Ground layer high-resolution heterogeneity processing method, system and equipment and medium

Abstract

The invention provides a layer high-resolution heterogeneity processing method, system and device and a medium, and the method comprises the following steps: S1, decompiling and processing imaging logging curve data, deleting invalid values and middle finger filtering to obtain effective curve data, and windowing the effective curve data to obtain a windowing data matrix; s2, based on the windowing data matrix, calculating a harmonic average value of each row in the window length, and obtaining a heterogeneous characterization harmonic parameter according to all harmonic average values; s3, calculating an equivalent average value of each row in the window length based on the windowing data matrix, and obtaining a heterogeneous representation equivalent parameter according to all the equivalent average values; s4, obtaining a heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter; s5, moving the windowing data matrix downwards, repeating the steps S2-S4 to obtain a continuous heterogeneity coefficient, and evaluating the reservoir based on the continuous heterogeneity coefficient; according to the method, reservoir effectiveness evaluation and permeability prediction work are well achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and in particular relates to a method, system, equipment and medium for processing high-resolution formation heterogeneity. Background Art

[0002] Reservoir heterogeneity usually refers to the fact that the oil and gas reservoirs have undergone the combined influence of sedimentation, diagenesis and later tectonic actions in their long geological history, which has caused extremely uneven changes in the spatial distribution and various internal properties of the reservoirs. Such changes include intra-layer, inter-layer, planar and microscopic heterogeneity.

[0003] Reservoir heterogeneity controls reservoir permeability and effectiveness and is one of the important parameters in reservoir evaluation. Tight sandstone oil and gas exploration belongs to the field of unconventional oil and gas exploration. It has extremely strong heterogeneity and many thin layers. Conventional logging resolution cannot effectively distinguish this type of formation. Therefore, a high-resolution quantitative characterization method of heterogeneity is needed. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method, system, equipment and medium for processing high-resolution formation heterogeneity, which utilizes filtering method to eliminate redundant information on the basis of retaining the characteristics of original data, avoids the influence of extreme values ​​and outliers through harmonic mean and equivalent mean, realizes depth matching with conventional logging curves through windowing technology, and achieves the purpose of calculating heterogeneity coefficient.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for processing formation high-resolution heterogeneity comprises the following steps:

[0007] S1: Decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, open windows on the valid curve data, and obtain a windowed data matrix;

[0008] S2: Calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameters according to all the harmonic means;

[0009] S3: Calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the heterogeneous characterization according to all the equivalent average values;

[0010] S4: Obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter;

[0011] S5: Move the windowed data matrix downward and repeat steps S2-S4 to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir.

[0012] Furthermore, the process of decompilation and processing of imaging logging curve data in step S1 is as follows:

[0013] The logging data of the target reservoir is collected and processed, including data loading, well deviation correction, velocity correction, electric button alignment and image generation, to obtain a logging curve; the logging curve is generated using 256 color levels.

[0014] Furthermore, in step S1, invalid values ​​and middle finger filtering are deleted to obtain valid curve data, and the process of windowing the valid curve data is as follows:

[0015] After deleting invalid values, the logging curve data is input into the median filter program for noise reduction to obtain valid curve data;

[0016] Take the first i rows of effective curve data for processing, and call the overall matrix of the first i rows of data a window, where i is called the window length. The specific steps to obtain i are:

[0017] i=L / R e ;

[0018] Where: L is the window length, which is selected according to the research situation, and 0.1m is recommended; R e is the sampling interval of the imaging logging curve, usually 0.00508m, i is rounded up, and the median depth of the window is taken as the depth recording point.

[0019] Furthermore, in step S2, the harmonic mean of each row in the window length is calculated based on the windowed data matrix, and the process of obtaining the harmonic parameter representing the heterogeneity according to all the harmonic means is as follows:

[0020] The harmonic mean is:

[0021]

[0022] Where: H 1 is the harmonic mean of the first row output of the window matrix, j is the number of elements in the first row of the window matrix;

[0023] The heterogeneous characterization harmonic parameters are:

[0024]

[0025] Where: i is the number of rows in the input window data matrix; H m is the average of all harmonic means in row i; n is the order, reflecting the trend of data changes.

[0026] Furthermore, the step S3 calculates the equivalent average value of each row in the window length based on the windowed data matrix, and the process of obtaining the equivalent parameters of the heterogeneous characterization according to all the equivalent average values ​​is as follows:

[0027] The window contains j columns of data, and the j columns of data are divided into j / 2 columns of new data, and the equivalent average value is:

[0028]

[0029] Where: M 1n is the new element outputted in the first row of the window matrix, n is the element in the nth column of the original matrix, and j is the number of columns;

[0030] Make a histogram statistics and take the highest value of the distribution as the equivalent value of the row;

[0031] According to the equivalent value of each row in the i-th row of the windowed data matrix and the equivalent average value, the equivalent parameter of the heterogeneous characterization is obtained as follows:

[0032]

[0033] Where: i is the number of input data rows, F m is the average value of all equivalent values ​​in the i-row space, and n is the order, which reflects the trend of data changes.

[0034] Furthermore, the process of obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter in step S4 is:

[0035] The heterogeneity coefficient is half of the sum of the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter. Further, the distance k by which the windowed data matrix is ​​moved downward in step S5 is:

[0036] k=C / R e

[0037] Where: C is the conventional logging curve sampling interval, usually 0.125m, R e The sampling interval of the imaging logging curve is usually 0.00508m.

[0038] A high-resolution formation heterogeneity processing system, comprising:

[0039] A preprocessing module is configured to decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, and perform windowing on the valid curve data to obtain a windowing data matrix;

[0040] A first operation module is configured to calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameter according to all the harmonic means;

[0041] The second operation module is configured to calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the inhomogeneous characterization according to all the equivalent average values;

[0042] A third operation module is configured to obtain a heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter;

[0043] The output module is configured to move the windowed data matrix downwards and repeat the processes of the first operation module, the second operation module and the third operation module to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir based on the continuous heterogeneity coefficient.

[0044] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for high-resolution formation heterogeneity processing are implemented.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for high-resolution formation heterogeneity processing.

[0046] Compared with the prior art, the present invention has the following beneficial technical effects:

[0047] The present invention provides a method, system, device and medium for high-resolution formation heterogeneity processing, comprising the following steps: S1: decode and process imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, open windows for the valid curve data, and obtain a windowed data matrix; S2: calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization harmonic parameter according to all the harmonic mean values; S3: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S4: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S5: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S6: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S7: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S8: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S9: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values; S10: calculate the equivalent mean of each row within the window length based on the windowed data matrix, and obtain the heterogeneity characterization equivalent parameter according to all the equivalent mean values. The heterogeneity characterization harmonic parameters and the heterogeneity characterization equivalent parameters are used to obtain the heterogeneity coefficient; S5: move the windowed data matrix downward and repeat steps S2-S4 to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir based on the continuous heterogeneity coefficient; the present application uses the harmonic mean and the equivalent mean to eliminate the errors caused by data outliers, overcomes the problem of low resolution of heterogeneity coefficient calculated by conventional logging, extracts high-resolution heterogeneity parameters of the reservoir, and solves the problems of reservoir effectiveness evaluation and permeability prediction, providing a reliable basis for current oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for processing formation heterogeneity with high resolution in a specific embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of before and after comparison of deleting invalid values ​​and middle finger filtering in a specific embodiment of the present invention;

[0050] Figure 3 This is an interpretation result diagram of a certain well in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0052] 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.

[0053] 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.

[0054] The present invention provides a method for processing formation heterogeneity with high resolution, such as Figure 1 As shown, the following steps are included:

[0055] S1: Decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, open windows on the valid curve data, and obtain a windowed data matrix;

[0056] S2: Calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameters according to all the harmonic means;

[0057] S3: Calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the heterogeneous characterization according to all the equivalent average values;

[0058] S4: Obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter;

[0059] S5: Move the windowed data matrix downward and repeat steps S2-S4 to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir.

[0060] Preferably, the process of decompilation and processing of imaging logging curve data in step S1 is:

[0061] Collect the logging data of the target reservoir and process the data, including data loading, well deviation correction, velocity correction, electric button alignment and image generation, to obtain the logging curve; the logging curve is generated using 256 color levels;

[0062] In the embodiments of this specification, the imaging logging curve data is obtained by logging together. The measured reservoirs can be conventional sandstone and tight sandstone. The measurement signals of different reservoirs to be measured are affected by the mud type to different degrees. This embodiment does not specifically limit the method of obtaining logging data from the reservoir to be measured, and it can be selected according to actual working conditions.

[0063] Preferably, in step S1, invalid values ​​and middle finger filtering are deleted to obtain valid curve data, and the process of windowing the valid curve data is as follows:

[0064] After deleting invalid values, the logging curve data is input into the median filter program for noise reduction to obtain valid curve data;

[0065] Take the first i rows of effective curve data for processing, and call the overall matrix of the first i rows of data a window, where i is called the window length. The specific steps to obtain i are:

[0066] i=L / R e ;

[0067] Where: L is the window length, which is selected according to the research situation, and 0.1m is recommended; R e is the sampling interval of the imaging logging curve, usually 0.00508m, i is rounded up, and the median depth of the window is taken as the depth recording point.

[0068] Specifically, the invalid values ​​are used to delete invalid values ​​in the image curve, and there is no specific limitation on the different invalid values ​​generated by different software, including but not limited to values ​​such as (-9999, -99999, 32767); after deleting invalid values ​​and filtering by the middle finger, valid curve data is obtained, as shown in Figure 2, a schematic diagram of the comparison before and after deleting invalid values ​​and filtering by the middle finger in this embodiment.

[0069] Preferably, in step S2, the harmonic mean of each row in the window length is calculated based on the windowed data matrix, and the process of obtaining the harmonic parameter characterizing the heterogeneity according to all the harmonic means is as follows:

[0070] The harmonic mean is:

[0071]

[0072] Where: H 1 is the harmonic mean of the first row output of the window matrix, j is the number of elements in the first row of the window matrix;

[0073] The heterogeneous characterization harmonic parameters are:

[0074]

[0075] Where: i is the number of rows in the input window data matrix; H m is the average of all harmonic means in row i; n is the order, reflecting the trend of data changes.

[0076] Preferably, the step S3 calculates the equivalent average value of each row in the window length based on the windowed data matrix, and the process of obtaining the equivalent parameter of the heterogeneous characterization according to all the equivalent average values ​​is as follows:

[0077] It should be noted that, considering that the imaging logging data is circumferential logging, the data recorded by the symmetrical button electrodes are coaxial data. Therefore, the average of the opposite data is taken to calculate the matrix reflecting the semi-circular direction of the wellbore, so as to reduce the influence of extreme values ​​in data processing. Specifically:

[0078] The window contains j columns of data, and the j columns of data are divided into j / 2 columns of new data, and the equivalent average value is:

[0079]

[0080] Where: M 1n is the new element outputted in the first row of the window matrix, n is the element in the nth column of the original matrix, and j is the number of columns;

[0081] Make a histogram statistics and take the highest value of the distribution as the equivalent value of the row;

[0082] According to the equivalent value of each row in the i-th row of the windowed data matrix and the equivalent average value, the equivalent parameter of the heterogeneous characterization is obtained as follows:

[0083]

[0084] Where: i is the number of input data rows, F m is the average value of all equivalent values ​​in the i-row space, and n is the order, which reflects the trend of data changes.

[0085] Preferably, the process of obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter in step S4 is:

[0086] The heterogeneity coefficient is half of the sum of the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter, which can be expressed as:

[0087] F=(F 调和 +F 等效 ) / 2;

[0088] Where: F 调和 is the harmonic parameter for heterogeneity characterization, F 等效 is the equivalent parameter for characterizing heterogeneity.

[0089] Preferably, the distance k by which the windowed data matrix is ​​moved downward in step S5 is:

[0090] k=C / R e

[0091] Where: C is the conventional logging curve sampling interval, usually 0.125m, R e The sampling interval of the imaging logging curve is usually 0.00508m.

[0092] The present invention provides a formation high-resolution heterogeneity processing system, comprising:

[0093] A preprocessing module is configured to decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, and perform windowing on the valid curve data to obtain a windowing data matrix;

[0094] A first operation module is configured to calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameter according to all the harmonic means;

[0095] The second operation module is configured to calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the inhomogeneous characterization according to all the equivalent average values;

[0096] A third operation module is configured to obtain a heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter;

[0097] The output module is configured to move the windowed data matrix downwards and repeat the processes of the first operation module, the second operation module and the third operation module to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir based on the continuous heterogeneity coefficient.

[0098] In another embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for high-resolution formation heterogeneity processing.

[0099] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the computer device and the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a high-resolution formation heterogeneity processing method in the above embodiment.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing high-resolution formation heterogeneity, It is characterized in that The following steps are involved: S1: Decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, open windows on the valid curve data, and obtain a windowed data matrix; S2: Calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameters according to all the harmonic means; S3: Calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the heterogeneous characterization according to all the equivalent average values; S4: Obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter; S5: Move the windowed data matrix downward and repeat steps S2-S4 to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir.

2. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that The process of decompilation and processing of imaging logging curve data in step S1 is as follows: The logging data of the target reservoir is collected and processed, including data loading, well deviation correction, velocity correction, electric button alignment and image generation, to obtain a logging curve; the logging curve is generated using 256 color levels.

3. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that In step S1, invalid values ​​and middle finger filtering are deleted to obtain valid curve data. The process of windowing the valid curve data is as follows: After deleting invalid values, the logging curve data is input into the median filter program for noise reduction to obtain valid curve data; Take the first i rows of effective curve data for processing, and call the overall matrix of the first i rows of data a window, where i is called the window length. The specific steps to obtain i are: i=L / R e ; Where: L is the window length, which is selected according to the research situation, and 0.1m is recommended; R e is the sampling interval of the imaging logging curve, usually 0.00508m, i is rounded up, and the median depth of the window is taken as the depth recording point.

4. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that In step S2, the harmonic mean of each row in the window length is calculated based on the windowed data matrix, and the process of obtaining the harmonic parameter representing the heterogeneity according to all the harmonic means is as follows: The harmonic mean is: Where: H 1 is the harmonic mean of the first row output of the window matrix, j is the number of elements in the first row of the window matrix; The heterogeneous characterization harmonic parameters are: Where: i is the number of rows in the input window data matrix; H m is the average of all harmonic means in row i; n is the order, reflecting the trend of data changes.

5. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that The step S3 calculates the equivalent average value of each row in the window length based on the windowed data matrix, and the process of obtaining the equivalent parameters of the heterogeneous characterization according to all the equivalent average values ​​is as follows: The window contains j columns of data, and the j columns of data are divided into j / 2 columns of new data, and the equivalent average value is: Where: M 1n is the new element outputted in the first row of the window matrix, n is the element in the nth column of the original matrix, and j is the number of columns; Make a histogram statistics and take the highest value of the distribution as the equivalent value of the row; According to the equivalent value of each row in the i-th row of the windowed data matrix and the equivalent average value, the equivalent parameter of the heterogeneous characterization is obtained as follows: Where: i is the number of input data rows, F m is the average value of all equivalent values ​​in the i-row space, and n is the order, which reflects the trend of data changes.

6. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that The process of obtaining the heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter in step S4 is: The heterogeneity coefficient is half of the sum of the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter.

7. A method for processing formation heterogeneity with high resolution according to claim 1, It is characterized in that The distance k by which the windowed data matrix is ​​moved downward in step S5 is: k=C / R e Where: C is the conventional logging curve sampling interval, usually 0.125m, R e The sampling interval of the imaging logging curve is usually 0.00508m.

8. A high-resolution formation heterogeneity processing system, It is characterized in that A method for processing formation high-resolution heterogeneity based on any one of claims 1 to 7, comprising: A preprocessing module is configured to decode and process the imaging logging curve data, delete invalid values ​​and middle finger filtering, obtain valid curve data, and perform windowing on the valid curve data to obtain a windowing data matrix; A first operation module is configured to calculate the harmonic mean of each row within the window length based on the windowed data matrix, and obtain the inhomogeneous characterization harmonic parameter according to all the harmonic means; The second operation module is configured to calculate the equivalent average value of each row within the window length based on the windowed data matrix, and obtain the equivalent parameter of the inhomogeneous characterization according to all the equivalent average values; A third operation module is configured to obtain a heterogeneity coefficient based on the heterogeneity characterization harmonic parameter and the heterogeneity characterization equivalent parameter; The output module is configured to move the windowed data matrix downwards and repeat the processes of the first operation module, the second operation module and the third operation module to obtain a continuous heterogeneity coefficient, which is used to evaluate the reservoir based on the continuous heterogeneity coefficient.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the steps of a method for high-resolution formation heterogeneity processing as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program. It is characterized in that When the computer program is executed by a processor, the steps of a method for high-resolution formation heterogeneity processing as described in any one of claims 1 to 7 are implemented.

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