Method and apparatus for building a low frequency model for envelope guided seismic exploration of a subsurface formation

Abstract

A method and system for generating and displaying a low frequency model of a seismic survey area is provided. The method and system includes defining a seismic survey geometry of a seismic survey measurement; processing seismic data to generate stacked seismic data and well log data to obtain elastic attributes; importing the stacked seismic data and the processed well log data into the defined seismic survey geometry; generating envelope data using the stacked seismic data; generating a low frequency seismic trace for each well; calculating a least squares optimized coefficient model for each well location from the generated envelope data and the low frequency seismic trace for each well; interpolating the coefficient model into the seismic survey geometry using a covariance technique and the imported stacked seismic data; and generating a three-dimensional low frequency model by inversion using the envelope data and the interpolated coefficient model for display.

Description

Technical Field

[0001] This disclosure relates to establishing an improved low-frequency model for regularizing seismic inversion in order to improve the accuracy of seismic inversion and thereby enhance the subsurface structure image of the surveyed area for reservoir characterization. Background Technology

[0002] As is well known, lithology and fluid identification play crucial roles in seismic exploration and reservoir characterization. Seismic inversion is the process of converting seismic data into models and images for reservoir characterization. One or more sound sources generate seismic waves (acoustic waves) that come into contact with subsurface structures. Seismic data can be reflection and / or refraction data, representing the reflection and / or refraction of seismic waves upon contact with subsurface strata (subsurface structures). Seismic data is generally limited to the range of 6-8 Hz to 60-80 Hz; therefore, traditional seismic data lacks information in the low-frequency range, such as below 8 Hz or 12 Hz. Low-frequency models are used to regularize seismic inversion and fill the low-frequency gaps in seismic data. Reliable low-frequency models can significantly improve the accuracy of seismic inversion in reservoir characterization.

[0003] Low-frequency seismic traces at well locations within the exploration area can be created using a low-frequency filtered version of well logging data (based on seismic data). Low-frequency model building is the process of interpolating low-frequency seismic traces between several known well locations into a uniform three-dimensional (3D) sampled seismic exploration space. Low-frequency seismic traces at well locations are typically sparsely distributed within the seismic exploration space. However, in cases of sparse well locations, building a reliable 3D volumetric low-frequency model using conventional techniques is challenging.

[0004] In traditional techniques, a common method for building low-frequency models using inverse distance-weighted interpolation was proposed by Shephard (Shepard D., 1968, “A two-dimensional interpolation function for irregularly-spaced data,” Proceedings of the 1968 ACM National Conference, pp. 517-524). This method interpolates the low-frequency model as a function of continuous parameters and provides several known low-frequency seismic traces at discrete locations. However, in geological regions, this smoothing method can violate subsurface structures, for example, it may be inconsistent with subsurface structures. Another approach is to select strata in the seismic data that correspond to coherent reflections and use the Kriging method (Matheron G., 1963, “Principles of geostatistics,” Geol, Vol. 58, pp. 1246-1266; Davis JC, 2002, “Statistics and Data Analysis in Geology,” Vol. 3, Wiley, New York) to build a low-frequency model guided by the selected strata. However, stratigraphic selection is both cumbersome and time-consuming (Douma and Naeini, 2014, "Application of Image-guided Interpolation to Build Low Frequency Background Model Prior to Inversion", 76th International Conference & Exhibition, EAGE, Extended Abstract, We G106.05). Furthermore, these traditional interpolation methods fail when known discrete locations in the survey area are very sparse. These traditional methods introduce bullseye effects into the low-frequency model, reducing its accuracy.

[0005] In another traditional technique, Hale (Hale D., 2009, “Image-guided blended neighbor interpolation”, 79th International Congress of Seismic Engineering (SEG), Extended Abstract, pp. 1127-1131) employed a blended neighbor method, which uses tensor fields (or tectonic dip and azimuth angles) calculated from seismic images to build low-frequency models. However, the low-frequency models generated using the Hale method fail to capture the lithological variations implied in the seismic amplitude and phase information used for reservoir characterization.

[0006] Therefore, it is necessary to provide a new method for generating low-frequency models to improve the accuracy of seismic inversion, thereby enhancing the subsurface structure image of the surveyed area for reservoir characterization. Summary of the Invention

[0007] In one aspect, an envelope-guided low-frequency modeling technique is provided to address the aforementioned problems and improve the accuracy of seismic inversion, thereby improving the subsurface structure image of the surveyed area for reservoir characterization. One or more embodiments use envelope data as guidance to invert a low-frequency model onto a uniform three-dimensional (3D) sample of the seismic survey space (seismic survey geometry). One or more embodiments include an envelope-guided low-frequency modeling technique to overcome the inaccuracies of the bullseye effect in conventional low-frequency modeling methods. One or more embodiments include an envelope-guided low-frequency modeling technique to conform to the subsurface structure of the surveyed area and capture the amplitude and phase information implied in various features of the seismic data for reservoir characterization.

[0008] In one aspect, a method is provided for generating and displaying a low-frequency model of a seismic survey area. The method includes: placing seismic data recording sensors at different locations within the seismic survey area; placing a logging tool including one or more well logging data recording sensors in one or more wells within the seismic survey area; performing a blast at an incident point in the seismic survey area to generate seismic waves that penetrate subsurface structures; using the seismic data recording sensors to sense the seismic waves and record seismic data; using the well logging data recording sensors to sense and record well logging data; transmitting seismic data from the seismic data recording sensors to a computer system including one or more memories, and storing the seismic data in one or more memories; transmitting well logging data from the well logging sensors to the computer system including one or more memories, and storing the well logging data in one or more memories; and defining the seismic survey area. The process involves: defining a seismic exploration geometry; processing seismic data to generate stacked seismic data; processing well logging data to obtain elastic properties; importing the stacked seismic data into a defined seismic exploration geometry; importing the processed well logging data into a defined seismic exploration geometry; generating envelope data using the stacked seismic data in the defined seismic exploration geometry; generating low-frequency seismic traces for each well in the seismic exploration area; calculating a least-squares optimized coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces for each well; interpolating the coefficient model into the seismic exploration geometry using covariance techniques and the imported stacked seismic data; generating a three-dimensional low-frequency model through inversion using the envelope data and the interpolated coefficient model; and displaying an image of the generated three-dimensional low-frequency model of the seismic exploration area.

[0009] In one respect, elastic properties may include one or more of P-wave velocity, S-wave velocity, and density.

[0010] In one respect, one or more wells in a seismic survey area can be multiple wells, and multiple well locations can be one of multiple wells.

[0011] In one aspect, the seismic geometry of the seismic survey area includes envelope data for each well at each well location and low-frequency seismic traces for each well at each well location.

[0012] In one aspect, calculating the least-squares optimization coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (a) selecting a well location; (b) extracting the envelope seismic traces of the selected well location from the generated envelope data and extracting the low-frequency seismic traces of the selected well location from multiple low-frequency seismic traces; and (c) solving the least-squares optimization problem d′ of the coefficient model m′ at the well location. e =F′m′, where d′ e F' represents the envelope seismic trace extracted at the well location, and F' represents the operator containing the low-frequency seismic trace at the well location.

[0013] In one aspect, calculating the least-squares optimization coefficient model for each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (d) repeating operations (a) to (c) until the least-squares optimization problem for each well is solved; (e) outputting the calculated coefficient model for each well location.

[0014] In one aspect, using covariance techniques and interpolating coefficient models to a seismic survey geometry using imported stacked seismic data also includes the following operations: (f) extracting seismic traces for each well location; (g) selecting one well location from multiple well locations; (h) generating a correlation map by calculating the correlation coefficients between the seismic traces at the selected well location and all seismic traces in the seismic survey geometry; (i) repeating operations (g) and (h) until a correlation map for each well location is generated in the seismic survey geometry; (j) outputting the correlation coefficient map for all well locations; (k) calculating the weighted parameters for all well locations; (l) interpolating each coefficient model for each well location to the entire seismic survey geometry; and (m) outputting the interpolated coefficient models to the entire seismic survey geometry.

[0015] In one respect, the weighted parameters are calculated using the following formula:

[0016]

[0017] in, This represents a mathematical transformation from v including the power parameter, where i represents the well position index, and p... i (x) represents the weighted parameter p at a given point x, and N represents the number of well locations.

[0018] In one aspect, using covariance techniques and imported stacked seismic data to interpolate coefficient models into the seismic survey geometry also includes inputting the calculated coefficient model and the interpolated coefficient model for each well location into the seismic survey geometry according to the following formula:

[0019]

[0020] Where the subscript i represents the index of the well location, p i Let (x) denote the weighted parameter p at a given point x, N denote the number of well locations, m(x) denote the coefficient model at a given point x, and m′ i A coefficient model representing the well location.

[0021] In one aspect, generating a three-dimensional low-frequency model through inversion using envelope data and the interpolated coefficient model includes combining the interpolated coefficient model and envelope data using multiple regression.

[0022] Ψ=min||d e -Mx||2

[0023] Where Ψ represents the multiple regression objective function, d e This represents the envelope data calculated throughout the entire survey, M represents the operator containing the coefficient model, and x represents the low-frequency model.

[0024] In one aspect, a system for generating and displaying a low-frequency model of a seismic survey area is provided. The system includes: a blasting device placed at each incident point in the seismic survey area to generate seismic waves that penetrate underground structures; multiple seismic data recording sensors positioned at different locations in the seismic survey area to sense and record seismic data and transmit the seismic data to a computer system including one or more memories for storing the seismic data; and a logging tool including one or more logging data recording sensors placed in one or more wellbores in the seismic survey area to sense and record logging data and transmit the logging data to a computer system including one or more memories for storing the logging data. The computer system also includes at least one processor and stored instructions in one or more memories, wherein the processor executes the instructions stored in the one or more memories to: define a seismic survey geometry for a seismic survey area; process seismic data to generate stacked seismic data; process well logging data to obtain elastic properties; import the stacked seismic data into the defined seismic survey geometry; import the processed well logging data into the defined seismic survey geometry; generate envelope data using the stacked seismic data in the defined seismic survey geometry; generate low-frequency seismic traces for each well in the seismic survey area; calculate a least-squares optimized coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well; interpolate the coefficient model into the seismic survey geometry using covariance techniques and the imported stacked seismic data; generate a three-dimensional low-frequency model by inversion using the envelope data and the interpolated coefficient model; and display an image of the generated three-dimensional low-frequency model of the seismic survey area. Attached Figure Description

[0025] Figure 1 This is a schematic diagram showing a top view of a survey area containing incident points of various seismic sources according to one embodiment;

[0026] Figure 2 It is a schematic diagram showing a cross-sectional view of an environment according to one embodiment, including the incident point of the seismic source, seismic data recording sensors, well location, wellbore, various transmission rays and various incident angles;

[0027] Figure 3 This is a schematic cross-sectional view of an environment including a wellbore and a logging tool according to one embodiment, the logging tool including one or more acoustic generators and one or more logging data recording sensors;

[0028] Figure 4 This is a schematic diagram illustrating a high-performance computing system according to one embodiment;

[0029] Figure 5This is a flowchart illustrating a method for establishing a low-frequency model according to one embodiment, which can improve the accuracy of seismic inversion to enhance reservoir characterization;

[0030] Figure 6 This is an example of superimposed seismic data according to one embodiment;

[0031] Figure 7 Based on one embodiment, it is derived from Figure 6 An example of envelope data calculated from stacked seismic data is shown;

[0032] Figure 8 This is an example of processed well logging data according to one embodiment;

[0033] Figure 9 According to one embodiment Figure 8 An example of a low-frequency seismic trace generated from processed well logging data;

[0034] Figure 10 It is shown in Figure 5 The flowchart of the operation 550 for calculating the least squares optimization coefficient model at one or more well locations;

[0035] Figure 11 It is shown in Figure 5 The flowchart in section 560 shows the operation of using survey covariance techniques to interpolate a coefficient model to a seismic survey geometry representing a seismic survey area;

[0036] Figure 12 It is shown in Figure 5 The flowchart of the operation 570 in which a three-dimensional low-frequency model is generated by inverting using envelope data and interpolated coefficient model;

[0037] Figure 13 It displays the distribution map of stacked seismic data and low-frequency seismic traces at 8 well locations;

[0038] Figure 14 It is a color diagram showing the final frequency model established using the method according to one embodiment; and

[0039] Figure 15 It is a color diagram showing the final frequency model established according to a conventional method. Detailed Implementation

[0040] Reference will now be made in detail to embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. It should be noted that, where possible, similar or identical reference numerals are used in the drawings, and similar or identical elements may be represented.

[0041] The accompanying drawings depict embodiments of the present disclosure and are for illustrative purposes only. Those skilled in the art will readily recognize from the following description that alternative embodiments exist without departing from the general principles of the present disclosure.

[0042] Throughout the specification, the terms “method,” “means,” and “technique” are used interchangeably and have the same meaning.

[0043] Throughout the instruction manual, the terms “subsurface structure,” “stratum,” and “subsurface stratum” are used interchangeably.

[0044] Throughout the instruction manual, the terms "seismic properties" and "geological properties" are used interchangeably.

[0045] Throughout the instruction manual, the terms "recorder" and "receiver" are used interchangeably.

[0046] Throughout this manual, the terms “data space,” “working data space,” “working area,” and “working space” are used interchangeably.

[0047] This disclosure relates to establishing an improved low-frequency model to regularize seismic inversion, thereby filling the low-frequency gap in seismic data, improving the accuracy of seismic inversion, and thus improving the image of the subsurface structure (strata) of the surveyed area for reservoir characterization.

[0048] Figures 1 to 4 Exemplary embodiments of methods, apparatus, and media for acquiring and storing seismic data, which, after processing, can generate one or more high-resolution geological models for high-resolution imaging of lithological identification, fluid identification, and reservoir characterization of subsurface structures in an exploration area. The exploration area can be subsurface structures beneath land or beneath a body of water (such as the ocean). Seismic data and / or well logging data obtained from the exploration area and stored in one or more memories are used. Figures 5 to 14 Exemplary embodiments of apparatus, methods, and media for providing improved quality seismic inversion results for reservoir characterization are shown, wherein improved high-resolution images are generated by using improved low-frequency model techniques to improve lithological identification, fluid identification, and reservoir characterization of subsurface structures in the field of seismic exploration. For example, Figures 5 to 14 Exemplary embodiments of the apparatus, method, and medium are shown, wherein envelope data is provided as guidance to invert a low-frequency model onto a uniform three-dimensional (3D) sample of the seismic survey space.

[0049] Figure 1 This is a schematic diagram showing a top-down view of a survey area containing incident points of different seismic sources according to one embodiment. More specifically, Figure 1A seismic survey area (exploration area) 101 is shown, which is a land-based area indicated by reference numeral 102. Reference numeral 102 indicates the top strata 102 of the land-based area. Those skilled in the art will recognize that seismic survey areas can generate detailed images of the local geology to determine the location and size of possible hydrocarbon (oil and gas) reservoirs, thereby identifying potential well locations 103. In these survey areas, seismic waves are reflected back from the subsurface rock strata when emitted from one or more seismic sources located at different incident points 104. A blast is an example of a seismic source generated by a seismic device. The seismic waves reflected or refracted back to the surface are captured by a seismic data recording sensor 105, transmitted from the seismic data recording sensor 105 via one or more data transmission systems (typically wireless), and stored for post-processing and analysis by a high-performance computing system. While this example shows the top strata 102 of a land-based area, it should be understood that this is only an example, and the method and system can also be applied to survey areas at the bottom of bodies of water, such as the ocean. Users can define a survey area, which can be on land or at the bottom of a body of water (such as the ocean).

[0050] Figure 2 This illustrates a description according to one embodiment. Figure 1 This is a schematic diagram of the cross-sectional view of the seismic survey area 101, including the incident point of the seismic source, the seismic data recording sensor (seismograph), the well location, the well casing, various transmission rays, and various incident angles. More specifically, in Figure 2 In the figures, the cross-sectional view of the subsurface portion above the seismic survey area is indicated by reference numeral 201, and different types of strata are shown by reference numerals 102, 203, and 204. Although the seismic survey area in this example is based on land, it should be understood that this system and method can also be applied to survey areas at the bottom of bodies of water, such as oceans.

[0051] Figure 2 A common-center gather is shown, where seismic data is ordered according to surface geometry to simulate individual reflection points on Earth. Exploration seismic data can also be called traces, gathers, or image gathers. Figure 2 In this example, data from one or more shot points or blast points and detectors can be combined into a single image gather, or used individually depending on the type of analysis to be performed. Although Figure 2 The subsurface strata at different depths (102, 203, and 204) in the seismic survey area are shown, but it should be understood that these are merely examples. Figure 2The different subsurface strata present in the formation are indicated by reference numerals 102, 203, and 204. Alternatively, the user can define the seismic survey area as one or more strata within the top stratum 102, rather than all three distinct strata 102, 203, and 204 at different depths. The user can define the survey area, which can be on land or at the bottom of a body of water (such as the ocean).

[0052] like Figure 2 As shown, one or more shot points or blast points represent seismic sources located on the Earth's surface, indicated by reference numeral 104, at various incident points or sites where one or more sources are activated. Seismic energy or seismic sources from multiple incident points 104 will be reflected from interfaces between different strata. These reflections will be captured by multiple seismic data recording sensors 105, each placed at a different offset distance 210 and well location 103. Since all incident points 104 and all seismic data recording sensors 105 are placed at different offset distances 210, survey seismic data or sets (also referred to in the art as "gathers" or "image gathers") will be recorded at different incident angles 208. Incident points 104 generate downward propagating rays 205, which are captured at the surface by upward propagating reflections from the seismic data recording sensors 105. Although Figure 2 The data shows upward reflection and transmission, but it is understandable that the seismic data recorded by the seismic data recording sensor (detector) may be related to the reflection and / or refraction of seismic waves in response to the source beneath the surface. The source can be a seismic device capable of causing an explosion, or other source-generating devices capable of generating seismic waves beneath the surface. Additionally, in Figure 2 In the example shown, well location 103 illustrates an existing drilled well and logging tool 209, using techniques known in the art to obtain multiple measurements along logging tool 209. This logging tool 209 is used to acquire logging data, which may include P-wave velocity, S-wave velocity, and density. First, seismic data from a seismic survey area can be received and recorded by one or more logging data recording sensors. Then, logging data, including P-wave velocity, S-wave velocity, density, and other logging data, can be calculated from the seismic data using the logging data recording sensors. Alternatively, the logging data recording sensors may simply transmit the seismic data to a computer system, which calculates the logging data. The logging data recording sensors may be the same as seismic data recording sensor 105.

[0053] Seismic data captured by well logging data recording sensors can be used to examine the dependence of amplitude, signal-to-noise ratio, time difference, frequency content, phase, azimuth, and other seismic properties, which are crucial for data processing and imaging in seismic survey areas.

[0054] Figure 3This is a schematic diagram illustrating a cross-sectional view of a wellbore and logging tools according to one embodiment, wherein the logging tools include one or more acoustic wave generators and one or more logging data recording sensors. An acoustic wave generator is an example of a device that generates one or more acoustic waves (acoustic waves). An acoustic wave generator can be referred to as a sound source because it generates or produces one or more acoustic waves (acoustic waves), which are also called seismic waves. One or more logging data recording sensors are examples of one or more seismic data recording sensors (seismic detectors or seismic data recorders). However, logging data recording sensors can also additionally compute logging data, such as P-wave velocity, S-wave velocity, density, and other logging data. In embodiments of the invention, oil and / or gas production is suspended in order to generate seismic waves and record seismic data, including reflections of seismic waves and refractions of seismic data as they move through one or more subsurface strata in a seismic exploration area. The seismic exploration area may have one or more wellbores including one or more logging tools comprising one or more acoustic wave generators and one or more logging data recording sensors.

[0055] Figure 3An oil drilling system 300 on land 305 is shown, including a drilling rig 310. The drilling rig 310 supports the insertion of a logging tool 315 into a wellbore 320. The logging tool 315 includes one or more acoustic generators (sound sources) for generating one or more acoustic waves, which are transmitted to one or more formations to generate reflected or reflected waves in the formations. Although this example shows one or more formations in a land-based exploration area, it should be understood that this is only an example, and the method and system can also be applied to exploration areas on the surface or bottom of a body of water (such as the ocean). The logging tool 315 also includes one or more logging data recording sensors. As described above, the one or more logging data recording sensors (similar or identical to seismic data recording sensor 105) receive and record logging data, which includes reflected and / or refracted data received by the one or more logging data recording sensors in response to seismic waves (sound waves) transmitted to one or more formations by the one or more acoustic generators. In addition, the logging data recording sensors can calculate logging data from the recorded seismic data. Alternatively, seismic data recorded by logging data recording sensors can be transmitted to a computer to calculate logging data. Logging data may include data based on reflection data, such as compressive wave velocity (Vp), shear wave velocity (Vs), and density as an indicator of porosity. This logging process for recording logging data can also be called sonic logging or wireline logging. Logging vehicle 325 can be coupled to logging tool 315 to assist in the running-in and lifting of logging tool 315 and to communicate with logging tool 315 to obtain logging data. Alternatively, in methods and systems targeting exploration areas on the surface or bottom of water bodies (such as oceans), other equipment or systems can be used to assist in the running-in and lifting of logging tool 315 and to communicate with logging tool 315 to obtain logging data.

[0056] Figure 4 This is a schematic diagram illustrating a high-performance computing system according to one embodiment, which receives (typically wirelessly) data from... Figure 1 and Figure 2 Earthquake data recording sensor 105 and / or Figure 3 Earthquake data recording sensors (in) Figure 3 Seismic wave data (also known as well logging data recording sensor). Figure 4 A high-performance computer system stores the seismic data in at least one memory for post-processing and analysis via a computer-implemented method and apparatus according to one or more embodiments. The analyzed or processed seismic data can be accessed via a personal computer system. More specifically, Figure 4A data transmission system 400 is shown for wirelessly transmitting seismic data from a seismic data recording sensor 105 to a system computer 405 coupled to one or more storage devices 410 for storing the seismic data in a database. The data transmission system can also wirelessly transmit seismic data directly from the seismic data recording sensor 105 to one or more storage devices 410 for storing the seismic data in a database, which can be accessed by the system computer 405. As described above, a well logging data recording sensor is an example of a seismic data recording sensor 105 placed in a wellbore. Wireless transmission is indicated by reference numeral 402. The one or more storage devices 410 may also store other computer software instructions or programs to implement the apparatus and methods described in the embodiments. The system computer 405 may be coupled (e.g., wirelessly) to one or more output storage devices 420, which can receive the results of computer-implemented processes or methods executed by the system computer 405. The personal computer system 425 may be coupled (e.g., wirelessly) to one or more output storage devices 420 and / or system computer 405 so that a user can use the user interface of the personal computer system 425 to input information or obtain the results of a computer-implemented processor method executed by the system computer 405. One or more storage devices 420 may also store other computer software instructions or programs to implement the apparatus and methods described in the embodiments.

[0057] The user interface of the personal computer system 425 may include, for example, one or more of the following: a keyboard, mouse, joystick, button, switch, electronic pen or stylus, gesture recognition sensor (e.g., for recognizing gestures of the user including body part movements), input sound device or voice recognition sensor (e.g., microphone for receiving voice commands), output sound device (e.g., speaker), trackball, remote control, portable (e.g., cellular or smartphone) phone, tablet computer, pedal or foot switch, virtual reality device, etc. The user interface may also include a haptic device to provide haptic feedback to the user. The user interface may also include, for example, a touchscreen. Furthermore, the personal computer system 425 may be a desktop computer, laptop computer, tablet computer, mobile phone, or any other personal computing system.

[0058] The processes, functions, methods, and / or computer software instructions or programs in the apparatus and methods described in the embodiments herein may be recorded, stored, or fixed in one or more non-transitory computer-readable media (computer-readable storage (recording) media) including program instructions (computer-readable instructions) executable by a computer to cause one or more processors to execute (implement or implement) the program instructions. The media may also be included alone or in combination with program instructions, data files, data structures, etc. The media and program instructions may be specially designed and constructed, or may be well-known and usable by those skilled in the art of computer software. Examples of non-transitory computer-readable media include magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured for storing and executing program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, etc. Examples of program instructions include machine code (e.g., generated by a compiler) and files containing higher-level code that can be executed by a computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware device can be configured as one or more software modules that are recorded, stored, or embedded in one or more non-transitory computer-readable media to perform the above-described operations and methods, and vice versa. Furthermore, the non-transitory computer-readable media can be distributed among computer systems connected via a network, and program instructions can be stored and executed in a distributed manner. In addition, the computer-readable media can also be embodied in at least one application-specific integrated circuit (ASIC) or field-programmable logic array (FPGA).

[0059] One or more databases may include a collection of data and supporting data structures that can be stored, for example, in one or more storage devices 310 and 320. For example, one or more storage devices 310 and 320 may be embodied as one or more non-transitory computer-readable storage media, such as non-volatile memory devices, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and flash memory, USB drives, volatile memory devices (e.g., random access memory (RAM)), hard disks, floppy disks, Blu-ray discs, or optical media (e.g., CD-ROMs and DVDs), or combinations thereof. However, examples of storage devices 410 and 420 are not limited to those described above, and the storage can be implemented by a variety of other devices and structures understood by those skilled in the art.

[0060] Figure 5This is a flowchart illustrating a method for establishing a low-frequency model to fill low-frequency gaps in seismic data, thereby improving the accuracy of seismic inversion and enhancing the image of subsurface structures in an exploration area for reservoir characterization, according to one embodiment. The exploration area can be subsurface structures beneath land or beneath a body of water (such as the ocean). The exploration area can be the top surface of land or the top surface of the bottom of a body of water (such as the ocean). Furthermore, for example... Figure 4 The high-performance computer system shown can perform tasks such as Figure 5 One or more operations in the flowchart shown.

[0061] See Operation 500, which defines a seismic exploration area comprising one or more wells. In Operation 505, a seismic exploration geometry is defined (established or set up) representing a defined seismic exploration area comprising one or more wells. This seismic exploration geometry is also a defined, uniform three-dimensional (3D) sampling space representing the defined seismic exploration area. This seismic exploration geometry is a working data space (data space or working area) used to store seismic data, elastic properties, acoustic properties, well logging data, and other geological properties. Examples of other geological properties may include amplitude envelope, amplitude-weighted frequency, amplitude-weighted phase, mean frequency, apparent polarity, cosine instantaneous phase, seismic data derivative, instantaneous amplitude derivative, dominant frequency, instantaneous frequency, instantaneous phase, and integral absolute amplitude. As described above, the data, attributes, and / or characteristics of the seismic exploration area can be stored in the working data space of the defined seismic exploration geometry.

[0062] In Operation 505, the seismic survey geometry includes the main survey line numbering and tie line numbering within a certain range, as well as the spatial coordinates of the source and geophone locations. The source location can be the blast point (i.e., the blast location) or the location of a sonic generator. The geophone location can be the location of a geophone that receives seismic waves, which may be reflections and / or refractions of seismic waves initially generated by an explosion or seismic generator. The source and geophone receiver locations can be located within the seismic survey area. Alternatively, the source location may be the location of one or more sonic generators in one or more logging tools 315, and the geophone location may be the location of one or more logging data recording sensors in one or more logging tools 315. The location of the logging tool can be the wellbore.

[0063] In one example, the seismic survey geometry in Operation 505 can represent an actual two-dimensional (2D) seismic survey area, which may be rectangular in shape, with time being the third dimension of the geometry. An example of a rectangle is a square. For instance, the 2D seismic survey area could be 10 km by 10 km. In this example, the 2D seismic survey area could be the top of the Earth's surface (land surface) or the top of the bottom surface beneath a body of water (such as the ocean). If the three vertices of the actual rectangle are known, the fourth vertex can be easily calculated. The actual rectangle of the 2D survey area can be represented using two different axes: the master survey line number and the tie line number in the seismic survey geometry. For example, the master survey line number might appear on the vertical axis of the rectangle, and the tie line number might appear on the horizontal axis. In Operation 505, the spatial coordinates of the source and detector locations can be defined using the master survey line number and the tie line number. Regarding the geometry of the seismic survey, the main survey line number, the connecting line number, and the time define a uniform three-dimensional (3D) sampling space that represents the defined seismic survey area.

[0064] exist Figure 5 In the flowchart, operation 505 can be executed after operations 510 and 530 to obtain information about the seismic survey area. Operation 505 can also be executed after operations 515 and 535 to process the obtained information about the seismic survey area. However, operation 505 must... Figure 5 Operations 520 and 540 are executed before operation 550. Additionally, some operations in operations 510 through 545 may be executed simultaneously or at different times. For example, operations 510 and 530 may be executed simultaneously or at different times. However, operations 500 through 545 must be executed before operation 550.

[0065] Referring to operation 510, seismic exploration is conducted within the survey area to generate seismic data. For example, explosives may be detonated at specific locations within the seismic survey area to generate seismic waves, which are reflected and / or refracted through subsurface structures (strata). These reflections and / or refractions are referred to as seismic data. In operation 515, the seismic data from seismic data recording sensor 105 is processed to generate stacked seismic data for the seismic survey area. This seismic data can be transmitted through, for example,... Figure 4 The computer system shown is used to process and generate stacked seismic data. This stacked seismic data is a three-dimensional space with time as the vertical axis and the main survey line number and connecting line number as the horizontal axis. This stacked seismic data can be generated by stacking and then offsetting the imaging gathers along the reflection angle.

[0066] In operation 520, stacked seismic data is imported into a defined seismic survey geometry. Subsequently, in operation 525, the imported stacked seismic data is used to generate three-dimensional (3D) envelope data. In this example, the envelope of the seismic data (envelope data) is calculated from the complex seismic traces. This envelope data reveals strong events from the seismic data. The envelope data contains low-frequency components that are lacking in conventional seismic data and highlight key seismic features, such as major geological structures within the seismic data. These advantages of envelope data are necessary for building low-frequency models.

[0067] For example, the envelope technique proposed by Wu et al. (Wu RS, J. Luo and B. Wu, 2014, “Seismic envelope inversion and modulation signal model”, published in Geophysics, Vol. 79, No. 3, WA13-WA24) can extract ultra-low frequency signals contained in imported stacked seismic data, thereby recovering the structure of long-wavelength low-frequency models. This calculation process basically includes two steps: (1) calculating the analytical transformation of the stacked seismic data to generate an analytical signal, and (2) calculating the amplitude of the analytical signal by applying a power function to obtain three-dimensional (3D) envelope data. These three dimensions are the main seismic line, the connecting line, and time.

[0068] Figure 6 One example is that the superimposed seismic data must be transformed by applying a power function to obtain 3D envelope data before calculating the amplitude of the analytic signal. Figure 7 It shows from Figure 6 The example shown illustrates the calculation of envelope data from stacked seismic data. This envelope data tracks low-frequency components within the stacked seismic data and extracts the geological structure of the seismic data. Figure 6 and Figure 7 In this configuration, the horizontal axis represents the tie line, and the vertical axis represents time. In another embodiment, the superimposed seismic data and envelope data can be displayed by referencing the horizontal axis (i.e., the master survey line) and the vertical axis (i.e., time). It should be noted that operation 525 generates three-dimensional envelope data. Figure 7 This is a two-dimensional slice of the three-dimensional envelope data.

[0069] Referring to Operation 530, wireline logging is performed in one or more wells to obtain seismic data. Although wireline logging is shown in Operation 530, logging while drilling can also be used to obtain seismic data. Figure 3 An example of wireline logging in a well is shown. Figure 3The wireline logging operation shown can be performed in one or more wells within a seismic exploration area. Wireline logging is achieved by lowering the logging tool 315 at the end of the cable into the wellbore 320 and recording logging data using a logging data recording sensor. Subsequently, in operation 535, the logging data recording sensor processes the logging data to obtain the elastic properties of the seismic exploration area, including P-wave velocity, S-wave velocity, and density. The processed logging data may also be referred to as upsampled logging data. Figure 8 This is an example of processed well logging data according to one embodiment. In operation 540, processed well logging data, including P-wave velocity, S-wave velocity, and density of the seismic survey area, is imported into a defined seismic survey geometry. In operation 545, low-frequency seismic traces at one or more well locations are generated based on the processed well logging data. Operation 545 is similar to the process of acquiring known values ​​during interpolation. More specifically, in operation 545, a low-pass filter may be applied to the processed well logging data imported into the seismic survey geometry to retain the desired low-frequency components and remove high-frequency information at one or more well locations within the seismic survey geometry. Figure 9 Based on one embodiment Figure 8 An example of using processed well logging data to generate low-frequency seismic traces. Figure 8 and Figure 9 In this diagram, the horizontal axis represents the value, and the vertical axis represents time. Here, the value represents the measured logging data. For example, typical values ​​for P-wave velocity logging range from 2000 m / s to 6000 m / s.

[0070] Operation 550 is also performed based on the elastic envelope data generated in Operation 525 and the low-frequency seismic traces generated in Operation 545 at one or more well locations. In Operation 550, a least-squares optimized coefficient model is calculated for one or more well locations. This least-squares optimized coefficient model may be referred to as the coefficient model. The coefficient model is generated or calculated at one or more well locations within the seismic survey geometry. More specifically, the coefficient model may be generated at every well location in the seismic survey area. The coefficient model is used to estimate the relationship between the envelope data generated in Operation 525 and the low-frequency model. The low-frequency model needs to be calibrated from the amplitude and phase perspectives using the envelope data generated in Operation 525. The input data to Operation 550 is the envelope data from Operation 525 and the low-frequency seismic traces at one or more well locations. The coefficient model may be represented by m′ and can be defined as a two-dimensional (2D) table with coefficient samples as the first dimension and well locations as the second dimension.

[0071] Figure 10 It shows in more detail in Figure 5 The flowchart of the operation 550 for calculating the least squares optimization coefficient model at one or more well locations. Figure 10 As shown, the envelope data 521 and low-frequency seismic traces 541 (one at each well location) of the seismic area are input into operation 550. In operation 552, a well location is selected from one or more well locations in the seismic survey area. In operation 554, the low-frequency seismic traces of the selected well location are extracted, and the envelope seismic traces are extracted from the envelope data 521 of the selected well location. Then, in operation 556, the extracted envelope seismic traces and the extracted low-frequency seismic traces are used to solve the least-squares optimization problem d′ of the coefficient model m′. e =F′m′. A system of linear equations is constructed by combining these selected seismic traces, and the coefficient model is solved using the following formula of multiple regression:

[0072] Ψ=min||d′ e -F′m′||2, (1)

[0073] Where Ψ represents the multiple regression objective function, d′ e This represents the calculated envelope seismic trace at a specific well location, where m′ represents the coefficient model, and F′ represents the operator containing the low-frequency seismic trace at that specific well location. Multivariate regression problems can be solved using the conjugate gradient method, which utilizes a norm error ||d′ containing a user-defined tolerance. e -F′m′||2 performs a series of iterations until the norm error is less than the tolerance value, at which point the iteration stops. As mentioned above, the coefficient model m′ can be a two-dimensional (2D) table with the coefficient samples as the first dimension and the well locations as the second dimension.

[0074] Refer to operation 557. If the coefficient model has not yet been calculated for each well location, then... Figure 10 The process then returns to operation 552 to select another well location and obtain the coefficient model for that well location. For example... Figure 5 and Figure 10 As shown, when the coefficient model has been generated (calculated) at each well location, the coefficient model at each well location is output in operation 558 (which is also the output of operation 550).

[0075] refer to Figure 5 Operation 560 (its in) Figure 11 (As further explained in detail below), each coefficient model output by operation 550 is interpolated into the seismic geometry representing the seismic survey area using covariance techniques. More specifically, the coefficient model m′ of sparse well locations (which can be defined as a two-dimensional (2D) table with the sample of coefficients as the first dimension and the well locations as the second dimension) is interpolated into the coefficient model m in the three-dimensional (3D) sampling space of the seismic geometry defined by operation 505. Figure 11This is a flowchart illustrating the operation of using the survey covariance technique to interpolate the coefficient model m′ to the seismic survey geometry representing the seismic survey area.

[0076] refer to Figure 11 The superimposed seismic data 521 of the imported seismic survey geometry output from operation 520 and the coefficient model at each well location obtained from operation 558 will be input into... Figure 5 In operation 560, in operation 520, the stacked seismic data of the seismic survey area is imported into the three-dimensional sampling space of the seismic survey geometry. Therefore, the imported stacked seismic data 521 is three-dimensional. From the imported stacked seismic data 521, seismic traces for one or more well locations are extracted in operation 560. Then, a well location is selected (operation 562), and a correlation map is generated by calculating the correlation coefficients between the seismic traces of the selected well location and all seismic traces in the seismic survey geometry. In operation 564, if it is determined that no correlation map has been generated for any well location, the process returns to operation 562 to select another well location. Operations 562 to 564 are repeated until all well locations are selected; the result of this process is a correlation coefficient map 565 for all well locations.

[0077] Referring to operation 566, the correlation coefficient map of the well location is used to calculate the weighted parameter. The formula (equation) for calculating the weighted parameter p at a given point x in the survey, based on the values ​​extracted from the correlation map, is as follows:

[0078]

[0079] in, p represents a mathematical transformation from v, which includes an exponential parameter. i (x) represents the weighted parameter p at a given point x, and N represents the number of well locations.

[0080] Then, the coefficient model m of the three-dimensional sampling survey space can be calculated using the weighting parameters in equation (2) and the coefficient model calculated at the well location from operation 558, so as to interpolate the coefficient model of the seismic survey geometry using the following equation:

[0081]

[0082] Where the subscript i represents the well location index, p i Let (x) denote the weighted parameter p at a given point x, and N represent the number of well locations. Let m(x) denote the coefficient model at a given point x, and m′ i This represents a two-dimensional (2D) coefficient model at the well location. The process is not complete until all spatial locations within the survey geometry have been calculated, and the interpolated coefficient model representing the seismic survey geometry of the seismic survey area, indicated by reference numeral 568, is output.

[0083] As mentioned above, the coefficient model for the survey space is not simply an averaging of local coefficient models. Instead, it takes into account the correlation of seismic data. The method for obtaining the coefficient model typically involves three steps: (1) generating a correlation map by calculating the correlation coefficients between the selected seismic traces and all seismic traces in the survey; (2) calculating weighting parameters; and (3) interpolating the coefficient model across the entire survey space. This interpolation process does not affect the accuracy of any local coefficient model. Therefore, this method is applicable even if there is only one well in the entire survey space.

[0084] Figure 12 It shows the use of envelope data and Figure 5 The flowchart of operation 570, which uses the interpolation coefficient model from operation 560 to generate a 3D low-frequency model, is as follows: Operation 570 receives envelope data 526 from the seismic survey area of ​​operation 525 as input, and takes the interpolation coefficient model from the seismic survey geometry of operation 560 as input, so as to generate a three-dimensional low-frequency model in operation 570 through inversion. Figure 12 As shown, operation 572 generates a system of linear equations. This system of linear equations is constructed by combining the 3D coefficient model with the calculated envelope data using the following multiple regression:

[0085] Ψ=min||d e -Mx||2 (4)

[0086] Where Ψ represents the multiple regression objective function, d e This represents the envelope data calculated throughout the entire survey, and M represents the operator containing the coefficient model. The output of operation 570 is the low-frequency model x. This inversion process can be solved using the conjugate gradient method.

[0087] Once a three-dimensional low-frequency model of the seismic survey geometry is generated (this model represents the low-frequency model of the seismic survey area), an image of the three-dimensional low-frequency model is displayed on the monitor in operation 580.

[0088] Figure 13 The distribution of stacked seismic data and low-frequency seismic traces at each well location is shown. Image 700 displays time slices of the imported stacked seismic data (stacked in three dimensions) with eight sparse well locations. The black dots in image 700 represent sparse well locations numbered 1 to 8. Figure 5In operation 545, lines 705, 710, 715, 720, 725, 730, 735, and 740 show the corresponding low-frequency seismic traces at eight well locations from 1 to 8, respectively. These low-frequency seismic traces, along with envelope data, are input into operation 550 to calculate the least-squares optimized coefficient model for each well location, which is then interpolated in operation 560 to generate, as shown in operation 570. Figure 14 The three-dimensional low-frequency model shown is illustrated. This example includes two horizontally distributed channels, and the amplitude variations in the channels in image 700 are primarily caused by lithological variations. In this example, Figure 13 This displays the amplitude and phase variations in the lower channels (from bottom left to top right). The color changes represent phase variations in the seismic data. (Reference) Figure 13 In the lower left corner, the color changes represent amplitude changes in the seismic data. The methods and apparatus of this invention are used to apply these features from the seismic data to the creation of low-frequency models.

[0089] Figure 14 It shows the use according to Figure 5 One embodiment of the method outputs a distribution map of the low-frequency model from operation 570, and Figure 15 This is a distribution plot showing the low-frequency model output by a conventional method using the same data. Figure 14 It shows the use of Figure 5 The illustrated embodiment generates an accurate low-frequency model for reservoir characterization. However, as... Figure 15 As shown, traditional methods (such as inverse distance-weighted interpolation, Kriging, and Hale methods) cannot accurately capture the amplitude and phase information contained in seismic data, which is necessary for generating accurate low-frequency models. These traditional methods do not generate envelope data that is combined with the generated low-frequency seismic traces, which are subsequently processed by... Figure 5 Operations 550 to 570 are applied to generate a 3D low-frequency model, which in Figure 5 The operation is shown in 580. Therefore, Figure 15 This illustrates a problem with traditional methods, the so-called bullseye effect. In contrast, Figure 14 It eliminates the bullseye effect of traditional methods and provides enhanced images using the same set of seismic data.

[0090] Therefore, this embodiment constructs an improved low-frequency model to regularize seismic inversion, in order to fill the low-frequency gap in seismic data, thereby improving the accuracy of seismic inversion, thus improving high-resolution images, and further enhancing the lithological identification, fluid identification and reservoir characterization of the underground structure in the survey area in the field of seismic exploration.

[0091] While embodiments of the present disclosure have been shown and described, modifications can be made by those skilled in the art without departing from the spirit or teaching of the invention. The embodiments described herein are merely examples and not limiting. Many variations and modifications of the methods, systems, and apparatus are possible and are within the scope of the invention. Therefore, the scope of protection is not limited to the embodiments described herein, but is defined solely by the claims. The scope of the claims should include all equivalents of the subject matter described in the claims.

Claims

1. A method for generating and displaying a low-frequency model of a seismic survey area, the method comprising: Seismic data recording sensors were placed at different locations within the seismic survey area; A logging tool, comprising one or more logging data recording sensors, is placed in one or more wells in the seismic survey area; Detonation is carried out at the incident point in the seismic survey area to generate seismic waves that penetrate underground structures. The earthquake data recording sensor is used to sense the seismic waves and record earthquake data. The well logging data recording sensor is used to sense and record well logging data; The seismic data from the seismic data recording sensor is transmitted to a computer system including one or more memories, and the seismic data is stored in one or more memories; The logging data from the logging data recording sensor is transmitted to a computer system including one or more memories, and the logging data is stored in one or more memories; Define the seismic survey geometry of the seismic survey area; Process seismic data to generate stacked seismic data; Process well logging data to obtain elastic properties; Import the overlaid seismic data into the defined seismic survey geometry; Import the processed well logging data into the defined seismic exploration geometry; Envelope data is generated using the superimposed seismic data within a defined seismic survey geometry. Generate low-frequency seismic traces for each well within the seismic survey area; The least-squares optimization coefficient model at each well location is calculated based on the generated envelope data and the low-frequency seismic traces of each well. The coefficient model is interpolated into the seismic survey geometry using covariance techniques and the imported stacked seismic data. The envelope data and the interpolated coefficient model are used to generate a three-dimensional low-frequency model through inversion; and This displays an image of the generated three-dimensional low-frequency model of the seismic survey area.

2. The method according to claim 1, wherein, The elastic properties include one or more of P-wave velocity, S-wave velocity, and density.

3. The method according to claim 1, wherein, The one or more wells in the seismic survey area are multiple wells, and the multiple well locations include one of the multiple wells.

4. The method according to claim 3, wherein, The seismic geometry of the seismic survey area includes envelope data for each well location and low-frequency seismic traces for each well location.

5. The method according to claim 4, wherein, The calculation of the least-squares optimization coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (a) Selecting a well location; (b) Extract the envelope seismic traces of the selected well location from the generated envelope data, and extract the low-frequency seismic traces of the selected well location from multiple low-frequency seismic traces; as well as (c) Solve the coefficient model at the well location. Least squares optimization problem ,in, This represents the extracted envelope seismic trace at the stated well location. This represents an operator that includes the envelope seismic trace at the well location.

6. The method according to claim 5, wherein, The calculation of the least-squares optimization coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (d) Repeat steps (a) to (c) until the least-squares optimization problem for each well is solved; and (e) Output the calculated coefficient model for each well location.

7. The method according to claim 6, wherein, The process of using covariance techniques and imported stacked seismic data to interpolate the coefficient model into the seismic survey geometry includes the following operations: (f) Extract the seismic traces for each well location; (g) Select one well location from the plurality of well locations; (h) A correlation map is generated by calculating the correlation coefficient between the seismic trace at the selected well location and all seismic traces in the seismic survey geometry; (i) Repeat operations (g) and (h) until a correlation map for each well location is generated in the seismic survey geometry; (j) Output the correlation diagrams for all well locations; (k) Calculate the weighted parameters for all well locations; (l) Interpolate each coefficient model for each well location into the entire seismic survey geometry; and (m) Output the interpolated coefficient model to the entire seismic survey geometry.

8. The method according to claim 7, wherein, The weighted parameters are calculated using the following formula: in, Indicates that it comes from including the power parameter. mathematical transformations, p represents the well location index. i (x) represents the weighted parameter p at a given point x, and N represents the number of well locations.

9. The method according to claim 8, wherein, The method of using covariance techniques and imported stacked seismic data to interpolate the coefficient model into the seismic survey geometry also includes inputting the calculated coefficient model and the interpolated coefficient model for each well location into the seismic survey geometry according to the following formula: Among them, subscript Index indicating the well location. Indicates at a given point Weighted parameters at the location , N Indicates the number of well locations. Let x represent the coefficient model at a given point x, and A coefficient model representing the well location.

10. The method according to claim 9, wherein, The process of generating a three-dimensional low-frequency model through inversion using the envelope data and the interpolated coefficient model includes using a multiple regression to combine the interpolated coefficient model and the envelope data. Ψ = ‖ ‖2 Where Ψ represents the objective function of the multiple regression. This represents the envelope data calculated throughout the entire survey. This represents an operator that includes the coefficient model, and This represents a low-frequency model.

11. A system for generating and displaying a low-frequency model of a seismic survey area, the system comprising: A blasting device placed at each incident point in the seismic survey area is used to generate seismic waves that penetrate underground structures. Multiple seismic data recording sensors are installed at different locations in the seismic survey area to sense and record seismic data and transmit the seismic data to a computer system including one or more memories, wherein the computer system stores the seismic data in one or more memories; and A logging tool including one or more logging data recording sensors is placed in one or more wellbores in the seismic survey area to sense and record logging data and transmit the logging data to a computer system including the one or more memories for storing the logging data; The computer system further includes at least one processor and stored instructions in the one or more memories, and the one or more processors execute the instructions stored in the one or more memories to implement: Define the seismic survey geometry of the seismic survey area; Process seismic data to generate stacked seismic data; Process well logging data to obtain elastic properties; Import the overlaid seismic data into the defined seismic survey geometry; Import the processed well logging data into the defined seismic exploration geometry; Envelope data is generated using the superimposed seismic data within a defined seismic survey geometry. Generate low-frequency seismic traces for each well within the seismic survey area; The least-squares optimization coefficient model at each well location is calculated based on the generated envelope data and the low-frequency seismic traces of each well. The coefficient model is interpolated into the seismic survey geometry using covariance techniques and the imported stacked seismic data. A three-dimensional low-frequency model is generated by inversion using the envelope data and the interpolated coefficient model; and This displays an image of the generated three-dimensional low-frequency model of the seismic survey area.

12. The system according to claim 11, wherein, The elastic properties include one or more of P-wave velocity, S-wave velocity, and density.

13. The system according to claim 11, wherein, The one or more wells in the seismic survey area are multiple wells, and the multiple well locations include one of the multiple wells.

14. The system according to claim 13, wherein, The seismic geometry of the seismic survey area includes envelope data for each well location and low-frequency seismic traces for each well location.

15. The system according to claim 14, wherein, The calculation of the least-squares optimization coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (a) Selecting a well location; (b) Extract the envelope seismic traces of the selected well location from the generated envelope data, and extract the low-frequency seismic traces of the selected well location from multiple low-frequency seismic traces; as well as (c) Solve the coefficient model at the well location. Least squares optimization problem ,in, This represents the extracted envelope seismic trace at the stated well location. This represents an operator that includes the envelope seismic trace at the well location.

16. The system according to claim 15, wherein, The calculation of the least-squares optimization coefficient model at each well location based on the generated envelope data and the low-frequency seismic traces of each well also includes the following operations: (d) Repeat steps (a) to (c) until the least-squares optimization problem for each well is solved; and (e) Output the calculated coefficient model for each well location.

17. The system according to claim 16, wherein, The process of using covariance techniques and imported stacked seismic data to interpolate the coefficient model into the seismic survey geometry includes the following operations: (f) Extract the seismic traces for each well location; (g) Select one well location from the plurality of well locations; (h) A correlation map is generated by calculating the correlation coefficient between the seismic trace at the selected well location and all seismic traces in the seismic survey geometry; (i) Repeat operations (g) and (h) until a correlation map for each well location is generated in the seismic survey geometry; (j) Output the correlation diagrams for all well locations; (k) Calculate the weighted parameters for all well locations; (l) Interpolate each coefficient model for each well location into the entire seismic survey geometry; and (m) Output the interpolated coefficient model to the entire seismic survey geometry.

18. The system according to claim 17, wherein, The weighted parameters are calculated using the following formula: in, Indicates that it comes from including the power parameter. mathematical transformations, p represents the well location index. i (x) represents the weighted parameter p at a given point x, and N represents the number of well locations.

19. The system according to claim 18, wherein, The method of using covariance techniques and imported stacked seismic data to interpolate the coefficient model into the seismic survey geometry also includes inputting the calculated coefficient model and the interpolated coefficient model for each well location into the seismic survey geometry according to the following formula: Among them, subscript Index indicating the well location. Indicates at a given point Weighted parameters at the location , N Indicates the number of well locations. Let x represent the coefficient model at a given point x, and A coefficient model representing the well location.

20. The system according to claim 19, wherein, The process of generating a three-dimensional low-frequency model through inversion using the envelope data and the interpolated coefficient model includes using a multiple regression to combine the interpolated coefficient model and the envelope data. Ψ = ‖ ‖2 Where Ψ represents the objective function of the multiple regression. This represents the envelope data calculated throughout the entire survey. This represents an operator that includes the coefficient model, and This represents a low-frequency model.

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