Method and device for determining strata parameters of salinized lacustrine facies mixed rock

Abstract

The invention discloses a strata parameter determination method and device for salinized lacustrine facies mixed rock. The method comprises the following steps: carrying out gray processing on a rock slice image of the salinized lacustrine facies mixed rock to obtain a gray slice image; extracting a gray value in the gray sheet image to obtain a target gray curve spectrum; and determining strata parameters of the salinized lacustrine facies mixed rock according to the target gray curve spectrum. According to the technical scheme of the application, the rock slice image of the salinized lacustrine facies mixed rock is subjected to gray processing to obtain the gray slice image, the gray value in the gray slice image is extracted to obtain the target gray curve spectrum, and the strata parameters of the salinized lacustrine facies mixed rock are determined according to the target gray curve spectrum, so that the strata parameters of the salinized lacustrine facies mixed rock are determined. The determination result of the strata parameters of the salinized lacustrine facies mixed rock is more accurate, and the determination process is clearer. Through the use of the steps, when the texture parameters are determined, the accurate texture parameters can be obtained in a simple mode.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum geological exploration, and in particular to a method and a device for determining laminar parameters of saline lake phase mixed sedimentary rocks. Background Art

[0002] In recent years, with the advancement of oil and gas exploration technology, lacustrine mixed sedimentary rocks, as an important field of unconventional oil and gas exploration, have been continuously discovered.

[0003] Sweet spot evaluation and optimization is a key geological problem faced by lacustrine mixed rock exploration. Studies have shown that the more developed the laminae in the sweet spot section of lacustrine mixed rock, the higher its organic carbon content and the better its oil content. However, the determination of the sweet spot evaluation and optimization depends on the determination of the laminae parameters. Therefore, the accurate identification of the laminae parameters of saline lacustrine mixed rock has become an important factor affecting the evaluation results. Therefore, a method that can accurately identify the laminae parameters of saline lacustrine mixed rock is urgently needed. Summary of the invention

[0004] The present invention provides a method and device for determining laminae parameters of saline lacustrine facies mixed sedimentary rocks, so as to solve the problem that the laminae parameter identification process of saline lacustrine facies mixed sedimentary rocks is complicated and difficult to simplify on the basis of maintaining a certain accuracy.

[0005] According to one aspect of the present invention, a method for determining laminae parameters of saline lacustrine mixed sedimentary rocks is provided, the method comprising:

[0006] The rock thin section image of the saline lake phase mixed sedimentary rock is gray-scale processed to obtain a gray-scale thin section image;

[0007] Extract the grayscale value in the grayscale slice image to obtain the target grayscale curve spectrum;

[0008] According to the target grayscale curve spectrum, the laminar parameters of saline lake facies mixed sedimentary rocks are determined.

[0009] According to another aspect of the present invention, a device for determining laminae parameters of saline lacustrine mixed sedimentary rocks is provided, the device comprising:

[0010] A grayscale image acquisition module is used to grayscale the rock thin section image of the saline lake phase mixed rock to obtain a grayscale thin section image;

[0011] A target spectrum determination module is used to extract the grayscale value in the grayscale slice image to obtain a target grayscale curve spectrum;

[0012] The laminae parameter determination module is used to determine the laminae parameters of saline lake phase mixed sedimentary rocks according to the target grayscale curve spectrum.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for determining the laminae parameters of saline lake phase mixed sedimentary rocks according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to implement the method for determining laminae parameters of saline lake facies mixed sedimentary rocks according to any embodiment of the present invention when executed by a processor.

[0018] The technical solution of the embodiment of the present invention performs grayscale processing on the rock thin section image of the saline lake phase mixed rock to obtain a grayscale thin section image, extracts the grayscale value in the grayscale thin section image to obtain a target grayscale curve spectrum, and determines the laminae parameters of the saline lake phase mixed rock according to the target grayscale curve spectrum, so that the determination result of the laminae parameters of the saline lake phase mixed rock is more accurate and the determination process is clearer. By using the above steps, when determining the laminae parameters, more accurate laminae parameters can be obtained in a simpler way.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a method for determining laminar parameters of saline lacustrine mixed sedimentary rocks provided in Example 1 of the present invention;

[0022] Figure 2 A flowchart of another method for determining laminae parameters of saline lacustrine mixed sedimentary rocks is provided for an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a regional grayscale slice image applicable to an embodiment of the present invention;

[0024] Figure 4A schematic diagram of a regional normalized curve spectrum C1 of a first region applicable to an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of a regional normalized curve spectrum C2 of a second region applicable to the embodiment of the present invention;

[0026] Figure 6 A schematic diagram of a regional normalized curve spectrum C3 of a third region applicable to the embodiment of the present invention;

[0027] Figure 7 A schematic diagram of a regional normalized curve spectrum C4 of a fourth region applicable to the embodiment of the present invention;

[0028] Figure 8 A schematic diagram of a regional normalized curve spectrum C5 of a fifth region applicable to the embodiment of the present invention;

[0029] Fig. 9 A schematic diagram of a target grayscale curve spectrum applicable to an embodiment of the present invention;

[0030] Fig.10 A schematic diagram of the number of peaks and troughs and the half-amplitude width applicable to an embodiment of the present invention;

[0031] Fig.11 A schematic diagram of the laminar density applicable to the embodiment of the present invention;

[0032] Fig.12 A schematic diagram of the thickness of the laminae applicable to the embodiment of the present invention;

[0033] Fig.13 2 is a schematic diagram of the structure of a device for determining laminar parameters of saline lacustrine mixed sedimentary rocks according to Embodiment 3 of the present invention;

[0034] Fig.14 It is a structural schematic diagram of an electronic device for implementing the method for determining laminae parameters of saline lacustrine mixed sedimentary rocks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment 1

[0038] Figure 1 A flowchart of a method for determining laminae parameters of saline lacustrine mixed sedimentary rocks is provided for the first embodiment of the present invention. This embodiment can be applied to the identification of laminae parameters of saline lacustrine mixed sedimentary rocks more conveniently, and the identification result can still maintain a certain accuracy. The method can be executed by a laminae parameter determination device for saline lacustrine mixed sedimentary rocks. The laminae parameter determination device for saline lacustrine mixed sedimentary rocks can be implemented in the form of hardware and / or software. The laminae parameter determination device for saline lacustrine mixed sedimentary rocks can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0039] S110, grayscale processing is performed on the rock thin section image of the saline lake phase mixed sedimentary rock to obtain a grayscale thin section image.

[0040] The rock thin section image can be an image obtained by photographing a rock thin section of a saline lake phase mixed rock, and has a clear scale and laminar bands. The grayscale thin section image can be an image obtained by grayscale processing the rock thin section image using an algorithm.

[0041] The rock thin-section images of saline lake-phase mixed rocks with scale and laminae are obtained by optical digital microscope equipment. Gray-scale thin-section images are obtained by calculation using floating-point method or Gamma correction algorithm.

[0042] Among them, since the rock slice image is composed of a matrix of three primary color pixels: red (R), green (G), and blue (B), it can be processed using the floating point method. The processing method is:

[0043] Gray value = R*0.3+G*0.59+B*0.11

[0044] The gamma correction algorithm can also be used to process rock thin section images. The processing method is:

[0045]

[0046] Among them, since the floating-point method has a poor effect in processing 23-bit true color images, the Gamma correction algorithm can be selected.

[0047] S120, extracting grayscale values ​​from the grayscale slice image to obtain a target grayscale curve spectrum.

[0048] The target grayscale curve spectrum may be an image used to represent grayscale values ​​at different positions in the grayscale sheet image and the corresponding sheet heights.

[0049] After obtaining the grayscale thin-section image, the grayscale value at each position in the grayscale thin-section image can be extracted by using ImageJ software, etc., so as to generate a target grayscale curve spectrum according to the grayscale value at each position.

[0050] In an optional solution, extracting the grayscale value in the grayscale slice image to obtain the target grayscale curve spectrum may include steps A1-A2:

[0051] Step A1: divide the grayscale slice image into regions to obtain at least two regional grayscale slice images.

[0052] Step A2: extract grayscale values ​​from at least two regional grayscale slice images to obtain a target grayscale curve spectrum.

[0053] When extracting grayscale values ​​through software or other methods, if there are too many pixels in the grayscale slice image, it will inevitably lead to a long calculation time and easily affect the accuracy of the final calculation result.

[0054] Therefore, it is necessary to divide the grayscale slice image into regions, so as to divide at least two regional grayscale slice images.

[0055] And by using software processing and other methods, the grayscale slice images of different areas are processed separately to obtain the target grayscale curve spectrum, so that when the target grayscale curve spectrum is determined, the time consumed by the operation can be reduced and the accuracy of the operation result can be improved.

[0056] In an optional solution, grayscale values ​​of at least two regional grayscale slice images are extracted to obtain a target grayscale curve spectrum, which may include steps B1-B2:

[0057] Step B1, extracting grayscale values ​​from at least two regional grayscale slice images to obtain at least two regional grayscale value curve spectra.

[0058] Step B2: determining a target grayscale curve spectrum according to at least two regional grayscale value curve spectra.

[0059] The regional gray value curve spectrum may be an image of gray values ​​at different positions in each regional gray slice image and the corresponding slice height.

[0060] After obtaining at least two regional grayscale slice images, grayscale values ​​of the at least two regional grayscale slice images are extracted by software processing or other methods, thereby obtaining at least two regional grayscale value curve spectra.

[0061] After obtaining at least two regional grayscale value curve spectra, the at least two regional grayscale value curve spectra may be fitted to obtain a target grayscale curve spectrum.

[0062] In an optional solution, determining a target grayscale curve spectrum according to at least two regional grayscale value curve spectra may include steps C1-C2:

[0063] Step C1, normalizing at least two regional grayscale value curve spectra to obtain at least two regional normalized curve spectra.

[0064] Step C2: determining a target grayscale curve spectrum according to at least two region normalized curve spectra.

[0065] The regional normalized curve spectrum may be determined after normalizing the regional gray value curve spectrum, and may be a curve spectrum that enables the program to calculate quickly.

[0066] After obtaining at least two regional grayscale value curve spectra, in order to enable the grayscale value curve spectra to run quickly in the program, it is necessary to normalize the at least two regional grayscale value curve spectra to obtain at least two regional normalized curve spectra.

[0067] And at least two regional normalized curve spectra are fitted to obtain a target grayscale curve spectrum.

[0068] In an optional solution, determining a target grayscale curve spectrum according to at least two regional normalized curve spectra may include steps D1-D2:

[0069] Step D1, calculating the average of at least two region normalized curve spectra to obtain a mean grayscale spectrum.

[0070] Step D2: filter the mean grayscale spectrum to obtain a target grayscale curve spectrum.

[0071] The mean grayscale spectrum may be a mean grayscale spectrum determined by calculating the mean of at least two regional normalized curve spectra.

[0072] After obtaining at least two regional normalized curve spectra, probability analysis is performed on the at least two regional normalized curve spectra, and then the mean is calculated to obtain the mean grayscale spectrum.

[0073] S130. Determine laminar parameters of saline lake-phase mixed sedimentary rocks according to the target grayscale curve spectrum.

[0074] The laminae parameters may be data used to describe the laminae of saline lake-facies mixed sedimentary rocks.

[0075] After obtaining the target grayscale curve spectrum, the development of the laminae of the saline lake phase mixed sedimentary rock can be judged according to the curve recorded in the target grayscale curve spectrum, and the laminae parameters of the saline lake phase mixed sedimentary rock can be determined.

[0076] By determining the laminae parameters of saline lacustrine facies mixed sedimentary rocks according to the target grayscale curve spectrum, the determination results of the laminae parameters of saline lacustrine facies mixed sedimentary rocks are more accurate and the determination process is clearer.

[0077] By adopting the technical solution of the present application, the rock thin section image of the saline lake phase mixed sedimentary rock is gray-processed to obtain a gray thin section image, the gray value in the gray thin section image is extracted to obtain a target gray curve spectrum, and the laminae parameters of the saline lake phase mixed sedimentary rock are determined according to the target gray curve spectrum, so that the determination result of the laminae parameters of the saline lake phase mixed sedimentary rock is more accurate and the determination process is clearer. By using the above steps, when determining the laminae parameters, more accurate laminae parameters can be obtained in a simpler way.

[0078] Embodiment 2

[0079] Figure 2 The present invention provides another flow chart of a method for determining laminae parameters of saline lake phase mixed sedimentary rocks. Based on the above embodiment, this embodiment further optimizes the process of determining laminae parameters of saline lake phase mixed sedimentary rocks according to the target grayscale curve spectrum in the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method for determining laminae parameters of saline lacustrine diamictite in this embodiment may include the following steps:

[0080] S210, grayscale processing is performed on the rock thin section image of the saline lake phase mixed sedimentary rock to obtain a grayscale thin section image.

[0081] S220, extracting grayscale values ​​from the grayscale slice image to obtain a target grayscale curve spectrum.

[0082] S230 , determining the number of peaks, the number of troughs, and the half-amplitude width according to the target grayscale curve spectrum.

[0083] S240, determining the density of laminae according to the number of wave crests, the number of wave troughs, and the half-amplitude width.

[0084] S250, determining the thickness of the laminae according to the number of wave crests, the number of wave troughs, and the half-amplitude width.

[0085] After obtaining the target grayscale curve spectrum, the number of peaks, the number of troughs and the half-amplitude width of the curve in the target grayscale curve spectrum can be determined from the curve recorded on the target grayscale curve spectrum.

[0086] The number of wave crests and the number of wave troughs are summed to determine the number of laminae in the rock thin section image, and then the laminae density is determined.

[0087] The thickness of each lamina can be determined by the half-amplitude width and the number of peaks and troughs, thereby determining the thickness of the temperature layer.

[0088] Exemplarily, rock thin section images were obtained using a Leica DVM6 instrument. Figure 3 Schematic diagram of a regional grayscale slice image applicable to an embodiment of the present invention. Figure 3 , the grayscale of the thin section image is processed to obtain a rock thin section image with a scale (width 1.43mm, height 1.09mm), the grayscale value is obtained by using the Gamma correction algorithm, and the thin section image is divided into 5 areas according to the equal length spacing in the vertical laminae direction.

[0089] Figure 4 It is a schematic diagram of a regional normalized curve spectrum C1 of a first region applicable to an embodiment of the present invention. Figure 5 It is a schematic diagram of a regional normalized curve spectrum C2 of a second region applicable to the embodiment of the present invention. Figure 6 It is a schematic diagram of a regional normalized curve spectrum C3 of a third region applicable to the embodiment of the present invention. Figure 7 It is a schematic diagram of a regional normalized curve spectrum C4 of a fourth region applicable to the embodiment of the present invention. Figure 8 FIG. 5 is a schematic diagram of a regional normalized curve spectrum C5 of the fifth region applicable to the embodiment of the present invention. Figure 4 , 5 , 6, 7 and 8, the gray value of each area of ​​the gray slice image was extracted using ImageJ software to generate the gray value curve spectrum C1-C5, using the formula y nor =(yy MinValue ) / (y MaxValue -y MinValue ) Normalize the curve spectrum C1-C5 to obtain the normalized curve spectrum C1 ， -C5 ， .

[0090] Fig. 9 Schematic diagram of the target grayscale curve spectrum applicable to the embodiment of the present invention. Fig. 9 , for the normalized curve spectrum C1 ， -C5 ，Perform probability analysis, calculate the mean and perform smoothing filtering to obtain the target grayscale curve spectrum C f , .

[0091] Fig.10 It is a schematic diagram of the number of peaks and troughs and the half-amplitude width applicable to the embodiment of the present invention. Fig.11 Schematic diagram of the laminar density applicable to the embodiment of the present invention. Fig.11 and 10 , statistical filtering gray spectrum C f , The number of peaks, troughs and half-amplitude width can be used to calculate the layer density L within the image range. d In this example, there are five low grayscale troughs, L1, L3, L5, L7, and L9, corresponding to dark plaster bands, and four high grayscale peaks, L2, L4, L6, and L8, corresponding to bright felsic bands. The number of laminae within the image range is 9 ( Figure 5 ), the slice image height is 1.09 mm, and the number of laminae per meter thick is 8257, that is, L d =8257 pieces / meter.

[0092] Fig.12 Schematic diagram of the thickness of the laminae applicable to the embodiment of the present invention. Fig.12 , according to the filtered gray spectrum C f , The intersection with the probability baseline can determine the thickness of the single layer L h In this example, the thicknesses of the nine layers are 0.18 mm, 0.10 mm, 0.11 mm, 0.09 mm, 0.06 mm, 0.15 mm, 0.17 mm, 0.09 mm, and 0.17 mm, respectively, i.e. h The value range is 0.06-0.18mm.

[0093] By adopting the technical solution of the present application, the density and thickness of the laminae are determined according to the number of wave peaks, the number of wave troughs and the half-amplitude width, so that the determination method is relatively simple while still maintaining the accuracy of the calculation results.

[0094] Embodiment 3

[0095] Fig.13 The present invention provides a structural block diagram of a device for determining laminae parameters of saline lacustrine facies mixed sedimentary rocks. This embodiment can be applied to the situation where the laminae parameters of saline lacustrine facies mixed sedimentary rocks can be more conveniently identified, and the identification results still need to maintain a certain accuracy. The device for determining laminae parameters of saline lacustrine facies mixed sedimentary rocks can be implemented in the form of hardware and / or software, and the device for determining laminae parameters of saline lacustrine facies mixed sedimentary rocks can be configured in an electronic device with data processing capabilities. Fig.13 As shown, the laminae parameter determination device of saline lacustrine mixed sedimentary rock of this embodiment may include: a grayscale image acquisition module 310 , a target map determination module 320 and a laminae parameter determination module 330 .

[0096] in:

[0097] A grayscale image acquisition module 310 is used to grayscale the rock thin section image of the saline lake phase mixed rock to obtain a grayscale thin section image;

[0098] The target spectrum determination module 320 is used to extract the grayscale value in the grayscale slice image to obtain a target grayscale curve spectrum;

[0099] The laminae parameter determination module 330 is used to determine the laminae parameters of the saline lacustrine mixed sedimentary rock according to the target grayscale curve spectrum.

[0100] Based on the above embodiment, optionally, the target map determination module 320 includes:

[0101] A regional image acquisition unit, used for dividing the grayscale slice image into regions to obtain at least two regional grayscale slice images;

[0102] The target grayscale image acquisition unit is used to extract grayscale values ​​from at least two regional grayscale slice images to obtain a target grayscale curve spectrum.

[0103] Based on the above embodiment, optionally, the target grayscale image acquisition unit includes:

[0104] A regional curve spectrum determination subunit is used to extract grayscale values ​​from at least two regional grayscale slice images to obtain at least two regional grayscale value curve spectra;

[0105] The target grayscale curve spectrum determination subunit is used to determine the target grayscale curve spectrum according to at least two regional grayscale value curve spectra.

[0106] Based on the above embodiment, optionally, the target grayscale curve spectrum determination subunit includes:

[0107] A normalization processing sub-subunit, used for normalizing at least two regional gray value curve spectra to obtain at least two regional normalized curve spectra;

[0108] The target grayscale curve spectrum determination sub-subunit is used to determine the target grayscale curve spectrum according to at least two regional normalized curve spectra.

[0109] Based on the above embodiment, optionally, the target grayscale curve spectrum determination sub-subunit includes:

[0110] The mean grayscale spectrum determination subunit is used to calculate the mean of at least two region normalized curve spectra to obtain the mean grayscale spectrum;

[0111] The target grayscale curve spectrum determines a small unit, which is used to filter the mean grayscale spectrum to obtain the target grayscale curve spectrum.

[0112] Based on the above embodiment, optionally, the lamination parameters at least include lamination density;

[0113] Accordingly, the lamination parameter determination module 330 includes:

[0114] A value determination unit, used for determining the number of peaks, the number of troughs and the half-amplitude width according to the target grayscale curve spectrum;

[0115] The laminae density determination unit is used to determine the laminae density according to the number of wave crests, the number of wave troughs and the half-amplitude width.

[0116] Based on the above embodiment, optionally, the lamination parameters at least include lamination thickness;

[0117] Accordingly, the lamination parameter determination module 330 includes:

[0118] A value determination unit, used for determining the number of peaks, the number of troughs and the half-amplitude width according to the target grayscale curve spectrum;

[0119] The laminae thickness determination unit is used to determine the laminae thickness according to the number of wave crests, the number of wave troughs and the half-amplitude width.

[0120] The device for determining lamina parameters of saline lake-facies mixed sedimentary rocks provided in an embodiment of the present invention can execute the method for determining lamina parameters of saline lake-facies mixed sedimentary rocks provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0121] Embodiment 4

[0122] Fig.14 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0123] like Fig.14As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining laminae parameters of saline lacustrine facies mixed sedimentary rocks.

[0126] In some embodiments, the method for determining the laminae parameters of saline lacustrine facies diamictite may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the laminae parameters of the saline lacustrine facies diamictite described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the laminae parameters of the saline lacustrine facies diamictite in any other appropriate manner (e.g., by means of firmware).

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0133] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0134] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining laminar parameters of saline lake-facies mixed sedimentary rocks, characterized in that: include: The rock thin section image of the saline lake phase mixed sedimentary rock is gray-scale processed to obtain a gray-scale thin section image; Extracting grayscale values ​​from the grayscale slice image to obtain a target grayscale curve spectrum; According to the target grayscale curve spectrum, the laminar parameters of the saline lake phase mixed sedimentary rock are determined.

2. The method according to claim 1, characterized in that The step of extracting the grayscale value in the grayscale slice image to obtain a target grayscale curve spectrum comprises: Dividing the grayscale thin slice image into regions to obtain at least two regional grayscale thin slice images; Grayscale values ​​are extracted from the at least two regional grayscale slice images to obtain the target grayscale curve spectrum.

3. The method according to claim 2, characterized in that The step of extracting grayscale values ​​from the at least two regional grayscale slice images to obtain the target grayscale curve spectrum comprises: Extracting grayscale values ​​from the at least two regional grayscale slice images to obtain at least two regional grayscale value curve spectra; The target grayscale curve spectrum is determined according to the at least two regional grayscale value curve spectra.

4. The method according to claim 3, characterized in that: The step of determining the target grayscale curve spectrum according to the at least two regional grayscale value curve spectra comprises: Normalizing the at least two regional grayscale value curve spectra to obtain at least two regional normalized curve spectra; The target grayscale curve spectrum is determined according to the at least two region normalized curve spectra.

5. The method according to claim 4, characterized in that The step of determining the target grayscale curve spectrum according to the at least two region normalized curve spectra comprises: Calculating the average of the normalized curve spectra of the at least two regions to obtain an average grayscale spectrum; The mean grayscale spectrum is filtered to obtain the target grayscale curve spectrum.

6. The method according to claim 1, characterized in that The laminae parameters at least include laminae density; Correspondingly, determining the laminar parameters of saline lacustrine mixed sedimentary rocks according to the target grayscale curve spectrum includes: According to the target grayscale curve spectrum, determining the number of peaks, the number of troughs and the half-amplitude width; The laminar density is determined according to the number of the wave crests, the number of the wave troughs, and the half-amplitude width.

7. The method according to claim 1, characterized in that The lamination parameters at least include lamination thickness, Correspondingly, determining the laminar parameters of saline lacustrine mixed sedimentary rocks according to the target grayscale curve spectrum includes: According to the target grayscale curve spectrum, determining the number of peaks, the number of troughs and the half-amplitude width; The thickness of the corrugation layer is determined according to the number of the wave peaks, the number of the wave troughs and the half-amplitude width.

8. A device for determining laminar parameters of saline lake-phase mixed sedimentary rocks, characterized in that: include: A grayscale image acquisition module is used to grayscale the rock thin section image of the saline lake phase mixed rock to obtain a grayscale thin section image; A target spectrum determination module is used to extract the grayscale value in the grayscale slice image to obtain a target grayscale curve spectrum; The laminae parameter determination module is used to determine the laminae parameters of the saline lake phase mixed sedimentary rock according to the target grayscale curve spectrum.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining laminae parameters of saline lake-facies mixed sedimentary rocks described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining laminae parameters of saline lake-facies mixed sedimentary rocks according to any one of claims 1 to 7 when executed.

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