methods, while improving accuracy, the time cost can be significantly reduced, thereby achieving rapid and accurate rock sample type identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a rock type identification method according to an embodiment of the present disclosure;

[0066] Figure 2 A schematic diagram of a process for obtaining a characteristic curve of a rock sample according to an embodiment of the present disclosure;

[0067] Figure 3 A schematic flow chart of another rock type identification method provided in an embodiment of the present disclosure;

[0068] Figure 4 A schematic flow chart of another rock type identification method provided in an embodiment of the present disclosure;

[0069] Figure 5aA schematic structural diagram of a CT scanning device provided in an embodiment of the present disclosure;

[0070] Figure 5b A schematic diagram of a low-energy full-diameter core CT scan image provided by an embodiment of the present disclosure;

[0071] Figure 5c A schematic diagram of a high-energy full-diameter core CT scan image provided by an embodiment of the present disclosure;

[0072] Figure 5d A schematic cross-sectional view of a full-diameter core density data volume provided by an embodiment of the present disclosure;

[0073] Figure 5e A schematic cross-sectional view of a full-diameter core effective atomic number data volume provided by an embodiment of the present disclosure;

[0074] Figure 5f A schematic diagram of a full-diameter core density and effective atomic number curve provided in an embodiment of the present disclosure;

[0075] Figure 5g A schematic diagram of a process for identifying rock types in a full-diameter core according to an embodiment of the present disclosure;

[0076] Figure 6 A schematic structural diagram of a rock type identification device provided in an embodiment of the present disclosure;

[0077] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure. In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be arbitrarily combined with each other. The steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. In addition, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0079] It is understandable that the description of each embodiment in this disclosure focuses on the differences between the embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0080] The main methods used to identify the type of rock samples are well logging analysis, core analysis and cuttings analysis.

[0081] Well logging analysis: This method primarily utilizes a surface gamma ray testing system to continuously measure the natural gamma ray intensity of full-diameter core samples, thereby analyzing the core sample's shale content and determining rock type. This method offers advantages such as low cost and fast scanning speed, but its disadvantage is that it can only identify sandstone and mudstone samples and cannot provide a more detailed classification of different mudstone types.

[0082] Core analysis method: It mainly analyzes full-diameter cores and small-scale plug-type cores. Full-diameter core analysis is mainly based on the naked eye observation of core samples by geologists. Through direct observation of core color, grain size, sedimentary structure, etc., a rough classification of rock types is made. The advantages of this method are low cost and intuitive results, but the disadvantages are that it cannot provide quantitative identification and can only provide a rough rock classification. At the same time, the analysis results of different geologists may vary greatly. The analysis methods for small-scale plug samples are relatively mature, including optical thin section analysis, scanning electron microscopy analysis, and X-ray diffraction analysis of sample powder. The advantages of this type of analysis method are mature technology and reliable results, but the disadvantages are limited sample representativeness, high analysis cost, and long analysis time.

[0083] Rock chip analysis: This method analyzes rock debris generated during oil and gas well drilling. Geologists use visual inspection or handheld instruments (such as a handheld fluorescence spectrometer) to qualitatively determine rock type. This method offers advantages in cost and speed, but drawbacks include high accuracy, which is highly dependent on the geologist's experience.

[0084] Figure 1 A schematic diagram of a rock type identification method provided in an embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method may include:

[0085] S11, acquiring a first image and a second image obtained by scanning a rock sample using X-rays of different energy values.

[0086] The rock sample can be a core extracted from a reservoir or formation, for example, a full-diameter core drilled from underground using a special coring tool. The rock sample scanned with different energy radiation is contained in a core tank, which can be formed, for example, of aluminum, polyvinyl chloride (PVC), cardboard, polyethylene (PE), polypropylene (PP), carbon fiber, glass fiber, or other non-metallic materials.

[0087] In some examples, to more quickly identify rock types, the rock samples scanned using different energy beams are multiple rock samples. These rock samples can be arranged in a predetermined order. This predetermined order can be an order associated with the original underground locations of the rock samples, or can be arranged in order from lightest to darkest rock color. The spacing between the rock samples can be set based on actual application needs, for example, within a range of 0.1 cm to 5 cm.

[0088] In some examples, X-rays of different energy values ​​include X-rays emitted using a first tube voltage and X-rays emitted using a second tube voltage. Different tube voltages correspond to X-rays of different energies. A higher tube voltage indicates a higher X-ray energy. Here, tube voltage refers to the voltage used by the X-ray tube to emit X-rays. Images obtained by scanning the same material with X-rays of different energy values ​​will have different grayscales.

[0089] In some examples, the first image is a CT (Computed Tomography) image obtained by scanning a rock sample using X-rays at a first tube voltage, and the second image is a CT image obtained by scanning the rock sample using X-rays at a second tube voltage. In this embodiment, the first tube voltage is greater than a preset value, and the second tube voltage is less than a preset value. The preset value can be set according to actual application needs, for example, 140 kV, 100 kV, or other preset values.

[0090] Specifically, first scanning data is obtained by scanning the rock sample with X-rays at a first tube voltage, and second scanning data is obtained by scanning the rock sample with X-rays at a first tube voltage. Image reconstruction is performed on the first scanning data and the second scanning data, respectively. Through image reconstruction, the CT values ​​in the first scanning data and the second scanning data are converted into image grayscales, respectively, to obtain a first image and a second scanning image of the rock sample, wherein the CT value represents the attenuation value of the X-ray after passing through the rock sample and being absorbed.

[0091] In this embodiment, by scanning the rock sample using X-rays of different energy values, more information inside the core can be obtained without damaging the core, which is beneficial to improving the accuracy of the final rock type identification.

[0092] S12, acquiring a characteristic curve of the rock sample according to the first image and the second image; wherein the characteristic curve includes: a density curve and / or an effective atomic number curve.

[0093] The density curve can reflect the density variation pattern at different locations of a rock sample. In other words, the density curve can show the corresponding relationship between the location of a rock sample and its density. The density value of a rock varies greatly with the type of rock. For example, the density value of limestone is 2.6 to 2.9 g / cm 3 The density of mudstone is 2.0~2.5g / cm 3 .

[0094] The effective atomic number curve reflects the variation in effective atomic number at different locations within a rock sample. In other words, it represents the relationship between the location of a rock sample and its effective atomic number. The effective atomic number varies significantly depending on the rock type. For example, limestone has an effective atomic number of 14 to 16, while mudstone has an effective atomic number of 11 to 18.

[0095] In this embodiment, the density curve includes density values ​​corresponding to a plurality of preset positions in the rock sample, and the effective atomic number curve includes effective atomic numbers corresponding to a plurality of preset positions in the rock sample.

[0096] Among them, the preset position can be set according to the actual application. For example, the preset position can be set according to the scanning layer thickness (i.e., the thickness of the scanning layer) when scanning the rock sample. For example, the preset position can be set to a spatial position on the rock sample where the thickness is a multiple of the scanning layer thickness.

[0097] It can be understood that if the rock sample consists of multiple rock samples, in order to identify the type of each rock sample, each rock sample is required to have at least one preset position. The density curve of the rock sample includes the density value corresponding to at least one preset position of each rock sample, and the effective atomic number curve of the rock sample includes the effective atomic number corresponding to at least one preset position of each rock sample.

[0098] Specifically, based on the attenuation values ​​of X-rays of different energy values ​​in the first scanning image and the second scanning image, the photoelectric effect model and the Compton effect model are used to calculate the density information and effective atomic number information of the rock sample, and based on the calculated density information and effective atomic number information of the rock sample, the density curve and the effective atomic number curve are obtained.

[0099] S13, determining the target type of the rock sample according to the characteristic curve.

[0100] The rock types may include but are not limited to organic mudstone, shale, carbonate, sandstone, limestone, dolomite and other porous rocks.

[0101] Specifically, the target type of the rock sample is determined according to the density value on the density curve and / or the effective atomic number on the effective atomic number curve.

[0102] It can be understood that when the density curve is composed of density curves corresponding to multiple rock samples, or when the effective atomic number curve is composed of effective atomic number curves corresponding to multiple rock samples, the target type of each rock sample can be determined separately according to the density curve and / or effective atomic number curve corresponding to each rock sample.

[0103] In the rock type identification method provided by the embodiment of the present disclosure, since the first image and the second image are obtained by scanning the rock sample under X-rays of different energy values, a characteristic curve of the rock sample can be obtained. The characteristic curve includes a density curve and / or an effective atomic number curve. Both the density curve and the effective atomic number curve can be used as quantitative parameters to determine the target type of the rock sample, thereby greatly improving the reliability of rock type identification. Compared with traditional rock type identification methods, while improving accuracy, it can significantly reduce time costs, thereby realizing rapid and accurate type identification of rock samples.

[0104] In one embodiment, Figure 2 As shown, in the above step S12, obtaining the characteristic curve of the rock sample according to the first image and the second image may include:

[0105] S121, analyzing the first image to obtain a first three-dimensional data volume of the rock sample.

[0106] Images of rock samples scanned by X-rays of different energy values ​​may be stored in a DICOM (Digital Imaging and Communications in Medicine) format.

[0107] In order to facilitate the analysis and processing of images of rock samples scanned by X-rays of different energy values, the DICOM format images can be converted into .RAW format data using a conversion tool.

[0108] Specifically, the first image is converted from a DICOM format into a first three-dimensional data volume in a .RAW format.

[0109] S122, analyzing the second image to obtain a second three-dimensional data volume of the rock sample.

[0110] Specifically, the second image is converted from a DICOM format into a second three-dimensional data volume in a .Raw format.

[0111] S123: Acquire a density data volume and an effective atomic number data volume of the rock sample based on the first three-dimensional data volume and the second three-dimensional data volume.

[0112] Here, both the density data volume and the effective atomic number data volume are data volumes in three-dimensional space. The density data volume is the set of density values ​​at each location of the rock sample in three-dimensional space. The effective atomic number data volume is the set of effective atomic numbers at each location of the rock sample in three-dimensional space.

[0113] Specifically,

[0114] The CT number information contained in the first three-dimensional data volume and the second three-dimensional data volume of the standard sample (i.e., the rock standard sample) can be used, and the statistical model coefficients (A, B, C, D, E) can be obtained at the time of standard sample scanning.

[0115] After completing the scanning of the standard sample, the data volume scanning of the rock sample is started. The density data volume and the effective atomic coefficient data volume can be directly calculated through the first three-dimensional data volume and the second three-dimensional data volume of the rock sample.

[0116] A more specific calculation method is as follows:

[0117] Obtain statistical model coefficients, decouple the photoelectric effect and Compton scattering effect through high and low energy X-ray scanning, and use the following relationship between core electron density, effective atomic number and CT number to obtain the density data volume and effective atomic number data volume of the rock sample:

[0118] ρ e =A×CT high +B×CT low +C

[0119] Ze n ×ρ e =D×(CT low -CT high )+E

[0120] Where: ①ρ: sample electron density, unit: g / cm 3 , when dealing with minerals with a small number of hydrogen atoms, it is considered to be approximately equal to the sample density; ②CT high : CT number of high-energy CT scan images, the unit is Hounsfield (abbreviated Hu); ③ CT low : CT number of low-energy CT scan image, unit is Hounsfield; ④Ze: effective atomic number, dimensionless; ⑤n: effective atomic number index, dimensionless, preferably 3.6; ⑥A~E: statistical model coefficients, A, B and D are in cm 3 / g, C and E are dimensionless;

[0121] The calculation formula of CT value is as follows:

[0122]

[0123] Where: ①CT number : CT number at a specific energy, ②μ: attenuation coefficient of the CT scan through the object; ③μ water : attenuation coefficient of water; ④μ air : attenuation coefficient of air;

[0124] Since the CT value is related to the density of the sample, the X-ray energy, and the algorithm, it is usually necessary to scan water or air to perform CT value correction before obtaining the CT value of the sample.

[0125] It is understandable that in addition to the above-mentioned methods, other methods can be used to obtain the density data body and effective atomic number data body of the rock sample. For example, professional digital core analysis software (such as PerGeos software) can be used to calculate the first three-dimensional data body and the second three-dimensional data body to obtain the density data body and effective atomic number data body of the rock sample. The embodiments of the present disclosure do not limit the specific acquisition process.

[0126] S124: Obtain a characteristic curve of the rock sample based on the density data volume and the effective atomic number data volume of the rock sample.

[0127] Among them, the characteristic curve of the rock sample may include a density curve, which is generated based on the density data body of the rock sample; the characteristic curve of the rock sample may also include an effective atomic number curve, which is generated based on the effective atomic number data body of the rock sample; the characteristic curve of the rock sample may also include a density curve and an effective atomic number curve at the same time.

[0128] In one example, the implementation process of step S124 may include:

[0129] generating a density curve for the rock sample based on the density data volume; and / or

[0130] An effective atomic number curve of a rock sample is generated based on the effective atomic number data body.

[0131] As mentioned above, the density curve of the rock sample can be generated based on the density data volume in the following way:

[0132] The density value corresponding to each preset position in the rock sample is determined according to the density data body, and the density curve of the rock sample is generated according to the density value corresponding to each preset position.

[0133] Specifically, for each of the plurality of preset positions, the density values ​​within the thickness range corresponding to the preset position in the density data volume are averaged (or median), and the averaged (or median) density value is used as the density value corresponding to the preset position. Then, based on the density value corresponding to each preset position, a density curve of the rock sample is generated. Here, the thickness range corresponding to each preset position can be equal to N times the scanning layer thickness, where N is a positive integer greater than or equal to 1.

[0134] As described above, the generation of the effective atomic number curve of the rock sample based on the effective atomic number data body can be achieved in the following way:

[0135] According to the effective atomic number data body, the effective atomic number corresponding to each preset position in the rock sample is determined, and according to the effective atomic number corresponding to each preset position, an effective atomic number curve of the rock sample is generated.

[0136] Determining effective atomic numbers corresponding to a plurality of preset positions in a rock sample based on the effective atomic number data volume may include:

[0137] Specifically, for each of the multiple preset positions, the effective atomic numbers within the thickness range corresponding to the preset position in the density data body are averaged (or the median is calculated), and the effective atomic number after averaging (or calculating the median) is used as the effective atomic number corresponding to the preset position. Then, based on the effective atomic number corresponding to each preset position, an effective atomic number curve of the rock sample is generated.

[0138] In this embodiment, by obtaining the density curve and / or effective atomic number curve of the rock sample based on the first image and the second image, the type of the rock sample can be determined using the density curve and / or effective atomic number curve of the rock sample.

[0139] In one embodiment, before determining the target type of the rock sample according to the characteristic curve in step S13, the method may further include:

[0140] Perform a correction on the density curve and / or the effective atomic number curve.

[0141] In one example, the density curve of the rock sample to be identified can be corrected using the density curve of the rock standard sample, for example, by performing a translation process on the density curve of the rock sample to be identified based on the density curve of the rock standard sample. The effective atomic number curve of the rock standard sample can also be corrected using the effective atomic number curve of the rock sample to be identified, for example, by performing a translation process on the effective atomic number curve of the rock sample to be identified based on the effective atomic number curve of the rock standard sample. The type of the rock sample is then identified based on the corrected density curve and / or effective atomic number curve. The rock standard sample here refers to a rock prepared in advance for curve correction of the density curve and effective atomic number curve of the rock sample.

[0142] In the embodiment of the present disclosure, by correcting the density curve and / or the effective atomic number curve, the systematic error generated by the scanning system can be reduced to a certain extent, so that the density curve and / or the effective atomic number curve obtained after calibration is more accurate, thereby making the rock type identification result more accurate.

[0143] In one embodiment, in the above step S13, determining the target type of the rock sample according to the characteristic curve can be achieved by one of the following methods:

[0144] Method 1: If the characteristic curve includes a density curve, a first range interval of the value range of the density curve is determined, and the rock type corresponding to the first range interval is determined as the target type of the rock sample.

[0145] Here, the first range refers to the density range. Different density ranges correspond to different rock types. For example, the density range of limestone is 2.6-2.9 g / cm 3 The density of mudstone ranges from 2.0 to 2.5 g / cm 3 .

[0146] In this embodiment, when the density curve includes density curves for multiple rock samples, in order to identify the target type of each rock sample, the value range of the density curve of each rock sample in the density curve needs to be compared with different density range intervals to determine the first range interval within which the density value range of each rock sample lies. The value range of the density curve of each rock sample is the value range of the density value corresponding to at least one predetermined position of each rock sample.

[0147] For example, assuming that the density curves include the density curves of rock samples 1 to 5, if the density curve of rock sample 1 has a value range of 2.7 to 2.9 g / cm 3 , then according to the first range interval of 2.6~2.9g / cm 3, it can be determined that the type of rock sample 1 is limestone.

[0148] Method 2: If the characteristic curve includes an effective atomic number curve, a second range interval of the value range of the effective atomic number curve is determined, and the rock type corresponding to the second range interval is determined as the target type of the rock sample.

[0149] Here, the second range refers to the effective atomic number range. Different effective atomic number ranges correspond to different rock types. For example, the effective atomic number of limestone is 14-16, and the effective atomic number of mudstone is 11-18.

[0150] In this embodiment, when the effective atomic number includes effective atomic number curves for multiple rock samples, in order to identify the target type of each rock sample, it is necessary to compare the value range of the effective atomic number curve of each rock sample in the effective atomic number curve with different effective atomic number range intervals to determine the second range interval within which the effective atomic number value range of each rock sample lies. The value range of the effective atomic number curve of each rock sample is the value range of the effective atomic number value corresponding to at least one preset position of each rock sample.

[0151] Method 3: If the characteristic curve includes a density curve and an effective atomic number curve, the rock type corresponding to both the first range interval and the second range interval is determined as the target type of the rock sample.

[0152] In this embodiment, the target type of the rock sample is determined by combining the density curve and the effective atomic number curve, which can further improve the accuracy and reliability of rock type identification.

[0153] In one embodiment, Figure 3 As shown, based on Figure 1 If the characteristic curve includes: a density curve and an effective atomic number curve, the method may further include:

[0154] S14, in the same rectangular coordinate system, aligning the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located.

[0155] Specifically, the density curve and the effective atomic number curve are plotted in the same rectangular coordinate system, and the coordinate region where the density curve is located is aligned with the coordinate region where the effective atomic number curve is located.

[0156] In some examples, each effective atomic number on the effective atomic number curve may be mapped to a coordinate region where the density curve is located, so as to align the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located.

[0157] For example, each effective atomic number on the effective atomic number curve can be mapped to the coordinate area where the density curve is located according to a preset mapping relationship. The preset mapping relationship can be understood as a multiple relationship between the maximum effective atomic number on the effective atomic number curve and the maximum density value of the density curve. For example, the maximum effective atomic number on the effective atomic number curve is 18, and the maximum density on the density curve is 2.5g / cm 3 , each effective atomic number on the effective atomic number curve can be mapped to the coordinate area where the density curve is located according to a mapping relationship of 7.2 times.

[0158] S15, determining the subtype of the rock sample from among multiple subtypes included in the target type based on the distance range between the aligned density curve and the effective atomic number curve; wherein different subtypes correspond to different distance ranges.

[0159] Rock types can be further divided into multiple subtypes. For example, mudstone can be further divided into siliceous mudstone, calcareous mudstone, and calcareous mudstone.

[0160] Specifically, the curve distance between the aligned density curve and the effective atomic number curve at each predetermined position of the rock sample is determined. This curve distance at each predetermined position is compared with the distance ranges corresponding to each of the multiple subtypes of the target type. Based on the comparison results, the subtype of the rock sample to which each predetermined position belongs is determined.

[0161] In this embodiment, by determining the subtype of the rock sample among the multiple subtypes contained in the target type based on the distance range between the aligned density curve and the effective atomic number curve, a finer level of rock type division can be achieved, making the rock type division more accurate.

[0162] In one embodiment, the method may further include:

[0163] Obtain well logs of rock samples.

[0164] Well logs are curves generated during well logging that reflect the characteristics of different lithologies (i.e., rock types) and different horizons. These include, but are not limited to, acoustic transit time logs (AC), density logs (DEN), compensated neutron logs (CNL), natural gamma ray logs (GR), spomtaneous potential logs (SP), caliper logs (CAL), microspherically focused logs (MFSL), and deep investigate double lateral resistivity logs (RLLD).

[0165] In the above step S13, determining the target type of the rock sample according to the characteristic curve may include:

[0166] Determine the target type of rock sample based on its logging curve and characteristic curve.

[0167] Specifically, the target type of the rock sample is determined based on one or more well logging curves and a characteristic curve of the rock sample. Here, the well logging curve can be selected according to actual needs and is not specifically limited here.

[0168] In this embodiment, by combining the well logging curve and characteristic curve of the rock sample, the type of the rock sample can be further accurately identified, thereby improving the accuracy of rock type identification.

[0169] The technical solutions provided by the embodiments of the present disclosure are described below in conjunction with specific embodiments.

[0170] Given that existing technologies are unable to quickly and accurately identify the rock types of full-diameter core samples, the embodiments of the present disclosure provide a rock type identification method that uses two-energy X-ray CT to scan full-diameter cores to obtain three-dimensional images, density curves, and effective atomic number curves of the cores. While ensuring timeliness, quantitative parameters are used to classify rock types. The generated core curves can be calibrated and calibrated against well logging curves widely used in the oil and gas industry, facilitating the rapid and accurate evaluation of oil and gas reservoirs.

[0171] like Figure 4 As shown in Figure 2, the main steps of the rock type identification method include:

[0172] Step 1: Rock sample scanning

[0173] Arrange the full-diameter core samples to be scanned from shallow to deep, remove dust from the rock surface as appropriate, and place them on a core groove made of polyvinyl chloride.

[0174] The rock sample is first scanned using a voltage of 100 kV, and then scanned using a scanning voltage of 140 kV. The current can remain unchanged, both at 7 mA.

[0175] The reconstructed images at different energies are directly exported from the console computer in DICOM format, and the format is converted into .Raw format to obtain the full-diameter core data volume at different energies.

[0176] Step 2: 3D image processing

[0177] The full-diameter core data volume at different energies is imported into the image processing software to calculate the density data volume and the effective atomic number data volume;

[0178] The density data volume and the effective atomic number data volume are averaged respectively to generate a density curve and an effective atomic number curve.

[0179] Step 3: Rock type identification

[0180] The density curve and the effective atomic number curve are calibrated using the density curve and the effective atomic number curve of the standard sample to obtain the calibrated density curve and the effective atomic number curve.

[0181] Rock type determination is performed based on the existing full-diameter core data volumes with two energies and combined with density and effective atomic number curves.

[0182] Next, combine Figures 5a to 5g The technical solutions provided in the embodiments of the present disclosure are illustrated by way of example.

[0183] This disclosed embodiment performs dual-energy CT scanning on a full-diameter core sample, using specialized image processing software for image processing and calculations. The scanning device is Neurological's CereTom NL3000, and the image processing software is ThermoFisher Scientific's digital core analysis software Pergeos 2021.1 and Shuyan Technology's proprietary full-diameter core CT scanning image and parameter query software, Whole Core Viewer.

[0184] Reference Figure 5aThe CT scanning device shown here primarily consists of an X-ray source, a collimator, and a detector. The X-ray source and detector rotate around a common axis, with the object being inspected positioned between them. The basic principle of CT rock scanning is as follows: the X-ray tube within the CT device emits X-rays, which are collimated by the collimator and then pass through the object being inspected to the detector. The amount of transmitted X-rays is measured, digitized, and then calculated to determine the absorption coefficient for each unit volume of the irradiated tissue layer. These absorption coefficients form a digital matrix. The high-speed computer within the device performs digital-to-analog conversion, which can be displayed on a screen or photographed. The reconstructed image also provides the X-ray attenuation coefficient for each pixel, typically expressed as a CT value.

[0185] The rock type identification method provided in the embodiment of the present disclosure mainly includes the following steps:

[0186] 1. Image scanning

[0187] ① Perform air calibration on the instrument and enter the scanning cabin only after calibration is completed.

[0188] ② Sample placement: Place the core holder into the CT rack hole, then place the sorted cores on the holder in order from top to bottom. The user controls the CT scan in the control room outside the scanning cabin.

[0189] ③ Set parameters and scan: Set the scanning parameters for high and low energy voltages and scan the sample. The high energy voltage is 140kV and the current is 7mA. The low energy voltage is adjusted to 100kV, and the current remains unchanged.

[0190] 2. Data processing

[0191] ①Data export: Export reconstructed images directly from the console computer in DICOM format.

[0192] ② Quality control: Check the reconstructed image to ensure that the image is clear and has no faults, and there is no obvious difference in the grayscale value of the slice image displayed in different directions.

[0193] ③ Import the full-diameter core sample scanned at high energy and low energy into Whole coreViewer and convert it into a 3D data volume in .RAW format. Figure 5b and Figure 5c shown.

[0194] ④ Import the two energy .RAW into Pergeos software, use the dual energy CT module to calculate the density data volume and effective atomic number data volume of the sample, and use the density data volume and effective atomic number data volume of the sample to generate two density curves and effective atomic number curves after averaging the data volume values. Among them, the cross-section of the density data volume and the cross-section of the effective atomic number data volume of the full diameter core sample can be referred to respectively. Figure 5d and Figure 5e The density curve and effective atomic number curve of the sample can be referred to Figure 5f As shown, Figure 5f The curve pointed by arrow a is the density curve, and the curve pointed by arrow b is the effective atomic number curve.

[0195] 3. Rock type determination

[0196] ① Rock classification: First, based on density and effective atomic number values, the major rock types can be divided. For example, the density of limestone is 2.6 to 2.9 g / cm 3 , the effective atomic number is 14 to 16, and the density of mudstone is 2.0 to 2.5 g / cm 3 , the effective atomic number is 11 to 18. Figure 5f As shown in the figure, it can be determined that the types of multiple rock samples are all mudstone. It is understandable that the density of different lithologies in different regions may vary, and a reasonable classification should be made based on actual conditions.

[0197] ② Rock type subdivision: In a certain category of lithology, if the density and effective atomic number data and curve values ​​show obvious differences, the lithology can be further subdivided. Figure 5g As shown, this mudstone segment clearly exhibits characteristics such as partial overlap between the density curve and the effective atomic number curve, significant separation in some areas, and slight separation in others. Based on the order of the rock samples, both the density curve and the effective atomic number exhibit a three-segment pattern: high, low, and medium values ​​on the density curve, while low, medium, and high values ​​on the effective atomic number curve. Combined with the curve data, the mudstone samples can be further classified into siliceous mudstone, calcareous mudstone, and calcareous mudstone. This type identification result matches well with the core observation results, further verifying the rationality of the type identification.

[0198] In summary, the embodiments of the present disclosure achieve the classification of rock types of full-diameter core samples by quantitative means, greatly improving the reliability of rock type judgment. By obtaining the full-diameter core density and effective atomic number curves, it can be combined with the logging curves used for reservoir evaluation in the oil and gas industry to further improve the accuracy of rock type identification. Compared with traditional geological observation methods, rock type identification based on the method provided by the embodiments of the present disclosure can improve accuracy while significantly reducing time costs.

[0199] Figure 6 A schematic diagram of a rock type identification device provided in an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the apparatus may include:

[0200] A first acquisition module 601 is configured to acquire a first image and a second image of a rock sample obtained by scanning the rock sample with X-rays of different energy values;

[0201] The second acquisition module 602 is configured to acquire a characteristic curve of the rock sample based on the first image and the second image; wherein the characteristic curve includes a density curve and / or an effective atomic number curve;

[0202] The determination module 603 is used to determine the target type of the rock sample according to the characteristic curve.

[0203] In one embodiment, the second acquisition module 602 includes:

[0204] An analysis submodule, configured to analyze the first image to obtain a first three-dimensional data volume of the rock sample, and to analyze the second image to obtain a second three-dimensional data volume of the rock sample;

[0205] A first acquisition submodule is configured to acquire a density data volume and an effective atomic number data volume of the rock sample based on the first three-dimensional data volume and the second three-dimensional data volume;

[0206] The second acquisition submodule is used to acquire the characteristic curve of the rock sample based on the density data body and the effective atomic number data body of the rock sample.

[0207] In one embodiment, the second acquisition submodule is specifically configured to:

[0208] generating a density curve for the rock sample based on the density data volume; and / or

[0209] An effective atomic number curve of a rock sample is generated based on the effective atomic number data body.

[0210] In one embodiment, the apparatus further comprises:

[0211] The correction module is used to correct the density curve and / or the effective atomic number curve.

[0212] In one embodiment, the determination module 603 is specifically configured to:

[0213] If the characteristic curve includes a density curve, determining a first range interval in which a value range of the density curve lies;

[0214] determining the rock type corresponding to the first range interval as the target type of the rock sample;

[0215] or,

[0216] If the characteristic curve includes an effective atomic number curve, determining a second range interval in which the value range of the effective atomic number curve lies;

[0217] determining the rock type corresponding to the second range interval as the target type of the rock sample;

[0218] or,

[0219] If the characteristic curve includes a density curve and an effective atomic number curve, the rock type corresponding to both the first range interval and the second range interval is determined as the target type of the rock sample.

[0220] In one embodiment, if the characteristic curve includes: a density curve and an effective atomic number curve, the determination module 603 is further configured to:

[0221] In the same rectangular coordinate system, align the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located;

[0222] According to the distance range between the aligned density curve and the effective atomic number curve, the subtype of the rock sample is determined among the multiple subtypes contained in the target type; different subtypes correspond to different distance ranges.

[0223] In one embodiment, the apparatus further comprises a third acquisition module;

[0224] The third acquisition module is used to obtain the logging curve of the rock sample;

[0225] The determination module is also used to determine the target type of the rock sample based on the logging curve and characteristic curve of the rock sample.

[0226] It should be noted that the rock type identification device provided in the above embodiment, when executing the rock type identification method, is merely illustrated by the division of the aforementioned program modules. In actual applications, the aforementioned processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the aforementioned processing. Furthermore, the rock type identification device provided in the above embodiment and the rock type identification method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0227] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure; Figure 7 As shown, the computer device 700 includes: a memory 701 and a processor 702. The memory 701 stores a computer program, and the processor 702 is configured to run the computer program to perform the following operations:

[0228] Acquire a first image and a second image obtained by scanning a rock sample using X-rays of different energy values;

[0229] Acquire a characteristic curve of the rock sample according to the first image and the second image; wherein the characteristic curve includes: a density curve and / or an effective atomic number curve;

[0230] According to the characteristic curve, the target type of rock sample is determined.

[0231] When the processor runs the computer program, the corresponding processes in the various methods of the embodiments of the present disclosure are implemented. For the sake of brevity, they are not described here in detail.

[0232] In actual application, the computer device 700 may further include: at least one network interface 703. The various components in the computer device 700 are coupled together via a bus system 704. It is understood that the bus system 704 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 7 In the figure, various buses are labeled as bus system 704. There can be at least one processor 701. The network interface 703 is used for wired or wireless communication between the computer device 700 and other devices.

[0233] The memory 702 in the embodiment of the present disclosure is used to store various types of data to support the operation of the computer device 700 .

[0234] The methods disclosed in the above embodiments of the present disclosure can be applied to or implemented by the processor 701. The processor 701 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 701 or by software instructions. The above processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 701 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present disclosure can be directly implemented as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium located in the memory 702. The processor 701 reads the information in the memory 702 and completes the steps of the above method in conjunction with its hardware.

[0235] In an exemplary embodiment, the computer device 700 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned methods.

[0236] The present disclosure also provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to perform the following operations when executed:

[0237] Acquire a first image and a second image obtained by scanning a rock sample using X-rays of different energy values;

[0238] Acquire a characteristic curve of the rock sample according to the first image and the second image; wherein the characteristic curve includes: a density curve and / or an effective atomic number curve;

[0239] According to the characteristic curve, the target type of rock sample is determined.

[0240] When the computer program is executed by the processor, the corresponding processes in the various methods of the embodiments of the present disclosure are implemented, and for the sake of brevity, they are not described here in detail.

[0241] In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0242] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0243] In addition, all functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0244] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0245] Alternatively, if the above-mentioned integrated unit of the present disclosure is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0246] It should be noted that: "first", "second", "third", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0247] In addition, the technical solutions described in the embodiments of the present disclosure can be arbitrarily combined without conflict.

[0248] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A rock type identification method, characterized in that: The method comprises: Acquire a first image and a second image obtained by scanning a rock sample using X-rays of different energy values; Acquire a characteristic curve of the rock sample according to the first image and the second image; wherein the characteristic curve includes: a density curve and an effective atomic number curve; determining the target type of the rock sample according to the characteristic curve; In the same rectangular coordinate system, aligning the coordinate region where the density curve is located with the coordinate region where the effective atomic number curve is located; determining a curve distance between the aligned density curve and the effective atomic number curve at each preset position of the rock sample, and comparing the curve distance at each preset position with respective distance ranges corresponding to a plurality of subtypes included in the target type; wherein different subtypes have different distance ranges; According to the comparison result at each preset position, the subtype of the rock sample to which each preset position belongs is determined.

2. The method according to claim 1, characterized in that The step of obtaining a characteristic curve of the rock sample according to the first image and the second image includes: Analyzing the first image to obtain a first three-dimensional data volume of the rock sample; Analyzing the second image to obtain a second three-dimensional data volume of the rock sample; Acquire a density data volume and an effective atomic number data volume of the rock sample based on the first three-dimensional data volume and the second three-dimensional data volume; A characteristic curve of the rock sample is obtained based on the density data volume and the effective atomic number data volume of the rock sample.

3. The method according to claim 2, characterized in that The step of obtaining a characteristic curve of the rock sample based on the density data volume and the effective atomic number data volume of the rock sample includes: A density curve of the rock sample is generated based on the density data volume; and an effective atomic number curve of the rock sample is generated based on the effective atomic number data volume.

4. The method according to claim 1, wherein Before the step of determining the target type of the rock sample according to the characteristic curve, the method further includes: The density curve and / or the effective atomic number curve are calibrated.

5. The method according to claim 1, wherein Determining the target type of the rock sample according to the characteristic curve includes: Determining a first range interval of the value range of the density curve, and determining a second range interval of the value range of the effective atomic number curve; The rock type corresponding to both the first range interval and the second range interval is determined as the target type of the rock sample.

6. The method according to claim 1, wherein The method further comprises: obtaining a well logging curve of the rock sample; Determining the target type of the rock sample according to the characteristic curve includes: The target type of the rock sample is determined according to the well logging curve of the rock sample and the characteristic curve.

7. A rock type identification device, characterized in that: The device comprises: A first acquisition module is used to acquire a first image and a second image obtained by scanning a rock sample with X-rays of different energy values; a second acquisition module, configured to acquire a characteristic curve of the rock sample based on the first image and the second image; wherein the characteristic curve includes a density curve and an effective atomic number curve; A determination module is used to determine the target type of the rock sample based on the characteristic curve; and to align the coordinate area where the density curve is located with the coordinate area where the effective atomic number curve is located in the same rectangular coordinate system; determine the curve distance between the aligned density curve and the effective atomic number curve at each preset position of the rock sample, and compare the curve distance at each preset position with the distance ranges corresponding to each of the multiple subtypes included in the target type; wherein different subtypes correspond to different distance ranges; and determine the subtype of the rock sample to which each preset position belongs based on the comparison result at each preset position.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the rock type identification method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the rock type identification method according to any one of claims 1 to 6 are implemented.

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