A multi-stage phase control constraint modeling method and device

Abstract

The application provides a multi-level phase control constraint modeling method and device, a computer readable storage medium and an electronic device. The method comprises the following steps: dividing a well logging sedimentary phase by using well logging data of a research area, and respectively interpreting lithofacies in each sedimentary phase; counting P-wave velocity, S-wave velocity and density in each lithofacies, and determining the mean value and variance of the P-wave velocity, the S-wave velocity and the density in each lithofacies; obtaining a first-level phase control lithofacies probability under different sedimentary phase controls according to a seismic sedimentary phase and a well logging lithofacies interpretation result; obtaining a second-level phase control lithofacies probability under different seismic data conditions according to a pre-stack inversion result; obtaining a multi-level phase control lithofacies probability according to the first-level phase control lithofacies probability and the second-level phase control lithofacies probability; and obtaining a random modeling result of the P-wave velocity, the S-wave velocity and the density according to the multi-level phase control lithofacies probability and the mean value and variance of the P-wave velocity, the S-wave velocity and the density in each lithofacies.

Description

Technical Field

[0001] This invention relates to the field of oil and gas seismic exploration technology, and in particular to a multi-level phase control constraint modeling method, apparatus, computer-readable storage medium, and electronic device. Background Technology

[0002] Continental clastic reservoirs are characterized by strong heterogeneity and rapid facies transitions, and are heavily influenced by sedimentary and lithofacies facies. To address these issues, current reservoir prediction techniques mainly fall into two categories. One category includes conventional techniques such as post-stack impedance inversion and pre-stack three-parameter inversion, which do not consider sedimentary facies constraints, resulting in predictions that often do not conform to geological patterns and have low accuracy. The second category is facies-controlled inversion techniques, which typically consider sedimentary facies constraints when establishing the low-frequency inversion model, or add a geological regularization term to the inversion objective function for constraint, but still have insufficient constraints on geological conditions. Summary of the Invention

[0003] To address the aforementioned problems, embodiments of the present invention provide a multi-level phase control constraint modeling method, apparatus, computer-readable storage medium, and electronic device.

[0004] In a first aspect, embodiments of the present invention provide a multi-level phase control constraint modeling method, including:

[0005] S100, using well logging data of the study area to divide well logging sedimentary facies, and interpreting lithofacies within each sedimentary facies;

[0006] S200, statistically analyze the P-wave velocity, S-wave velocity and density in each lithofacies, and determine the mean and variance of the P-wave velocity, S-wave velocity and density in each lithofacies;

[0007] S300, seismic attributes are extracted using post-stack data of the study area, and sensitive attributes that can reflect sedimentary facies are selected from the seismic attributes based on the well logging sedimentary facies interpretation results, and the sensitive attribute thresholds for each sedimentary facies are determined.

[0008] S400, determine the upper and lower layers of the target layer to be studied, and calculate the sensitive attribute slices of the entire seismic data volume between the upper and lower layers;

[0009] S500, based on the sensitive attribute threshold obtained in step S300, determine the seismic sedimentary facies and well logging lithofacies interpretation results corresponding to the sensitive attribute slices between the upper and lower layers;

[0010] S600, based on the seismic sedimentary facies and well logging lithofacies interpretation results obtained in step 500, the probabilities of different well logging lithofacies within each sedimentary facies are statistically analyzed, thereby obtaining the probabilities of first-order facies-controlled lithofacies under different sedimentary facies control.

[0011] S700: Based on the pre-stack seismic angle stacking data of the study area, pre-stack inversion is performed to obtain the inversion results of P-wave velocity, S-wave velocity and density.

[0012] S800: Combining the P-wave velocity, S-wave velocity and density obtained in step S200 within each lithofacies, determine the probability that the P-wave velocity, S-wave velocity and density obtained in step S700 belong to each lithofacies, thereby obtaining the probability of secondary facies-controlled lithofacies under different seismic data conditions.

[0013] S900, determine the multi-level facies probability based on the first-level facies probability and the second-level facies probability;

[0014] S1000: Based on the multi-level phase-controlled lithofacies probabilities obtained in step S900, and the mean and variance of P-wave velocity, S-wave velocity, and density in each lithofacies obtained in step S200, obtain the stochastic modeling results of P-wave velocity, S-wave velocity, and density.

[0015] According to an embodiment of the present invention, in step S200 above, the mean and variance of the longitudinal wave velocity, transverse wave velocity and density in each rock facies are determined by Gaussian distribution fitting.

[0016] According to an embodiment of the present invention, the post-stack data of the above-mentioned study area is well-side post-stack data.

[0017] According to embodiments of the present invention, the above-mentioned seismic attributes include amplitude-type and coherence-type seismic attributes.

[0018] According to an embodiment of the present invention, in step S800 above, the probability of the P-wave velocity, S-wave velocity and density obtained in step S700 belonging to each rock facies is determined by using a Naive Bayes classification algorithm.

[0019] According to an embodiment of the present invention, in step S900 above, the multi-level facies-controlled lithofacies probability is determined according to the following formula:

[0020]

[0021] in, τ1 and τ2 are used to adjust the contributions of the data F and d, where F represents sedimentary facies, π represents lithofacies, P(π|F) is the lithofacies probability under different sedimentary facies control, d represents seismic data, P(π|d) is the lithofacies probability under seismic data conditions, and P(π) is the sedimentary facies probability.

[0022] According to an embodiment of the present invention, τ1=τ2=1, so that P(π|F) and P(π|d) contribute the same to the posterior probability.

[0023] Secondly, the present invention also provides a multi-level phase control constraint modeling device, characterized in that it comprises:

[0024] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-level phase control constraint modeling method as described in the first aspect above.

[0025] Fourthly, embodiments of the present invention provide an electronic device comprising:

[0026] processor;

[0027] Memory used to store the processor's executable instructions;

[0028] The processor is configured to execute the instructions to implement a multi-level phase control constraint modeling method as described in the first aspect above.

[0029] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0030] In response to the characteristics of strong heterogeneity and rapid facies transformation in terrestrial clastic reservoirs, embodiments of this invention propose a multi-level facies-controlled constraint modeling method that fully considers the influence of sedimentary facies and lithofacies. This method establishes an initial model that is more consistent with geological conditions for subsequent seismic inversion, which is beneficial to improving the accuracy of seismic inversion and reservoir prediction and reducing exploration and development risks. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart of the steps of the multi-level phase control constraint modeling method provided in the embodiments of the present invention;

[0033] Figure 2 This is a schematic diagram of the theoretical lithofacies model provided in an embodiment of the present invention;

[0034] Figure 3 yes Figure 1 A schematic diagram of the P-wave velocity, S-wave velocity, and density corresponding to the theoretical lithofacies model;

[0035] Figure 4 This is a schematic diagram of the P-wave velocity, S-wave velocity, and density model obtained by the multi-level phased-controlled constraint modeling method provided in the embodiments of the present invention;

[0036] Figure 5 This is a schematic diagram of the composition of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1

[0039] like Figure 1 As shown, the multi-level phase control constraint modeling method provided in this embodiment of the invention mainly includes the following steps.

[0040] Step 1: Collect post-stack data, pre-stack seismic angle stacking data, target layer data, and well logging data (P-wave velocity, S-wave velocity, and density) for the target area;

[0041] Step 2: Use well logging data to classify sedimentary facies, and interpret lithofacies within each sedimentary facies;

[0042] Step 3: Statistically analyze the P-wave velocity, S-wave velocity, and density in each lithofacies, and perform Gaussian distribution fitting to obtain the mean and variance of the P-wave velocity, S-wave velocity, and density in different lithofacies.

[0043] Step 4: Extract seismic attributes such as amplitude and coherence from well-side post-stack data, combine with well logging sedimentary facies interpretation results, select sensitive attributes that can reflect sedimentary facies, and determine the sensitive attribute thresholds for each sedimentary facies.

[0044] Step 5: Assuming the target layers are H1 and H2, where H1 and H2 are the upper and lower layers respectively, calculate the sensitive attribute slices of the entire seismic data volume between layers H1 and H2;

[0045] Step 6: Obtain the seismic sedimentary facies and well logging lithofacies interpretation results corresponding to the sensitive attribute slices between H1 and H2 based on the thresholds described in Step 4 above;

[0046] Step 7: Based on the results of Step 6, calculate the probability P(π|F) of different well-logging lithofacies within each sedimentary facies, where F represents the sedimentary facies, π represents the lithofacies, and P(π|F) is the lithofacies probability under different sedimentary facies control. This is the first-order facies-controlled lithofacies probability.

[0047] Step 8: Perform pre-stack inversion based on the pre-stack angle stacking data to obtain the inversion results of P-wave velocity, S-wave velocity, and density;

[0048] Step 9: Based on the data from Step 3, determine the probability P(π|d) of the P-wave velocity, S-wave velocity and density in Step 8 belonging to each lithofacies using the Naive Bayes classification algorithm, where d represents the seismic data, π represents the lithofacies, and P(π|d) is the lithofacies probability under different seismic data conditions. This is the second-order facies-controlled lithofacies probability.

[0049] Step 10: Based on the following formula, combine the first-level and second-level facies-controlled lithofacies probabilities to obtain the multi-level facies-controlled lithofacies probabilities;

[0050]

[0051] in,

[0052]

[0053] P(π) is the probability of the sedimentary phase.

[0054] τ1 and τ2 are used to adjust the contributions of the regulation data F and d.

[0055] τ1=τ2=1 is equivalent to assuming that P(π|F) and P(π|d) contribute the same to the posterior probability.

[0056] Step 11: Based on the lithofacies probabilities obtained in Step 10 and the mean and variance of P-wave velocity, S-wave velocity and density of different lithofacies in Step 3, random sampling can be used to obtain the random modeling results of P-wave velocity, S-wave velocity and density.

[0057] The following example illustrates the implementation process of this invention. Models for three lithofacies—sandstone, siltstone, and mudstone—are selected. Pre-stack angular seismic data is synthesized using seismic wavelet data, and then this synthesized data is used for stochastic modeling to test the method proposed in this invention.

[0058] Appendix Figure 2 For theoretical lithofacies model; attached Figure 3 (a), (b), and (c) in the figure represent the theoretical models for P-wave velocity, S-wave velocity, and density, respectively. (See appendix.) Figure 4 (a), (b), and (c) in the figure are the initial models of P-wave velocity, S-wave velocity, and density obtained using the present invention, respectively. It can be seen that the main geological bodies presented by the results of the present invention, such as channel morphology and mudstone distribution characteristics, are in good agreement with the lithofacies model. Furthermore, the initial models of P-wave velocity, S-wave velocity, and density are also close to the theoretical models. This indicates that the initial model established by the present invention conforms to the geological conditions and can lay the foundation for improving the accuracy of reservoir prediction.

[0059] Example 2

[0060] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0061] This embodiment provides a multi-level phase control constraint modeling device, characterized in that it includes:

[0062] The interpretation module is used to divide well logging sedimentary facies using well logging data of the study area, and to interpret lithofacies within each sedimentary facies.

[0063] The first determination module is used to statistically analyze the P-wave velocity, S-wave velocity, and density in each lithofacies, and to determine the mean and variance of the P-wave velocity, S-wave velocity, and density in each lithofacies.

[0064] The second determination module is used to extract seismic attributes using post-stack data of the study area, and select sensitive attributes that can reflect sedimentary facies from the seismic attributes based on the well logging sedimentary facies interpretation results, and determine the sensitive attribute threshold for each sedimentary facies.

[0065] The calculation module is used to determine the upper and lower layers of the target layer to be studied, and to calculate the sensitive attribute slices of the entire seismic data volume between the upper and lower layers.

[0066] The third determining module is used to determine the seismic sedimentary facies and well logging lithofacies interpretation results corresponding to the sensitive attribute slices between the upper and lower layers based on the sensitive attribute thresholds obtained by the second determining module.

[0067] The first statistical module is used to calculate the probability of different well logging facies within each sedimentary facies based on the seismic sedimentary facies and well logging lithofacies interpretation results obtained from the third determination module, thereby obtaining the probability of first-order facies-controlled lithofacies under different sedimentary facies.

[0068] The inversion module is used to perform pre-stack inversion based on the pre-stack seismic angle stacking data of the study area to obtain the inversion results of P-wave velocity, S-wave velocity and density.

[0069] The second statistical module is used to combine the P-wave velocity, S-wave velocity and density in each lithofacies obtained by the first determination module to determine the probability that the P-wave velocity, S-wave velocity and density obtained by the inversion module belong to each lithofacies, thereby obtaining the probability of secondary facies controlled lithofacies under different seismic data conditions.

[0070] The fourth determining module is used to determine the multi-level facies probability based on the first-level facies probability and the second-level facies probability;

[0071] The stochastic modeling module is used to obtain stochastic modeling results for P-wave velocity, S-wave velocity, and density based on the multi-level phase-controlled lithofacies probabilities obtained by the fourth determination module and the mean and variance of P-wave velocity, S-wave velocity, and density in each lithofacies obtained by the first determination module.

[0072] Example 3

[0073] This embodiment provides a computer-readable medium storing a computer program that, when executed by a processor, implements the various steps of a multi-level phase control constraint modeling method as described in the above embodiment.

[0074] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0075] Example 4

[0076] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 5 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0077] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only line segments are used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0078] A memory is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. The processor executes the program stored in the memory to perform all the steps in the aforementioned multi-level phase control constraint modeling method.

[0079] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0080] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0081] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0082] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0083] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0084] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0085] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0086] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0087] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0091] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A multi-level phase control constraint modeling method, characterized in that, Includes the following steps: S100, using well logging data of the study area to divide well logging sedimentary facies, and interpreting lithofacies within each sedimentary facies; S200, statistically analyze the P-wave velocity, S-wave velocity and density in each lithofacies, and determine the mean and variance of the P-wave velocity, S-wave velocity and density in each lithofacies; S300, seismic attributes are extracted using post-stack data of the study area, and sensitive attributes that can reflect sedimentary facies are selected from the seismic attributes based on the well logging sedimentary facies interpretation results, and the sensitive attribute thresholds for each sedimentary facies are determined. S400, determine the upper and lower layers of the target layer to be studied, and calculate the sensitive attribute slices of the entire seismic data volume between the upper and lower layers; S500, based on the sensitive attribute threshold obtained in step S300, determine the seismic sedimentary facies and well logging lithofacies interpretation results corresponding to the sensitive attribute slices between the upper and lower layers; S600, based on the seismic sedimentary facies and well logging lithofacies interpretation results obtained in step 500, the probabilities of different well logging lithofacies within each sedimentary facies are statistically analyzed, thereby obtaining the probabilities of first-order facies-controlled lithofacies under different sedimentary facies control. S700: Based on the pre-stack seismic angle stacking data of the study area, pre-stack inversion is performed to obtain the inversion results of P-wave velocity, S-wave velocity and density. S800: Combining the P-wave velocity, S-wave velocity and density obtained in step S200 within each lithofacies, determine the probability that the P-wave velocity, S-wave velocity and density obtained in step S700 belong to each lithofacies, thereby obtaining the probability of secondary facies-controlled lithofacies under different seismic data conditions. S900, determine the multi-level facies probability based on the first-level facies probability and the second-level facies probability; S1000: Based on the multi-level phase-controlled lithofacies probabilities obtained in step S900, and the mean and variance of P-wave velocity, S-wave velocity and density in each lithofacies obtained in step S200, obtain the stochastic modeling results of P-wave velocity, S-wave velocity and density.

2. The multi-level phase control constraint modeling method as described in claim 1, characterized in that, In step S200, the mean and variance of the P-wave velocity, S-wave velocity and density in each rock facies are determined by Gaussian distribution fitting.

3. The multi-level phase control constraint modeling method as described in claim 2, characterized in that, The post-stack data for the study area are well-side post-stack data.

4. The multi-level phase control constraint modeling method as described in claim 1, characterized in that, The earthquake attributes include amplitude-based and coherence-based earthquake attributes.

5. The multi-level phase control constraint modeling method as described in claim 1, characterized in that, In step S800, the Naive Bayes classification algorithm is used to determine the probability that the P-wave velocity, S-wave velocity and density obtained in step S700 belong to each rock facies.

6. The multi-level phase control constraint modeling method as described in claim 5, characterized in that, In step S900, the multi-level facies-controlled lithofacies probability is determined according to the following formula: in, , , , , and The contributions of the adjustment data F and d are used to adjust the sedimentary phase. Indicates lithofacies, Let d represent the lithofacies probabilities controlled by different sedimentary facies, and d represent the seismic data. Lithofacies probability under seismic data conditions denoted as the probability of the sedimentary phase.

7. The multi-level phase control constraint modeling method as described in claim 6, characterized in that, to make and They contribute equally to the posterior probability.

8. A multi-level phase control constraint modeling device, characterized in that, include: The interpretation module is used to divide well logging sedimentary facies using well logging data of the study area and interpret lithofacies within each sedimentary facies. The first determination module is used to statistically analyze the P-wave velocity, S-wave velocity, and density in each lithofacies, and to determine the mean and variance of the P-wave velocity, S-wave velocity, and density in each lithofacies. The second determination module is used to extract seismic attributes using post-stack data of the study area, and select sensitive attributes that can reflect sedimentary facies from the seismic attributes based on the well logging sedimentary facies interpretation results, and determine the sensitive attribute threshold for each sedimentary facies. The calculation module is used to determine the upper and lower layers of the target layer to be studied, and to calculate the sensitive attribute slices of the entire seismic data volume between the upper and lower layers. The third determining module is used to determine the seismic sedimentary facies and well logging lithofacies interpretation results corresponding to the sensitive attribute slices between the upper and lower layers based on the sensitive attribute thresholds obtained by the second determining module. The first statistical module is used to calculate the probability of different well logging facies within each sedimentary facies based on the seismic sedimentary facies and well logging lithofacies interpretation results obtained from the third determination module, thereby obtaining the probability of first-order facies-controlled lithofacies under different sedimentary facies. The inversion module is used to perform pre-stack inversion based on the pre-stack seismic angle stacking data of the study area to obtain the inversion results of P-wave velocity, S-wave velocity and density. The second statistical module is used to combine the P-wave velocity, S-wave velocity and density in each lithofacies obtained by the first determination module to determine the probability that the P-wave velocity, S-wave velocity and density obtained by the inversion module belong to each lithofacies, thereby obtaining the probability of secondary facies controlled lithofacies under different seismic data conditions. The fourth determining module is used to determine the multi-level facies probability based on the first-level facies probability and the second-level facies probability; The stochastic modeling module is used to obtain stochastic modeling results for P-wave velocity, S-wave velocity, and density based on the multi-level phase-controlled lithofacies probabilities obtained by the fourth determination module and the mean and variance of P-wave velocity, S-wave velocity, and density in each lithofacies obtained by the first determination module.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a multi-level phase control constraint modeling method as described in any one of claims 1 to 7.

10. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement a multi-level phase control constraint modeling method as described in any one of claims 1 to 7.

Images