Fluid identification method and device based on far-near fluid mobility attribute

Abstract

The invention relates to the field of geophysical exploration, and particularly discloses a fluid identification method and device based on a far-reduced fluid mobility attribute, and the method comprises the steps: obtaining the far-reduced fluid mobility attribute based on the pre-stack angle gather data of a target interval; determining a far-near fluid mobility attribute threshold value; and identifying the target layer fluid based on the threshold value. According to the fluid identification method based on the far-reduced fluid mobility attribute, the far-reduced fluid mobility attribute is obtained based on the pre-stack angle gather data of the target interval, the seismic response characteristic difference under the condition that the fluid is contained and the condition that the fluid is not contained is displayed more visually, and the fluid identification precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration, and in particular to a fluid identification method and device based on the fluidity property of far-minus-near fluids. Background Art

[0002] Compared with post-stack seismic data, pre-stack seismic data contains richer reservoir fluid information. Therefore, fluid identification based on pre-stack data has gradually become the focus of reservoir fluid identification. At present, there are three main reservoir fluid identification methods based on seismic data:

[0003] (1) Fluid identification method based on post-stack seismic attributes. This method is limited by the lack of universal attributes that are sensitive to fluids and is only applicable to certain work areas. (2) Fluid identification method based on pre-stack and post-stack inversion. This method is limited by the influence of low-frequency models and inversion multi-solutions, and the fluid prediction results are highly uncertain. (3) Fluid identification method based on AVO attributes. This method is limited by the influence of AVO attributes on fluid sensitivity and is only effective in certain work areas. The above methods for fluid identification all have certain limitations, and a highly adaptable fluid identification method is urgently needed.

[0004] Based on this technical background, the present invention studies a fluid identification method and device based on the fluidity property of far-minus-near fluid. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a fluid identification method and device based on the far-minus-near fluid mobility attributes. The method obtains the far-minus-near fluid mobility attributes based on the pre-stack angle gather data of the target layer segment, which more intuitively shows the difference in seismic response characteristics between the case of containing fluid and the case of not containing fluid, thereby improving the accuracy of fluid identification.

[0006] In order to achieve the above object, a first aspect of the present invention provides a fluid identification method based on the far-reducing-near fluid flow property, comprising:

[0007] Based on the pre-stack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained;

[0008] Determining a threshold value of the far-minus-near fluid flow attribute;

[0009] The target layer fluid is identified based on the threshold value.

[0010] A second aspect of the present invention provides a fluid identification device based on the far-reducing-near fluid velocity property, comprising:

[0011] The module for calculating the far-minus-near fluid mobility attributes obtains the far-minus-near fluid mobility attributes based on the pre-stack angle gather data of the target layer;

[0012] A threshold value determination module, used to determine a threshold value of the far-minus-near fluid flow attribute;

[0013] The fluid identification module is used to identify the target layer fluid based on the threshold value.

[0014] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0015] A memory storing executable instructions;

[0016] A processor runs the executable instructions in the memory to implement the fluid identification method based on the far-minus-near fluid flow property of the first aspect.

[0017] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the fluid identification method based on the far-minus-near fluid flow property described in the first aspect.

[0018] The beneficial effects of the present invention include:

[0019] (1) The fluid identification method based on the far-minus-near fluid mobility attribute provided by the present invention obtains the far-minus-near fluid mobility attribute based on the pre-stack angle gather data of the target layer segment, which more intuitively shows the difference in seismic response characteristics between the case with fluid and the case without fluid, thereby improving the accuracy of fluid identification.

[0020] (2) The fluid identification method based on the far-minus-near fluid mobility attribute provided by the present invention extends the fluid mobility attribute of post-stack data commonly used in the prior art to pre-stack data, fully exploits the fluid information in the pre-stack data, avoids the limitation of the limited sensitivity of post-stack data to fluids, and provides a new idea for the identification of fluid attributes.

[0021] (3) The fluid identification method based on the far-minus-near fluid mobility attribute provided by the present invention utilizes the difference in dispersion and attenuation characteristics in the far and near track data to obtain the far-minus-near fluid mobility attribute based on the pre-stack angle gather data of the target layer segment. The calculation process does not require wells and initial models, and the calculation results are stable, which has extremely high promotion and application value.

[0022] (4) The fluid identification method based on the far-to-near fluid mobility attribute provided by the present invention calibrates the far-to-near fluid mobility attribute based on the known well fluid content, obtains the far-to-near fluid mobility attribute threshold value, and identifies the fluid in the study area, with a high degree of consistency in fluid prediction.

[0023] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

[0025] Figure 1 This is a flow chart of the fluid identification method based on the far-minus-near fluid flow properties proposed by the present invention.

[0026] Figure 2 Schematic diagram of the target stacked front angle gather in Example 1 of the present invention.

[0027] Figure 3 It is a schematic diagram of the superimposed cross-section of the target layer near-path angle in the first embodiment of the present invention.

[0028] Figure 4 It is a schematic diagram of the long-distance angle superimposed cross-section of the target layer in Example 1 of the present invention.

[0029] Figure 5 Schematic diagram of the near-channel angle flow profile of the target layer in Example 1 of the present invention.

[0030] Figure 6 It is a schematic diagram of the long-distance angle flow profile of the target layer in Example 1 of the present invention.

[0031] Figure 7 It is a schematic cross-sectional diagram of the fluid mobility properties of the target layer from far to near in the first embodiment of the present invention. DETAILED DESCRIPTION

[0032] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0033] The present invention provides a fluid identification method based on the fluidity property of far-reducing near-reducing fluids, such as Figure 1 As shown, including:

[0034] Based on the pre-stack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained;

[0035] Determine the threshold value of the far minus near fluid flow property;

[0036] Identify target layer fluid based on threshold value.

[0037] In the present invention, the far-minus-near fluid mobility attribute is obtained based on the pre-stack angle gather data of the target layer, which more intuitively shows the difference in seismic response characteristics between the case with fluid and the case without fluid, and improves the accuracy of fluid identification.

[0038] According to the present invention, the far-minus-near fluid mobility attributes are obtained based on the pre-stack angle gather data of the target layer, including:

[0039] Filter out the effective angle range based on the pre-stack angle gather data of the target layer;

[0040] The effective angles are stacked by angles to obtain the long-distance and short-distance stacking data volume;

[0041] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively;

[0042] Subtract the flow properties of the far and near channels to obtain the flow properties of the far minus near channels.

[0043] According to the present invention, superimposing the effective angles by angles to obtain a long-distance and short-distance superimposed data body includes:

[0044] The effective angles are divided into far-path angles and near-path angles, and are superimposed respectively to obtain far-path superimposed data volumes and near-path superimposed data volumes;

[0045] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively, including:

[0046] Based on the far-path stacking data volume and the near-path stacking data volume, the far-path fluid mobility properties and the near-path fluid mobility properties are extracted respectively.

[0047] According to the present invention, the formula used for extraction is:

[0048]

[0049]

[0050] Among them, R 1 is a real coefficient representing the fluid properties, ω is the angular frequency of the seismic signal, κ is the fluid permeability, η is the fluid viscosity coefficient, C is the complex function of the porous rock parameters, ρ f is the bulk density of reservoir rock, and F is the fluid mobility property.

[0051] In the present invention, the fluid mobility attributes of post-stack data commonly used in the prior art are extended to pre-stack data, which fully mines the fluid information in the pre-stack data, avoids the limitation of the limited sensitivity of post-stack data to fluid, and provides a new idea for the identification of fluid attributes.

[0052] Preferably, subtracting the fluid flow properties of the far channel and the near channel to obtain the far minus near fluid flow property includes: subtracting the fluid flow property of the near channel from the fluid flow property of the far channel to obtain the far minus near fluid flow property.

[0053] In the present invention, the difference in dispersion and attenuation characteristics in the far and near trace data is utilized to obtain the far minus near fluid mobility attribute based on the prestack angle gather data of the target layer segment. The calculation process does not require wells and initial models, the calculation results are stable, and it has extremely high promotion and application value.

[0054] Preferably, determining the threshold value of the far-minus-near fluid flow property comprises:

[0055] The fluid mobility attributes of the bypass channel of the actual well are extracted and compared with the far-to-near fluid mobility attributes to determine the threshold value of the far-to-near fluid mobility attributes of the target layer.

[0056] According to the present invention, identifying the target layer fluid based on the threshold value includes:

[0057] Based on the threshold value, the fluid in the target layer is identified and the fluid distribution in the target layer is predicted.

[0058] In the present invention, the far-reduced near fluid mobility attribute is calibrated based on the known well fluid content, the far-reduced near fluid mobility attribute threshold value is obtained, and the fluid in the study area is identified, and the fluid prediction has a high degree of consistency.

[0059] The present invention will be described in more detail below by way of examples.

[0060] Embodiment 1:

[0061] This embodiment takes data from an actual work area in China as an example. The target layer is a sandstone-mudstone reservoir, and a fluid identification method based on the far-minus-near fluid mobility attribute is used to carry out fluid identification research.

[0062] The fluid identification method based on the far-minus-near fluid flow property in this embodiment is as follows: Figure 1 As shown, including:

[0063] Based on the pre-stack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained;

[0064] Determine the threshold value of the far minus near fluid flow property;

[0065] Identify the target layer fluid based on the threshold value;

[0066] Based on the prestack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained, including:

[0067] Filter out the effective angle range based on the pre-stack angle gather data of the target layer;

[0068] The effective angles are stacked by angles to obtain the long-distance and short-distance stacking data volume;

[0069] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively;

[0070] Subtract the flow properties of the far and near channels from each other to obtain the flow properties of the far-minus-near channels;

[0071] The effective angles are superimposed to obtain the long and short track superposition data volume, including:

[0072] The effective angles are divided into far-path angles and near-path angles, and are superimposed respectively to obtain far-path superimposed data volumes and near-path superimposed data volumes;

[0073] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively, including:

[0074] Based on the long-path stacking data volume and the short-path stacking data volume, the long-path fluid mobility attribute and the short-path fluid mobility attribute are extracted respectively;

[0075] The formula used for extraction is:

[0076]

[0077]

[0078] Among them, R 1 is a real coefficient representing the fluid properties, ω is the angular frequency of the seismic signal, κ is the fluid permeability, η is the fluid viscosity coefficient, C is the complex function of the porous rock parameters, ρ f is the bulk density of reservoir rock, and F is the fluid mobility property;

[0079] Subtracting the mobility attributes of the far path fluid from the near path fluid to obtain the far minus near path fluid mobility attribute includes: subtracting the mobility attribute of the far path fluid from the mobility attribute of the near path fluid to obtain the far minus near path fluid mobility attribute;

[0080] Determining the threshold value of the far-minus-near fluid flow attribute includes:

[0081] Extract the fluid mobility attribute of the bypass channel of the actual well, compare it with the far-to-near fluid mobility attribute, and determine the threshold value of the far-to-near fluid mobility attribute of the target layer;

[0082] Identifying target layer fluid based on threshold value includes:

[0083] Based on the threshold value, the fluid in the target layer is identified and the fluid distribution in the target layer is predicted.

[0084] In this embodiment, the fluid identification process based on the far-to-near fluid flow property specifically includes the following steps:

[0085] Step 1: Determine the effective angle range. Analyze the pre-stack angle gather data of the target layer and screen out the effective angle range. Figure 2 This is the pre-stack gather of the side track of Well A1 in the study area. Through analysis, the effective angle is 1-34 degrees.

[0086] Step 2: Angle stacking: Divide the effective angles into far-path angles and near-path angles, and stack them separately to obtain near-path stacking data volumes and far-path stacking data volumes. Figure 3 and Figure 4 The well-connected profiles of the near-track stacked data volume and the far-track stacked data volume are shown respectively.

[0087] Step 3: Extract fluid flow properties. For the superimposed data volume of the near and far paths, extract the fluid flow properties respectively, such as Figure 5 and Figure 6 shown.

[0088] Step 4: Calculate the far minus near fluid flow property. Subtract the far fluid flow property from the near fluid flow property to obtain the far minus near fluid flow property, such as Figure 7 shown.

[0089] Step 5: Determine the threshold value of the far-minus-near fluid mobility attribute. Extract the fluid mobility attributes of the actual well bypass and compare them with the logging interpretation results to determine the threshold value of the far-minus-near fluid mobility attribute of the target layer as (>1.05*106).

[0090] Step 6: Fluid identification based on far-minus-near fluid mobility attributes. Based on the determined threshold value, fluid identification is carried out on the target layer to predict the fluid distribution in the study area. According to the actual drilling situation (Well A1 and Well A3 are high-yield gas wells, and Well A2 produces water), the far-minus-near fluid mobility attribute profile is consistent with the actual drilling situation, while the far-angle and near-angle fluid mobilities have certain deviations from the actual drilling situation, proving that the far-minus-near fluid mobility attribute fluid identification accuracy is significantly improved. At the same time, the far-minus-near fluid mobility attribute can effectively reduce fluid identification anomalies, such as Figure 7 Shown in the black circle.

[0091] Embodiment 2:

[0092] This embodiment provides a fluid identification method based on the fluidity property of far-reduced near-reduced fluids, such as Figure 1 As shown, including:

[0093] Based on the pre-stack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained;

[0094] Determine the threshold value of the far minus near fluid flow property;

[0095] Identify the target layer fluid based on the threshold value;

[0096] Based on the prestack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained, including:

[0097] Filter out the effective angle range based on the pre-stack angle gather data of the target layer;

[0098] The effective angles are stacked by angles to obtain the long-distance and short-distance stacking data volume;

[0099] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively;

[0100] Subtract the flow properties of the far and near channels from each other to obtain the flow properties of the far-minus-near channels;

[0101] The effective angles are superimposed to obtain the long and short track superposition data volume, including:

[0102] The effective angles are divided into far-path angles and near-path angles, and are superimposed respectively to obtain far-path superimposed data volumes and near-path superimposed data volumes;

[0103] Based on the superimposed data of the far and near channels, the fluid flow properties of the far and near channels are extracted respectively, including:

[0104] Based on the long-path stacking data volume and the short-path stacking data volume, the long-path fluid mobility attribute and the short-path fluid mobility attribute are extracted respectively;

[0105] The formula used for extraction is:

[0106]

[0107]

[0108] Among them, R 1 is a real coefficient representing the fluid properties, ω is the angular frequency of the seismic signal, κ is the fluid permeability, η is the fluid viscosity coefficient, C is the complex function of the porous rock parameters, ρ f is the bulk density of reservoir rock, and F is the fluid mobility property;

[0109] Subtracting the mobility attributes of the far path fluid from the near path fluid to obtain the far minus near path fluid mobility attribute includes: subtracting the mobility attribute of the far path fluid from the mobility attribute of the near path fluid to obtain the far minus near path fluid mobility attribute;

[0110] Determining the threshold value of the far-minus-near fluid flow attribute includes:

[0111] Extract the fluid mobility attribute of the bypass channel of the actual well, compare it with the far-to-near fluid mobility attribute, and determine the threshold value of the far-to-near fluid mobility attribute of the target layer;

[0112] Identifying target layer fluid based on threshold value includes:

[0113] Based on the threshold value, the fluid in the target layer is identified and the fluid distribution in the target layer is predicted.

[0114] Embodiment three:

[0115] This embodiment provides a fluid identification device based on the fluid velocity property of far-reducing-near fluids, such as Figure 1 As shown, including:

[0116] The module for calculating the far-minus-near fluid mobility attributes obtains the far-minus-near fluid mobility attributes based on the pre-stack angle gather data of the target layer;

[0117] A threshold value determination module, used to determine a threshold value of a far-minus-near fluid flow attribute;

[0118] A fluid identification module for identifying the fluid in the target layer based on a threshold value;

[0119] Obtaining the far-minus-near fluid mobility attribute based on the prestack angle gather data of the target layer section includes:

[0120] Filtering out the effective angle range based on the prestack angle gather data of the target layer section;

[0121] Performing angle stacking on the effective angles to obtain the far-and-near trace stacked data volume;

[0122] Extracting the far-and-near trace fluid mobility attributes respectively based on the far-and-near trace stacked data volume;

[0123] Subtracting the far-and-near trace fluid mobility attributes to obtain the far-minus-near fluid mobility attribute;

[0124] Performing angle stacking on the effective angles to obtain the far-and-near trace stacked data volume includes:

[0125] Dividing the effective angles into far trace angles and near trace angles, and performing stacking respectively to obtain the far trace stacked data volume and the near trace stacked data volume;

[0126] Extracting the far-and-near trace fluid mobility attributes respectively based on the far-and-near trace stacked data volume includes:

[0127] Extracting the far trace fluid mobility attribute and the near trace fluid mobility attribute respectively based on the far trace stacked data volume and the near trace stacked data volume;

[0128] The formula used for extraction is:

[0129]

[0130]

[0131] Wherein, R 1 is a real coefficient representing the fluid property, ω is the angular frequency of the seismic signal, κ is the fluid permeability, η is the fluid viscosity, C is a complex function of the porous rock parameter, ρ f is the bulk density of the reservoir rock, and F is the fluid mobility attribute;

[0132] Subtracting the far-and-near trace fluid mobility attributes to obtain the far-minus-near fluid mobility attribute includes: subtracting the near trace fluid mobility attribute from the far trace fluid mobility attribute to obtain the far-minus-near fluid mobility attribute;

[0133] Determining the threshold value of the far-minus-near fluid mobility attribute includes:

[0134] Extracting the fluid mobility attribute of the wellside trace of the actual well drilled, comparing it with the far-minus-near fluid mobility attribute, and determining the threshold value of the far-minus-near fluid mobility attribute of the target layer;

[0135] Identifying the fluid in the target layer based on the threshold value includes:

[0136] Based on the threshold value, the fluid in the target layer is identified and the fluid distribution in the target layer is predicted.

[0137] Embodiment 4:

[0138] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0139] A memory storing executable instructions;

[0140] The processor runs the executable instructions in the memory to implement a fluid identification method based on the far-minus-near fluid flow property.

[0141] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0142] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0143] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present invention.

[0144] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0145] Embodiment five:

[0146] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a fluid identification method based on a far-minus-near fluid velocity property is implemented.

[0147] The computer-readable storage medium according to the embodiment of the present invention stores non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of the embodiments of the present invention are executed.

[0148] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0149] The fluid identification method based on far-minus-near fluid mobility attributes provided by an embodiment of the present invention obtains far-minus-near fluid mobility attributes based on pre-stack angle gather data of the target layer segment, more intuitively demonstrates the difference in seismic response characteristics between fluid-containing and fluid-free conditions, and improves the accuracy of fluid identification.

[0150] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A fluid identification method based on the fluidity property of far-reduced near-reduced fluids, It is characterized in that include: Based on the pre-stack angle gather data of the target layer, the far-minus-near fluid mobility attributes are obtained; Determining a threshold value of the far-minus-near fluid flow attribute; The target layer fluid is identified based on the threshold value.

2. The prediction method according to claim 1, It is characterized in that The far-minus-near fluid mobility attributes obtained based on the pre-stack angle gather data of the target layer include: Filter out the effective angle range based on the pre-stack angle gather data of the target layer; The effective angles are stacked at different angles to obtain a long-range and short-range stacking data volume; Extracting the fluidity attributes of the far and near channels respectively based on the far and near channel superimposed data volume; Subtract the flow properties of the far and near channels to obtain the flow properties of the far minus near channels.

3. The prediction method according to claim 2, It is characterized in that The effective angle is superimposed at different angles to obtain a long-distance and short-distance superimposed data volume, which includes: The effective angle is divided into a far path angle and a near path angle, and the two angles are superimposed to obtain a far path superimposed data volume and a near path superimposed data volume; Extracting the fluid flow properties of the far and near channels based on the far and near channel superimposed data volume includes: Based on the long-path stacking data volume and the short-path stacking data volume, the long-path fluid mobility attribute and the short-path fluid mobility attribute are extracted respectively.

4. The prediction method according to claim 3, It is characterized in that The formula used for the extraction is: Among them, R 1 is a real coefficient representing the fluid properties, ω is the angular frequency of the seismic signal, κ is the fluid permeability, η is the fluid viscosity coefficient, C is the complex function of the porous rock parameters, ρ f is the bulk density of reservoir rock, and F is the fluid mobility property.

5. The prediction method according to claim 3, It is characterized in that Subtracting the fluid flow properties of the far and near channels from each other to obtain the fluid flow property of the far-minus-near channel includes: subtracting the fluid flow property of the near-channel fluid from the fluid flow property of the far channel to obtain the fluid flow property of the far-minus-near channel.

6. The prediction method according to claim 5, It is characterized in that Determining the threshold value of the far-minus-near fluid flow attribute includes: The fluid mobility attribute of the bypass channel of the actual well is extracted and compared with the far-to-near fluid mobility attribute to determine the far-to-near fluid mobility attribute threshold value of the target layer.

7. The prediction method according to claim 6, It is characterized in that Identifying the target layer fluid based on the threshold value includes: Based on the threshold value, the fluid of the target layer is identified and the fluid distribution of the target layer is predicted.

8. A fluid identification device based on the fluidity property of far-reduced near-reduced fluids, It is characterized in that include: The module for calculating the far-minus-near fluid mobility attributes obtains the far-minus-near fluid mobility attributes based on the pre-stack angle gather data of the target layer; A threshold value determination module, used to determine a threshold value of the far-minus-near fluid flow attribute; The fluid identification module is used to identify the target layer fluid based on the threshold value.

9. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the fluid identification method based on the far-minus-near fluid flow property according to any one of claims 1-7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fluid identification method based on the far-to-near fluid flow property described in any one of claims 1 to 7 is implemented.

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