Method, system, storage medium and equipment for identifying thin coal seam reservoir

Abstract

The present application provides a kind of thin coal seam reservoir identification method, system, storage medium and equipment, belong to petroleum and natural gas exploration and development field.The method is: for the determined target interval, the full reflection coefficient sequence of thin coal seam is calculated in combination with well logging data and one-dimensional forward modeling is carried out to obtain the waveform and energy of thin coal seam reservoir, then the reservoir reflection coefficient sequence after removing coal seam is obtained and one-dimensional forward modeling is carried out to obtain the waveform and energy of the reservoir after removing coal seam, the reservoir identification characteristics are determined by comparing the energy change of the two reservoirs.The present application determines the identification and prediction in the thin coal seam development area without removing coal seam by analyzing the characteristic change of reservoir identification before and after thin coal seam, improves the identification accuracy of thin coal seam reservoir, so as to carry out efficient oil and gas exploration and development.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of oil and gas exploration and development, and particularly relates to a thin coal seam containing reservoir identification method, system, storage medium and equipment. BACKGROUND

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

[0003] At present, the identification of coal seam reservoirs mainly focuses on technologies such as multi-wavelet decomposition and reconstruction, matching pursuit and compressed sensing. For example, previous studies have recombined frequency band signals of seismic data by multi-wavelet decomposition and reconstruction to achieve the effect of removing coal interference, eliminated strong reflections of coal seams by multi-channel matching pursuit algorithm, achieved the effect of stripping strong shielding by introducing layer information into the matching pursuit algorithm to achieve the effect of strong shielding, and achieved the effect of removing strong shielding by the L2 norm constraint of the reflection coefficient domain along the layer through compressed sensing. To some extent, such methods can solve the shielding effect of strong radiation caused by the development of coal seams, but they have certain limitations in actual reservoir prediction work. First, the study area suitable for such methods usually develops a large set of thick coal seams, such as coal seams in Daniudi area, which are usually more than 15 m thick, or even thicker. Existing methods have discussed the influence of coal seam thickness and structural changes on the response of coal seam reflection, and the thickness change is usually 2 m or more. However, actual coal-bearing strata are composed of thin interbeds of different lithology, and the thickness of coal seams is usually only about 1-2 m, and multiple sets of 1-2 m thick thin coal seams develop in a set of reservoirs, and the resolution of seismic data cannot distinguish and remove coal seams. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art, and to provide a thin coal seam containing reservoir identification method, system, storage medium and equipment, which improves the thin coal seam containing reservoir identification and prediction accuracy, thereby achieving efficient oil and gas exploration and development.

[0005] The present application is achieved by the following technical solutions:

[0006] In a first aspect of the present application, a thin coal seam containing reservoir identification method is provided. For a determined target interval, the full reflection coefficient sequence of the thin coal seam containing reservoir is calculated in combination with logging data and one-dimensional forward modeling is performed to obtain the waveform and energy of the thin coal seam containing reservoir. Then, the reservoir reflection coefficient sequence after removing the coal seam is obtained and one-dimensional forward modeling is performed to obtain the waveform and energy of the reservoir after removing the coal seam. The reservoir identification characteristics are determined by comparing the energy changes of the two reservoirs.

[0007] Further improvements of the present application are as follows:

[0008] The method specifically comprises the following steps:

[0009] The first step is to determine the target layer section.

[0010] The second step is to obtain a full reflection coefficient sequence of the reservoir containing thin coal seams.

[0011] The third step is to obtain a waveform corresponding to each reflection coefficient by using one-dimensional reflection coefficient forward modeling.

[0012] The fourth step is to determine the maximum amplitude energy value of the wave peak formed by the reservoir containing thin coal seams by synthesizing the seismic record.

[0013] The fifth step is to obtain a reflection coefficient sequence of the reservoir after removing the thin coal seams.

[0014] The sixth step is to obtain the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seams.

[0015] The seventh step is to compare the maximum amplitude energy value of the wave peak formed by the reservoir containing thin coal seams with the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seams, so as to determine the identification characteristics of the coal-bearing reservoir.

[0016] The further improvement of the present application is that:

[0017] In the first step of determining the target layer section, the specific operation includes:

[0018] Obtaining the well logging data, the geological stratification data and the seismic data of the target reservoir area, combining the well logging data with the seismic data and determining the target layer section.

[0019] The obtained well logging data mainly includes acoustic wave and density.

[0020] The further improvement of the present application is that:

[0021] In the second step of obtaining the full reflection coefficient sequence of the reservoir containing thin coal seams, the specific operation includes:

[0022] According to the acoustic logging data, the formation interval velocity of the research area is obtained, then the reflection coefficient is calculated from the density and the formation interval velocity, and the full 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] Wherein, R(n) represents the initial reflection coefficient sequence nth sampling point reflection coefficient, rho (n) is the density value corresponding to the nth sampling point, v(n) is the formation interval velocity corresponding to the nth sampling point, rho (n+1) is the density value corresponding to the n+1th sampling point, v(n+1) is the formation interval velocity corresponding to the n+1th sampling point.

[0026] Further improvement of the present application is that:

[0027] In the third step, the one-dimensional reflection coefficient forward modeling is used to obtain the waveform corresponding to each reflection coefficient, and the specific operation includes:

[0028] The one-dimensional reflection coefficient forward modeling is used to obtain the waveform corresponding to each reflection coefficient by convolution model of the seismic trace beside the well.

[0029] The convolution model is:

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

[0031] Wherein, S n (t) is the synthetic seismogram 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] Further improvement of the present application is that:

[0033] In the fourth step, the maximum amplitude energy value of the wave peak formed by the thin coal seam reservoir is determined through the synthetic seismogram, and the specific operation includes:

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

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

[0036]

[0037] Wherein, S n (t) is the synthetic seismogram of the nth sampling point, and S(t) is the synthetic seismogram of the full reflection coefficient sequence.

[0038] Further improvement of the present application is that:

[0039] In the fifth step, the reservoir reflection coefficient sequence after removing the thin coal seam is obtained, and the specific operation includes:

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

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

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

[0043] The second aspect of the present application provides a thin coal seam containing reservoir identification system, comprising:

[0044] The target layer section determination unit is used for determining the target layer section.

[0045] The first reflection coefficient sequence acquisition unit is used for acquiring the total reflection coefficient sequence of the thin coal seam containing reservoir.

[0046] The waveform acquisition unit acquires the waveform corresponding to each reflection coefficient by using one-dimensional reflection coefficient forward simulation.

[0047] The first maximum amplitude energy value acquisition unit is used for determining the maximum amplitude energy value of the wave peak formed by the thin coal seam containing reservoir through the synthetic seismogram.

[0048] The second reflection coefficient sequence acquisition unit is used for acquiring the reservoir reflection coefficient sequence after removing the thin coal seam.

[0049] The second maximum amplitude energy value acquisition unit is used for acquiring the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seam.

[0050] The identification feature determination unit is used for comparing the maximum amplitude energy value of the wave peak formed by the thin coal seam containing reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seam, and determining the coal seam identification feature.

[0051] The third aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores at least one computer executable program, when the at least one program is executed by the computer, the computer executes the steps of the thin coal seam containing reservoir identification method as described above.

[0052] The fourth aspect of the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program, when the computer program is executed by the processor, the processor executes the steps of the thin coal seam containing reservoir identification method as described above.

[0053] Compared with the prior art, the present application has the advantages of:

[0054] (1) The present application fully respects the characteristics of original logging data, so that the reservoir seismic attribute analysis has high reliability, and lays a foundation for excluding the influence of thin coal seams in subsequent reservoir prediction;

[0055] (2) The present application 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, even if the resolution of seismic data is improved, the thin coal seams cannot be identified. The present application restores the original seismic amplitude energy information of the reservoir through one-dimensional reflection coefficient forward modeling, without the need to identify or remove the thin coal seams;

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

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

[0058] Figure 2 It is a planar change of the coal seam development range and thickness of the target interval;

[0059] Figure 3 It is a schematic view of the total reflection coefficient sequence obtained based on the logging data;

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

[0061] Figure 5 It is a schematic view of the reflection coefficient sequence after removing the thin coal seams;

[0062] Figure 6 It is a one-dimensional reflection coefficient forward modeling result after removing the thin coal seams;

[0063] Figure 7 It is a comparison of the reservoir seismic amplitude energy information before and after removing the coal seams;

[0064] Figure 8 It is a comparison of the reservoir amplitude planar attribute containing coal seams. DETAILED DESCRIPTION

[0065] The present application will be described in further detail below with reference to the accompanying drawings:

[0066] The present application considers the characteristics of thin and irregular development of coal seams in the study area, and in the case that the coal seams cannot be accurately positioned and removed, the characteristics of the reservoirs before and after the thin coal seams are analyzed to determine that the thin coal seam development area can be identified and predicted without removing the coal seams, thereby improving the identification accuracy of the thin coal seam reservoirs and achieving efficient oil and gas exploration and development.

[0067] For a determined target layer, the present application calculates the total reflection coefficient sequence of the thin coal seam and performs one-dimensional forward modeling to obtain the waveform and energy of the thin coal seam reservoir, then obtains the reflection coefficient sequence of the reservoir after removing the thin coal seam and performs one-dimensional forward modeling to obtain the waveform and energy of the reservoir after removing the thin coal seam, and determines the reservoir identification characteristics by comparing the energy changes of the two reservoirs.

[0068] Example 1

[0069] The present application provides a thin coal seam reservoir identification method, comprising the following steps:

[0070] First, determine the target layer;

[0071] Obtain the well logging data, geological stratification data and seismic data of the target reservoir area, and determine the target layer by well-seismic combination;

[0072] The obtained well logging data mainly includes acoustic wave and density.

[0073] Second, obtain the total reflection coefficient sequence of the thin coal seam reservoir;

[0074] According to the acoustic logging data, the formation velocity of the study area is obtained, and then the reflection coefficient is calculated from the density and the formation velocity, and the total reflection coefficient sequence of the thin coal seam reservoir 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] Wherein, 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+1)th sampling point, and v(n+1) is the formation velocity corresponding to the (n+1)th sampling point.

[0078] Third, use one-dimensional reflection coefficient forward modeling to obtain the waveform corresponding to each reflection coefficient;

[0079] The one-dimensional reflection coefficient forward modeling is used, the seismic wavelet is obtained from the convolution model of the seismic trace beside the well, and the waveform corresponding to each reflection coefficient is obtained, wherein the convolution model is:

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

[0081] Wherein, 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] In the fourth step, the maximum amplitude energy value of the wave peak formed by the reservoir containing the thin coal seam is determined through the synthetic seismic record.

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

[0084]

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

[0086] In the fifth step, the reservoir reflection coefficient sequence after removing the thin coal seam is obtained.

[0087] Based on the reflection characteristics of the coal seam, the time window in which the coal seam development section is located and the energy reflection intensity of the coal seam development section are determined, the reflection coefficient R(m) related to the time window in which the coal seam development section is located and meeting the coal seam development characteristics is extracted from the full reflection coefficient sequence, and the reflection coefficient is removed, that is, the coal seam is replaced by the surrounding rock (mudstone), and the reservoir reflection coefficient sequence R2 after removing the thin coal seam is obtained.

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

[0089] Wherein, R(n) is the full reflection coefficient sequence, R(m) is the coal seam reflection coefficient sequence of the target layer system, and R2 is the reservoir reflection coefficient sequence after removing the thin coal seam.

[0090] In the sixth step, the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seam is obtained.

[0091] Based on the reservoir reflection coefficient sequence after removing the thin coal seam, one-dimensional reflection coefficient forward modeling is carried out, the waveform corresponding to each reservoir reflection coefficient after removing the thin coal seam is obtained, and then the maximum amplitude energy value F2 of the wave peak formed by the reservoir after removing the thin coal seam is obtained. The specific method is the same as that of the third step and the fourth step, and will not be described here.

[0092] The seventh step is to compare the maximum amplitude energy value of the peak formed by the thin coal seam reservoir 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-bearing reservoir.

[0093] By comparing the maximum amplitude energy value of the peak formed by the thin coal seam reservoir with the maximum amplitude energy value of the peak formed by the reservoir after removing the thin coal seam, the change in the maximum amplitude energy of the peak formed by the reservoir clarifies the identification characteristics of the coal seam reservoir, and this characteristic can be used to help subsequent reservoir identification.

[0094] By extracting the plane properties of the amplitude of the target layer, the identification markers of the coal-bearing reservoir are clearly defined.

[0095] Examples of applying the method of the present invention are as follows:

[0096]

Example 2

[0097] The study area is located in a transitional marine-continental sedimentary environment, with significant lateral variations in physical properties and thickness. It features abundant coal seams that frequently interbedde with sandstone and mudstone, existing as thin interbedded layers. Statistical analysis of the coal seams contained in the target strata in the study area shows that ( Figure 2 The coal seams are mostly around 1 meter thick, with poor lateral contrast between wells and no obvious planar pattern. Most reservoirs are less than 30 meters thick. The dominant frequency of seismic data in the study area is around 28 Hz, and the layer velocity is 3600-4000 m / s. Using Rayleigh's resolution limit of 1 / 4 wavelength between two adjacent reflection interfaces as a standard, it can be determined that the reservoirs in the study area are all within the tuned thickness range (32-35 meters), and the 1-meter thick coal seam is indistinguishable in the seismic data. Based on this background, analysis of the seismic data clarifies that the coal seam has low velocity and low density characteristics, forming a strong reflection interface with the surrounding rock. This interface information interferes with reservoir reflection identification. Statistical analysis of well logging data clarifies that the reservoir exhibits high impedance compared to the surrounding rock, while the coal seam exhibits low impedance compared to the surrounding rock. The total reflection coefficient sequence was calculated using well logging data (…). Figure 3 It can be seen that the reflection coefficient at the top of the reservoir is positive, and the reflection coefficient at the top of the coal seam is negative. Based on the total reflection coefficient sequence, a one-dimensional forward modeling of the reflection coefficient is performed. Figure 4 The results showed that the reservoir exhibited a peak response, while the coal seam exhibited a trough response. The synthesized record indicated that the amplitude energy corresponding to the coal-bearing reservoir was F1. In the total reflection coefficient sequence, the negative reflection coefficient corresponding to the coal seam in the target section was found. Figure 4 The red reflectance coefficient in the image is removed to obtain the reflectance coefficient sequence after removing the thin coal seam. Figure 5 Based on this reflection coefficient sequence, a one-dimensional forward modeling simulation of the reflection coefficients is performed. Figure 6), from the results can be obtained, no coal seam reservoir corresponding to the amplitude energy is F2. In the actual case shows, F1 is 0.15, F2 is 0.08( Figure 7 ), therefore the existence of the coal seam in this area is to strengthen the amplitude energy of the reservoir, but does not change the relative relationship of the reservoir amplitude energy, that is, the positive value is still positive, and will not be converted into negative value due to the development of thin coal seam. By extracting the maximum amplitude plane attribute on the fixed time window of the target layer, it can be known( Figure 8 ), in well A and C, the logging result shows that the reservoir and the coal seam are combined, and the amplitude attribute is strong amplitude; in well B, the mudstone and the coal seam are combined, and the amplitude attribute is very weak (or negative). Therefore, when the reservoir contains a thin coal seam with small thickness, the wave peak can still represent the reservoir, and the reservoir can be identified on the plane through the amplitude attribute, and there is no need to think of more methods to remove the thin coal seam.

[0098]

Embodiment 3

[0099] The embodiment provides a thin coal seam reservoir identification system, comprising:

[0100] A target layer determination unit is configured to determine a target layer;

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

[0102] A waveform acquisition unit is configured to acquire a waveform corresponding to each reflection coefficient by using one-dimensional reflection coefficient forward modeling;

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

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

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

[0106] An identification feature determination unit is configured 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 removing the thin coal seam, and determine a thin coal seam reservoir identification feature.

[0107]

Embodiment 4

[0108] The embodiment provides a computer readable storage medium, the computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the thin coal seam reservoir identification method according to the embodiment 1.

[0109] Example 5

[0110] The embodiment provides a computer device, comprising a memory and a processor, the memory stores a computer program, the computer program is executed by the processor to make the processor execute the steps of the thin coal seam reservoir identification method in the embodiment 1.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by a computer program instructing the relevant hardware, 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 above-mentioned embodiment method. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0112] The above technical solutions are only one embodiment of the present application. For those skilled in the art, on the basis of the principles disclosed in the present application, various types of improvements or modifications can be easily made, and are not limited to the technical solutions described in the above embodiment. Therefore, the above description is only preferred, and is not limited in nature.

Claims

1. A method for identifying thin coal seam containing reservoirs, characterized by, For a determined target interval, a full reflection coefficient sequence of a thin coal-bearing reservoir is calculated in combination with logging data, and one-dimensional forward modeling is performed to obtain the waveform and energy of the thin coal-bearing reservoir, then a reflection coefficient sequence of a reservoir without coal seams is obtained, and one-dimensional forward modeling is performed to obtain the waveform and energy of the reservoir without coal seams, and the reservoir identification features are determined by comparing the energy changes of the two reservoirs; The method specifically comprises the following steps: First, determine the target interval; Second, obtain the full reflection coefficient sequence of the thin coal-bearing reservoir; Third, obtain the waveform corresponding to each reflection coefficient by one-dimensional reflection coefficient forward modeling; Fourth, determine the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir through synthetic seismogram; Fifth, obtain the reflection coefficient sequence of the reservoir without thin coal seams; Sixth, obtain the maximum amplitude energy value of the wave peak formed by the reservoir without thin coal seams; Seventh, compare the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir without thin coal seams to determine the coal-bearing reservoir identification features.

2. The thin coal seam containing reservoir identification method of claim 1, wherein, In the first step of determining the target interval, the specific operation comprises: Obtain the logging data, geological stratification data and seismic data of the target reservoir area, combine wells with seismic data and determine the target interval; The obtained logging data includes acoustic wave and density.

3. The thin coal seam containing reservoir identification method of claim 2, wherein, In the second step of obtaining the full reflection coefficient sequence of the thin coal-bearing reservoir, the specific operation comprises: According to the acoustic logging data, obtain the formation velocity of the study area, then calculate the reflection coefficient from the density and the formation velocity to obtain the full reflection coefficient sequence of the thin coal-bearing reservoir; 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) Wherein, 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+1)th sampling point, v(n+1) is the formation velocity corresponding to the (n+1)th sampling point.

4. The thin coal seam containing reservoir identification method of claim 3, wherein, In the third step of obtaining the waveform corresponding to each reflection coefficient by one-dimensional reflection coefficient forward modeling, the specific operation comprises: Obtain the seismic wavelet from the convolution model by one-dimensional reflection coefficient forward modeling in combination with the seismic trace beside the well to obtain the waveform corresponding to each reflection coefficient; The convolution model is: S n (t) = W(t)*R(n) where S n (t) is the synthetic seismogram at the nth sample point, W(t) is the seismic wavelet, * is the convolution operation, and R(n) is the reflection coefficient at the nth sample point.

5. The thin coal seam containing reservoir identification method of claim 4, wherein, In the fourth step of determining the maximum amplitude energy value of the wave peak formed by the thin coal-bearing reservoir through synthetic seismogram, the specific operation comprises: Obtain the synthetic seismogram S(t) of the full reflection coefficient sequence by synthetic seismogram to determine the maximum amplitude energy value F1 of the wave peak formed by the thin coal-bearing reservoir; The synthetic seismogram is calculated by formula (2): （2） where S n (t) is the synthetic seismogram at the nth sample point, and S(t) is the synthetic seismogram of the sequence of the total reflection coefficients.

6. The thin coal seam containing reservoir identification method of claim 1, wherein, In the fifth step of obtaining the reflection coefficient sequence of the reservoir without thin coal seams, the specific operation comprises: Based on the coal seam reflection characteristics, the time window where the coal seam development section is located and the energy reflection intensity of the coal seam development section are determined, the reflection coefficient R(m) related to the coal seam development characteristics in the time window where the coal seam development section is located is extracted from the total reflection coefficient sequence, the reflection coefficient of this kind is removed, that is, the set of coal seams is replaced by surrounding rock, and the reservoir reflection coefficient sequence R2 after removing the thin coal seam is obtained; R2 = R(n)-R(m) Wherein, R(n) is the total reflection coefficient sequence, R(m) is the coal seam reflection coefficient sequence of the target layer system, and R2 is the reservoir reflection coefficient sequence after removing the thin coal seam.

7. A thin coal seam containing reservoir identification system characterized by, For the determined target layer section, the total reflection coefficient sequence containing the thin coal seam is calculated combined with the logging data, and one-dimensional forward modeling is performed to obtain the waveform and energy of the thin coal seam containing reservoir, then the reservoir reflection coefficient sequence after removing the coal seam is obtained and one-dimensional forward modeling is performed to obtain the waveform and energy of the reservoir after removing the coal seam, the reservoir identification characteristics are determined by comparing the energy changes of the two reservoirs, and the system comprises: A target layer section determination unit is configured to determine a target layer section. A first reflection coefficient sequence acquisition unit is configured to acquire a total reflection coefficient sequence of a reservoir containing a thin coal seam. A waveform acquisition unit is configured to acquire a waveform corresponding to each reflection coefficient by one-dimensional reflection coefficient forward modeling. A first maximum amplitude energy value acquisition unit is configured to determine a maximum amplitude energy value of a wave peak formed by a thin coal seam containing reservoir by synthetic seismogram. A second reflection coefficient sequence acquisition unit is configured to acquire a reservoir reflection coefficient sequence after removing the thin coal seam. A second maximum amplitude energy value acquisition unit is configured to acquire a maximum amplitude energy value of a wave peak formed by a reservoir after removing the thin coal seam. An identification characteristic determination unit is configured to compare the maximum amplitude energy value of the wave peak formed by the thin coal seam containing reservoir with the maximum amplitude energy value of the wave peak formed by the reservoir after removing the thin coal seam, and determine the coal reservoir identification characteristics.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the thin coal seam containing reservoir identification method of any one of claims 1-6.

9. A computer device, comprising: The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the thin coal seam containing reservoir identification method of any one of claims 1-6. The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the thin coal seam containing reservoir identification method of any one of claims 1-6.

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