A method, system, storage medium and device for establishing an initial model for seismic inversion

Abstract

The present invention provides a method, system, storage medium, and device for establishing an initial model for seismic inversion. The method comprises: establishing basic low-frequency models using different interpolation methods; performing top-bottom comparison on the target layer, converting the seismic data using a 90-degree phase shift, extracting root mean square amplitude attribute stratigraphic slices at specific intervals from the 90-degree phase-shifted seismic data, and conducting sedimentological interpretation; testing the basic low-frequency models established based on the different interpolation methods through sedimentological interpretation of the stratigraphic slices, and selecting an interpolation method and the corresponding basic low-frequency model as a preselected interpolation method and initial low-frequency model; reestablishing the initial low-frequency model using the preselected interpolation method using the attribute slices of each sublayer as corresponding constraints, thereby obtaining a new low-frequency model constrained by the stratigraphic attribute slices for subsequent inversion. The present invention effectively avoids the problem that conventional methods, which rely solely on mathematical interpolation, cause the inversion results to deviate from the actual underground conditions.

Description

Technical Field

[0001] The present invention relates to the field of oil seismic exploration technology, in particular to the field of seismic data interpretation technology for oil seismic exploration, and is applied to reservoir prediction work in seismic data interpretation in industries such as oil and gas exploration, and specifically to seismic inversion modeling. Background Art

[0002] In recent years, with the continued advancement of oil and gas exploration and development, the requirements for reservoir prediction and sand body characterization have become increasingly precise. Seismic inversion technology is one of the key tools for reservoir prediction. After years of development, inversion technology and its applications have made great progress, but some limitations still exist, resulting in low prediction accuracy.

[0003] Currently, the most popular inversion methods are model inversion. The basic principle is to use well logging data as a constraint and iterate through a combination of forward and inversion to determine the subsurface impedance. This method utilizes both high- and low-frequency information from well logging data, significantly broadening the frequency band of the seismic signal and enabling better acquisition of impedance information for thin layers and interbedded layers.

[0004] However, this conventional inversion method has certain limitations, and its accuracy is significantly affected by the initial model. The initial model is typically established by integrating wave impedance curves, horizon interpretation results, and lithologic information derived from seismic, well logging, and geological data. However, in practice, it has been found that existing well information significantly influences the prediction of thin interbedded sandstone reservoirs. This often results in mismatches between the planar distribution trends of sand bodies predicted by inversion and those predicted by seismic attributes, such as circles around well points and areas with low well density. This poor agreement with geological understanding and patterns is problematic.

[0005] Therefore, in view of the above-mentioned shortcomings and problems in the existing technology, it is necessary to propose an optimized method for establishing the initial model of seismic inversion to avoid the phenomenon that the accuracy is greatly affected by the initial model, the drilling information interferes too much with the prediction, the planar distribution trend of the inversion predicted sand body does not match the sand body predicted by seismic attributes, and the problem of poor consistency with geological understanding and laws. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to propose an improved method, system, storage medium and device for establishing an initial model for seismic inversion, so as to solve the above-mentioned problems existing in the prior art.

[0007] Based on the above objectives, in one aspect, the present invention provides a method for establishing an initial model for seismic inversion, wherein the method comprises the following steps:

[0008] Different interpolation methods are used to establish basic low-frequency models;

[0009] Top-bottom comparison of the target layer is performed, and seismic data is converted using a 90-degree phase shift. The corresponding root mean square amplitude attribute stratigraphic slices are extracted at specific intervals in the 90-degree phase-shifted seismic data for sedimentological interpretation.

[0010] Through sedimentological interpretation of stratigraphic slices, the basic low-frequency models established based on different interpolation methods are tested and an interpolation method and the corresponding basic low-frequency model are selected as the pre-selected interpolation method and initial low-frequency model;

[0011] The attribute slices of each small layer are used as corresponding constraints to re-establish the initial low-frequency model using a pre-selected interpolation method, thereby obtaining a new low-frequency model based on the constraints of the formation attribute slices for subsequent inversion.

[0012] In some embodiments of the method for establishing an initial model for seismic inversion according to the present invention, the method of re-establishing the initial low-frequency model using the attribute slices of each small layer as corresponding constraints using a pre-selected interpolation method, thereby obtaining a new low-frequency model constrained by the formation attribute slices for subsequent inversion, further includes:

[0013] By analyzing the correlation between the seismic attributes at the well point and the well curve, the relationship is further updated using the verification well;

[0014] Constraints are made using predefined cutoff values ​​to ensure the longitudinal stability of the interpolation model;

[0015] Eliminate local outliers through iterative weighting.

[0016] In some embodiments of the method for establishing an initial model for seismic inversion according to the present invention, the initial low-frequency model is re-established using a preselected interpolation method using the attribute slices of each small layer as corresponding constraints, thereby obtaining a new low-frequency model constrained by the formation attribute slices for subsequent inversion, further comprising:

[0017] Kriging method is applied to eliminate the prediction error of well point locations.

[0018] In some embodiments of the method for establishing an initial model for seismic inversion according to the present invention, verifying basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratum slices and selecting an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model further includes:

[0019] Interpolation methods are screened and constrained based on the planar sedimentary trends interpreted for each slice and used in subsequent inversion.

[0020] In some embodiments of the method for establishing an initial model for seismic inversion according to the present invention, verifying basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratum slices and selecting an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model further includes:

[0021] The plane attributes of the target layer segment obtained by different interpolation methods are extracted and compared with the plane morphology of the target geological body. The interpolation method and the corresponding basic low-frequency model with the highest matching degree are selected as the pre-selected interpolation method and initial low-frequency model.

[0022] In some embodiments of the method for establishing an initial seismic inversion model according to the present invention, top-bottom comparison is performed on the target layer, seismic data is converted using a 90-degree phase shift, corresponding root mean square amplitude attribute stratigraphic slices are extracted at specific intervals in the 90-degree phase-shifted seismic data, and sedimentological interpretation is further performed by:

[0023] The longitudinal interval time window of the stratigraphic slice is determined according to the seismic response characteristics and longitudinal resolution capability corresponding to the target geological body, and the screened attribute stratigraphic slices are extracted as plane constraints.

[0024] In some embodiments of the method for establishing an initial seismic inversion model according to the present invention, top-bottom comparison is performed on the target layer, seismic data is converted using a 90-degree phase shift, corresponding root mean square amplitude attribute stratigraphic slices are extracted at specific intervals in the 90-degree phase-shifted seismic data, and sedimentological interpretation is further performed by:

[0025] The interval time window is selected to be less than half of the vertical deposition time thickness of the target geological body, preferably less than one-third of the vertical deposition time thickness of the target geological body, and more preferably less than one-quarter of the vertical deposition time thickness of the target geological body.

[0026] Another aspect of the present invention provides a system for establishing an initial model for seismic inversion, comprising:

[0027] A basic model building module, wherein the basic model building module is configured to respectively build basic low-frequency models using different interpolation methods;

[0028] a stratigraphic slice production module configured to perform top-bottom comparison of a target layer, convert seismic data using a 90-degree phase shift, extract corresponding root mean square amplitude attribute stratigraphic slices at specific intervals in the 90-degree phase-shifted seismic data, and perform sedimentological interpretation;

[0029] An interpolation method and model preselection module configured to test basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratigraphic slices and select an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model;

[0030] The model constraint reconstruction module is configured to use the attribute slices of each small layer as corresponding constraints to re-establish the initial low-frequency model using a preselected interpolation method, thereby obtaining a new low-frequency model based on the constraints of the formation attribute slices for subsequent inversion.

[0031] In another aspect of the present invention, a computer-readable storage medium is provided, storing computer program instructions, which, when executed, implement any of the above-mentioned methods for establishing an initial model for seismic inversion according to the present invention.

[0032] In another aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any one of the above-mentioned methods for establishing an initial model for seismic inversion according to the present invention is executed.

[0033] The present invention has at least the following beneficial technical effects: the method of the present invention performs seismic inversion initial model production based on stratigraphic slices, and innovatively incorporates seismic attribute slices into the initial model production process of conventional inversion, so that the distribution trend of the initial model on the plane can be made more reasonable and more in line with the actual underground sedimentation law, effectively avoiding the problem that the results obtained by conventional methods relying solely on mathematical differences often deviate from the actual underground sedimentation conditions, thereby solving the problem at the source and ultimately achieving the purpose of making the inversion results consistent with the seismic attribute trends. This method can better assist in the optimization and demonstration of well location targets in oil field production, greatly improving the rationality that is lacking in conventional inversion results, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying any creative work.

[0035] In the figure:

[0036] Figure 1 A schematic block diagram showing an embodiment of a method for establishing an initial model for seismic inversion according to the present invention is shown;

[0037] Figure 2A schematic diagram of fine model establishment for a conventional low-frequency model according to the present invention is shown;

[0038] Figure 3 A schematic diagram of a small layer attribute slice extracted according to the method of the present invention is shown;

[0039] Figure 4 A comparison diagram of the application effects of the models established according to the prior art and the method of the present invention is shown, wherein a is a schematic diagram of the earthquake root mean square amplitude attribute, b is the plane of the low-frequency model of the conventional method, and c is the plane of the low-frequency model of the present invention;

[0040] Figure 5 A comparison diagram of model plane results obtained according to the prior art and the method according to the present invention is shown, wherein a is the model plane result of the conventional method, and b is the model plane result of the present invention;

[0041] Figure 6 A comparison diagram of inversion results according to the prior art and the method according to the present invention is shown;

[0042] Figure 7 A schematic block diagram of an embodiment of a system for establishing an initial model for seismic inversion according to the present invention is shown;

[0043] Figure 8 A schematic diagram showing an embodiment of a computer-readable storage medium for implementing a method for establishing an initial model for seismic inversion according to the present invention;

[0044] Figure 9 A schematic diagram of the hardware structure of an embodiment of a computer device for implementing a method for establishing an initial model for seismic inversion according to the present invention is shown. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0046] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are intended to distinguish two non-identical entities or non-identical parameters with the same name. Therefore, "first" and "second" are used for convenience of expression only and should not be understood as limitations on the embodiments of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, other steps or units inherent to a process, method, system, product, or device that includes a series of steps or units.

[0047] To address this issue with conventional inversion methods, this paper proposes a method for creating an initial inversion model based on stratigraphic slices. This method, for the first time, incorporates seismic attributes as constraints into the inversion process, thereby improving the mismatch between inversion results and attribute predictions. Applying this improved model to seismic inversion ensures that the predicted sand bodies are essentially consistent with the attribute prediction trends on the plane, thus avoiding the discrepancies between inversion and attribute results that can occur with conventional methods.

[0048] In previous conventional methods, the initial models used in inversion were generated by wellpoint interpolation and were therefore significantly affected by mathematical interpolation methods. This invention creatively introduces stratigraphic attribute slicing based on conventional methods, utilizing the attribute slices of small layers to control the planar interpolation morphology of each layer model. Under attribute control, conventional results are updated with secondary control, and the newly generated model is then used in subsequent inversions. This can further reduce the uncertainty of mathematical interpolation results, thereby improving the inconsistency between conventional results and seismic attribute trends and more accurately assisting oilfield production and applications.

[0049] It mainly includes three core technical links:

[0050] First, the original seismic data is subjected to a 90-degree phase shift, so that the reflection characteristics of the sand body correspond to the target sand body at the same position, thereby generating a stratigraphic attribute slice that corresponds one-to-one with the sand body in the vertical direction;

[0051] Second, conduct seismic sedimentological interpretation of stratigraphic slices, optimize and constrain interpolation methods based on the planar sedimentary trends interpreted for each slice, and achieve a transition from "surface" constraints to "volume" constraints, which will be used for subsequent inversion.

[0052] Third, the new model will be applied to seismic inversion to carry out reservoir prediction.

[0053] To this end, a first aspect of the present invention provides a method 100 for establishing an initial model for seismic inversion. Figure 1 FIG. 1 is a schematic block diagram showing an embodiment of a method for establishing an initial model for seismic inversion according to the present invention. Figure 1 In the illustrated embodiment, the method includes:

[0054] Step S110: using different interpolation methods to establish basic low-frequency models respectively;

[0055] Step S120: performing top-bottom comparison on the target layer, converting the seismic data using a 90-degree phase shift, extracting corresponding root mean square amplitude attribute stratigraphic slices at specific intervals in the 90-degree phase-shifted seismic data, and performing sedimentological interpretation;

[0056] Step S130: verifying the basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratigraphic slices, and selecting an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model;

[0057] Step S140: Using the attribute slices of each small layer as corresponding constraints, the initial low-frequency model is re-established using a pre-selected interpolation method, thereby obtaining a new low-frequency model based on the constraints of the formation attribute slices for subsequent inversion.

[0058] In general, to address the above-mentioned problems in existing technologies, stratigraphic slices are added when establishing low-frequency models and the seismic sedimentological interpretations based on them are used as constraints. This makes the planar trend of the low-frequency model more consistent with seismic phases and geological understanding and has higher vertical resolution, which is superior to the traditional method of modeling using only well interpolation. Commonly used interpolation methods in establishing low-frequency models include triangular interpolation, local weighted interpolation, Kriging interpolation, and other interpolation methods. Regardless of the interpolation method, it starts from the well point and gradually extrapolates to the area without wells to obtain the model framework of the entire area. Since it is a purely mathematical algorithm, the calculation mode of each method is different, and the results obtained are also different. In addition, the different weights assigned to the attribute values ​​at the well point will lead to the formation of local anomaly areas within a small range centered on the well point, namely the "bull's eye" phenomenon, which causes the final inversion result to deviate from the actual underground sedimentary law.

[0059] Therefore, according to the method of the present invention, in step S110, different interpolation methods are used to establish basic low-frequency models. Simultaneously, in step S120, top-bottom comparison is performed on the target layer, seismic data is converted using a 90-degree phase shift, and root mean square amplitude attribute stratigraphic slices are extracted from the 90-degree phase-shifted seismic data at specific time intervals. Multiple sets of stratigraphic attribute slices are generated and sedimentological interpretation is performed. Preferably, the time intervals are determined based on the resolution of the seismic data of the target geological body.

[0060] On this basis, in step S130, the basic low-frequency models established based on different interpolation methods are verified through sedimentological interpretation of stratigraphic slices. Thus, a relatively optimal interpolation method and corresponding basic low-frequency model suitable for the study area are selected from the various basic low-frequency models established based on different interpolation methods as the preselected interpolation method and initial low-frequency model.

[0061] Finally, in step S140, the initial low-frequency model is rebuilt using the attribute slices of each small layer as corresponding constraints using a preselected interpolation method, thereby obtaining a new low-frequency model constrained by the formation attribute slices for subsequent inversion.

[0062] The following is an example of a work area in Songliao Basin. Figures 2 to 6 , further explaining the method according to the present invention.

[0063] The main research target of this area is the Grape Flower oil layer, which is a delta front deposit with relatively developed sandstone. Based on the conventional low-frequency model, the reflection characteristics of the sand bodies in the area are analyzed to determine the reflection thickness of the target sand bodies. The appropriate small layer time window is selected to further refine the model. The total time thickness of the P oil layer group in the study area is about 60ms. In order to make the model as fine as possible, the model is refined at 2ms intervals and the low-frequency model is rebuilt. Figure 2 As shown in FIG. 4 , the model making of this region is improved mainly in the following key points according to the present invention.

[0064] (1) Optimal interpolation method under the constraints of stratigraphic slice interpretation

[0065] In this example, six interpolation methods were tested using sedimentological interpretation of stratigraphic slices. Inverse distance weighting and local weighting produced significant anomalies, while the other methods showed similar results. Ultimately, the natural neighbor interpolation method was chosen as the preferred method.

[0066] (2) Refined analysis based on stratigraphic attribute slices

[0067] First, the top and bottom of the target layer are compared, and then the seismic data is converted using a 90-degree phase shift technique so that the converted seismic waveform reflection characteristics are in the same position vertically as the target sand body.

[0068] Secondly, stratigraphic slices were prepared and seismic sedimentological interpretation was performed. In this example, a total of 41 sets of stratigraphic attribute slices were prepared and sedimentological interpretation was performed, as shown in the attached figure. Figure 3 As shown, the sedimentary laws of the target reservoir space can be fully demonstrated, making the inversion results more consistent with the actual geological laws.

[0069] It should be noted that the selection of the time interval window will change according to the different working areas. Preferably, the longitudinal time interval window of the stratum slice is determined according to the seismic response characteristics and the vertical resolution capability corresponding to the target geological body, and the attribute stratum slice after the optimization is extracted as the plane constraint condition. In other words, the model refinement with 2ms intervals adopted in the present embodiment is non-restrictive. Different time interval windows can be determined according to different situations, as long as the requirement of precision can be met. Preferably, the time interval window is selected to be less than half of the longitudinal deposition time thickness of the target geological body, preferably less than one-third of the longitudinal deposition time thickness of the target geological body, more preferably less than one-quarter of the longitudinal deposition time thickness of the target geological body, and the smaller the time interval window, the more refined the model obtained.

[0070] (3) Attribute constraints

[0071] By analyzing the correlation between seismic attributes at the well point and the well curve, and using validation wells to further optimize the relationship, a predefined cutoff value constraint is applied to ensure the vertical stability of the interpolation model, and local outliers are eliminated through iterative weighting, the application of Kriging can eliminate the prediction error of the well point location.

[0072] Finally, the optimized model is compared with the conventional method and is more consistent with the planar deposition law, such as Figure 4 The model is applied to seismic inversion to obtain prediction results that are more consistent with planar sedimentary laws.

[0073] Here, the method according to the present invention has also been advantageously applied in the characterization of the upper and lower river channels of the Sa-1 oil layer group in the JQ area. The river channels in this area correspond to weak peak amplitude reflection characteristics, and the characteristics are not obvious in conventional seismic attributes. After the frequency of the data is enhanced according to the present invention, the river channel characteristics are enhanced. The river channel characteristics are more obvious on the inversion plane attributes obtained by constraining the enhanced seismic attributes, and the boundary with the surrounding rock is clearer, which can better determine the distribution range of the river channel, such as Figure 6 shown.

[0074] To illustrate the interactive interpretation process, we selected the H work area in the western Songliao Basin as an example. The target reservoir is the S0 oil formation, which belongs to the Nen Formation 1. During its deposition, two deltaic progradation events occurred within a deep-to-semi-deep lake setting, forming a retrogradational sedimentary sequence.

[0075] Regional sedimentary facies reveal that the majority of the area is composed of shallow lake deposits, with a small area in the southeast comprising low-water deltaic deposits. Lithology revealed at various well sites indicates that the entire upper S0 oil formation is dominated by mudstone deposits, with the river channel located in the lower S0 region being the primary target for this seismic inversion. This channel manifests as a lens-shaped, strong peak reflection feature on the seismic profile, with a temporal thickness of approximately 6 to 10 milliseconds. Using the method described in this invention, an initial inversion model was constructed for this region based on stratigraphic slices.

[0076] (1) Model subdivision and reconstruction

[0077] First, low-frequency models were constructed using different interpolation methods and compared with the original seismic attributes on a plane. The comparison showed significant differences in the ability of different interpolation methods to represent the river channel. Consequently, the natural neighbor interpolation method was primarily used for low-frequency model construction in subsequent modeling. It should be noted that the natural neighbor interpolation method is not an inevitable choice; depending on the characteristics of the target geological body, the optimal interpolation method may vary.

[0078] (2) Hourly window attribute slice extraction and reconstruction model

[0079] In the 90-degree phase-shifted seismic data, the channel depth under S0 is typically between 6 and 10 ms. Therefore, we extracted stratum slices with root-mean-square amplitude attributes at 2-ms intervals from the top of S0 to the bottom of S0. Each slice reveals the evolution of the channel from its initial development to its gradual disappearance, and the planar morphology of the channel becomes clearer. The attribute slices of each sublayer serve as constraints in reconstructing the low-frequency model, resulting in an inversion result constrained by the stratigraphic attribute slices.

[0080] Comparing the conventional method with the method according to the present invention, the plane trends of the models obtained by the two methods show that the abnormal phenomena such as the "bull's eye" produced by the conventional method have been eliminated, and the plane distribution characteristics of the river channel in the area have been retained to the maximum extent, such as Figure 5 As shown in the figure. The comparison of the plane attributes of the final inversion result and the original seismic attributes shows that the river channel features are not clear in the conventional inversion results, and the overall trend transition is unnatural. After the attribute constraints, the detailed features of the river channel are fully preserved, and the transition of the area outside the river channel on the plane is more natural, as shown in the figure. Figure 6 shown.

[0081] The second aspect of the present invention further provides a seismic inversion initial model establishment system 200. Figure 7 FIG. 2 shows a schematic block diagram of an embodiment of a system 200 for establishing an initial model for seismic inversion according to the present invention. Figure 7 As shown, the system includes:

[0082] A basic model building module 210, wherein the basic model building module 210 is configured to respectively build basic low-frequency models using different interpolation methods;

[0083] a stratigraphic slice production module 220 configured to perform top-bottom comparison of a target layer, convert seismic data using a 90-degree phase shift, extract corresponding root mean square amplitude attribute stratigraphic slices at specific intervals in the 90-degree phase-shifted seismic data, and perform sedimentological interpretation;

[0084] An interpolation method and model preselection module 230 is configured to test basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratigraphic slices and select an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model;

[0085] The model constraint reconstruction module 240 is configured to use the attribute slices of each small layer as corresponding constraints to re-establish the initial low-frequency model using a preselected interpolation method, thereby obtaining a new low-frequency model based on the constraints of the formation attribute slices for subsequent inversion.

[0086] A third aspect of the embodiments of the present invention further provides a computer-readable storage medium. Figure 8 FIG. 1 is a schematic diagram showing a computer-readable storage medium for a method for establishing an initial model for seismic inversion according to an embodiment of the present invention. Figure 8 As shown, the computer-readable storage medium 300 stores computer program instructions 310, which can be executed by a processor. When the computer program instructions 310 are executed, the method of any one of the above embodiments is implemented.

[0087] It should be understood that, unless they conflict with each other, all the embodiments, features and advantages described above for the method for establishing an initial seismic inversion model according to the present invention are also applicable to the system and storage medium for establishing an initial seismic inversion model according to the present invention.

[0088] According to a fourth aspect of the embodiments of the present invention, a computer device 400 is provided, including a memory 420 and a processor 410. The memory stores a computer program, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[0089] like Figure 9 FIG. 1 is a schematic diagram of the hardware structure of a computer device for executing the method for establishing an initial model for seismic inversion provided by the present invention. Figure 9 Taking the computer device 400 shown as an example, the computer device includes a processor 410 and a memory 420, and may also include: an input device 430 and an output device 440. The processor 410, the memory 420, the input device 430 and the output device 440 can be connected via a bus or other means. Figure 9 The bus connection is used as an example. The input device 430 can receive input digital or character information and generate signal input related to the establishment of the initial seismic inversion model. The output device 440 can include a display device such as a display screen.

[0090] The memory 420, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the resource monitoring method in the embodiment of the present application. The memory 420 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; and the data storage area may store data created by the use of the resource monitoring method, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 420 may optionally include a memory remotely located relative to the processor 410, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0091] The processor 410 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 420, that is, implements the resource monitoring method of the above method embodiment.

[0092] Finally, it should be noted that the computer-readable storage medium (e.g., memory) herein may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. By way of example and not limitation, the non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which may act as an external cache memory. By way of example and not limitation, RAM may be obtained in a variety of forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to include, but are not limited to, these and other suitable types of memory.

[0093] It will also be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits and algorithmic steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given of the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0094] The various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure herein may be implemented or executed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and / or any other such configuration.

[0095] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless expressly limited to the singular.

[0096] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0097] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples. Within the spirit of the embodiments of the present invention, the technical features of the above embodiments or different embodiments may be combined, and there are many other variations of different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of the embodiments of the present invention.

Claims

1. A method for establishing an initial model for seismic inversion, characterized in that: The following steps are involved: Different interpolation methods are used to establish basic low-frequency models; Top-bottom comparison of the target layer is performed, and seismic data is converted using a 90-degree phase shift. The corresponding root mean square amplitude attribute stratigraphic slices are extracted at specific intervals in the 90-degree phase-shifted seismic data. Sedimentary interpretation is then performed, which includes: determining the longitudinal interval time window of the stratigraphic slices based on the seismic response characteristics and vertical resolution of the target geological body, and extracting the selected attribute stratigraphic slices as plane constraints; Through sedimentological interpretation of stratigraphic slices, the basic low-frequency models established based on different interpolation methods are tested and an interpolation method and the corresponding basic low-frequency model are selected as the pre-selected interpolation method and initial low-frequency model; The initial low-frequency model is rebuilt using the attribute slices of each small layer as corresponding constraints using a preselected interpolation method, thereby obtaining a new low-frequency model constrained by the formation attribute slices for subsequent inversion, which further includes: By analyzing the correlation between the seismic attributes at the well point and the well curve, the relationship is further updated using the verification well; Constraints are made using predefined cutoff values ​​to ensure the longitudinal stability of the interpolation model; Eliminate local outliers through iterative weighting.

2. The method according to claim 1, characterized in that The method of re-establishing the initial low-frequency model by using the attribute slices of each small layer as corresponding constraints using a pre-selected interpolation method, thereby obtaining a new low-frequency model based on the constraints of the formation attribute slices for subsequent inversion further includes: Kriging method is applied to eliminate the prediction error of well point locations.

3. The method according to any one of claims 1 to 2, characterized in that The method of verifying the basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratum slices and selecting an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model further includes: Interpolation methods are screened and constrained based on the planar sedimentary trends interpreted for each slice and used in subsequent inversion.

4. The method according to claim 3, characterized in that The method of verifying the basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratum slices and selecting an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model further includes: The plane attributes of the target layer segment obtained by different interpolation methods are extracted and compared with the plane morphology of the target geological body. The interpolation method and the corresponding basic low-frequency model with the highest matching degree are selected as the pre-selected interpolation method and initial low-frequency model.

5. The method according to claim 1, wherein The top-bottom comparison of the target layer is performed, the seismic data is converted using a 90-degree phase shift, and corresponding root mean square amplitude attribute stratigraphic slices are extracted at specific interval time windows on the 90-degree phase-shifted seismic data to perform sedimentological interpretation, which further includes: The interval time window is selected to be less than half of the vertical deposition time thickness of the target geological body.

6. A seismic inversion initial model establishment system, characterized in that: include: A basic model building module, wherein the basic model building module is configured to respectively build basic low-frequency models using different interpolation methods; a stratigraphic slice production module configured to perform top-bottom comparison of a target layer, convert seismic data using a 90-degree phase shift, extract corresponding root mean square amplitude attribute stratigraphic slices at specific intervals in the 90-degree phase-shifted seismic data, and perform sedimentological interpretation; An interpolation method and model preselection module configured to test basic low-frequency models established based on different interpolation methods through sedimentological interpretation of stratigraphic slices and select an interpolation method and a corresponding basic low-frequency model as a preselected interpolation method and an initial low-frequency model; A model constraint reconstruction module, wherein the model constraint reconstruction module is configured to use the attribute slices of each small layer as corresponding constraints to re-establish the initial low-frequency model using a preselected interpolation method, thereby obtaining a new low-frequency model constrained by the formation attribute slices for subsequent inversion; The stratigraphic slice production module further includes: determining a stratigraphic slice longitudinal interval time window according to the seismic response characteristics and longitudinal resolution capability corresponding to the target geological body, and extracting the selected attribute stratigraphic slices as plane constraints; The model constraint reconstruction module further includes: analyzing the correlation between the seismic attributes at the well point and the well curve, and using verification wells to further update the relationship; using pre-defined cutoff values ​​to constrain to ensure the vertical stability of the interpolation model; and eliminating local outliers through iterative repeated weighting.

7. A computer-readable storage medium, characterized in that Computer program instructions are stored, and when the computer program instructions are executed, the method for establishing the seismic inversion initial model according to any one of claims 1 to 5 is implemented.

8. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the processor executes the computer program, the method for establishing an initial model for seismic inversion according to any one of claims 1 to 5 is executed.

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