Lithofacies earthquake prediction method, electronic equipment and medium

Abstract

The invention discloses a lithofacies earthquake prediction method, electronic equipment and a medium. The method comprises the steps that 1, a target interval is determined, and the interval number range of each lithofacies and the range of the proportion of the thickness of each lithofacies in the target interval are set; 2, obtaining the combination of lithofacies types, and calculating the thickness of the corresponding lithofacies type; 3, according to the calculation result of the step 2, random implementation of all lithofacies combination models is carried out; 4, seismic wavelets are extracted through well seismic calibration, and the frequency band range is determined; 5, performing seismic forward modeling on the lithofacies combination model, and establishing a model space for lithofacies prediction; 6, searching a lithofacies combination model which is optimally matched with the seismic reflection waveform from the model space for each piece of seismic data of the three-dimensional work area seismic data; and step 7, repeating the step 6 for all seismic traces in the work area to realize spatial distribution prediction of lithofacies combination in the whole area. The lithofacies spatial distribution prediction is obtained through the seismic data and the logging data.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development, and more specifically, to a lithofacies seismic prediction method, electronic equipment, and medium. Background Technology

[0002] Lithofacies information reflects reservoir lithology and fluid characteristics, playing a crucial role in seismic reservoir prediction. Therefore, conducting lithofacies seismic prediction is of great significance. Conventional methods mainly fall into two categories. One category utilizes elastic parameters closely related to lithofacies information to qualitatively or quantitatively convert them into lithofacies information. The acquisition of these elastic parameters primarily relies on pre-stack seismic inversion techniques, but the accuracy of pre-stack seismic inversion varies among different elastic parameters, thus affecting the prediction accuracy of lithofacies. The other category, based on the analysis of lithofacies reservoir characteristics and seismic response characteristics, establishes a fitting relationship between geological parameters and sensitive seismic attributes to characterize the planar distribution of geological parameters. Then, cluster analysis is performed using geological parameters as variables to achieve planar prediction of lithofacies. However, cluster analysis algorithms are data-driven methods with significant uncertainties.

[0003] A lithofacies earthquake prediction method still needs to be developed.

[0004] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention proposes a lithofacies seismic prediction method, electronic equipment, and medium, which can obtain lithofacies spatial distribution prediction through seismic data and well logging data, and be used for subsequent reservoir and fluid prediction to support oil and gas exploration and development.

[0006] In a first aspect, embodiments of this disclosure provide a method for predicting lithofacies earthquakes, including:

[0007] Step 1: Interpret the lithofacies data of the work area, determine the target layer, and set the range of the number of layers for each lithofacies and the range of the proportion of the thickness of each lithofacies in the target layer.

[0008] Step 2: Determine the initial lithofacies type, obtain the combination of lithofacies types through random simulation, and calculate the thickness of the corresponding lithofacies type;

[0009] Step 3: Based on the calculation results of Step 2, perform random implementation of all lithofacies combination models;

[0010] Step 4: Extract seismic wavelets through well-seismic calibration to determine the frequency band range;

[0011] Step 5: Perform seismic forward modeling on the lithofacies assemblage model to obtain the corresponding seismic records, and then establish the model space for lithofacies prediction;

[0012] Step 6: For each seismic data point in the 3D seismic data of the work area, find the lithofacies combination model that best matches the seismic reflection waveform in the model space;

[0013] Step 7: Repeat step 6 for all seismic traces in the work area to achieve spatial distribution prediction of lithofacies assemblages throughout the area.

[0014] Preferably, determining the target stratigraphic segment and setting the range of the number of layers for each lithofacies and the range of the proportion of the thickness of each lithofacies in the target stratigraphic segment includes:

[0015] The number of layers of each lithofacies within the target stratum of each well is counted based on the target stratum, and then the range of the number of layers is set.

[0016] The total thickness of each lithofacies in each well is statistically analyzed, and the proportion of the total thickness of each lithofacies in the total thickness of the target layer is calculated. Then, the range of the proportion of each lithofacies in the target layer is set.

[0017] Preferably, step 2 includes:

[0018] Determine the single-layer thickness of each lithofacies within the target section, and then determine the probability distribution of the thickness variation of each lithofacies, as well as the probability distribution of the P-wave velocity, S-wave velocity, and density of each lithofacies.

[0019] Given an initial lithofacies type based on well logging statistics, a random simulation is performed for the lithofacies type, and the thickness of the corresponding lithofacies type is obtained by random sampling of the lithofacies thickness probability distribution.

[0020] Preferably, stochastic simulation based on lithofacies type includes:

[0021]

[0022] Where, π t (t=1,2,...,T) represents the lithofacies type at time t, p(π t+1 p(π0) represents the probability of the lithofacies type at time t+1, p(π0) represents the probability of the initial lithofacies type, and p(|) represents the conditional probability.

[0023] Preferably, step 3 includes:

[0024] Determine whether the calculation results of step 2 conform to the range of the number of layers for each lithofacies and the proportion of the thickness of each lithofacies in the target layer. If not, repeat step 2 until it conforms. Then, randomly sample according to the probability distribution of P-wave velocity, S-wave velocity and density of each lithofacies to obtain the corresponding P-wave velocity, S-wave velocity and density of each lithofacies. This obtains one random realization of the lithofacies combination. Repeat steps 2-3 to obtain random realizations of all lithofacies combination models.

[0025] Preferably, step 4 includes:

[0026] Seismic wavelets are extracted through well-seismic calibration, and their spectrum W(f) is calculated to determine their frequency band range [f]. b f u ], where f b f is the lower limit of the frequency band. u This represents the upper limit of the frequency band.

[0027] Preferably, seismic forward modeling is performed on the lithofacies assemblage model, that is, the P-wave velocity, S-wave velocity, and density corresponding to each lithofacies assemblage model are used to calculate according to the following formula:

[0028]

[0029] in, i is the complex unit. p = sin i1 / α1, ξ1 = cos i1 / α1, i1 is the angle, α1 and α2 are the longitudinal wave velocities of the upper and lower media respectively, β1 and β2 are the transverse wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, J0 is the zero-order Bessel function, J1 is the first-order Bessel function, θ is the incident angle of the ray, r is the offset distance, h and z are the vertical distances from the source and receiver to the reflecting interface respectively, and R is the propagation distance of the seismic wave from the source to the receiver and R = (h + z) / cosθ;

[0030] Obtain multiple lithofacies combination models f model and its corresponding seismic forward modeling record d model ;

[0031] Combining actual data, namely the well logging lithofacies interpretation results f real And the corresponding well-side seismic traces, the well-side seismic traces are bandpass filtered, and the processed result is d real , so that it is with d model They have the same frequency band;

[0032] Construct a model space for lithofacies prediction, where the space formed by lithofacies assemblages is {f}. model f real The corresponding space formed by the seismic data is {d}.model d real}

[0033] Preferably, the seismic data that best matches the seismic reflection waveform is found from the model space using the following formula, and the lithofacies assemblage corresponding to this seismic data is the lithofacies prediction result:

[0034] J = ||d syn -d obs ||2+λ2cor(d syn -d obs )

[0035] Where, d syn d represents seismic data in the model space for lithofacies prediction. obs For actual seismic data after bandpass filtering, and d syn They have the same frequency band [f b f u ], λ2 is the weighting coefficient, which is a positive number, and cor(,) represents the correlation coefficient between the two data.

[0036] Secondly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0037] Memory, which stores executable instructions;

[0038] A processor that executes the executable instructions in the memory to implement the lithofacies seismic prediction method.

[0039] Thirdly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the described lithofacies seismic prediction method.

[0040] Its beneficial effects are as follows:

[0041] This invention makes full use of data such as well logging and seismic data to establish a model space for lithofacies assemblages, and directly finds the lithofacies assemblage model that best matches the seismic reflection waveform from the model space, ultimately realizing the prediction of the spatial distribution of lithofacies assemblages.

[0042] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0043] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0044] Figure 1 A flowchart illustrating the steps of a lithofacies seismic prediction method according to an embodiment of the present invention is shown.

[0045] Figure 2 A schematic diagram of seismic data according to an embodiment of the present invention is shown.

[0046] Figure 3 A schematic diagram of lithofacies prediction results according to an embodiment of the present invention is shown. Detailed Implementation

[0047] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0048] To facilitate understanding of the solutions and effects of the embodiments of the present invention, four specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0049] Example 1

[0050] Figure 1 A flowchart illustrating the steps of a lithofacies seismic prediction method according to an embodiment of the present invention is shown.

[0051] like Figure 1 As shown, this lithofacies earthquake prediction method includes:

[0052] Step 1: Interpret the lithofacies data of the work area, determine the target layer, and set the range of the number of layers for each lithofacies and the range of the proportion of the thickness of each lithofacies in the target layer.

[0053] Step 2: Determine the initial lithofacies type, obtain the combination of lithofacies types through random simulation, and calculate the thickness of the corresponding lithofacies type;

[0054] Step 3: Based on the calculation results of Step 2, perform random implementation of all lithofacies combination models;

[0055] Step 4: Extract seismic wavelets through well-seismic calibration to determine the frequency band range;

[0056] Step 5: Perform seismic forward modeling on the lithofacies assemblage model to obtain the corresponding seismic records, and then establish the model space for lithofacies prediction;

[0057] Step 6: For each seismic data point in the 3D seismic data of the work area, find the lithofacies combination model that best matches the seismic reflection waveform in the model space;

[0058] Step 7: Repeat step 6 for all seismic traces in the work area to achieve spatial distribution prediction of lithofacies assemblages throughout the area.

[0059] In one example, the target stratigraphic segment is determined, and the range of the number of layers for each lithofacies and the range of the proportion of each lithofacies thickness in the target stratigraphic segment are defined, including:

[0060] The number of layers of each lithofacies within the target stratum of each well is counted based on the target stratum, and then the range of the number of layers is set.

[0061] The total thickness of each lithofacies in each well is statistically analyzed, and the proportion of the total thickness of each lithofacies in the total thickness of the target layer is calculated. Then, the range of the proportion of each lithofacies in the target layer is set.

[0062] In one example, step 2 includes:

[0063] Determine the single-layer thickness of each lithofacies within the target section, and then determine the probability distribution of the thickness variation of each lithofacies, as well as the probability distribution of the P-wave velocity, S-wave velocity, and density of each lithofacies.

[0064] Given an initial lithofacies type based on well logging statistics, a random simulation is performed for the lithofacies type, and the thickness of the corresponding lithofacies type is obtained by random sampling of the lithofacies thickness probability distribution.

[0065] In one example, stochastic simulations for lithofacies types include:

[0066]

[0067] Where, π t (t=1,2,...,T) represents the lithofacies type at time t, p(π t+1 p(π0) represents the probability of the lithofacies type at time t+1, p(π0) represents the probability of the initial lithofacies type, and p(|) represents the conditional probability.

[0068] In one example, step 3 includes:

[0069] Determine whether the calculation results of step 2 conform to the range of the number of layers for each lithofacies and the proportion of the thickness of each lithofacies in the target layer. If not, repeat step 2 until it conforms. Then, randomly sample according to the probability distribution of P-wave velocity, S-wave velocity and density of each lithofacies to obtain the corresponding P-wave velocity, S-wave velocity and density of each lithofacies. This obtains one random realization of the lithofacies combination. Repeat steps 2-3 to obtain random realizations of all lithofacies combination models.

[0070] In one example, step 4 includes:

[0071] Seismic wavelets are extracted through well-seismic calibration, and their spectrum W(f) is calculated to determine their frequency band range [f]. b f u ], where f b f is the lower limit of the frequency band. u This represents the upper limit of the frequency band.

[0072] In one example, a seismic forward modeling is performed on a lithofacies assemblage model, which involves calculating the P-wave velocity, S-wave velocity, and density corresponding to each lithofacies assemblage model using the following formula:

[0073]

[0074] in, i is the complex unit. p = sin i1 / α1, ξ1 = cos i1 / α1, i1 is the angle, α1 and α2 are the longitudinal wave velocities of the upper and lower media respectively, β1 and β2 are the transverse wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, J0 is the zero-order Bessel function, J1 is the first-order Bessel function, θ is the incident angle of the ray, r is the offset distance, h and z are the vertical distances from the source and receiver to the reflecting interface respectively, and R is the propagation distance of the seismic wave from the source to the receiver and R = (h + z) / cosθ;

[0075] Obtain multiple lithofacies combination models f model and its corresponding seismic forward modeling record d model ;

[0076] Combining actual data, namely the well logging lithofacies interpretation results f real And the corresponding well-side seismic traces, the well-side seismic traces are bandpass filtered, and the processed result is d real , so that it is with d model They have the same frequency band;

[0077] Construct a model space for lithofacies prediction, where the space formed by lithofacies assemblages is {f}. model f real The corresponding space formed by the seismic data is {d}. model d real}

[0078] In one example, the seismic data that best matches the seismic reflection waveform is found from the model space using the following formula; the lithofacies assemblage corresponding to this seismic data is the lithofacies prediction result:

[0079] J = ||d syn -dobs ||2+λ2cor(d syn -d obs )

[0080] Where, d syn d represents seismic data in the model space for lithofacies prediction. obs For actual seismic data after bandpass filtering, and d syn They have the same frequency band [f b f u ], λ2 is the weighting coefficient, which is a positive number, and cor(,) represents the correlation coefficient between the two data.

[0081] Specifically, this method includes the following steps:

[0082] Step 1: Interpret the lithofacies data of the work area. Assume there are N lithofacies, denoted as 1 for lithofacies 1, 2 for lithofacies 2, ..., and N for lithofacies N. Select the target interval to be studied and count the number c of each lithofacies layer within the target interval for each well. k Assume the range of the statistical layers is h. k ~j k Calculate the total thickness of each lithofacies and its percentage in the target stratum. (Example d) k ~l k k takes values ​​from 1 to N.

[0083] Step 2: Calculate the thickness of each facies layer within the target section, and perform probability fitting on the thickness of each facies layer, usually using an exponential distribution, to determine the probability distribution satisfied by the thickness variation of each facies layer; at the same time, obtain the probability distribution satisfied by the P-wave velocity, S-wave velocity and density of each facies layer, usually a Gaussian distribution.

[0084] Given an initial lithofacies type based on well logging statistics, the lithofacies type is then randomly simulated using the following formula:

[0085] Assuming the data length to be simulated is T, random simulation is performed according to the following formula.

[0086]

[0087] Where, π t (t=1,2,...,T) represents the lithofacies type at time t, p(π t ) and p(π t+1 ) refers to the probability of lithofacies type at times t and t+1, and p(π0) is the probability of the initial lithofacies type.

[0088] The thickness of the corresponding lithofacies type is obtained by random sampling based on the probability distribution in step 2 until the simulation of the entire length T is completed.

[0089] Step 3: Then, determine the result of Step 2: whether the number of each lithofacies layer is within h. k ~j k Within the range, are the percentages of each lithofacies within d? k ~l k If the range is not met, repeat step 2 until the requirements are met. Then, randomly sample according to the probability distribution of P-wave velocity, S-wave velocity, and density of each lithofacies to obtain the corresponding P-wave velocity, S-wave velocity, and density for each lithofacies. This completes one random realization of the lithofacies assemblage. Repeat steps 2-3 until 2000 random realizations of lithofacies assemblage models are obtained.

[0090] Step 4: Extract the seismic wavelet through well-seismic calibration, calculate its spectrum W(f), and determine its frequency band range, f b f is the lower limit of the frequency band. u This represents the upper limit of the frequency band.

[0091] Step 5: Perform seismic forward modeling on the lithofacies assemblage model established in Step 3 according to the following formula to obtain the corresponding seismic records:

[0092]

[0093] in, i is the complex unit. p = sin i1 / α1, ξ1 = cos i1 / α1, where i1 is the angle, α1 and α2 are the P-wave velocities of the upper and lower media respectively, β1 and β2 are the S-wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, J0 is the zero-order Bessel function, J1 is the first-order Bessel function, θ is the incident angle of the ray, r is the offset distance, h and z are the vertical distances from the source and receiver to the reflecting interface respectively, and R is the propagation distance of the seismic wave from the source to the receiver, and R = (h + z) / cosθ. The P-wave velocity, S-wave velocity, and density are obtained by random sampling from the probability distribution fitted in step 2.

[0094] Obtain multiple lithofacies combination models f model and its corresponding seismic forward modeling record d model Combined with actual data, namely the well logging lithofacies interpretation results f real and the corresponding well-side seismic channel d real For the seismic channel d next to the well real Perform bandpass filtering to make it consistent with d model They have the same frequency band; construct a model space for lithofacies prediction, where the space formed by lithofacies assemblages is {f model f real The corresponding space formed by the seismic data is {d}. model d real}

[0095] Step 6: Based on each randomly simulated lithofacies assemblage model and its corresponding seismic record, as well as the well logging lithofacies interpretation results and the corresponding well-side seismic traces, establish a lithofacies prediction model space. For each seismic trace of the 3D seismic data for the work area, find the lithofacies assemblage model that best matches the seismic reflection waveform from the model space according to the following formula:

[0096] J = ||d syn -d obs ||2+λ2cor(d syn -d obs ).

[0097] Step 7: Repeat step 6 for all seismic traces in the work area to achieve spatial distribution prediction of lithofacies assemblages throughout the area.

[0098] Example 2

[0099] Figure 2 A schematic diagram of seismic data according to an embodiment of the present invention is shown.

[0100] Figure 3 A schematic diagram of lithofacies prediction results according to an embodiment of the present invention is shown. An example is provided to illustrate the implementation process of the present invention. Figure 2 This is the actual post-stack seismic data for a certain work area; Figure 3 The lithofacies prediction results obtained using this invention include three lithofacies: sandstone, mudstone, and siltstone, represented by orange, gray, and yellow, respectively. The well logging curves in the figure represent clay content; generally, sandstone has low clay content, mudstone has high clay content, and siltstone has a moderate clay content. Figure 3 It is evident that the lithofacies prediction results and the well logging curves show a good agreement, thus proving that this method has high lithofacies prediction accuracy.

[0101] Example 3

[0102] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that runs the executable instructions in the memory to implement the above-described lithofacies earthquake prediction method.

[0103] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0104] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0105] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0106] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0107] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0108] Example 4

[0109] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the described lithofacies seismic prediction method.

[0110] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0111] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0112] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0113] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting lithofacies earthquakes, characterized in that, include: Step 1: Interpret the lithofacies data of the work area, determine the target layer, and set the range of the number of layers for each lithofacies and the range of the proportion of the thickness of each lithofacies in the target layer. Step 2: Determine the initial lithofacies type, obtain the combination of lithofacies types through random simulation, and calculate the thickness of the corresponding lithofacies type; Step 3: Based on the calculation results of Step 2, perform random implementation of all lithofacies combination models; Step 4: Extract seismic wavelets through well-seismic calibration to determine the frequency band range; Step 5: Perform seismic forward modeling on the lithofacies assemblage model to obtain the corresponding seismic records, and then establish the model space for lithofacies prediction; Step 6: For each seismic data point in the 3D seismic data of the work area, find the lithofacies combination model that best matches the seismic reflection waveform in the model space; Step 7: Repeat step 6 for all seismic traces in the work area to achieve spatial distribution prediction of lithofacies assemblages throughout the area.

2. The lithofacies seismic prediction method according to claim 1, wherein, Determine the target stratigraphic segment, and define the range of the number of layers for each lithofacies and the range of the proportion of the thickness of each lithofacies within the target stratigraphic segment, including: The number of layers of each lithofacies within the target stratum of each well is counted based on the target stratum, and then the range of the number of layers is set. The total thickness of each lithofacies in each well is statistically analyzed, and the proportion of the total thickness of each lithofacies in the total thickness of the target layer is calculated. Then, the range of the proportion of each lithofacies in the target layer is set.

3. The lithofacies seismic prediction method according to claim 1, wherein, Step 2 includes: Determine the single-layer thickness of each lithofacies within the target section, and then determine the probability distribution of the thickness variation of each lithofacies, as well as the probability distribution of the P-wave velocity, S-wave velocity, and density of each lithofacies. Given an initial lithofacies type based on well logging statistics, a random simulation is performed for the lithofacies type, and the thickness of the corresponding lithofacies type is obtained by random sampling of the lithofacies thickness probability distribution.

4. The lithofacies seismic prediction method according to claim 3, wherein, Stochastic simulations based on lithofacies type include: Where, π t (t=1,2,...,T) represents the lithofacies type at time t, p(π t+1 p(π0) represents the probability of the lithofacies type at time t+1, p(π0) represents the probability of the initial lithofacies type, and p(|) represents the conditional probability.

5. The lithofacies seismic prediction method according to claim 1, wherein, Step 3 includes: Determine whether the calculation results of step 2 conform to the range of the number of layers for each lithofacies and the proportion of the thickness of each lithofacies in the target layer. If not, repeat step 2 until it conforms. Then, randomly sample according to the probability distribution of P-wave velocity, S-wave velocity and density of each lithofacies to obtain the corresponding P-wave velocity, S-wave velocity and density of each lithofacies. This obtains one random realization of the lithofacies combination. Repeat steps 2-3 to obtain random realizations of all lithofacies combination models.

6. The lithofacies seismic prediction method according to claim 1, wherein, Step 4 includes: Seismic wavelets are extracted through well-seismic calibration, and their spectrum W(f) is calculated to determine their frequency band range [f]. b f u ], where f b f is the lower limit of the frequency band. u This represents the upper limit of the frequency band.

7. The lithofacies seismic prediction method according to claim 1, wherein, Seismic forward modeling is performed on the lithofacies assemblage model, which involves calculating the P-wave velocity, S-wave velocity, and density corresponding to each lithofacies assemblage model according to the following formula: in, It is a plural unit. r0 = 1, p = sin1 / α1, ξ1 = cosi1 / α1, i1 is the angle, α1 and α2 are the longitudinal wave velocities of the upper and lower media respectively, β1 and β2 are the transverse wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, J0 is the zero-order Bessel function, J1 is the first-order Bessel function, θ is the incident angle of the ray, r is the offset distance, h and z are the vertical distances from the source and receiver to the reflecting interface respectively, and R is the propagation distance of the seismic wave from the source to the receiver and R = (h + z) / cosθ; Obtain multiple lithofacies combination models f model and its corresponding seismic forward modeling record d model ; Combining actual data, namely the well logging lithofacies interpretation results f real And the corresponding well-side seismic traces, the well-side seismic traces are bandpass filtered, and the processed result is d real , so that it is with d model They have the same frequency band; Construct a model space for lithofacies prediction, where the space formed by lithofacies assemblages is {f}. model f real The corresponding space formed by the seismic data is {d}. model d real } 8. The lithofacies seismic prediction method according to claim 1, wherein, The best-matching seismic reflection waveform is found in the model space using the following formula; the corresponding lithofacies assemblage is the lithofacies prediction result: J=||d syn -d obs ||2+λ2cor(d syn -d obs ) Where, d syn d represents seismic data in the model space for lithofacies prediction. obs For actual seismic data after bandpass filtering, and d syn They have the same frequency band [f b f u }], λ2 is the weight coefficient, which is a positive number, and cor(,) represents the correlation coefficient between the two data.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the lithofacies seismic prediction method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the lithofacies seismic prediction method according to any one of claims 1-8.

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