Method and apparatus for establishing formation velocity model, and electronic device, medium and product

Abstract

A method for establishing a formation velocity model, comprising: acquiring a root-mean-square amplitude of each first test location in a target formation (S101); acquiring a measured vertical seismic profile velocity and a measured acoustic logging velocity of each first test location, and on the basis of the root-mean-square amplitude of each first test location and the vertical seismic profile velocity and acoustic logging velocity of each first test location, obtaining a characteristic relationship between root-mean-square amplitudes and formation velocities (S102); and on the basis of the characteristic relationship, and the root-mean-square amplitude of each second test location in the target formation, obtaining formation velocities, and on the basis of the formation velocities, establishing a formation velocity model for the target formation (S103). Further provided are an apparatus for establishing a formation velocity model, and an electronic device and a product. Thus, the accuracy of formation velocity modeling results can be improved.

Description

Methods, apparatus, electronic equipment, media and products for establishing rock strata velocity models

[0001] This application claims priority to Chinese Patent Application No. 202411587612.5, filed on November 8, 2024, entitled “Method, Apparatus, Electronic Equipment, Medium and Product for Establishing Rock Strata Velocity Model”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of petroleum seismic exploration data processing technology, and in particular to a method, apparatus, electronic equipment, medium and product for establishing a rock stratum velocity model. Background Technology

[0003] In petroleum exploration, the accuracy of velocity modeling directly affects the accuracy of structural mapping and the assessment of oil and gas resources. For example, in areas where gypsum-salt rocks are distributed, the thickness of these rocks varies greatly and is not clearly reflected in seismic profiles, making continuous tracking difficult. Therefore, it is necessary to conduct velocity modeling studies using multiple velocity information sources to establish high-precision gypsum-salt rock models, thereby better guiding seismic exploration work in the study area.

[0004] Establishing velocity models plays a crucial role in oil exploration and development. During oil production, the location of gypsum-salt layers is essential for determining key parameters such as reservoir height, area, and thickness. Gypsum-salt velocity modeling allows for precise calculation of gypsum-salt layer velocities, enabling the identification of their specific locations in seismic data and providing accurate geological information for subsequent exploration and development. Gypsum-salt velocity modeling also indirectly assesses reservoir properties such as porosity and permeability, providing vital information for reservoir evaluation and development planning. Furthermore, gypsum-salt layer velocity models offer significant guidance during drilling and production. By comparing actual drilling logging data with velocity model predictions, formation changes can be detected promptly, allowing for adjustments to drilling plans and production strategies to ensure smooth drilling and production operations.

[0005] Currently, some velocity modeling schemes use the velocity at the test location to build the velocity model. However, when the rock strata structure is complex and the spatial distribution varies greatly, the velocity varies greatly. When using the velocity at the test location to build the model, it is difficult to adapt to the drastic local lateral velocity changes. Due to the large velocity error, the model will be inaccurate. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, medium, and product for establishing a rock stratum velocity model, in order to improve the accuracy of velocity modeling results.

[0007] In a first aspect, this application provides a method for establishing a rock stratum velocity model, the method comprising: obtaining the root mean square amplitude of each first test location in the target rock stratum; obtaining the measured vertical seismic profile velocity and sonic logging velocity of each first test location; obtaining the characteristic relationship between the root mean square amplitude and the rock stratum velocity based on the root mean square amplitude of each first test location and the vertical seismic profile velocity and sonic logging velocity of each first test location; obtaining the rock stratum velocity based on the characteristic relationship and the root mean square amplitude of each second test location in the target rock stratum; and establishing a rock stratum velocity model of the target rock stratum based on the rock stratum velocity.

[0008] In one possible implementation, obtaining the root mean square amplitude at each first test location in the target rock stratum includes: obtaining pre-stack depth migration pure wave data of the target rock stratum; and obtaining the root mean square amplitude at each first test location in the target rock stratum based on the pre-stack depth migration data and a root mean square amplitude calculation method.

[0009] In one possible implementation, acquiring pre-stack depth migration pure wave data of the target rock strata includes: acquiring raw seismic data; performing pre-stack depth migration processing on the raw seismic data to obtain migration imaging results; and extracting pre-stack depth migration pure wave data from the migration imaging results.

[0010] In one possible implementation, the method further includes: calibrating the top and bottom boundaries of the target rock strata; and using the top and bottom boundaries as constraints on the rock strata velocity model to optimize the rock strata velocity model.

[0011] In one possible implementation, the method further includes: dividing the target rock strata into different depth positions according to a preset step size, and selecting a preset number of positions from the different depth positions as each second test position.

[0012] In one possible implementation, the characteristic relationship between the root mean square amplitude and the rock layer velocity is obtained based on the root mean square amplitude at each first test location, the vertical seismic profile velocity at each first test location, and the sonic logging velocity; including: fitting a fitting formula for the root mean square amplitude and the rock layer velocity based on the root mean square amplitude at each first test location, the vertical seismic profile velocity at each first test location, and the sonic logging velocity.

[0013] In one possible implementation, the target rock formation includes multiple well locations; before obtaining the root mean square amplitude attribute of each first test location in the target rock formation, the method further includes: dividing the multiple well locations to obtain a set of test well locations and a set of verification well locations; dividing the set of test well locations to obtain each first test location and each second test location;

[0014] In one possible implementation, the method further includes: obtaining the strata velocity at each verification location in the set of verification well locations based on the strata velocity model; verifying the strata velocity model based on the strata velocity at each verification location, as well as the measured vertical seismic profile velocity and sonic logging velocity at each verification location, to obtain the verification results.

[0015] Secondly, this application provides a rock stratum velocity model building device, comprising: an acquisition module for acquiring the root mean square amplitude of each first test location in the target rock stratum; the acquisition module is further configured to acquire the measured vertical seismic profile velocity and sonic logging velocity of each first test location; a processing module for obtaining the characteristic relationship between the root mean square amplitude and the rock stratum velocity based on the root mean square amplitude of each first test location and the vertical seismic profile velocity and sonic logging velocity of each first test location; and a modeling module for obtaining the rock stratum velocity based on the characteristic relationship and the root mean square amplitude of each second test location in the target rock stratum; and establishing a rock stratum velocity model of the target rock stratum based on the rock stratum velocity.

[0016] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, causing the at least one processor to perform the first aspect above and various possible rock strata velocity modeling methods involved in the first aspect.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the first aspect above and various possible rock strata velocity modeling methods involved in the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect above and various possible rock strata velocity modeling methods involved in the first aspect.

[0019] This application discloses a method, apparatus, electronic equipment, medium, and product for establishing a rock layer velocity model. It obtains the root-mean-square (RMS) amplitude at each first test location in the target rock layer, as well as the measured vertical seismic profile velocity and sonic logging velocity at each first test location. Based on the RMS amplitude and the vertical seismic profile velocity and sonic logging velocity at each first test location, a characteristic relationship between the RMS amplitude and the rock layer velocity is derived. Based on this characteristic relationship, the rock layer velocity is obtained from the RMS amplitude at each second test location in the target rock layer. A rock layer velocity model is then established based on this rock layer velocity. This application's scheme improves the accuracy of rock layer velocity model establishment by obtaining the characteristic relationship between the RMS amplitude and the rock layer velocity from the RMS amplitude at each first test location in the target rock layer, the vertical seismic profile velocity, and the sonic logging velocity, and by obtaining the rock layer velocity from the RMS amplitude at each second test location in the target rock layer. Rock layer velocity modeling is then performed based on the obtained rock layer velocity. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 illustrates a flowchart of a method for establishing a rock stratum velocity model.

[0022] Figure 2 shows the distribution of root mean square amplitude and well velocity.

[0023] Figure 3 is a schematic diagram comparing the rock strata velocity field and the pre-stack depth migration profile before and after using the method of this application;

[0024] Figure 4 illustrates a flowchart of a method for establishing a rock stratum velocity model.

[0025] Figure 5 shows the pre-stack depth migration pure wave profile of the target rock strata;

[0026] Figure 6 is a schematic diagram of the root mean square amplitude attribute distribution;

[0027] Figure 7 illustrates a flowchart of a method for establishing a rock stratum velocity model;

[0028] Figure 8 is an overlay diagram of the rock formation velocity and the verification well velocity;

[0029] Figure 9 is a cross-sectional view of the pre-stack depth migration results before and after using the scheme of this application;

[0030] Figure 10 shows a schematic diagram of a rock stratum velocity model building device;

[0031] Figure 11 shows an exemplary structural diagram of an electronic device.

[0032] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0034] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning. The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to be omnipresent but not exclusive. For example, a product or device that comprises a series of components is not necessarily limited to those components that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such products or devices. The term "module" as used in this application refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code capable of performing the functions associated with that element.

[0035] In petroleum exploration, the accuracy of velocity modeling directly affects the accuracy of structural mapping and the assessment of oil and gas resources. For example, in areas where gypsum-salt rocks are distributed, the thickness of these rocks varies greatly and is not clearly reflected in seismic profiles, making continuous tracking difficult. Therefore, it is necessary to conduct velocity modeling studies using multiple velocity information sources to establish high-precision gypsum-salt rock models, thereby better guiding seismic exploration work in the study area.

[0036] Establishing velocity models plays a crucial role in oil exploration and development. During oil production, the location of gypsum-salt layers is essential for determining key parameters such as reservoir height, area, and thickness. Gypsum-salt velocity modeling allows for precise calculation of gypsum-salt layer velocities, enabling the identification of their specific locations in seismic data and providing accurate geological information for subsequent exploration and development. Gypsum-salt velocity modeling also indirectly assesses reservoir properties such as porosity and permeability, providing vital information for reservoir evaluation and development planning. Furthermore, gypsum-salt layer velocity models offer significant guidance during drilling and production. By comparing actual drilling logging data with velocity model predictions, formation changes can be detected promptly, allowing for adjustments to drilling plans and production strategies to ensure smooth drilling and production operations.

[0037] Currently, some velocity modeling schemes use the velocity at the test location to build the velocity model. However, when the rock strata structure is complex and the spatial distribution varies greatly, the velocity varies greatly. When using the velocity at the test location to build the model, it is difficult to adapt to the drastic local lateral velocity changes. Due to the large velocity error, the model will be inaccurate.

[0038] The technical content provided in this application aims to solve the aforementioned technical problems in related technologies. The rock layer velocity model establishment method, apparatus, electronic equipment, medium, and product of this application involve obtaining the root mean square amplitude (RMS) at each first test location in the target rock layer; obtaining the measured vertical seismic profile velocity and sonic logging velocity at each first test location; obtaining the characteristic relationship between the RMS amplitude and rock layer velocity based on the RMS amplitude, vertical seismic profile velocity, and sonic logging velocity at each first test location; obtaining the rock layer velocity based on the RMS amplitude at each second test location in the target rock layer based on the characteristic relationship; and establishing a rock layer velocity model for the target rock layer based on the rock layer velocity. The solution of this application, by obtaining the characteristic relationship between the RMS amplitude and rock layer velocity based on the RMS amplitude, vertical seismic profile velocity, and sonic logging velocity at each first test location in the target rock layer, and obtaining the rock layer velocity from the RMS amplitude at each second test location in the target rock layer; and performing rock layer velocity modeling based on the obtained rock layer velocity, improves the accuracy of rock layer velocity model establishment.

[0039] The technical solutions of this application will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. In the description of this application, unless otherwise expressly specified and limited, the terms should be broadly understood within the art. The embodiments of this application will now be described with reference to the accompanying drawings.

[0040] Example 1

[0041] Figure 1 illustrates a flowchart of a method for establishing a rock stratum velocity model; as shown in Figure 1, the method includes:

[0042] Step 101: Obtain the root mean square amplitude of each first test location in the target rock stratum;

[0043] Step 102: Obtain the measured vertical seismic profile velocity and sonic logging velocity at each first test location; based on the root mean square amplitude and the vertical seismic profile velocity and sonic logging velocity at each first test location, obtain the characteristic relationship between the root mean square amplitude and the rock layer velocity.

[0044] Step 103: Based on the characteristic relationship, obtain the rock layer velocity according to the root mean square amplitude of each second test position of the target rock layer; establish a rock layer velocity model of the target rock layer based on the rock layer velocity.

[0045] Specifically, the root mean square (RMS) amplitude of seismic waves at each first test location within the target rock stratum is first obtained; for example, the target rock stratum is a gypsum-salt layer. Gypsum-salt layers are highly plastic, and their spatial velocity distribution varies greatly. The first test location refers to the three-dimensional position of each spatial point used for testing within the target rock stratum. The RMS amplitude attribute of seismic waves at each location is obtained, and the RMS amplitude is obtained based on the RMS amplitude attribute; this example does not limit the method for obtaining the RMS amplitude. In practical applications, before fitting the spatial velocity, the vertical surface profile velocity and sonic logging velocity at each first test location within the target rock stratum are simultaneously obtained; the vertical surface profile velocity is obtained by receiving seismic wave signals by placing a geophone in the well. The seismic wave signals include both upward waves from underground and downward waves from the surface; the vertical surface profile velocity refers to the speed at which seismic waves propagate vertically in the underground medium. Sonic logging is a logging method that uses the difference in the propagation speed of sound waves in rocks to classify lithology and estimate reservoir porosity; sonic logging velocity refers to the speed at which sound waves propagate in rocks. Sonic logging involves placing a transmitting probe and a receiving probe in the well, recording the time difference of sound waves traveling from the transmitting probe through the formation to the receiving probe, and then calculating the propagation speed of sound waves in the rock. Based on the measured vertical seismic profile velocity, sonic logging velocity, and root-mean-square (RMS) amplitude at each of the first test locations, a characteristic relationship between RMS amplitude and formation velocity is obtained. In this example, based on the obtained RMS amplitude, vertical seismic profile velocity, and sonic logging velocity at each of the first test locations, a characteristic relationship between RMS amplitude and formation velocity is obtained. Based on this characteristic relationship, the formation velocity at each of the second test locations is obtained, and a formation velocity model is established based on the formation velocity, making the established formation velocity model more accurate.

[0046] Furthermore, based on the characteristic relationship between the root mean square amplitude and the rock layer velocity, the rock layer velocity is obtained according to the root mean square amplitude of each second test position of the target rock layer, and a rock layer velocity model of the target rock layer is established based on the rock layer velocity.

[0047] As an example, based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity, the characteristic relationship between the root mean square amplitude and the rock layer velocity is obtained; including: based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity, a fitting formula for the root mean square amplitude and the rock layer velocity is obtained.

[0048] Specifically, the n root mean square amplitudes at n first test locations, along with the corresponding n vertical seismic profile velocities and sonic logging velocities within the same range, are used as a set of known sample data. Based on their distribution shape on the plane, this example selects the optimal polynomial fitting formula. Using this formula, the best fitting parameters a, b, and c are calculated and found using the least squares method, minimizing the error between the fitted curve and the data points.

[0049] The fitting formula is expressed as: V = aA 2 +bA+c

[0050] Where V is the rock velocity at the first test location, A is the root mean square amplitude, a and b are the coefficients of the polynomial, and c is the adjustment term. The adjustment term can optimize the fitting formula, improve the predictive ability of the rock velocity model, and make the model more accurate.

[0051] Figure 2 shows the distribution of root-mean-square amplitude and well velocity. As shown in Figure 2, the horizontal axis represents well velocity, which is obtained by integrating the aforementioned vertical seismic profile velocity and sonic logging velocity; the vertical axis represents the root-mean-square amplitude value. Figure 2 shows that the root-mean-square amplitude value is relatively focused, creating favorable conditions for accurately establishing a fitting formula between the root-mean-square amplitude and the rock layer velocity. Specifically, the root-mean-square amplitude values ​​at each of the second test locations below the target rock layer are input into the fitting formula to obtain the rock layer velocity at each of the second test locations. Based on the obtained rock layer velocities at each of the second locations, a rock layer velocity model of the target rock layer can be established. This rock layer velocity model is used to describe the spatial variation of velocity within the target rock layer. Figure 3 is a schematic diagram comparing the velocity field of the rock strata with the pre-stack depth migration profile before and after using the method of this application. In Figure 3, DB401 represents a drilled well location within the target rock stratum, E1-2km1 represents the top boundary of the rock stratum, and T8 represents the target drilling formation below the rock stratum. As can be seen from Figure 3, the left figure is the superimposed diagram of the velocity field and the pre-stack depth migration profile before using the method of this application. Due to the inability to establish an accurate velocity model of the target rock stratum segment, the velocity field within the target rock stratum is not obvious, and therefore the color depth reflected in the left figure is relatively light. The right figure is the superimposed diagram of the velocity field and the pre-stack depth migration profile after using this method. It can be seen that the velocity field of the target rock stratum is more obvious, and the color reflected in the figure is darker. This indicates that the method of this application improves the accuracy of establishing the velocity model of the target rock stratum.

[0052] In this example, a fitting formula is obtained by fitting the root mean square amplitude, vertical seismic profile velocity, and sonic logging velocity. A target rock layer velocity model is established based on the velocity at each location within the target rock layer space, thereby improving the accuracy of target rock layer velocity modeling.

[0053] Specifically, the root mean square amplitude needs to be obtained from the seismic pure wave data; correspondingly, as an example, Figure 4 illustrates a flowchart of a method for establishing a rock layer velocity model. Based on any example, step 101 specifically includes:

[0054] Step 201: Obtain pre-stack depth migration pure wave data of the target rock strata;

[0055] Step 202: Based on the pre-stack depth migration data, obtain the root mean square amplitude of each first test position in the target rock layer using the root mean square amplitude calculation method.

[0056] In practical applications, it is necessary to obtain the root mean square (RMS) amplitude from the pre-stack depth migration pure wave data of the target rock strata. For example, firstly, the pre-stack depth migration pure wave data is superimposed without any other non-uniform energy processing, preserving the original amplitude relationship of the seismic waves. The RMS amplitude attributes of gypsum-salt rocks are extracted within the top and bottom boundaries of the target rock strata, generating an RMS amplitude attribute volume. After obtaining the amplitude attributes and attribute volume, the RMS amplitude can be extracted and calculated. Specifically, by statistically analyzing various characteristic attributes of the seismic traces within a given analysis window, various seismic attribute information can be obtained. In one example, for each seismic trace, the RMS amplitude is calculated within a specific analysis window, and then the RMS amplitude attribute volume is obtained through comprehensive statistical analysis across the entire work area. Because the square root operation is performed on the pre-averaged amplitude, the RMS amplitude shows a significant change in amplitude. Then, based on the seismic amplitude attribute volume, the RMS amplitude value is calculated using the RMS amplitude. Figure 5 shows the pre-stack depth migration pure wave profile of the target rock strata; Figure 6 shows the root mean square amplitude attribute distribution. Combining Figures 5 and 6, it can be seen that the dark areas within the salt top and salt bottom range in Figure 6 represent locations with strong amplitude attributes. The root mean square amplitude at these locations has a clear relationship with the velocity within the same salt top and salt bottom range in Figure 5, and they are quite consistent. It can be seen that the distribution of strong root mean square amplitude values ​​can basically reflect the distribution range of gypsum-salt rocks.

[0057] In this example, the root mean square amplitude attribute is obtained from the pre-stack depth migration pure wave data. No non-consistent energy processing is performed on the pre-stack depth migration pure wave data of the seismic waves, thus maintaining the original amplitude relationship of the seismic waves. This makes the amplitude value more accurate, improves the accuracy of the fitting formula, and further improves the accuracy of the target rock layer velocity model.

[0058] Furthermore, before obtaining the root mean square amplitude attribute from the target pre-stack depth migration pure wave data, it is necessary to first obtain the pre-stack depth migration pure wave data. Accordingly, as an example, Figure 7 illustrates a flowchart of a method for establishing a rock stratum velocity model. Based on any example, step 201 specifically includes:

[0059] Step 301: Acquire raw seismic data;

[0060] Step 302: Perform pre-stack depth migration processing on the original seismic data to obtain the migration imaging results;

[0061] Step 303: Extract pre-stack depth migration pure wave data from the migration imaging results.

[0062] Specifically, the process begins with acquiring raw seismic data. Preprocessing of this data includes denoising, as seismic waveform data typically contains noise such as environmental and instrument noise. To obtain a clearer signal, data denoising is necessary. Common denoising methods include filtering (such as high-pass, low-pass, and band-pass filtering) and baseline drift removal. This example does not specify a particular denoising method. After denoising, data correction is required, which involves converting the raw seismic waveform data into standard seismic quantities such as displacement, velocity, or acceleration to ensure data accuracy and reliability. Following data preprocessing, pre-stack depth migration imaging is performed using the processed seismic data and a velocity model established based on the propagation velocity of seismic waves in the target rock layer, yielding pre-stack depth migration stacked pure wave data. The specific steps are as follows: First, perform time-domain analysis to analyze the time characteristics of the seismic waveform data, such as peak value, amplitude, and half-period, to understand the basic characteristics of the seismic waves; convert the waveform data into a spectral image and obtain parameters such as the frequency, energy, and energy density of the seismic waves through spectral analysis; based on the propagation characteristics of the seismic waves and the spectral analysis results, select an appropriate filter to filter the seismic data to extract the pure wave signal of interest; for example, a bandpass filter can be used to filter out noise and unwanted wave components while retaining the target wave component; to further improve the signal-to-noise ratio and clarity of the pure wave signal, signal enhancement techniques can be used to further process the filtered signal; finally, the extracted pure wave data needs to be verified and corrected to ensure the accuracy and reliability of the extraction results. In one example, raw data is acquired to ensure the integrity and quality of seismic data acquisition. Data preprocessing, including denoising, filtering, and waveform correction, is performed to improve data quality. An established velocity model is used as the initial model for pre-stack depth migration. Based on imaging results and geological interpretation, the velocity model is corrected and iterated. The corrected velocity model is then used for re-processing pre-stack depth migration imaging to obtain more accurate imaging results. Common reflection gathers are extracted from each migrated common offset profile. The reflected waves from each trace are then stacked to form pre-stack depth migration stacked pure wave data. In this example, pre-stack depth migration stacked pure wave data is obtained from the raw seismic data. The root mean square amplitude is obtained from the pre-stack depth migration stacked pure wave data and fitted using a formula, improving the accuracy of the fitting results and thus enhancing the accuracy of the target rock layer velocity model.

[0063] Specifically, the established velocity model needs to define the boundaries of its target rock strata in order to optimize the model. Accordingly, as an example, the method also includes:

[0064] Mark the top and bottom boundaries of the target rock strata;

[0065] The top and bottom boundaries are used as constraints for the rock strata velocity model, and the model is optimized accordingly.

[0066] Specifically, before establishing a velocity model for the target rock strata, it is necessary to collaborate with geologists to understand the geological overview, collect data on geological stratification and well logging velocities, and accurately identify the top and bottom boundaries of the gypsum-salt rock strata by continuously tracing the same set of reflection phase axes through geological stratification and layer calibration. These top and bottom boundaries serve as the control interfaces for the spatial distribution of gypsum-salt rocks, pinpointing the target area for the velocity model of this scheme. In one example, various geological data on underground rock strata are first collected, including seismic data, well logging data, drilling data, and geological exploration reports; these data form the basis for delineating the top and bottom boundaries of the rock strata. Seismic data is interpreted by identifying the characteristics of seismic reflection wave groups, such as amplitude, frequency, and phase, to determine the rock strata interfaces. Well logging data is compared with the seismic interpretation results to correct and verify the seismic interpretation results. Well logging data can provide more detailed and accurate rock strata information, helping to improve the accuracy of the delineation. On the seismic profile, the strata are traced along the strike to ensure the continuity of the top and bottom boundaries. Finally, based on the results of seismic interpretation and well logging data, the top and bottom boundaries of the rock strata are delineated. Calibrating the top and bottom boundaries accurately reflects the structure of underground rock strata, improves the accuracy of velocity models, and guides exploration and development. In practical applications, geological stratification refers to the process of dividing crustal rocks into different layers based on lithology, fossils, age, and other characteristics. Geological stratification data mainly includes methods such as classifying strata into different rock types or rock assemblages based on the mineral composition, structure, and texture of the rocks. Well logging velocity data typically refers to the data on the propagation velocity of underground rocks using well logging methods; well logging velocity data mainly includes sonic transit time data, sonic velocity curves, porosity information, and various other well logging data (such as resistivity, density, spontaneous potential, etc.). Based on the above geological structure and well logging velocity data, the top and bottom boundaries of the target rock strata are calibrated to accurately depict the distribution range of the target rock strata. The root mean square amplitude used for fitting within this range is then determined based on the distribution range, and fitting processing is performed. In this example, by accurately calibrating the top and bottom boundaries of the target rock layer, the amplitude within the range of the top and bottom boundaries of the target rock layer is extracted, fitted, and a velocity model is established, thereby improving the accuracy of velocity modeling of the target rock layer.

[0067] Specifically, when modeling the velocity of a target rock stratum, it is necessary to obtain the velocity at each location in space based on the fitting formula, and then establish a velocity model based on the velocity. Correspondingly, based on any example, the method also includes:

[0068] The target rock strata are divided into different depth positions according to a preset step size, and a preset number of positions are selected from each different depth position as the second test positions.

[0069] Specifically, after obtaining the fitting formula for the root mean square amplitude and rock layer velocity based on multiple sample data from multiple first test locations, the target rock layer is divided into locations at different depths according to a preset step size. In one example, multiple preset number of second test locations are divided at a depth of 5 meters. The root mean square amplitudes at multiple second test locations are substituted into the fitted formula for the root mean square amplitude and rock layer velocity to obtain the rock layer velocity at each second test location. A velocity model of the target rock layer is then constructed based on the rock layer velocities at each second test location. In this example, the rock layer velocity at multiple locations is used to construct the rock layer velocity model, which improves the accuracy of the rock layer velocity model.

[0070] After constructing the rock formation velocity model, its accuracy needs to be assessed. Accordingly, as an example, the target rock formation includes multiple well locations; before obtaining the root mean square amplitude at each first test location in the target rock formation, the following steps are also included:

[0071] Multiple well locations are divided into a test well location set and a verification well location set;

[0072] The set of test well locations is divided into first test locations and second test locations.

[0073] Specifically, the target rock formation includes multiple well locations. Before obtaining the root mean square amplitude of each first test location in the target rock formation, the multiple well locations within the target rock formation are divided into sets of test well locations and sets of verification well locations. The test well location set is then further divided into first test locations and second test locations. In this example, dividing the multiple well locations in the target rock formation into test well locations and verification well locations, and further dividing the test well locations into first test locations and second test locations, provides a foundation for accurately establishing the velocity model of the target rock formation, further improving the accuracy of the rock formation velocity model.

[0074] After dividing the target rock formation into well locations, each verification well location is obtained; correspondingly, as an example, the method also includes:

[0075] Based on the rock strata velocity model, the rock strata velocity at each verification location under the verification well location set is obtained;

[0076] The rock velocity model was validated based on the rock strata velocity at each validation location, as well as the measured vertical seismic profile velocity and sonic logging velocity at each validation location, and the validation results were obtained.

[0077] In one example, 70% of the well locations within the target rock formation are used as test wells to establish a velocity model for the target rock formation; correspondingly, the remaining 30% of well locations are used as verification wells to test the accuracy of the velocity model. Specifically, based on the established rock formation velocity model, the rock formation velocity at each verification location within the verification well set is obtained; the rock formation velocity at each verification location within the verification well set is compared with the measured vertical seismic profile velocity and sonic logging velocity at each verification location. The square of the velocity difference at each verification location is calculated, and then the average value is calculated. If the average value is less than 10%, i.e., the consistency (consistency = (1 - average value) x 100%) is greater than 90%, then the velocity accuracy is considered to meet the requirements for pre-stack depth migration velocity modeling. Figure 8 shows the overlay diagram of rock formation velocity and verification well velocity. As shown in Figure 8, the upper coordinate represents velocity, with units of meters per second. E1-2km1 represents the top boundary marker layer of the target rock strata, and E1-2km4 represents the bottom boundary marker layer. The section from E1-2km1 to E1-2km4 is a gypsum-salt rock segment. The section between E1-2km1 and E1-2km2 is a high-velocity mudstone segment, and the section between E1-2km2 and E1-2km4 is a high-velocity gypsum-rock segment. The curves marked with mudstone and gypsum-rock high-velocities in the figure represent the rock strata velocities obtained using the velocity model of this scheme. The other curve represents the well velocity, which is obtained by integrating the vertical seismic profile velocity and the sonic logging velocity. It can be seen that the velocity curves obtained using the velocity model of this scheme have a high degree of overlap with the well velocity, indicating that the velocity model of this application has high accuracy and can realistically reflect the rock strata velocities. In this example, the velocity model of the target rock strata was validated using a verification well, and the validation results were obtained. This further validated the accuracy of the target rock strata velocity model.

[0078] Figure 9 compares the pre-stack depth migration profiles before and after using the proposed scheme, using any of the aforementioned examples. As shown in Figure 9, the left image is the pre-stack depth migration profile before using the scheme, and the right image is the pre-stack depth migration profile after using the scheme. Dabei 401 is a test well location within the target strata range in an example; E1-2km1 is the top boundary marker layer of the target strata, and E1-2km4 is the bottom boundary marker layer of the target strata. "+" indicates a deeper seismic profile; "-" indicates a shallower seismic profile. As can be seen from Figure 9, after obtaining the pre-stack depth migration imaging results based on the velocity model established in this scheme, the depth error between the seismic profile and the wellbore is reduced from -320 meters to 60 meters, achieving the required accuracy for production applications.

[0079] The method for establishing a rock layer velocity model provided in this embodiment obtains the root mean square (RMS) amplitude at each first test location in the target rock layer; and measures the vertical seismic profile velocity and sonic logging velocity at each first test location. Based on the RMS amplitude and the vertical seismic profile velocity and sonic logging velocity at each first test location, a characteristic relationship between the RMS amplitude and the rock layer velocity is obtained. Based on this characteristic relationship, the rock layer velocity is obtained from the RMS amplitude at each second test location in the target rock layer. A rock layer velocity model is then established based on this rock layer velocity. This solution improves the accuracy of the rock layer velocity model by obtaining the characteristic relationship between the RMS amplitude and the rock layer velocity from the RMS amplitude at each first test location in the target rock layer, the vertical seismic profile velocity, and the sonic logging velocity, and obtaining the rock layer velocity from the RMS amplitude at each second test location in the target rock layer. The rock layer velocity model is then built based on the obtained rock layer velocity.

[0080] Example 2

[0081] The following example illustrates the specific application of the rock stratum velocity model established using this scheme in the aforementioned examples.

[0082] Taking the target rock strata in a certain region as the object, the target rock strata in this region are gypsum-salt rock strata, including pure salt layer, gypsum rock layer, gypsum mud rock layer, etc. Gypsum-salt rock is widely distributed, gypsum mud salt undergoes intense plastic deformation, and the characteristics and thickness distribution of gypsum mud salt vary greatly laterally, with the thickest reaching 4800m.

[0083] First, the top and bottom boundaries of the gypsum-salt rock are determined according to the method described in the previous example. The processing personnel need to work closely with the seismic interpreters to finely characterize the top and bottom boundaries of the gypsum-salt rock according to the geological stratification. Then, the seismic data with amplitude preservation characteristics are obtained. Using the pre-stack depth migration pure wave data, that is, without any energy processing, the original amplitude relative relationship of the seismic waves is maintained. This relationship can be regarded as the correspondence between the gypsum-salt rock and the mudstone velocity.

[0084] Using the method described in the previous example, the formulas for rock strata velocity and root mean square amplitude were created. The fitting formulas for the rock strata velocity and root mean square amplitude within the target rock strata's top and bottom boundaries in this region are: V = 0.0097A 2 -56.498A+70357

[0085] By substituting the root mean square amplitude value A obtained from pre-stack depth migration pure wave data within the top and bottom boundaries of the gypsum-salt rock into the fitting formula, the rock strata velocity within this range can be calculated. A rock strata velocity model can then be constructed using the velocities at points within this spatial range. This velocity model can reflect the velocity variation trends of gypsum-salt rock and mudstone with small errors.

[0086] Based on the established velocity model, seismic imaging processing is performed according to this rock layer velocity model. This processing includes techniques such as pre-stack depth migration and post-stack time migration to generate seismic profiles or 3D seismic data volumes reflecting the subsurface geological structure. Geological interpretation of the seismic imaging results identifies subsurface geological structural features, such as strata, faults, and lithological variations. Based on this geological interpretation, a 3D model of the subsurface geological structure is established. This 3D model is a mathematical model that reflects the morphology of geological structures, the relationships between various structural elements, and the spatial distribution of geological properties, realistically expressing the morphology, characteristics, and distribution patterns of three-dimensional spatial physical parameters of geological structures.

[0087] A three-dimensional model of the underground geological structure, derived from the rock strata velocity model, provides a more intuitive understanding of the underground geological structure and offers a more reliable geological basis for relevant decision-making. This three-dimensional model of the geological structure can be applied in fields including resource exploration and development, oil and gas exploration and development, mineral resource assessment, engineering design and evaluation, and environmental impact assessment.

[0088] Example 3

[0089] Figure 10 illustrates a schematic diagram of a rock stratum velocity model building device. As shown in Figure 10, the device includes:

[0090] The acquisition module 21 is used to acquire the root mean square amplitude of each first test location in the target rock stratum;

[0091] The acquisition module 21 is also used to acquire the measured vertical seismic profile velocity and sonic logging velocity at each of the first test locations.

[0092] Processing module 22 is used to obtain the characteristic relationship between root mean square amplitude and rock layer velocity based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity.

[0093] Modeling module 23 is used to obtain the rock layer velocity based on the root mean square amplitude of each second test position of the target rock layer according to the characteristic relationship; and to establish a rock layer velocity model of the target rock layer based on the rock layer velocity.

[0094] Specifically, the root mean square (RMS) amplitude of seismic waves at each first test location within the target rock stratum is first obtained; for example, the target rock stratum is a gypsum-salt layer. Gypsum-salt layers are highly plastic, and their spatial velocity distribution varies greatly. The first test location refers to the three-dimensional position of each spatial point used for testing within the target rock stratum. The RMS amplitude attribute of seismic waves at each location is obtained, and the RMS amplitude is obtained based on the RMS amplitude attribute; this example does not limit the method for obtaining the RMS amplitude. In practical applications, before fitting the spatial velocity, the vertical surface profile velocity and sonic logging velocity at each first test location within the target rock stratum are simultaneously obtained; the vertical surface profile velocity is obtained by receiving seismic wave signals by placing a geophone in the well. The seismic wave signals include both upward waves from underground and downward waves from the surface; the vertical surface profile velocity refers to the speed at which seismic waves propagate vertically in the underground medium. Sonic logging is a logging method that uses the difference in the propagation speed of sound waves in rock to classify lithology and estimate reservoir porosity; sonic logging velocity refers to the speed at which sound waves propagate in rock. Sonic logging involves placing a transmitting probe and a receiving probe in the well, recording the time difference of sound waves traveling from the transmitting probe through the formation to the receiving probe, and then calculating the propagation speed of sound waves in the rock. Based on the measured vertical seismic profile velocity, sonic logging velocity, and root-mean-square (RMS) amplitude at each first test location, a characteristic relationship between RMS amplitude and formation velocity is obtained. In this example, based on the obtained RMS amplitude, vertical seismic profile velocity, and sonic logging velocity at each first test location, a characteristic relationship between RMS amplitude and formation velocity is obtained. Based on this characteristic relationship, the formation velocity at each second test location is obtained, and a formation velocity model is established based on the formation velocity, making the established formation velocity model more accurate.

[0095] Furthermore, based on the characteristic relationship between the root mean square amplitude and the rock layer velocity, the rock layer velocity is obtained according to the root mean square amplitude of each second test position of the target rock layer, and a rock layer velocity model of the target rock layer is established based on the rock layer velocity.

[0096] As an example, based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity, the characteristic relationship between the root mean square amplitude and the rock layer velocity is obtained; including: based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity, a fitting formula for the root mean square amplitude and the rock layer velocity is obtained.

[0097] Specifically, the n root mean square amplitudes at n first test locations, along with the corresponding n vertical seismic profile velocities and sonic logging velocities within the same range, are used as a set of known sample data. Based on their distribution shape on the plane, this example selects the optimal polynomial fitting formula. Using this formula, the best fitting parameters a, b, and c are calculated and found using the least squares method, minimizing the error between the fitted curve and the data points.

[0098] The fitting formula is expressed as: V = aA 2 +bA+c

[0099] Where V is the rock velocity at the first test location, A is the root mean square amplitude, a and b are the coefficients of the polynomial, and c is the adjustment term. The adjustment term can optimize the fitting formula, improve the predictive ability of the rock velocity model, and make the model more accurate.

[0100] In this example, a fitting formula is obtained by fitting the root mean square amplitude, vertical seismic profile velocity, and sonic logging velocity. A target rock layer velocity model is established based on the velocity at each location within the target rock layer space, thereby improving the accuracy of target rock layer velocity modeling.

[0101] Specifically, the root mean square amplitude needs to be obtained from the seismic pure wave data; correspondingly, as an example, module 21 is specifically used for:

[0102] Acquire pre-stack depth-migrated pure wave data of the target rock strata;

[0103] Based on the pre-stack depth migration data, the root mean square amplitude of each first test location in the target rock layer is obtained using the root mean square amplitude calculation method.

[0104] In practical applications, it is necessary to obtain the root mean square (RMS) amplitude from the pre-stack depth migration pure wave data of the target rock strata. For example, firstly, the pre-stack depth migration pure wave data is superimposed without any other non-uniform energy processing, preserving the original amplitude relationship of the seismic waves. The RMS amplitude attributes of gypsum-salt rocks are extracted within the top and bottom boundaries of the target rock strata, generating an RMS amplitude attribute volume. After obtaining the amplitude attributes and attribute volume, the RMS amplitude can be extracted and calculated. Specifically, by statistically analyzing various characteristic attributes of the seismic traces within a given analysis window, various seismic attribute information can be obtained. In one example, for each seismic trace, the RMS amplitude is calculated within a specific analysis window, and then the RMS amplitude attribute volume is obtained through comprehensive statistical analysis across the entire work area. Because the square root operation is performed on the pre-averaged amplitude, the RMS amplitude shows a significant change in amplitude. Then, based on the seismic amplitude attribute volume, the RMS amplitude value is calculated using the RMS amplitude. In this example, the root mean square amplitude attribute is obtained from the pre-stack depth migration pure wave data. No non-consistent energy processing is performed on the pre-stack depth migration pure wave data of the seismic waves, thus maintaining the original amplitude relationship of the seismic waves. This makes the amplitude value more accurate, improves the accuracy of the fitting formula, and further improves the accuracy of the target rock layer velocity model.

[0105] Furthermore, before obtaining the root mean square amplitude attribute from the target pre-stack depth-migrated pure wave data, it is necessary to first acquire the pre-stack depth-migrated pure wave data. Accordingly, as an example, the acquisition module 21 is also used for:

[0106] Collect raw seismic data;

[0107] The migration imaging results are obtained by pre-stack depth migration processing based on the original seismic data;

[0108] Pre-stack depth migration pure wave data are extracted from the migration imaging results.

[0109] Specifically, the process begins with acquiring raw seismic data. Preprocessing of this data includes denoising, as seismic waveform data typically contains noise such as environmental and instrument noise. To obtain a clearer signal, data denoising is necessary. Common denoising methods include filtering (such as high-pass, low-pass, and band-pass filtering) and baseline drift removal. This example does not specify a particular denoising method. After denoising, data correction is required, which involves converting the raw seismic waveform data into standard seismic quantities such as displacement, velocity, or acceleration to ensure data accuracy and reliability. Following data preprocessing, pre-stack depth migration imaging is performed using the processed seismic data and a velocity model established based on the propagation velocity of seismic waves in the target rock layer, yielding pre-stack depth migration stacked pure wave data. The specific steps are as follows: First, perform time-domain analysis to analyze the time characteristics of the seismic waveform data, such as peak value, amplitude, and half-period, to understand the basic characteristics of the seismic waves; convert the waveform data into a spectral image and obtain parameters such as the frequency, energy, and energy density of the seismic waves through spectral analysis; based on the propagation characteristics of the seismic waves and the spectral analysis results, select an appropriate filter to filter the seismic data to extract the pure wave signal of interest; for example, a bandpass filter can be used to filter out noise and unwanted wave components while retaining the target wave component; to further improve the signal-to-noise ratio and clarity of the pure wave signal, signal enhancement techniques can be used to further process the filtered signal; finally, the extracted pure wave data needs to be verified and corrected to ensure the accuracy and reliability of the extraction results. In one example, raw data is acquired to ensure the integrity and quality of seismic data acquisition. Data preprocessing, including denoising, filtering, and waveform correction, is performed to improve data quality. An established velocity model is used as the initial model for pre-stack depth migration. Based on imaging results and geological interpretation, the velocity model is corrected and iterated. The corrected velocity model is then used for re-processing pre-stack depth migration imaging to obtain more accurate imaging results. Common reflection gathers are extracted from each migrated common offset profile. The reflected waves from each trace are then stacked to form pre-stack depth migration stacked pure wave data. In this example, pre-stack depth migration stacked pure wave data is obtained from the raw seismic data. The root mean square amplitude is obtained from the pre-stack depth migration stacked pure wave data and fitted using a formula, improving the accuracy of the fitting results and thus enhancing the accuracy of the target rock layer velocity model.

[0110] Specifically, the established velocity model needs to define the boundaries of its target rock layer in order to optimize the model. Accordingly, as an example, this scheme also includes: a calibration module 24, which is used for:

[0111] Mark the top and bottom boundaries of the target rock strata;

[0112] The top and bottom boundaries are used as constraints for the rock strata velocity model, and the model is optimized accordingly.

[0113] Specifically, before establishing a velocity model for the target rock strata, it is necessary to collaborate with geologists to understand the geological overview, collect data on geological stratification and well logging velocities, and accurately identify the top and bottom boundaries of the gypsum-salt rock strata by continuously tracing the same set of reflection phase axes through geological stratification and layer calibration. These top and bottom boundaries serve as the control interfaces for the spatial distribution of gypsum-salt rocks, pinpointing the target area for the velocity model of this scheme. In one example, various geological data on underground rock strata are first collected, including seismic data, well logging data, drilling data, and geological exploration reports; these data form the basis for delineating the top and bottom boundaries of the rock strata. Seismic data is interpreted by identifying the characteristics of seismic reflection wave groups, such as amplitude, frequency, and phase, to determine the rock strata interfaces. Well logging data is compared with the seismic interpretation results to correct and verify the seismic interpretation results. Well logging data can provide more detailed and accurate rock strata information, helping to improve the accuracy of the delineation. On the seismic profile, the strata are traced along the strike to ensure the continuity of the top and bottom boundaries. Finally, based on the results of seismic interpretation and well logging data, the top and bottom boundaries of the rock strata are delineated. Calibrating the top and bottom boundaries accurately reflects the structure of underground rock strata, improves the accuracy of velocity models, and guides exploration and development. In practical applications, geological stratification refers to the process of dividing crustal rocks into different layers based on lithology, fossils, age, and other characteristics. Geological stratification data mainly includes methods such as classifying strata into different rock types or rock assemblages based on the mineral composition, structure, and texture of the rocks. Well logging velocity data typically refers to the data on the propagation velocity of underground rocks using well logging methods; well logging velocity data mainly includes sonic transit time data, sonic velocity curves, porosity information, and various other well logging data (such as resistivity, density, spontaneous potential, etc.). Based on the above geological structure and well logging velocity data, the top and bottom boundaries of the target rock strata are calibrated to accurately depict the distribution range of the target rock strata. The root mean square amplitude used for fitting within this range is then determined based on the distribution range, and fitting processing is performed. In this example, by accurately calibrating the top and bottom boundaries of the target rock layer, the amplitude within the range of the top and bottom boundaries of the target rock layer is extracted, fitted, and a velocity model is established, thereby improving the accuracy of velocity modeling of the target rock layer.

[0114] Specifically, when modeling the velocity of a target rock stratum, it is necessary to obtain the velocity at each location in space according to the fitting formula, and then establish a velocity model based on the velocity. Accordingly, as an example, the target rock stratum is divided into different depth positions according to a preset step size, and a preset number of positions are selected from each depth position as the second test positions.

[0115] Specifically, after obtaining the fitting formula for the root mean square amplitude and rock layer velocity based on multiple sample data from multiple first test locations, the target rock layer is divided into locations at different depths according to a preset step size. In one example, multiple preset number of second test locations are divided at a depth of 5 meters. The root mean square amplitudes at multiple second test locations are substituted into the fitted formula for the root mean square amplitude and rock layer velocity to obtain the rock layer velocity at each second test location. A velocity model of the target rock layer is then constructed based on the rock layer velocities at each second test location. In this example, the rock layer velocity at multiple locations is used to construct the rock layer velocity model, which improves the accuracy of the rock layer velocity model.

[0116] After constructing the rock formation velocity model, its accuracy needs to be assessed. Accordingly, as an example, the target rock formation includes multiple well locations; before obtaining the root mean square amplitude at each first test location in the target rock formation, the following steps are also included:

[0117] Multiple well locations are divided into a test well location set and a verification well location set;

[0118] The set of test well locations is divided into first test locations and second test locations.

[0119] Specifically, the target rock formation includes multiple well locations. Before obtaining the root mean square amplitude of each first test location in the target rock formation, the multiple well locations within the target rock formation are divided into sets of test well locations and sets of verification well locations. The test well location set is then further divided into first test locations and second test locations. In this example, dividing the multiple well locations in the target rock formation into test well locations and verification well locations, and further dividing the test well locations into first test locations and second test locations, provides a foundation for accurately establishing the velocity model of the target rock formation, further improving the accuracy of the rock formation velocity model.

[0120] This solution also includes a verification module 25, which is used for:

[0121] Based on the rock strata velocity model, the rock strata velocity at each verification location under the verification well location set is obtained;

[0122] The rock velocity model was validated based on the rock strata velocity at each validation location, as well as the measured vertical seismic profile velocity and sonic logging velocity at each validation location, and the validation results were obtained.

[0123] In one example, 70% of the well locations within the target rock formation are used as test wells to establish a velocity model for the target rock formation; correspondingly, the remaining 30% of well locations are used as verification wells to test the accuracy of the established velocity model. Specifically, based on the established rock formation velocity model, the rock formation velocity at each verification location within the verification well set is obtained. The rock formation velocity at each verification location within the verification well set is compared with the measured vertical seismic profile velocity and sonic logging velocity at each verification location. The square of the velocity difference at each verification location is calculated, and then the average value is calculated. If the average value is less than 10%, i.e., the consistency (consistency = (1 - average value) x 100%) is greater than 90%, the velocity accuracy is considered to meet the requirements for pre-stack depth migration velocity modeling. In this example, the velocity model of the target rock formation is verified using verification wells, and the verification results are obtained. This further verifies the accuracy of the target rock formation velocity model.

[0124] The rock strata velocity model establishment device provided in this embodiment obtains the root mean square amplitude of each first test location in the target rock stratum, as well as the measured vertical seismic profile velocity and sonic logging velocity at each first test location. Based on the root mean square amplitude of each first test location and the vertical seismic profile velocity and sonic logging velocity at each first test location, a characteristic relationship between the root mean square amplitude and the rock stratum velocity is obtained. Based on this characteristic relationship, the rock stratum velocity is obtained from the root mean square amplitude of each second test location in the target rock stratum. A rock stratum velocity model of the target rock stratum is then established based on the rock stratum velocity. The solution of this application improves the accuracy of rock stratum velocity model establishment by obtaining the characteristic relationship between the root mean square amplitude and the rock stratum velocity from the root mean square amplitude of each first test location in the target rock stratum, the vertical seismic profile velocity, and the sonic logging velocity, and obtaining the rock stratum velocity from the root mean square amplitude of each second test location in the target rock stratum. Rock stratum velocity modeling is then performed based on the obtained rock stratum velocity.

[0125] Example 4

[0126] Figure 11 illustrates a schematic diagram of the structure of an electronic device, which includes:

[0127] The device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods described in the example above.

[0128] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0129] The memory 292, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above method examples.

[0130] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0131] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method in any of the embodiments.

[0132] This application also provides a computer program product, including a computer program that, when executed by a processor, is used to implement the method in any of the embodiments.

[0133] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0134] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0135] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0136] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0137] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0138] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0139] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0140] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only.

[0141] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

[0142] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for establishing a rock stratum velocity model, characterized in that, The method includes: Obtain the root mean square amplitude at each first test location in the target rock stratum; The vertical seismic profile velocity and sonic logging velocity at each of the first test locations are obtained; based on the root mean square amplitude at each of the first test locations, the vertical seismic profile velocity at each of the first test locations, and the sonic logging velocity, the characteristic relationship between the root mean square amplitude and the rock layer velocity is obtained. Based on the aforementioned characteristic relationship, the rock layer velocity is obtained according to the root mean square amplitude of each second test position of the target rock layer; a rock layer velocity model of the target rock layer is established based on the rock layer velocity.

2. The method according to claim 1, characterized in that, The step of obtaining the root mean square amplitude at each first test location in the target rock stratum includes: Acquire pre-stack depth-migrated pure wave data of the target rock strata; Based on the pre-stack depth migration data, the root mean square amplitude of each first test location in the target rock layer is obtained using the root mean square amplitude calculation method.

3. The method according to claim 2, characterized in that, The acquisition of pre-stack depth migration pure wave data of the target rock strata includes: Collect raw seismic data; The migration imaging results are obtained by performing pre-stack depth migration processing on the original seismic data. The pre-stack depth migration pure wave data is extracted from the migration imaging results.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Define the top and bottom boundaries of the target rock strata; The top and bottom boundaries are used as constraints on the rock stratum velocity model to optimize it.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: The target rock strata are divided into different depth positions according to a preset step size, and a preset number of positions are selected from the different depth positions as the second test positions.

6. The method according to any one of claims 1-5, characterized in that, The step of obtaining the characteristic relationship between the root mean square amplitude and the rock layer velocity based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity includes: Based on the root mean square amplitude at each of the first test locations, the vertical seismic profile velocity at each of the first test locations, and the sonic logging velocity, a fitting formula for the root mean square amplitude and the rock layer velocity is obtained.

7. The method according to any one of claims 1-6, characterized in that, The target rock formation includes multiple well locations; before obtaining the root mean square amplitude of each first test location in the target rock formation, the method further includes: The multiple well locations are divided into a test well location set and a verification well location set; The set of test well locations is divided into the first test locations and the second test locations.

8. The method according to claim 7, characterized in that, The method further includes: Based on the rock strata velocity model, the rock strata velocity at each verification location under the verification well location set is obtained; The rock strata velocity model is validated based on the rock strata velocity at each validation location, as well as the measured vertical seismic profile velocity and sonic logging velocity at each validation location, and validation results are obtained.

9. A device for establishing a rock stratum velocity model, characterized in that, include: The acquisition module is used to acquire the root mean square amplitude of each first test location in the target rock stratum; The acquisition module is also used to acquire the measured vertical seismic profile velocity and sonic logging velocity at each of the first test locations. The processing module is used to obtain the characteristic relationship between the root mean square amplitude and the rock layer velocity based on the root mean square amplitude of each first test location, the vertical seismic profile velocity of each first test location, and the sonic logging velocity. The modeling module is used to obtain the rock layer velocity based on the characteristic relationship and the root mean square amplitude of each second test position of the target rock layer; A rock stratum velocity model is established based on the rock stratum velocity.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-8.

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