Recognition method and system for reservoir containing thin coal seam, storage medium and equipment

Abstract

The invention provides a thin coal seam-containing reservoir identification method and system, a storage medium and equipment, and belongs to the field of petroleum and natural gas exploration and development. The method comprises the following steps: for a determined target layer section, calculating a total reflection coefficient sequence of the thin coal seam in combination with logging data, and performing one-dimensional forward modeling on the total reflection coefficient sequence to obtain a waveform and energy of a reservoir of the thin coal seam; then, a reservoir reflection coefficient sequence after the coal seam is removed is obtained, one-dimensional forward modeling is conducted on the reservoir reflection coefficient sequence, the waveform and energy of the reservoir after the coal seam is removed are obtained, and reservoir recognition features are determined by comparing two reservoir energy changes. According to the method, identification and prediction can be clearly carried out in the thin coal seam development area without removing the coal seam by analyzing the characteristic change of the reservoir identification before and after the thin coal seam, so that the identification precision of the reservoir containing the thin coal seam is improved, and efficient oil-gas exploration and development are carried out.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas exploration and development, and specifically relates to a method, system, storage medium and equipment for identifying a reservoir containing thin coal seams. Background Art

[0002] The development of coal seams will form low-frequency strong reflections in seismic reflections that have strong interference signals on the effective signals of the overlying or underlying reservoirs. In the process of reservoir prediction, how to distinguish and eliminate such interference is often a key and difficult point.

[0003] At present, the identification of coal seam reservoirs mainly revolves around technologies such as multi-wavelet decomposition and reconstruction, matching pursuit and compressed sensing. For example, predecessors reorganized the frequency band signals of seismic data through multi-wavelet decomposition and reconstruction technology to achieve the effect of removing coal interference, eliminated the strong reflection of coal seams through multi-channel matching pursuit algorithm, introduced the information along the layer into the matching pursuit algorithm to achieve the strong coal seam shielding effect stripping effect, and achieved the strong shielding effect through the L2 norm constraint along the layer in the reflection coefficient domain of compressed sensing. This type of method can solve the shielding effect of strong radiation caused by coal seam development to a certain extent, but it has certain limitations in the reservoir prediction work in actual work areas. First of all, the research area where this type of method is applicable usually develops large sets of thick coal seams. For example, the coal seams in the Daniudi area are often greater than 15m, or even thicker. The existing methods have discussed the influence of the thickness and structural changes of the coal seams themselves on the reflection wave response of the coal seams. When setting the thickness change, it is usually 2m or more. However, the actual coal-bearing strata are composed of thin interbeds of different lithologies. The thickness of the coal seams is usually only about 1-2 meters, and multiple sets of 1-2 meter thick thin coal seams are developed in a set of reservoirs. The resolution of seismic data cannot achieve the effect of distinguishing and eliminating coal seams. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a method, system, storage medium and equipment for identifying reservoirs containing thin coal seams, thereby improving the identification and prediction accuracy of reservoirs containing thin coal seams, thereby carrying out efficient oil and gas exploration and development.

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

[0006] The first aspect of the present invention provides a method for identifying a reservoir containing a thin coal seam. For a determined target layer segment, a total reflection coefficient sequence containing a thin coal seam is calculated in combination with logging data and a one-dimensional forward simulation is performed on it to obtain the waveform and energy of the reservoir containing a thin coal seam. Then, a reflection coefficient sequence of the reservoir after removing the coal seam is obtained and a one-dimensional forward simulation is performed on it to obtain the waveform and energy of the reservoir after removing the coal seam. The reservoir identification characteristics are clarified by comparing the two reservoir energy changes.

[0007] A further improvement of the present invention is:

[0008] The method specifically comprises the following steps:

[0009] The first step is to determine the target layer segment;

[0010] The second step is to obtain the total reflection coefficient sequence of the reservoir containing thin coal seams;

[0011] The third step is to use one-dimensional reflection coefficient forward modeling to obtain the waveform corresponding to each reflection coefficient;

[0012] The fourth step is to determine the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir through synthetic seismic records;

[0013] The fifth step is to obtain the reservoir reflection coefficient sequence after removing the thin coal seam;

[0014] Step 6, obtaining the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed;

[0015] The seventh step is to compare the maximum amplitude energy value of the peak formed by the reservoir containing the thin coal seam with the maximum amplitude energy value of the peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification characteristics of the coal-containing reservoir.

[0016] A further improvement of the present invention is:

[0017] In the first step, the target layer is determined. The specific operations include:

[0018] Obtain well logging data, geological stratification data and seismic data in the target reservoir area, combine well logging and seismic data and determine the target layer interval;

[0019] The acquired logging data mainly include acoustic wave and density.

[0020] A further improvement of the present invention is:

[0021] In the second step, the total reflection coefficient sequence of the reservoir containing thin coal seams is obtained. The specific operations include:

[0022] Based on the sonic logging data, the formation velocity of the study area is obtained, and then the reflection coefficient is calculated from the density and formation velocity, and then the total reflection coefficient sequence of the reservoir containing thin coal seams is obtained;

[0023] The reflection coefficient is calculated by formula (1):

[0024] R(n)=[ρ(n+1)×v(n+1)-ρ(n)×v(n)] / [ρ(n +1)×v(n +1)+ρ(n)×v(n)] (1)

[0025] Among them, R(n) represents the reflection coefficient of the nth sampling point in the initial reflection coefficient sequence, ρ(n) is the density value corresponding to the nth sampling point, v(n) is the formation velocity corresponding to the nth sampling point, ρ(n+1) is the density value corresponding to the n+1th sampling point, and v(n+1) is the formation velocity corresponding to the n+1th sampling point.

[0026] A further improvement of the present invention is:

[0027] In the third step, one-dimensional reflection coefficient forward modeling is used to obtain the waveform corresponding to each reflection coefficient. The specific operations include:

[0028] Using one-dimensional reflection coefficient forward modeling, combined with the well-side seismic trace, the seismic wavelet is obtained from the convolution model to obtain the waveform corresponding to each reflection coefficient.

[0029] The convolution model is:

[0030] S n (t)=W(t)*R(n)

[0031] Among them, S n (t) is the synthetic seismic record of the nth sampling point, W(t) is the seismic wavelet, * is the convolution operation, and R(n) is the reflection coefficient of the nth sampling point.

[0032] A further improvement of the present invention is:

[0033] In the fourth step, the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir is determined by synthesizing seismic records. The specific operations include:

[0034] The synthetic seismic record S(t) of the total reflection coefficient sequence is obtained through the synthetic seismic record, and the maximum amplitude energy value F1 of the wave peak formed by the thin coal seam reservoir is determined;

[0035] The synthetic seismic record is calculated by formula (2):

[0036]

[0037] Among them, S n (t) is the synthetic seismic record of the nth sampling point, and S(t) is the synthetic seismic record of the total reflection coefficient sequence.

[0038] A further improvement of the present invention is:

[0039] In the fifth step, the reservoir reflection coefficient sequence after removing the thin coal seam is obtained. The specific operations include:

[0040] Based on the reflection characteristics of the coal seam, the time window where the coal seam development section is located and the energy reflection intensity of the coal seam development section are determined, and the reflection coefficient R (m) related to the time window where the coal seam development section is located and in line with the coal seam development characteristics is extracted from the total reflection coefficient sequence. This type of reflection coefficient is removed, that is, it is understood that the set of coal seams is replaced by surrounding rocks (mudstone), and the reservoir reflection coefficient sequence R2 after removing the thin coal seam is obtained;

[0041] R2=R(n)-R(m)

[0042] Among them, R(n) is the total reflection coefficient sequence, R(m) is the reflection coefficient sequence of the target stratum coal seam, and R2 is the reservoir reflection coefficient sequence after removing the thin coal seam.

[0043] A second aspect of the present invention provides a system for identifying a thin coal seam reservoir, comprising:

[0044] A target layer segment determination unit, used for determining a target layer segment;

[0045] A first reflection coefficient sequence acquisition unit is used to acquire a total reflection coefficient sequence of a reservoir containing a thin coal seam;

[0046] The waveform acquisition unit uses one-dimensional reflection coefficient forward modeling to acquire the waveform corresponding to each reflection coefficient;

[0047] A first maximum amplitude energy value acquisition unit is used to determine the maximum amplitude energy value of a wave peak formed by a reservoir containing a thin coal seam by synthesizing seismic records;

[0048] A second reflection coefficient sequence acquisition unit is used to acquire a reservoir reflection coefficient sequence after removing the thin coal seam;

[0049] A second maximum amplitude energy value acquisition unit is used to acquire the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed;

[0050] The identification feature determination unit is used to compare the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification feature of the coal-containing reservoir.

[0051] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for identifying a thin coal seam reservoir as described above.

[0052] A fourth aspect of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying a thin coal seam reservoir as described above.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] (1) The present invention fully respects the characteristics of the original logging data, making the reservoir seismic attribute analysis highly reliable, laying the foundation for excluding the influence of thin coal seams in subsequent reservoir prediction;

[0055] (2) The present invention can well restore the original seismic amplitude energy information of the reservoir. Since thin coal seams often have the characteristics of "multiple sets and thinness" in the study area, such thin coal seams cannot be identified even if the resolution of seismic data is improved. The present invention restores the original seismic amplitude energy information of the reservoir through one-dimensional reflection coefficient forward simulation, without identifying or eliminating thin coal seams;

[0056] (3) The present invention performs forward modeling on each reflection coefficient of the seismic data to accurately locate the waveform characteristics corresponding to the thin coal seam and the amplitude energy information formed, which is helpful for analyzing the impact of the thin coal seam. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a technical flow chart of the method for identifying a reservoir containing thin coal seams of the present invention;

[0058] Figure 2 Plane changes of coal seam development range and thickness in the target layer;

[0059] Figure 3 is a schematic diagram of a total reflection coefficient sequence obtained based on well logging data;

[0060] Figure 4 This is the forward modeling result of one-dimensional reflection coefficient of thin coal seam;

[0061] Figure 5 This is a schematic diagram of the reflection coefficient sequence after removing the thin coal seam;

[0062] Figure 6 This is the forward modeling result of one-dimensional reflection coefficient of thin coal seam;

[0063] Figure 7 Comparison of reservoir seismic amplitude energy information before and after coal seam removal;

[0064] Figure 8 Comparison of amplitude plane attributes of coal-bearing reservoirs. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0066] The present invention takes into account the characteristic of "thin and irregular" coal seam development in the study area. When the coal seam cannot be accurately located and removed, by analyzing the characteristic changes of reservoir identification before and after the thin coal seam is contained, it is clarified that in the thin coal seam development area, identification and prediction can be carried out without removing the coal seam, thereby improving the identification accuracy of reservoirs containing thin coal seams, thereby carrying out efficient oil and gas exploration and development.

[0067] For a determined target layer segment, the present invention calculates a total reflection coefficient sequence containing thin coal seams in combination with logging data and performs a one-dimensional forward simulation on it to obtain the waveform and energy of the reservoir containing the thin coal seams. Then, the reflection coefficient sequence of the reservoir after the thin coal seams are removed is obtained and a one-dimensional forward simulation is performed on it to obtain the waveform and energy of the reservoir after the thin coal seams are removed. The reservoir identification characteristics are clarified by comparing the two reservoir energy changes.

[0068] [Example 1]

[0069] This embodiment provides a method for identifying a reservoir containing thin coal seams, comprising the following steps:

[0070] The first step is to determine the target layer segment;

[0071] Obtain well logging data, geological stratification data and seismic data in the target reservoir area, combine well logging and seismic data and determine the target layer interval;

[0072] The acquired logging data mainly include acoustic wave and density.

[0073] The second step is to obtain the total reflection coefficient sequence of the reservoir containing thin coal seams;

[0074] Based on the sonic logging data, the formation velocity of the study area is obtained, and then the reflection coefficient is calculated from the density and formation velocity, and then the total reflection coefficient sequence of the reservoir containing thin coal seams is obtained;

[0075] The reflection coefficient is calculated by the following formula:

[0076] R(n)=[ρ(n+1)×v(n+1)-ρ(n)×v(n)] / [ρ(n+1)×v(n+1)+ρ(n)×v(n)]

[0077] Among them, R(n) represents the reflection coefficient of the nth sampling point in the initial reflection coefficient sequence, ρ(n) is the density value corresponding to the nth sampling point, v(n) is the formation velocity corresponding to the nth sampling point, ρ(n+1) is the density value corresponding to the n+1th sampling point, and v(n+1) is the formation velocity corresponding to the n+1th sampling point.

[0078] The third step is to use one-dimensional reflection coefficient forward modeling to obtain the waveform corresponding to each reflection coefficient;

[0079] Using one-dimensional reflection coefficient forward modeling, combined with the well-side seismic trace, the seismic wavelet is obtained by the convolution model to obtain the waveform corresponding to each reflection coefficient, where the convolution model is:

[0080] S n (t)=W(t)*R(n)

[0081] Among them, S n (t) is the synthetic seismic record of the nth sampling point, W(t) is the seismic wavelet, * is the convolution operation, and R(n) is the reflection coefficient of the nth sampling point.

[0082] The fourth step is to determine the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir through synthetic seismic records;

[0083] The synthetic seismic record S(t) of the total reflection coefficient sequence is obtained through synthetic seismic record, and the maximum amplitude energy value F1 of the wave peak formed by the thin coal seam reservoir is determined;

[0084]

[0085] Among them, S n (t) is the synthetic seismic record of the nth sampling point, S(t) is the synthetic seismic record of the total reflection coefficient sequence, and m is the number of sampling points.

[0086] The fifth step is to obtain the reservoir reflection coefficient sequence after removing the thin coal seam;

[0087] Based on the reflection characteristics of the coal seam, the time window where the coal seam development section is located and the energy reflection intensity of the coal seam development section are determined, and the reflection coefficient R (m) related to the time window where the coal seam development section is located and in line with the coal seam development characteristics is extracted from the total reflection coefficient sequence. This type of reflection coefficient is removed, that is, it is understood that the set of coal seams is replaced by surrounding rocks (mudstone), and the reservoir reflection coefficient sequence R2 after removing the thin coal seam is obtained;

[0088] R2=R(n)-R(m)

[0089] Among them, R(n) is the total reflection coefficient sequence, R(m) is the reflection coefficient sequence of the target stratum coal seam, and R2 is the reservoir reflection coefficient sequence after removing the thin coal seam.

[0090] Step 6, obtaining the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed;

[0091] Based on the reservoir reflection coefficient sequence after removing the thin coal seam, a one-dimensional reflection coefficient forward simulation is performed to obtain the waveform corresponding to each reservoir reflection coefficient after removing the thin coal seam, and then obtain the peak maximum amplitude energy value F2 formed by the reservoir after removing the thin coal seam. The specific method is the same as the third and fourth steps and will not be repeated here.

[0092] The seventh step is to compare the maximum amplitude energy value of the wave peak formed by the reservoir containing the thin coal seam with the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification characteristics of the coal-containing reservoir;

[0093] By comparing the maximum amplitude energy value of the peak formed by the reservoir containing thin coal seams with the maximum amplitude energy value of the peak formed by the reservoir after the thin coal seams are removed, the change in the maximum amplitude energy of the peak formed by the reservoir clearly defines the identification characteristics of the reservoir containing coal seams, and this feature is used to assist in subsequent reservoir identification.

[0094] By extracting the amplitude plane attributes of the target layer segment, the identification marks of the coal-bearing reservoir are clearly identified.

[0095] The embodiments of the method of the present invention are as follows:

[0096] [Example 2]

[0097] The study area is located in a sedimentary environment of marine-continental transition phase, with large lateral variations in physical properties and thickness. The developed coal seams are rich and frequently interbedded with sandstone and mudstone in the form of thin interlayers. The results of statistical analysis of the coal seams contained in the target layer of the study area show that ( Figure 2 ), the thickness of coal seams is mostly about 1m, the lateral contrast between wells is poor, there is no obvious pattern in the plane, and the reservoirs are mostly below 30m. The main frequency of the seismic data in the study area is about 28Hz, and the layer velocity is 3600-4000m / s. Taking the resolution limit of two adjacent reflection interfaces given by Rayleigh as 1 / 4 wavelength as the standard, it can be determined that the reservoirs in the study area are all within the tuning thickness (32-35 meters), and the 1m thick coal seams are even more indistinguishable in the seismic data. With this as the research background, through the analysis of seismic data, it is clear that the coal seam has the characteristics of low speed and low density, forming a strong reflection interface with the surrounding rock. The interface information has a certain interference with the reflection identification of the reservoir. Through the statistical analysis of logging data, it is clear that the reservoir is characterized by high impedance compared to the surrounding rock, and the coal seam is characterized by low impedance compared to the surrounding rock. The total reflection coefficient sequence ( Figure 3 ), it can be seen that the top of the reservoir has a positive reflection coefficient and the top of the coal seam has a negative reflection coefficient. Based on the total reflection coefficient sequence, a one-dimensional reflection coefficient forward modeling is performed ( Figure 4 ), the result shows that the reservoir shows a peak response, while the coal seam shows a trough response. At this time, the result of the synthetic record shows that the amplitude energy corresponding to the coal-bearing reservoir is F1. In the total reflection coefficient sequence, find the negative reflection coefficient corresponding to the coal seam contained in the target layer ( Figure 4 The red reflection coefficient in the image is removed, and the reflection coefficient sequence after removing the thin coal seam is obtained ( Figure 5 ). Based on this reflection coefficient sequence, a one-dimensional reflection coefficient forward modeling is performed ( Figure 6), it can be seen from the results that the amplitude energy corresponding to the reservoir without coal seams is F2. In the actual case, F1 is 0.15 and F2 is 0.08 ( Figure 7 ), so the existence of coal seams in this area strengthens the amplitude energy of the reservoir, but does not change the relative relationship of the reservoir amplitude energy, that is, the positive value remains positive and will not turn into a negative value due to the development of thin coal seams. By opening the upper and lower fixed time windows of the target layer to extract the maximum amplitude plane attributes, we can get ( Figure 8 ), in wells A and C, the logging results show a combination of reservoir and coal seam, and the amplitude attribute is strong amplitude; in well B, the mudstone and coal seam are combined, and the amplitude attribute is very weak amplitude (or negative). Therefore, when the reservoir contains thin coal seams with a small thickness, the peak can still be used to represent the reservoir, and the reservoir can be identified on the plane through the amplitude attribute, without having to think of more methods to remove the thin coal seams.

[0098] [Example 3]

[0099] This embodiment provides a system for identifying a thin coal seam reservoir, comprising:

[0100] A target layer segment determination unit, used for determining a target layer segment;

[0101] A first reflection coefficient sequence acquisition unit is used to acquire a total reflection coefficient sequence of a reservoir containing a thin coal seam;

[0102] The waveform acquisition unit uses one-dimensional reflection coefficient forward modeling to acquire the waveform corresponding to each reflection coefficient;

[0103] A first maximum amplitude energy value acquisition unit is used to determine the maximum amplitude energy value of a wave peak formed by a reservoir containing a thin coal seam by synthesizing seismic records;

[0104] A second reflection coefficient sequence acquisition unit is used to acquire a reservoir reflection coefficient sequence after removing the thin coal seam;

[0105] A second maximum amplitude energy value acquisition unit is used to acquire the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed;

[0106] The identification feature determination unit is used to compare the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification feature of the coal-containing reservoir.

[0107] [Example 4]

[0108] The present invention provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the thin coal seam reservoir identification method described in Example 1.

[0109] [Example 5]

[0110] This embodiment provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying a thin coal seam reservoir as described in Example 1.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A method for identifying a reservoir containing thin coal seams, characterized in that: For the determined target layer section, the total reflection coefficient sequence containing thin coal seams is calculated in combination with logging data, and a one-dimensional forward simulation is performed on it to obtain the waveform and energy of the reservoir containing thin coal seams. Then, the reflection coefficient sequence of the reservoir after removing the coal seam is obtained, and a one-dimensional forward simulation is performed on it to obtain the waveform and energy of the reservoir after removing the coal seam. The reservoir identification characteristics are clarified by comparing the two reservoir energy changes.

2. The method for identifying a thin coal seam reservoir according to claim 1, characterized in that: The method specifically comprises the following steps: The first step is to determine the target layer segment; The second step is to obtain the total reflection coefficient sequence of the reservoir containing thin coal seams; The third step is to use one-dimensional reflection coefficient forward modeling to obtain the waveform corresponding to each reflection coefficient; The fourth step is to determine the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir through synthetic seismic records; The fifth step is to obtain the reservoir reflection coefficient sequence after removing the thin coal seam; Step 6, obtaining the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed; The seventh step is to compare the maximum amplitude energy value of the peak formed by the reservoir containing the thin coal seam with the maximum amplitude energy value of the peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification characteristics of the coal-containing reservoir.

3. The method for identifying a thin coal seam reservoir according to claim 2, characterized in that: In the first step, the target layer segment is determined, and the specific operations include: Obtain well logging data, geological stratification data and seismic data in the target reservoir area, combine well logging and seismic data and determine the target layer interval; The acquired logging data mainly include acoustic wave and density.

4. The method for identifying a thin coal seam reservoir according to claim 3, characterized in that: In the second step, the total reflection coefficient sequence of the reservoir containing the thin coal seam is obtained, and the specific operations include: Based on the sonic logging data, the formation velocity of the study area is obtained, and then the reflection coefficient is calculated from the density and formation velocity, and then the total reflection coefficient sequence of the reservoir containing thin coal seams is obtained; The reflection coefficient is calculated by formula (1): R(n)=[ρ(n+1)×v(n+1)-ρ(n)×v(n)] / [ρ(n +1)×v(n +1)+ρ(n)×v(n)] (1) Among them, R(n) represents the reflection coefficient of the nth sampling point in the initial reflection coefficient sequence, ρ(n) is the density value corresponding to the nth sampling point, v(n) is the formation velocity corresponding to the nth sampling point, ρ(n+1) is the density value corresponding to the n+1th sampling point, and v(n+1) is the formation velocity corresponding to the n+1th sampling point.

5. The method for identifying a thin coal seam reservoir according to claim 4, characterized in that: In the third step, a one-dimensional reflection coefficient forward modeling is used to obtain a waveform corresponding to each reflection coefficient. The specific operations include: Using one-dimensional reflection coefficient forward modeling, combined with the well-side seismic trace, the seismic wavelet is obtained from the convolution model to obtain the waveform corresponding to each reflection coefficient. The convolution model is: S n (t)=W(t)*R(n) Among them, S n (t) is the synthetic seismic record of the nth sampling point, W(t) is the seismic wavelet, * is the convolution operation, and R(n) is the reflection coefficient of the nth sampling point.

6. The method for identifying a thin coal seam reservoir according to claim 5, characterized in that: In the fourth step, the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir is determined by synthesizing the seismic record, and the specific operations include: The synthetic seismic record S(t) of the total reflection coefficient sequence is obtained through the synthetic seismic record, and the maximum amplitude energy value F1 of the wave peak formed by the thin coal seam reservoir is determined; The synthetic seismic record is calculated by formula (2): Among them, S n (t) is the synthetic seismic record of the nth sampling point, and S(t) is the synthetic seismic record of the total reflection coefficient sequence.

7. The method for identifying a thin coal seam reservoir according to claim 2, characterized in that: The fifth step of obtaining the reservoir reflection coefficient sequence after removing the thin coal seam includes: Based on the reflection characteristics of coal seams, the time window where the coal seam development section is located and the energy reflection intensity of the coal seam development section are determined, and the reflection coefficient R(m) related to the time window where the coal seam development section is located and in line with the coal seam development characteristics is extracted from the total reflection coefficient sequence. This type of reflection coefficient is removed, that is, it is understood that the set of coal seams is replaced by surrounding rocks, and the reservoir reflection coefficient sequence R2 after removing the thin coal seam is obtained; R2=R(n)-R(m) Among them, R(n) is the total reflection coefficient sequence, R(m) is the reflection coefficient sequence of the target stratum coal seam, and R2 is the reservoir reflection coefficient sequence after removing the thin coal seam.

8. A system for identifying a thin coal seam reservoir, characterized in that: include: A target layer segment determination unit, used for determining a target layer segment; A first reflection coefficient sequence acquisition unit is used to acquire a total reflection coefficient sequence of a reservoir containing a thin coal seam; The waveform acquisition unit uses one-dimensional reflection coefficient forward modeling to acquire the waveform corresponding to each reflection coefficient; A first maximum amplitude energy value acquisition unit is used to determine the maximum amplitude energy value of a wave peak formed by a reservoir containing a thin coal seam by synthesizing seismic records; A second reflection coefficient sequence acquisition unit is used to acquire a reservoir reflection coefficient sequence after removing the thin coal seam; A second maximum amplitude energy value acquisition unit is used to acquire the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed; The identification feature determination unit is used to compare the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir after the thin coal seam is removed, so as to clarify the identification feature of the coal-containing reservoir.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps of the method for identifying a thin coal seam reservoir as described in any one of claims 1-7.

10. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying a thin coal seam reservoir as described in any one of claims 1-7.

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