Tight reservoir stoneley wave hydrocarbon reservoir identification method and system and medium

Abstract

The invention discloses a tight reservoir stoneley wave hydrocarbon reservoir identification method, a tight reservoir stoneley wave hydrocarbon reservoir identification system and a medium. The tight reservoir stoneley wave hydrocarbon reservoir identification method comprises the following steps: S1, comprehensively dividing a reservoir and a dry layer by utilizing one or more of well logging information, well wall coring, rock debris logging and gas logging; s2, taking the stoneley wave logging curve as a main curve and the transverse wave as an auxiliary curve in the same logging channel, selecting a proper scale, determining the overlapping relationship between the stoneley wave and the transverse wave of the reservoir section and the size of an overlapping region, and calculating the size of the overlapping region, namely a fluid movement index; s3, establishing interpretation standards of an oil-gas layer, an oil-water layer and a dry layer by utilizing the fluid movement index and oil testing and production testing data; and S4, comprehensively identifying an oil-gas layer, an oil-water layer and a dry layer by utilizing the fluid movement index and the interpretation standard in the step S2. The method solves the problem that the tight reservoir resistivity logging cannot effectively identify the hydrocarbon reservoir.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical well logging, in particular to a method for identifying a tight reservoir Stoneley wave oil and gas layer, a system and a medium thereof. Background Art

[0002] Reservoirs with medium to high porosity and permeability (Φ > 10%, K > 10 md) have large reservoir spaces, low bound water content, and generally high saturation. Oil and gas make up a large proportion of the reservoir, and the contribution of oil (gas) content to resistivity increases, demonstrating the traditional electrical characteristic of "resistivity increasing with porosity." Therefore, this characteristic is commonly used to identify oil and gas reservoirs. Tight reservoirs (Φ < 10%, K < 10 md) have high bound water content and a small proportion of oil and gas. Therefore, resistivity logging generally weakens the information provided by formation pore fluids, resulting in less distinct logging signatures for oil, gas, and oil-water layers. The resistivity differences between these three layers decrease, making identification more difficult. This makes fluid identification in tight reservoirs particularly challenging. Currently, imaging logging, two-dimensional nuclear magnetic resonance (NMR), and other logging technologies, combined with logging visualization, gas logging, cuttings logging, and sidewall coring, are commonly used to comprehensively address tight reservoir fluid identification and oil content assessment.

[0003] In recent years, Stoneley waves have become an important means of analyzing formation permeability. Williams was the first to point out that formation permeability is correlated with Stoneley wave slowness and amplitude. Hornbuy et al. used a joint inversion of the slowness lag caused by dispersion and the frequency shift caused by attenuation to calculate formation permeability. Xu Song et al. analyzed the effect of gas saturation on the slowness dispersion and attenuation of Stoneley waves, and believed that attenuation has a greater impact on gas saturation. Peng et al. used a sensitivity method to analyze the effects of factors such as pore fluid density, viscosity, porosity, pore tortuosity, and permeability on Stoneley waves in the 0-20 kHz frequency band. The study concluded that these factors have little effect on Stoneley wave slowness, but a significant impact on attenuation, and used the attenuation coefficient to distinguish between gas and water layers.

[0004] The above studies used the slowness and attenuation joint inversion method to calculate the formation permeability, used the attenuation characteristics to qualitatively identify the gas layer, and used the Stoneley wave to identify the tight reservoir oil layer. There is a gap in the research and application.

[0005] Publication No. CN116449429A discloses a method for evaluating fracture permeability based on the high- and low-frequency reflection coefficients and attenuation of Stoneley waves. Using array acoustic waveform data, the difference between the high- and low-frequency reflection coefficients of the Stoneley waves is calculated and normalized. The difference in attenuation between the high- and low-frequency bands at each depth point is calculated and normalized. The normalized reflection coefficient difference is multiplied by the attenuation difference to define the fracture permeability factor, which is used to evaluate formation fracture permeability. This prior art primarily uses the Stoneley wave reflection coefficient and attenuation coefficient to extract the fracture permeability factor associated with the fracture. However, fractured formations have a more significant impact on the slowness (the inverse of the velocity) of the low-frequency Stoneley waves, and this Stoneley wave slowness information is not utilized.

[0006] Publication No. CN116856921A discloses a method for evaluating the productivity of fractured buried-hill reservoirs. By analyzing the waveform and frequency spectrum of Stoneley waves in buried-hill reservoirs dominated by pores and fractures, the method determines the response characteristics of Stoneley waves in different types of buried-hill reservoirs. High-frequency models for inverting permeability using Stoneley waves are established for both reservoir types, while a low-frequency model for inverting permeability using Stoneley waves is established for fracture-pore buried-hill reservoirs. The permeabilities of the corresponding reservoirs are then derived. A target area productivity prediction model is then established based on the product of permeability, gas layer thickness, and open flow rate, providing a powerful method for evaluating the productivity of newly drilled gas fields in the target area. This prior art relies on analyzing reservoir permeability using Stoneley waves and using permeability models to predict target gas layer productivity, but fails to use Stoneley waves to distinguish between gas and water layers in the target area.

[0007] Publication No. CN107816348A discloses a method and device for identifying gas layers using P-waves and Stoneley waves. This method uses the P-wave and Stoneley wave energies of array acoustic waves to calculate a formation gas content indicator curve. This amplifies the response characteristics of natural gas to P-wave and Stoneley wave attenuation, eliminating the effects of certain gas layers responding only to P-wave or Stoneley wave attenuation. This effectively improves sensitivity and accurately determines the location of gas-bearing layers. This prior art utilizes Stoneley wave energy information and has been successfully applied to gas layers, but it does not address the identification of oil layers in tight reservoir conditions.

[0008] In short, the technical solutions and technical problems to be solved by the above-mentioned disclosed technologies are completely different from the present invention. Regarding more technical features and technical problems to be solved by the present invention, the above-mentioned disclosed technical documents do not provide any technical inspiration. Summary of the Invention

[0009] In response to the above-mentioned defects in the prior art, the idea and purpose of the present invention is to use the Stoneley wave slowness to extract the fluid migration index related to the formation permeability, and use this index to analyze the reservoir fluid properties under tight reservoir conditions, establish the interpretation standard of gas layers, oil layers, water layers or dry layers in the study area, and achieve the purpose of using Stoneley waves to identify oil and gas layers.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] In one aspect, the present invention provides a method for identifying Stoneley wave oil and gas layers in tight reservoirs. The method uses a Stoneley wave logging curve as a main curve and either a shear wave or a longitudinal wave as an auxiliary curve. The method employs a pairwise intersection method to extract a fluid mobility index and identify pore fluid properties. The steps are as follows:

[0012] S1: Using one or more of the following data, namely, well logging data, sidewall coring, rock cuttings logging, and gas logging data, the reservoir and dry layer are comprehensively divided;

[0013] S2: Using the Stoneley wave logging curve as the main curve and the shear wave as the auxiliary curve in the same well logging track, select an appropriate scale to determine the overlap relationship and overlap area size of the Stoneley wave and shear wave in the reservoir section, and calculate the overlap area size, i.e., the fluid movement index;

[0014] S3: Using fluid mobility index and test oil and production data, establish interpretation standards for oil and gas layers, oil and water layers, and dry layers;

[0015] S4: Comprehensively identify oil and gas layers, oil and water layers, and dry layers using the fluid migration index and interpretation criteria from step S2.

[0016] Furthermore, in S1, well sections with oil and gas shows in sidewall coring, cuttings logging, and gas logging, or with porosity and permeability calculated from logging data exceeding the lower limit of regional reservoir properties, are classified as reservoirs;

[0017] Reservoir sections with no oil or gas indications from sidewall coring, cuttings logging and gas logging, and with porosity and permeability calculated from logging data lower than the lower limit of regional reservoir physical properties, are classified as dry layers.

[0018] Furthermore, in step S2, a suitable calibration curve is selected to determine the overlapping relationship and the size of the overlapping area. The curve calibration principle is to overlap the Stoneley wave logging curve with the shear wave or longitudinal wave curve in the dry layer, and highlight the difference between the two curves of the reservoir.

[0019] Furthermore, in step S2, the size of the overlapping region, namely XQFM, is calculated using formula 1:

[0020] XQFM=DTST-((DTS-DTS min )×(DTST max -DTST min ) / (DTSmax -DTS min )

[0021] +DTST min )

[0022] Among them: XQFM - fluid movement index; DTST - Stoneley wave measurement; DTS - shear wave measurement, DTST min and DTST max —Stoneley wave scale minimum and maximum values; DTS max and DTS min — Maximum and minimum values ​​of the shear wave scale; the units of the above parameters are μs / ft.

[0023] Furthermore, in step S3, the oil and gas layer, oil and water layer or dry layer distribution interval is established, using the following steps:

[0024] A1. Using the test oil and production test results data of the example well area or adjacent area, determine the oil and gas layer, oil and gas layer, and dry layer of the example well;

[0025] A2. Establish fluid migration index and interpretation standards for oil and gas layers, oil and water layers, and dry layers, and use the interpretation standards to comprehensively classify oil and gas layers, oil and water layers, and dry layers;

[0026] Statistics on the relationship between the fluid mobility index of the well area or adjacent area and the results of oil testing and production testing are carried out. The fluid mobility index has a good distinction between different oil testing results.

[0027] Among them, the fluid movement index of the oil and gas layer is greater than 8μs / ft, the fluid movement index of the oil and water layer is 3-8μs / ft, and the fluid movement index of the dry layer is less than 3μs / ft.

[0028] Furthermore, in step S4, the Stoneley wave value increases due to mud sedimentation near the bottom of the well, and the Stoneley wave is corrected as necessary. The correction adopts formula 2:

[0029] DTST 校正 =DTST-A

[0030] Where: DTST is Stoneley wave, unit is μs / ft; A is a value to be determined, unit is μs / ft; A value range is 1-15μs / ft.

[0031] Furthermore, when the drilling fluid density is less than 1.5 g / cm 3 ,A value range is 1-5μs / ft; when the drilling fluid mud density is greater than 1.5g / cm 3 ,A value range is 5-15μs / ft.

[0032] In another aspect, the present invention provides a system for identifying Stoneley wave oil and gas layers in a tight reservoir, comprising a processor and a memory storing a computer program, wherein the processor is configured to execute the computer program to implement a method for identifying Stoneley wave oil and gas layers in a tight reservoir.

[0033] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying Stoneley wave oil and gas layers in tight reservoirs.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The use of Stoneley wave fluid identification provides a new method for evaluating the oil (gas) content of tight reservoirs, solving the problem that resistivity logging in tight reservoirs cannot effectively identify oil and gas layers. It has been applied in oil and gas fields in basins such as Bohai Bay, Tarim Shunbei Oilfield, and Junggar Deep Formation, and has achieved good results. It has improved the accuracy of oil and gas layer identification, provided a reliable basis for layer selection for oil testing and fracturing, and greatly improved economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a step diagram of a method for identifying Stoneley wave oil and gas layers in tight reservoirs according to the present invention;

[0037] Figure 2 This is the second embodiment of a method for identifying Stoneley wave oil and gas layers in tight reservoirs according to the present invention;

[0038] Figure 3 This is the third embodiment of the method for identifying Stoneley wave oil and gas layers in tight reservoirs of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0040] Example 1:

[0041] See also Figure 1 The present invention provides a method for identifying Stoneley wave oil and gas layers in tight reservoirs. The method uses the Stoneley wave logging curve as the main curve and either the shear wave or the longitudinal wave as the auxiliary curve. The method uses a pairwise intersection method to extract the fluid mobility index and identify the pore fluid properties. The steps are as follows:

[0042] S1: Reservoir and dry layers are comprehensively divided using one or more of the following data: well logging data, sidewall coring, cuttings logging, and gas logging. Well sections with oil and gas shows in sidewall coring, cuttings logging, and gas logging, or with porosity and permeability calculated from logging data exceeding the lower limit of the regional reservoir properties, are classified as reservoirs. Well sections with no oil and gas shows in sidewall coring, cuttings logging, and gas logging, or with porosity and permeability calculated from logging data below the lower limit of the regional reservoir properties, are classified as dry layers.

[0043] S2: Using the Stoneley wave logging curve as the main curve and the shear wave as the auxiliary curve in the same well logging track, select an appropriate scale to determine the overlap relationship and overlap area size of the Stoneley wave and shear wave in the reservoir section, and calculate the overlap area size, i.e., the fluid movement index;

[0044] In step S2, a suitable calibration curve is selected to determine the overlap relationship and the size of the overlap area. The calibration principle of the curve is to overlap the Stoneley wave logging curve with the shear wave or longitudinal wave curve in the dry layer, and highlight the difference between the two curves of the reservoir.

[0045] In step S2, the overlap region size (XQFM) is calculated using formula 1:

[0046] XQFM=DTST-((DTS-DTS min )×(DTST max -DTST min ) / (DTS max -DTS min )

[0047] +DTST min )

[0048] Among them: XQFM - fluid movement index; DTST - Stoneley wave measurement; DTS - shear wave measurement, DTST min and DTST max —Stoneley wave scale minimum and maximum values; DTS max and DTS min — Maximum and minimum values ​​of shear wave scale; the units of the above parameters are μs / ft;

[0049] S3: Using fluid mobility index and test oil and production data, establish interpretation standards for oil and gas layers, oil and water layers, and dry layers;

[0050] In step S3, the distribution interval of the oil and gas layer, the oil and water layer or the dry layer is determined by using the fluid movement index, and the following steps are adopted:

[0051] A1. Using the test oil and production test results data of the example well area or adjacent area, determine the oil and gas layer, oil and gas layer, and dry layer of the example well;

[0052] A2. Establish interpretation standards for fluid migration index and oil and gas layers, oil-water layers, and dry layers. Use these interpretation standards to comprehensively classify oil and gas layers, oil-water layers, and dry layers. Statistically analyze the relationship between fluid migration index and well testing and production test results in case studies or adjacent areas. The fluid migration index demonstrates good differentiation between different well testing results. The fluid migration index for oil and gas layers is greater than 8μs / ft, the fluid migration index for oil and water layers is 3-8μs / ft, and the fluid migration index for dry layers is less than 3μs / ft.

[0053] S4: Comprehensively identify oil and gas layers, oil and water layers, and dry layers using the fluid migration index and interpretation criteria in step S2;

[0054] In step S4, the Stoneley wave value increases due to mud sedimentation near the bottom of the well, so the Stoneley wave is corrected as necessary. The correction is done using formula 2:

[0055] DTST 校正 =DTST+A

[0056] Where: DTST is Stoneley wave, unit is μs / ft; A is the value to be determined, unit is μs / ft; when the drilling fluid density is less than 1.5g / cm 3 ,A value range is 1-5μs / ft; when the drilling fluid mud density is greater than 1.5g / cm 3 ,A value range is 5-15μs / ft.

[0057] Example 2:

[0058] Based on Example 1, Figure 2 .

[0059] This example is the deep layer of the Bohai Bay Basin. The target layer is buried at a depth of 4700-5000m. A large number of tight reservoirs are developed in the deep layer. Most of the key exploration wells in this area use array acoustic logging, which uses array acoustic waves to extract Stoneley waves, shear waves, longitudinal waves and other logging curves.

[0060] First, well logging data was used to calculate shale content, porosity, and permeability. This was then combined with rock cuttings and gas logging data to comprehensively classify reservoir and non-reservoir formations. The target interval had a logging porosity of 5-9%, averaging 6.4%. Fractures were not well developed, and conventional permeability was 0.1-1 md / day, averaging 0.32 md / day, indicating a tight reservoir with extremely low permeability.

[0061] In the same well logging track, the Stoneley wave logging curve is used as the main curve and the shear wave as the auxiliary curve to perform curve overlap. The 4870-4900m well section has the lowest porosity and the worst permeability, with obvious dry layer characteristics. In this section, the scales of the Stoneley wave curve and the shear wave curve are adjusted so that the shear wave curve and the Stoneley wave curve coincide. In the 4700-4790m gas layer abnormal section, the Stoneley wave curve is on the left and the shear wave curve is on the right. The Stoneley wave curve and the shear wave curve have an overlapping area, as shown in Figure 2. Figure 2 .

[0062] The fluid mobility index XQFM was calculated using the adjusted scale value and formula 1. The XQFM value in the 4700-4790m well section was high, while the XQFM values ​​in other sections were low.

[0063] The well area in the embodiment has array acoustic wave data of multiple wells, as well as detailed oil test and test production data. The correspondence between XQFM and oil and gas layers, oil-water layers and dry layers is statistically analyzed. Under tight reservoir conditions, the two have a good correspondence. The oil layer and gas layer have high XQFM values, the poor oil layer and oil-water layer have medium XQFM values, and the dry layer and water layer have the lowest XQFM values, see Table 1.

[0064]

[0065] Using the interpretation criteria in Table 1, we analyzed the oil and gas characteristics of Well Example 1. The upper section (4753.2-4784.3 m) showed a peak XQFM value of 18.0 μs / ft and an average of 11.8 μs / ft, indicating a gas formation. After fracturing and oil testing, the daily gas production reached 32,150 m³ and daily water production reached 18.2 m³, concluding that the formation was gas-bearing. The lower section (4826.0-4851.0 m) showed a peak XQFM value of 3.5 μs / ft and an average of only 1.4 μs / ft, indicating a dry formation (primarily producing water after fracturing). After fracturing and oil testing, the daily gas production reached 126 m³ and daily water production reached 6.85 m³, indicating a gas-bearing water formation. The interpretation was consistent with the oil testing results.

[0066] Example 3:

[0067] Based on Example 1, Figure 3 .

[0068] This example is the deep layer of the Bohai Bay Basin. The target layer is buried at a depth of 4300-4500m. A large number of tight reservoirs are developed in the deep layer. Most of the key exploration wells in this area use array acoustic logging, which uses array acoustic waves to extract Stoneley waves, shear waves, longitudinal waves and other logging curves.

[0069] First, well logging data was used to calculate shale content, porosity, and permeability. This was then combined with rock cuttings and gas logging data to comprehensively classify reservoir and non-reservoir zones. The target interval had a maximum porosity of 9.5% and an average of 4.1%. The permeability reached a maximum of 0.34 md / s and an average of 0.04 md / s, indicating a low-porosity, ultra-low-permeability, and dense reservoir.

[0070] In the same well logging track, the Stoneley wave logging curve is used as the main curve and the shear wave is used as the auxiliary curve to overlap the curves. The 4470-4490m well section has a high mud content, low formation porosity, poor permeability, and obvious dry layer characteristics. Adjust the scales of the Stoneley wave curve and the shear wave curve so that the shear wave curve and the Stoneley wave curve coincide. In the low natural gamma (GR < 63API) section, the Stoneley wave curve is on the left and the shear wave curve is on the right. The Stoneley wave curve and the shear wave curve have an overlapping area, as shown in Figure 2. Figure 3 .

[0071] In this example, due to the sedimentation of drilling fluid at a depth of 4490m, the Stoneley wave time difference is large, so that the Stoneley wave curve and the shear wave curve in the dry layer section do not coincide with each other. Formula 2 needs to be used for correction. The correction amount A is -2.0μs / ft. After correction, the Stoneley wave curve and the shear wave curve in the dry layer section at a depth of 4490m coincide with each other.

[0072] The fluid mobility index XQFM was calculated using the adjusted scale value and formula 1. The XQFM distribution range of the target layer was 0-64μs / ft. The XQFM values ​​of different reservoirs and within reservoirs were significantly different, reflecting the great differences in reservoir permeability and fluid viscosity coefficient.

[0073] Using the interpretation criteria in Table 1, the oil and gas characteristics of this example well were analyzed. The XQFM values ​​for gas layers peaked at 64 μs / ft, averaging 18 μs / ft. XQFM values ​​for oil layers ranged from 5-10 μs / ft, averaging 8.1 μs / ft. XQFM values ​​for poor oil layers ranged from 2-5 μs / ft, averaging 2.9 μs / ft. XQFM values ​​for dry layers were generally less than 2 μs / ft. Mid-course testing of this example well revealed a depth of 4302.8-4463.8 m, converting to 237 m³ of oil and 26,149 m³ of gas per day, indicating a light oil layer with a high gas-to-oil ratio.

[0074] Example 4:

[0075] Based on Example 1, this embodiment provides a system for identifying Stoneley wave oil and gas layers in tight reservoirs, comprising a processor and a memory storing a computer program;

[0076] The processor is configured to execute the computer program to implement a tight reservoir Stoneley wave oil and gas layer identification method.

[0077] Example 5:

[0078] Based on Example 1, this embodiment provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for identifying Stoneley wave oil and gas layers in tight reservoirs is implemented.

[0079] All components not discussed in detail in this application and the connection methods of the components in this application are well-known technologies in the technical field and can be directly applied without further explanation.

[0080] In the present invention, the term "plurality" refers to two or more, unless otherwise specified. Terms such as "installed," "connected," "connected," and "fixed" should be interpreted broadly. For example, "connected" can mean fixed, removable, or integral; and "connected" can mean directly or indirectly through an intermediary. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0081] In the description of the present invention, it should be understood that the directions or positional relationships indicated by terms such as "up", "down", "left", "right", "front" and "back" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0082] Throughout this specification, terms such as "one embodiment," "some embodiments," and "specific embodiments" mean that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0083] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for identifying Stoneley wave oil and gas layers in tight reservoirs, characterized in that: The Stoneley wave logging curve is used as the main curve, and either the shear wave or the longitudinal wave is used as the auxiliary curve. The pairwise intersection method is used to extract the fluid mobility index and identify the pore fluid properties. The steps are as follows: S1: Using one or more of the following data, namely, well logging data, sidewall coring, rock cuttings logging, and gas logging data, to comprehensively divide the reservoir and dry layer; S2: Using the Stoneley wave logging curve as the main curve and the shear wave as the auxiliary curve in the same well logging track, select an appropriate scale to determine the overlap relationship and overlap area size of the Stoneley wave and shear wave in the reservoir section, and calculate the overlap area size, i.e., the fluid movement index; S3: Using fluid mobility index and test oil and production data, establish interpretation standards for oil and gas layers, oil and water layers, and dry layers; S4: Comprehensively identify oil and gas layers, oil and water layers, and dry layers using the fluid migration index and interpretation criteria from step S2.

2. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 1, characterized in that: In S1, well sections with oil and gas shows from sidewall coring, cuttings logging, and gas logging, or with porosity and permeability calculated from logging data exceeding the lower limit of regional reservoir properties, are classified as reservoirs; Reservoir sections with no oil or gas indications from sidewall coring, cuttings logging and gas logging, and with porosity and permeability calculated from logging data lower than the lower limit of regional reservoir-forming properties, are classified as dry layers.

3. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 1, characterized in that: In step S2, a suitable calibration curve is selected to determine the overlapping relationship and the size of the overlapping area. The calibration principle of the curve is to overlap the Stoneley wave logging curve with the shear wave or longitudinal wave curve in the dry layer, and highlight the difference between the two curves of the reservoir.

4. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 3, characterized in that: In step S2, the overlap region size, XQFM, is calculated using formula 1: XQFM=DTST-((DTS-DTS min )×(DTST max -DTST min ) / (DTS max -DTS min )+DTST min ) Among them: XQFM - fluid movement index; DTST - Stoneley wave measurement; DTS - shear wave measurement, DTST min and DTST max —Stoneley wave scale minimum and maximum values; DTS max and DTS min —The maximum and minimum values ​​of the shear wave scale; the units of the above parameters are μs / ft.

5. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 1, characterized in that: In step S3, the oil and gas layer, oil and water layer or dry layer distribution interval is established by the following steps: A1. Using the test oil and production test results data of the example well area or adjacent area, determine the oil and gas layer, oil and gas layer, and dry layer of the example well; A2. Establish fluid migration index and interpretation standards for oil and gas layers, oil and water layers, and dry layers, and use the interpretation standards to comprehensively classify oil and gas layers, oil and water layers, and dry layers; Statistics on the relationship between the fluid mobility index of the well area or adjacent area and the results of oil testing and production testing are carried out. The fluid mobility index has a good distinction between different oil testing results. Among them, the fluid movement index of the oil and gas layer is greater than 8μs / ft, the fluid movement index of the oil and water layer is 3-8μs / ft, and the fluid movement index of the dry layer is less than 3μs / ft.

6. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 1, characterized in that: In step S4, the Stoneley wave value increases due to mud sedimentation near the bottom of the well, so the Stoneley wave is corrected as necessary. The correction is done using formula 2: DTST 校正 =DTST-A Where: DTST is Stoneley wave, unit is μs / ft; A is a value to be determined, unit is μs / ft; A value range is 1-15μs / ft.

7. The method for identifying Stoneley wave oil and gas layers in tight reservoirs according to claim 6, characterized in that: When the drilling fluid density is less than 1.5 g / cm 3 ,A value range is 1-5μs / ft; when the drilling fluid mud density is greater than 1.5g / cm 3 ,A value range is 5-15μs / ft.

8. A system for identifying tight reservoir Stoneley wave oil and gas layers, comprising a processor and a memory storing a computer program, characterized in that ； The processor is configured to execute the computer program to implement a tight reservoir Stoneley wave oil and gas layer identification method.

9. A computer storage medium having a computer program stored thereon, characterized in that ； When the computer program is executed by a processor, a method for identifying Stoneley wave oil and gas layers in tight reservoirs is implemented.

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