Methods, apparatus, equipment, storage media, and products for determining the velocity of sound waves.

Abstract

This application provides a method, apparatus, device, storage medium, and product for determining acoustic velocity, belonging to the field of seismic exploration technology. The method includes: acquiring migration seismic data within an exploration area and the location information of a well to be measured within the exploration area; the migration seismic data includes multiple seismic traces; based on the location information, determining a target seismic trace data corresponding to the location information from the multiple seismic traces; determining the attribute information of the target seismic trace data, and determining at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data, the attribute information including at least one of amplitude information, frequency information, and phase information; and determining the acoustic velocity of the entire formation within the well based on the target seismic trace data, at least one seismic trace attribute data, and an acoustic velocity prediction model. Since the target seismic trace data and at least one seismic trace attribute data can reflect the characteristics of the entire formation, the accuracy of the determined acoustic velocity is improved.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, and in particular to a method, apparatus, device, storage medium and product for determining sound wave velocity. Background Technology

[0002] Currently, P-wave logging is an important technique for exploring the distribution of oil and gas in reservoirs. To reduce costs, P-wave logging only measures the acoustic velocity of a few key target layers within the wellbore, and then determines the acoustic velocity of the entire formation based on the acoustic velocity of the key target layers.

[0003] In related technologies, the acoustic velocity of the entire formation during drilling is determined using the constant filling method. The specific steps are as follows: Multiple acoustic velocities corresponding to multiple target layers are obtained; from these multiple acoustic velocities, a first acoustic velocity corresponding to the first target layer and a second acoustic velocity corresponding to the second target layer are determined. The first target layer is the formation closest to the wellhead among the multiple target layers, and the second target layer is the formation farthest from the wellhead among the multiple target layers; the acoustic velocity of formations with depths less than the first target layer is determined to be the same as the first acoustic velocity; the acoustic velocity of formations with depths greater than the second target layer is determined to be the same as the second acoustic velocity, thus obtaining the acoustic velocity of the entire formation during drilling.

[0004] However, since the lithology of the formation in the well changes with depth, and the acoustic velocity corresponding to different lithologies is also different, the constant filling cannot accurately represent the acoustic velocity of the entire formation in the well. Therefore, the accuracy of the above method in determining the acoustic velocity is poor. Summary of the Invention

[0005] This application provides a method, apparatus, device, storage medium, and product for determining acoustic wave velocity, which can improve the accuracy of determining the acoustic wave velocity of the entire formation during drilling. The technical solution is as follows:

[0006] On one hand, this application provides a method for determining the velocity of sound waves, the method comprising:

[0007] Acquire migration seismic data within the exploration area and location information of the wells to be measured within the exploration area, wherein the migration seismic data includes multiple seismic traces;

[0008] Based on the location information, the target seismic trace data corresponding to the location information is determined from the plurality of seismic trace data;

[0009] Determine the attribute information of the target seismic trace data, and determine at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data, wherein the attribute information includes at least one of amplitude information, frequency information and phase information;

[0010] Based on the target seismic trace data, the attribute data of at least one seismic trace, and the acoustic velocity prediction model, the acoustic velocity of the entire formation in the well is determined.

[0011] In one possible implementation, determining the acoustic velocity of the entire formation in the wellbore based on the target seismic trace data, the at least one seismic trace attribute data, and the acoustic velocity prediction model includes:

[0012] The target seismic trace data and the at least one seismic trace attribute data are normalized to obtain normalized seismic trace data and at least one normalized attribute data.

[0013] The normalized seismic trace data and the at least one normalized attribute data are input into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well.

[0014] The normalized velocity is inversely normalized to obtain the relative velocity corresponding to the entire formation during drilling.

[0015] A first velocity trend parameter for the drilling is determined, which represents the trend of the acoustic velocity with depth.

[0016] The acoustic velocity of the entire formation is determined based on the sum of the relative velocity corresponding to the entire formation during drilling and the first velocity trend parameter.

[0017] In another possible implementation, the normalization process of the target seismic trace data and the at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data includes:

[0018] Determine multiple first amplitude data corresponding to the target seismic trace data and multiple second amplitude data corresponding to each seismic trace attribute data;

[0019] Determine the average and standard deviation of the plurality of first amplitude data and the average and standard deviation of the plurality of second amplitude data;

[0020] The target seismic trace data is normalized based on the first average value and first standard deviation of the plurality of first amplitude data to obtain normalized seismic trace data. Furthermore, the seismic trace attribute data is normalized based on the second average value and second standard deviation of the plurality of second amplitude data to obtain normalized attribute data.

[0021] In another possible implementation, the target seismic trace data is normalized based on the first average and first standard deviation of the plurality of first amplitude data to obtain normalized seismic trace data; and the seismic trace attribute data is normalized based on the second average and second standard deviation of the plurality of second amplitude data to obtain normalized attribute data, including:

[0022] Based on the first average value and first standard deviation of the plurality of first amplitude data, the target seismic trace data is normalized using the following formula 1 to obtain normalized seismic trace data; and based on the second average value and second standard deviation of the plurality of second amplitude data, the seismic trace attribute data is normalized using the following formula 2 to obtain normalized attribute data.

[0023] Formula 1:

[0024] Formula 2:

[0025] Wherein, O1 represents the normalized seismic trace data, x1 represents the target seismic trace data, μ1 represents the first average value, σ1 represents the first standard deviation, O2 represents the normalized attribute data, x2 represents the seismic trace attribute data, μ2 represents the second average value, and σ2 represents the second standard deviation.

[0026] In another possible implementation, the drilling includes a logged section and an unlogged section, wherein the logged section is a formation section for which velocity logging parameters reflecting formation properties have been obtained through logging instruments, and the unlogged section is a formation section for which velocity logging parameters have not been measured; determining the first velocity trend parameter of the drilling includes:

[0027] Determine the maximum and minimum drilling speeds;

[0028] Obtain the acoustic velocity corresponding to multiple depth values ​​within the measured well section;

[0029] The first velocity trend parameter of the drilling is determined based on the maximum velocity, the minimum velocity, and the acoustic velocity corresponding to the plurality of depth values.

[0030] In another possible implementation, the first velocity trend parameter of the drilling is determined based on the maximum velocity, the minimum velocity, and the acoustic velocities corresponding to the plurality of depth values, including:

[0031] Based on the maximum speed, the minimum speed, and the acoustic velocities corresponding to the multiple depth values, the first parameter and the second parameter are determined using the following formula three to obtain the first velocity trend parameter of the drilling.

[0032] Formula 3: V(x)1=V Top +(V Base -V Top )×m×e cx

[0033] Among them, V Top V represents the minimum speed. Base V(x)1 represents the maximum speed, x represents the depth value, V(x)1 represents the first speed trend parameter of the drilling, m represents the first parameter, and c represents the second parameter.

[0034] In another possible implementation, determining the target seismic trace data corresponding to the location information from the plurality of seismic trace data based on the location information includes:

[0035] Obtain the velocity logging parameters corresponding to the logged section within the well, wherein the velocity logging parameters are used to represent the correspondence between the vertical depth value and the sonic velocity of the logged section;

[0036] Determine the convolution between the wavelet of the migrated seismic data and the reflection coefficient of the velocity logging parameters;

[0037] Based on the location information, multiple first seismic trace data are determined within the target area surrounding the location information;

[0038] Determine the correlation coefficient between each first seismic trace data and the convolution;

[0039] The target seismic trace data with the highest correlation coefficient is determined from the plurality of first seismic trace data.

[0040] In another possible implementation, the process of training the acoustic velocity prediction model includes:

[0041] Acquire migration seismic data within the exploration area, and acquire sample velocity logging parameters of drilled wells within the exploration area. The sample velocity logging parameters are used to represent the correspondence between the vertical depth value and the sonic velocity of the logged section within the drilled well.

[0042] Determine the frequency range of the migrated seismic data, and determine the second velocity trend parameter corresponding to the sample velocity logging parameters;

[0043] Based on the frequency range and the second velocity trend parameter, determine the sample relative velocity corresponding to the sample velocity logging parameter;

[0044] Based on the location information of the drilled wells, sample seismic trace data corresponding to the location information are determined from the multiple seismic trace data;

[0045] Determine the attribute information of the sample seismic trace data, and determine at least one sample attribute data corresponding to the attribute information from the sample seismic trace data;

[0046] The sample seismic trace data, the sample relative velocity, and the at least one sample attribute data are normalized to obtain normalized sample seismic trace data, at least one normalized sample attribute data, and normalized sample relative velocity.

[0047] Based on the normalized sample seismic trace data, the normalized sample relative velocity, and the at least one normalized sample attribute data, the initial acoustic velocity prediction model is trained to obtain the acoustic velocity prediction model.

[0048] In another possible implementation, obtaining the sample velocity logging parameters of the drilled wells within the exploration area includes:

[0049] Obtain drilling information of the drilled wells in the exploration area. The drilling information includes the trajectory information of the drilled wells, the depth values ​​of the drilled wells, the platform height values ​​of the drilled wells, and the core filling elevation values ​​of the drilled wells.

[0050] Determine the initial velocity logging parameters of the drilled wells in the exploration area. The initial velocity logging parameters are used to represent the correspondence between the depth values ​​of the logged sections in the drilled wells and the sonic velocity.

[0051] Based on the track information of the drilled well, the depth values ​​of the drilled well, the platform height values ​​of the drilled well, and the core elevation values ​​of the drilled well, the depth values ​​of the drilled well section are converted into vertical depth values ​​to obtain the sample velocity logging parameters.

[0052] On the other hand, this application provides a device for determining the velocity of sound waves, the device comprising:

[0053] The acquisition module is used to acquire the migration seismic data within the exploration area and the location information of the wells to be measured within the exploration area. The migration seismic data includes multiple seismic traces.

[0054] The first determining module is used to determine the target seismic trace data corresponding to the location information from the plurality of seismic trace data based on the location information;

[0055] The second determining module is used to determine the attribute information of the target seismic trace data, and to determine at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data. The attribute information includes at least one of amplitude information, frequency information and phase information.

[0056] The third determining module is used to determine the acoustic velocity of the entire formation in the well based on the target seismic trace data, the attribute data of the at least one seismic trace, and the acoustic velocity prediction model.

[0057] In one possible implementation, the third determining module includes:

[0058] A normalization unit is used to normalize the target seismic trace data and the at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data.

[0059] The prediction unit is used to input the normalized seismic trace data and the at least one normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well.

[0060] The inverse normalization unit is used to perform inverse normalization processing on the normalized velocity to obtain the relative velocity corresponding to the entire formation of the well.

[0061] The first determining unit is used to determine the first velocity trend parameter of the drilling, the first velocity trend parameter being used to represent the changing trend of the acoustic velocity with depth.

[0062] The second determining unit is used to determine the acoustic velocity of the entire formation of the well based on the sum of the relative velocity corresponding to the entire formation of the well and the first velocity trend parameter.

[0063] In another possible implementation, the normalization unit is configured to: determine a plurality of first amplitude data corresponding to the target seismic trace data and a plurality of second amplitude data corresponding to each seismic trace attribute data; determine the average and standard deviation of the plurality of first amplitude data and the average and standard deviation of the plurality of second amplitude data; normalize the target seismic trace data according to the first average and first standard deviation of the plurality of first amplitude data to obtain normalized seismic trace data; and normalize the seismic trace attribute data according to the second average and second standard deviation of the plurality of second amplitude data to obtain normalized attribute data.

[0064] In another possible implementation, the drilling includes a logged section and an unlogged section. The logged section is a formation section for which velocity logging parameters reflecting formation properties have been obtained through logging instruments. The unlogged section is a formation section for which velocity logging parameters have not been measured. The first determining unit is used to determine the maximum and minimum velocity of the drilling; obtain the acoustic velocity corresponding to multiple depth values ​​within the logged section; and determine a first velocity trend parameter of the drilling based on the maximum velocity, the minimum velocity, and the acoustic velocity corresponding to the multiple depth values.

[0065] In another possible implementation, the first determining module is configured to acquire velocity logging parameters corresponding to the logged section within the well, the velocity logging parameters representing the correspondence between the vertical depth value and the acoustic velocity of the logged section; determine the convolution between the wavelet of the migrated seismic data and the reflection coefficient of the velocity logging parameters; based on the location information, determine multiple first seismic traces included in the target area surrounding the location information; determine the correlation coefficient between each first seismic trace and the convolution; and determine the target seismic trace with the highest correlation coefficient from the multiple first seismic traces.

[0066] In another possible implementation, the device further includes a training module; the training module is used to acquire migrated seismic data within the exploration area, and to acquire sample velocity logging parameters of drilled wells within the exploration area, the sample velocity logging parameters representing the correspondence between the vertical depth value and the sonic velocity of the logged section within the drilled well; determine the frequency range of the migrated seismic data, and determine a second velocity trend parameter corresponding to the sample velocity logging parameters; based on the frequency range and the second velocity trend parameter, determine the sample relative velocity corresponding to the sample velocity logging parameters; and based on the location information of the drilled wells, obtain data from the multiple seismic traces. The process involves: determining sample seismic trace data corresponding to the location information; determining attribute information of the sample seismic trace data; identifying at least one sample attribute data corresponding to the attribute information from the sample seismic trace data; normalizing the sample seismic trace data, the sample relative velocity, and the at least one sample attribute data to obtain normalized sample seismic trace data, at least one normalized sample attribute data, and normalized sample relative velocity; and training an initial acoustic velocity prediction model based on the normalized sample seismic trace data, the normalized sample relative velocity, and the at least one normalized sample attribute data to obtain the acoustic velocity prediction model.

[0067] On the other hand, embodiments of this application provide a computer device, the computer device including: a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor to implement the method for determining the speed of sound as described in any of the above possible implementations.

[0068] On the other hand, embodiments of this application provide a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement the method for determining the speed of sound as described in any of the above possible implementations.

[0069] On the other hand, embodiments of this application provide a computer program product, the computer program product including computer program code, the computer program code being stored in a computer-readable storage medium; a processor of a computer device reads the computer program code from the computer-readable storage medium, the processor executing the computer program code, causing the computer device to load and execute the method for determining the speed of sound in any of the above possible implementations.

[0070] The beneficial effects of the technical solutions provided in this application include at least the following:

[0071] This application provides a method for determining acoustic velocity. Since the acoustic velocity of the well is determined by using target seismic trace data, at least one seismic trace attribute data, and an acoustic velocity prediction model, and since the target seismic trace data and at least one seismic trace attribute data are actual data covering the entire formation of the well to be tested, that is, actual data covering the entire area of ​​the well to be tested, they can reflect the characteristics of the entire formation of the well to be tested, thus improving the accuracy of the determined acoustic velocity of the entire formation. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a flowchart illustrating a method for determining the velocity of a sound wave according to an exemplary embodiment;

[0074] Figure 2 This is a schematic diagram illustrating the importance weights of normalized attribute data according to an exemplary embodiment;

[0075] Figure 3 This is a schematic diagram illustrating a relative velocity and the velocity of sound according to an exemplary embodiment;

[0076] Figure 4 This is a schematic diagram illustrating a first velocity trend parameter according to an exemplary embodiment;

[0077] Figure 5 This is a flowchart illustrating a method for training a sound wave velocity prediction model according to an exemplary embodiment;

[0078] Figure 6 This is a schematic diagram illustrating sample velocity logging parameters and sample relative velocity according to an exemplary embodiment;

[0079] Figure 7 This is a block diagram illustrating a device for determining the velocity of a sound wave according to an exemplary embodiment;

[0080] Figure 8 This is a block diagram illustrating a device for determining the velocity of a sound wave according to an exemplary embodiment;

[0081] Figure 9 This is a structural block diagram of a computer device according to an exemplary embodiment. Detailed Implementation

[0082] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0083] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0084] Figure 1 This is a flowchart illustrating a method for determining the velocity of sound waves according to an exemplary embodiment, executed by a computer device. See also Figure 1 The method includes:

[0085] 101. Computer equipment acquires migration seismic data within the exploration area and location information of wells to be measured within the exploration area. The migration seismic data includes multiple seismic traces.

[0086] In one possible implementation, multiple shot points and multiple geophones are deployed within the exploration area; the shot points are used to emit seismic wave signals, and the geophones are used to receive the seismic wave signals emitted by the shot points. Optionally, the steps for the computer equipment to acquire migration seismic data within the exploration area are as follows: multiple geophones receive seismic wave signals and upload the received seismic wave signals to the computer equipment; the computer equipment receives the seismic wave signals uploaded by multiple geophones to obtain migration seismic data within the exploration area; wherein, a seismic trace data includes the seismic wave signal uploaded by one geophone within a first preset time period.

[0087] In another possible implementation, the computer receives seismic wave signals from multiple detectors, obtains migrated seismic data for the exploration area, and then stores this migrated seismic data. Alternatively, the computer can directly acquire migrated seismic data locally.

[0088] In one possible implementation, the location information includes the coordinates of the well. The computer device stores a correspondence between the coordinates and well identifiers. Accordingly, the steps for the computer device to obtain the location information of the well to be measured within the exploration area are as follows: the computer device obtains the target well identifier of the well to be measured within the exploration area; based on the target well identifier, it determines the coordinates corresponding to the target well identifier from the stored correspondence between coordinates and well identifiers. It should be noted that the well identifier is used to distinguish different wells to be measured. In one possible implementation, the well identifier includes at least one of letters, numbers, and text. For example, the well identifier might be "Well A to be measured".

[0089] 102. Based on location information, computer equipment determines the target seismic trace data corresponding to the location information from multiple seismic trace data.

[0090] In one possible implementation, the location information includes the coordinates of the exploration area, with each seismic trace corresponding to one set of coordinates. The computer equipment acquires the seismic trace corresponding to this coordinate information. Accordingly, this step involves the computer equipment determining the seismic trace with the specified coordinates from multiple seismic traces to obtain the target seismic trace.

[0091] In this embodiment of the application, since the seismic trace data with coordinate information can be directly determined from multiple seismic trace data through coordinate information, the efficiency of determining the target seismic trace data is improved.

[0092] In another possible implementation, the computer equipment acquires multiple first seismic traces near the location information, and determines the target seismic trace from these multiple first seismic traces based on a correlation coefficient. Accordingly, this step involves: the computer equipment acquiring velocity logging parameters corresponding to the logged section within the wellbore, where the velocity logging parameters represent the correspondence between the vertical depth value and the sonic velocity of the logged section; determining the convolution between the wavelet of the migrated seismic data and the reflection coefficient of the velocity logging parameters; based on the location information, determining multiple first seismic traces within the target area surrounding the location information; determining the correlation coefficient between each first seismic trace and the convolution; and determining the target seismic trace with the highest correlation coefficient from the multiple first seismic traces.

[0093] Optionally, the target area is a circle with the location information as the center and a preset distance as the radius. In this embodiment, the value of the preset distance is not specifically limited and can be set and modified as needed. Optionally, the preset distance is any value between 5m and 200m, for example, the preset distance is 5m, 10m, 15m, etc.

[0094] In this embodiment, since the target seismic trace data with the largest correlation coefficient is determined from multiple first seismic trace data based on the velocity logging parameters, a correlation relationship is established between the velocity logging parameters and the target seismic trace data. Since the velocity logging parameters are the actual data of the well, the target seismic trace data selected based on the velocity logging parameters can reflect the actual data of the well, thus improving the accuracy of the determined target seismic trace data.

[0095] 103. The computer equipment determines the attribute information of the target seismic trace data, and determines at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data. The attribute information includes at least one of amplitude information, frequency information and phase information.

[0096] In one possible implementation, the amplitude information includes at least one of instantaneous amplitude, average amplitude, maximum amplitude, minimum amplitude, root-mean-square amplitude, and amplitude kurtosis. The frequency information includes at least one of instantaneous frequency, average frequency, maximum frequency, and minimum frequency. The phase information includes at least one of instantaneous phase, average phase, maximum phase, and minimum phase. Correspondingly, the attribute information includes at least one of instantaneous amplitude, average amplitude, maximum amplitude, minimum amplitude, root-mean-square amplitude, amplitude kurtosis, instantaneous frequency, average frequency, maximum frequency, minimum frequency, instantaneous phase, average phase, maximum phase, and minimum phase. Optionally, the attribute information also includes average vibrational energy, highlight volume, arc length, entropy, single-frequency volume, reflection intensity, envelope slope, and sweet spot attribute. The average vibrational energy, highlight volume, arc length, entropy, single-frequency volume, reflection intensity, envelope slope, and sweet spot attribute are all attribute information obtained by transforming the amplitude information, frequency information, and phase information.

[0097] In one possible implementation, this step is as follows: For each attribute information, the computer device determines the corresponding seismic trace attribute data from the target seismic trace data, obtaining at least one seismic trace attribute data. For example, the attribute information includes actual amplitude and average amplitude; the computer device determines the seismic trace attribute data corresponding to the actual amplitude and the seismic trace attribute data corresponding to the average amplitude from the target seismic trace data, obtaining two seismic trace attribute data.

[0098] In another possible implementation, the computer device stores a correspondence between attribute information and seismic trace attribute data. Accordingly, this step involves the computer device, for each piece of attribute information, determining the corresponding seismic trace attribute data from the stored correspondence between attribute information and seismic trace attribute data, thereby obtaining at least one piece of seismic trace attribute data.

[0099] In this embodiment of the application, since the computer device can directly obtain the seismic trace attribute data corresponding to the attribute information from the correspondence between the stored attribute information and the seismic trace attribute data, the efficiency of determining the seismic trace attribute data is improved.

[0100] 104. The computer equipment determines the acoustic velocity of the entire formation in the well based on the target seismic trace data, at least one seismic trace attribute data, and the acoustic velocity prediction model.

[0101] In one possible implementation, the computer device determines the acoustic velocity throughout the drilling process via the following steps (1) to (5):

[0102] (1) The computer equipment performs normalization processing on the target seismic trace data and at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data.

[0103] In one possible implementation, this step is as follows: a computer device determines multiple first amplitude data corresponding to the target seismic trace data and multiple second amplitude data corresponding to each seismic trace attribute data; determines a first average and a first standard deviation of the multiple first amplitude data and a second average and a second standard deviation of the multiple second amplitude data; normalizes the target seismic trace data according to the first average and the first standard deviation of the multiple first amplitude data to obtain normalized seismic trace data; and normalizes the seismic trace attribute data according to the second average and the second standard deviation of the multiple second amplitude data to obtain normalized attribute data.

[0104] In one possible implementation, the steps for the computer device to determine the normalized seismic trace data and the normalized attribute data are as follows: the computer device normalizes the target seismic trace data according to the first average and first standard deviation of multiple first amplitude data using the following formula 1 to obtain normalized seismic trace data; and the computer device normalizes the seismic trace attribute data according to the second average and second standard deviation of multiple second amplitude data using the following formula 2 to obtain normalized attribute data.

[0105] Formula 1:

[0106] Formula 2:

[0107] Where O1 represents normalized seismic trace data, x1 represents target seismic trace data, μ1 represents the first mean, σ1 represents the first standard deviation, O2 represents normalized attribute data, x2 represents seismic trace attribute data, μ2 represents the second mean, and σ2 represents the second standard deviation.

[0108] (2) The computer equipment inputs the normalized seismic trace data and at least one normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well.

[0109] In one possible implementation, the computer device directly inputs normalized seismic trace data and at least one normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the well.

[0110] In the embodiments of this application, since the normalized velocity is determined based on normalized seismic trace data and all attribute data, the influence of all attribute data on the normalized velocity can be taken into account, thus improving the accuracy of the determined normalized velocity.

[0111] In another possible implementation, the computer device first filters a preset number of normalized attribute data from at least one normalized attribute data, and then inputs the normalized seismic trace data and the preset number of normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the drilling.

[0112] Optionally, the computer device determines the importance weight of each normalized attribute data based on the regression tree and the Gini coefficient, and sequentially filters a preset number of normalized attribute data from multiple sets of at least one normalized attribute data according to the importance weight from largest to smallest. In this embodiment, the value of the preset number is not specifically limited and can be set and modified as needed. Optionally, the preset number can be any value between 2 and 50.

[0113] For example, see Figure 2 The number of at least one normalized attribute data is 20; the preset number is 3. The computer device determines attribute data 1, attribute data 16, and attribute data 19 from the 20 normalized attribute data in descending order of importance weight.

[0114] In the embodiments of this application, since the importance weight of each normalized attribute data is determined based on the regression tree and the Gini coefficient, the main attribute data is determined from multiple attribute data, which reduces the amount of data required and thus improves the efficiency of determining the normalization speed.

[0115] In another possible implementation, the computer device performs dimensionality reduction and compression processing on a preset number of normalized attribute data based on the main attributes. The normalized attribute data of the main attributes obtained after dimensionality reduction and compression is then input into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the drilling. Optionally, the computer device performs dimensionality reduction and compression processing on the preset number of normalized attribute data using principal component analysis.

[0116] In this embodiment, since the preset number of normalized attribute data is compressed by using the main attributes, the amount of data that needs to be input is further reduced, thus improving the efficiency of determining the normalization speed.

[0117] (3) The computer equipment performs inverse normalization on the normalized velocity to obtain the relative velocity corresponding to the entire formation during drilling.

[0118] In one possible implementation, the computer equipment performs inverse normalization on the normalized velocity based on the acoustic velocities corresponding to multiple depth values ​​within the logged section. Accordingly, this step involves: the computer equipment determining the third average and third standard deviation of the acoustic velocities corresponding to multiple depth values ​​within the logged section; and, based on the third average and third standard deviation, performing inverse normalization on the normalized velocity using the following formula (Formula 7) to obtain the relative velocity corresponding to the entire formation during drilling.

[0119] Formula 7: V1=σ3x3+μ3

[0120] Where V1 represents the relative velocity corresponding to the entire formation during drilling, x3 represents the normalized velocity, σ3 represents the third standard deviation, and μ3 represents the third mean.

[0121] For example, see Figure 3 The computer equipment performs inverse normalization on the normalized speed to obtain the relative speed as shown in curve 1.

[0122] (4) The computer equipment determines the first velocity trend parameter of the drilling, which is used to represent the trend of the change of the sound wave velocity with the depth value.

[0123] In one possible implementation, the well includes a logged section and an unlogged section. The logged section is the formation segment for which velocity logging parameters reflecting formation properties have been obtained through logging instruments. The unlogged section is the formation segment for which velocity logging parameters have not been measured. The steps for determining the first velocity trend parameter of the well are as follows: computer equipment determines the maximum and minimum drilling velocities; the acoustic velocities corresponding to multiple depth values ​​within the logged section are acquired; and the first velocity trend parameter of the well is determined based on the maximum and minimum velocities and the acoustic velocities corresponding to the multiple depth values. The logged and unlogged sections together constitute the entire formation corresponding to the well.

[0124] In one possible implementation, the step of the computer device determining the first velocity trend parameter of drilling based on the maximum velocity, minimum velocity, and acoustic velocities corresponding to multiple depth values ​​is as follows: the computer device determines the first parameter and the second parameter based on the maximum velocity, minimum velocity, and acoustic velocities corresponding to multiple depth values ​​using the following formula three, thereby obtaining the first velocity trend parameter of drilling.

[0125] Formula 3: V(x)1=VTop +(V Base -V Top )×m×e cx

[0126] Among them, V Top V represents the minimum speed. Base V(x) represents the maximum speed, x represents the depth value, V(x)1 represents the first speed trend parameter, m represents the first parameter, and c represents the second parameter.

[0127] It should be noted that the velocity increases with increasing burial depth; the greater the depth, the greater the velocity. Correspondingly, the minimum velocity V... Top The velocity of the overlying rock strata corresponding to the strata, the maximum velocity V Base The velocity corresponding to the basement of the strata. In one possible implementation, the computer device determines the maximum and minimum velocities based on the geological parameters of the strata. The maximum velocity corresponds to the velocity of the deepest stratum (basement), and the minimum velocity corresponds to the velocity of the shallowest stratum, i.e., the velocity at the location where TVD is 0. See, for example. Figure 4 V Top 1850 m / s, V Base The first velocity trend parameter determined by the computer equipment is 6000 m / s. Figure 4 The curve in the figure is shown.

[0128] In this embodiment, the first velocity trend parameter is obtained by fitting the sonic velocity, maximum velocity, and minimum velocity corresponding to multiple depth values ​​of the measured well section. Since the sonic velocity, maximum velocity, and minimum velocity corresponding to multiple depth values ​​are all actual measured data, the accuracy of the drilling velocity trend parameter determined based on the above data is improved.

[0129] In another possible implementation, the computer equipment determines the formation property trend V(x)3 of the drilling formation based on the Hodrick-Prescott filter; the computer equipment then determines the final velocity trend parameter of the drilling based on the first velocity trend parameter and the formation property trend. Accordingly, the steps for the computer equipment to determine the final velocity trend parameter of the drilling are as follows: the computer equipment determines the length of the logged section and the distance from the unlogged section to the logged section; based on the length, distance, first velocity trend parameter, and formation property trend, the final velocity trend parameter of the drilling is determined using the following formula (Formula 4).

[0130] Formula 4:

[0131] Where V(x) represents the final velocity trend parameter of drilling, V(x)1 represents the first velocity trend parameter, and V(x)3 represents the formation property trend; d represents the distance from the unlogged section to the logged section, and l represents the length of the logged section.

[0132] In this embodiment, the final velocity trend parameter is determined by using actual data such as acoustic velocity, maximum velocity, and minimum velocity corresponding to multiple depth values, as well as the trend of formation properties. This takes into account both the measurement data and the changes in formation properties, thereby improving the accuracy of the determined velocity trend parameter.

[0133] (5) The computer equipment determines the acoustic velocity of the entire formation based on the sum of the relative velocity and the first velocity trend parameter corresponding to the entire formation of the well.

[0134] In one possible implementation, this step is as follows: The computer device determines the acoustic velocity of the entire formation during drilling based on the sum of the relative velocity and the first velocity trend parameter using the following formula five.

[0135] Formula 5: ln(y)=V1+lnV(x)1

[0136] Where y represents the acoustic velocity of the entire formation during drilling, V1 represents the relative velocity of the entire formation during drilling, and V(x)1 represents the first velocity trend parameter.

[0137] For example, see continue. Figure 3 The computer equipment determines the acoustic velocity of the entire formation during drilling based on the sum of the relative velocity and the first velocity trend parameter, as shown in curve 2.

[0138] It should be noted that the acoustic velocity of the entire formation determined by the computer equipment is a prediction of the acoustic velocity of the well, that is, a prediction of the acoustic velocity of the unlogged section of the well, while the acoustic velocity of the logged section is obtained directly through the measurement of the logging instrument.

[0139] In this embodiment of the application, the acoustic velocity of the well is determined by using target seismic trace data, at least one seismic trace attribute data and an acoustic velocity prediction model. Since the target seismic trace data and at least one seismic trace attribute data are actual data covering the entire formation of the well to be tested, that is, actual data covering the entire area of ​​the well to be tested, they can reflect the characteristics of the entire formation of the well to be tested, thus improving the accuracy of the determined acoustic velocity of the entire formation.

[0140] It should be noted that before determining the normalized drilling velocity using the acoustic velocity prediction model, the initial velocity prediction model needs to be trained using sample data to obtain the acoustic velocity prediction model. Optionally, the initial velocity prediction model is an LSTM (Long Short-Term Memory) model. See [link to relevant documentation] for one possible implementation. Figure 5 The process of training a sound wave velocity prediction model using computer equipment includes the following steps 501 to 507:

[0141] 501. Computer equipment acquires offset seismic data within the exploration area, as well as sample velocity logging parameters of drilled wells within the exploration area. The sample velocity logging parameters are used to represent the correspondence between the vertical depth value and the sonic velocity of the logged section within the drilled well.

[0142] In one possible implementation, the computer device acquires the migrated seismic data within the exploration area using the same method as in step 101, which will not be described again here.

[0143] In one possible implementation, the steps for the computer equipment to acquire sample velocity logging parameters of drilled wells within the exploration area are as follows: the computer equipment acquires drilling information of drilled wells within the exploration area, including the trajectory information of drilled wells, the depth values ​​of drilled wells, the platform height values ​​of drilled wells, and the core elevation values ​​of drilled wells; the computer equipment determines the initial velocity logging parameters of drilled wells within the exploration area, which represent the correspondence between the depth values ​​of the logged sections within the drilled wells and the sonic velocity; based on the trajectory information of drilled wells, the depth values ​​of drilled wells, the platform height values ​​of drilled wells, and the core elevation values ​​of drilled wells, the depth values ​​of the logged sections are converted into vertical depth values ​​to obtain the sample velocity logging parameters.

[0144] For example, see Figure 6 The sample velocity logging parameters determined by the computer equipment are shown in curve 3.

[0145] 502. Computer equipment determines the frequency range of the migrated seismic data and the second velocity trend parameter corresponding to the sample velocity logging parameters.

[0146] In one possible implementation, the computer device determines the second speed trend parameter in the same way as the computer device determines the first speed trend parameter in step 104, and will not be described again here.

[0147] 503. The computer equipment determines the relative velocity of the sample corresponding to the sample velocity logging parameters based on the frequency range and the second velocity trend parameter.

[0148] In one possible implementation, the frequency range of the migrated seismic data is smaller than the frequency range of the sample velocity logging parameters. The computer equipment filters the sample velocity logging parameters based on the frequency range of the migrated seismic data; then, the filtered data is detrended using a second velocity trend parameter to obtain the sample relative velocity. Accordingly, this step is as follows: the computer equipment determines the target velocity logging parameters in the sample velocity logging parameters that have the same frequency range as the migrated seismic data; based on the target velocity logging parameters and the second velocity trend parameter, the sample relative velocity is obtained using the following formula (Formula 6).

[0149] Formula 6: V² = ln(X) - lnV(x)²

[0150] Where V2 represents the relative velocity of the sample, X represents the target velocity logging parameter, and V(x)2 represents the second velocity trend parameter.

[0151] For example, see continue. Figure 6 The computer equipment performs detrending processing on the sample velocity logging parameters, and the resulting sample relative velocity is shown in curve 4.

[0152] 504. The computer equipment determines the sample seismic trace data corresponding to the location information from multiple seismic trace data based on the location information of the drilled well.

[0153] In one possible implementation, the method by which the computer device determines the sample seismic trace data is the same as the method by which the computer device determines the target seismic trace data in step 102, and will not be described again here.

[0154] 505. The computer equipment determines the attribute information of the sample seismic trace data, and determines at least one sample attribute data corresponding to the attribute information from the sample seismic trace data.

[0155] In one possible implementation, the method 4 for the computer device to determine at least one sample attribute data is the same as the method for the computer device to determine at least one seismic trace attribute data in step 103, and will not be described again here.

[0156] 506. The computer equipment normalizes the sample seismic trace data, sample relative velocity, and at least one sample attribute data to obtain normalized sample seismic trace data, at least one normalized sample attribute data, and normalized sample relative velocity.

[0157] In one possible implementation, the method for normalizing the computer device is the same as the method for normalizing the computer device in step 104, and will not be described again here.

[0158] In one possible implementation, the computer device adjusts the phase of the sample seismic trace data to obtain multiple sample seismic trace data with different phases. Optionally, the phase adjustment range is any value between 5 degrees and 15 degrees, for example, a phase adjustment range of 10 degrees. If the phase range of the sample seismic trace data is 0 degrees to 180 degrees, then the computer device adjusts the phase of the sample seismic trace data to obtain 19 sample seismic trace data with multiple different phases.

[0159] In this embodiment of the application, since the computer device adjusts the phase of the sample seismic trace data to obtain multiple sample seismic trace data with different phases, the number of normalized sample data with different phases is increased, thereby improving the accuracy of the acoustic velocity prediction model obtained by training based on the normalized sample data.

[0160] 507. The computer equipment trains the initial acoustic velocity prediction model based on normalized sample seismic trace data, normalized sample relative velocity, and at least one normalized sample attribute data to obtain the acoustic velocity prediction model.

[0161] In one possible implementation, this step involves: a computer device training an initial acoustic velocity prediction model based on normalized sample seismic trace data, normalized sample relative velocity, and at least one normalized sample attribute data, until the accuracy of the initial acoustic velocity prediction model reaches a preset threshold, thus obtaining the acoustic velocity prediction model. Here, the normalized sample seismic trace data and at least one normalized sample attribute data are input labels, and the normalized sample relative velocity is the output label. In this embodiment, the number of sample data is not specifically limited; optionally, the computer device uses 70% of the sample data as a training set and 30% as a validation set to train the initial acoustic velocity prediction model.

[0162] In this embodiment, the type and value of the preset threshold are not specifically limited, and can be set and modified according to needs or actual measurement data. Optionally, the type of preset threshold includes at least one of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Optionally, the preset threshold for the coefficient of determination can be any value between 80% and 100%; for example, the preset threshold is 80%, 85%, 90%, etc.

[0163] This application provides a method for determining acoustic velocity. Since the acoustic velocity of the well is determined by using target seismic trace data, at least one seismic trace attribute data, and an acoustic velocity prediction model, and since the target seismic trace data and at least one seismic trace attribute data are actual data covering the entire formation of the well to be tested, that is, actual data covering the entire area of ​​the well to be tested, they can reflect the characteristics of the entire formation of the well to be tested, thus improving the accuracy of the determined acoustic velocity of the entire formation.

[0164] Figure 7 This is a block diagram illustrating a device for determining the velocity of a sound wave according to an exemplary embodiment. See also... Figure 7 The device includes:

[0165] The acquisition module 701 is used to acquire the migration seismic data within the exploration area and the location information of the wells to be measured within the exploration area. The migration seismic data includes multiple seismic traces.

[0166] The first determining module 702 is used to determine the target seismic trace data corresponding to the location information from multiple seismic trace data based on the location information;

[0167] The second determining module 703 is used to determine the attribute information of the target seismic trace data, and to determine at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data. The attribute information includes at least one of amplitude information, frequency information and phase information.

[0168] The third determination module 704 is used to determine the acoustic velocity of the entire formation in the well based on the target seismic trace data, at least one seismic trace attribute data and the acoustic velocity prediction model.

[0169] In one possible implementation, see Figure 8 The third determining module 704 includes:

[0170] Normalization unit 7041 is used to normalize the target seismic trace data and at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data.

[0171] Prediction unit 7042 is used to input normalized seismic trace data and at least one normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well.

[0172] The inverse normalization unit 7043 is used to perform inverse normalization on the normalized velocity to obtain the relative velocity corresponding to the entire formation during drilling.

[0173] The first determining unit 7044 is used to determine the first velocity trend parameter of drilling, which is used to represent the trend of the change of acoustic velocity with depth.

[0174] The second determining unit 7045 is used to determine the acoustic velocity of the entire formation during drilling based on the sum of the relative velocity corresponding to the entire formation and the first velocity trend parameter.

[0175] In another possible implementation, the normalization unit 7041 is used to determine multiple first amplitude data corresponding to the target seismic trace data and multiple second amplitude data corresponding to each seismic trace attribute data; determine the average and standard deviation of the multiple first amplitude data and the average and standard deviation of the multiple second amplitude data; normalize the target seismic trace data according to the first average and first standard deviation of the multiple first amplitude data to obtain normalized seismic trace data; and normalize the seismic trace attribute data according to the second average and second standard deviation of the multiple second amplitude data to obtain normalized attribute data.

[0176] In another possible implementation, the drilling includes a logged section and an unlogged section. The logged section is the formation section for which velocity logging parameters reflecting formation properties are obtained through logging instruments. The unlogged section is the formation section for which velocity logging parameters have not been measured. The first determining unit 7044 is used to determine the maximum and minimum drilling velocity; acquire the sonic velocity corresponding to multiple depth values ​​within the logged section; and determine the first velocity trend parameter of the drilling based on the maximum velocity, minimum velocity, and sonic velocity corresponding to multiple depth values.

[0177] In another possible implementation, the first determining module 702 is used to acquire the velocity logging parameters corresponding to the logged section within the well, the velocity logging parameters being used to represent the correspondence between the vertical depth value and the acoustic velocity of the logged section; determine the convolution between the wavelet of the migrated seismic data and the reflection coefficient of the velocity logging parameters; based on the location information, determine multiple first seismic trace data included in the target area around the location information; determine the correlation coefficient between each first seismic trace data and the convolution; and determine the target seismic trace data with the largest correlation coefficient from the multiple first seismic trace data.

[0178] In another possible implementation, see [link to previous section]. Figure 8The device also includes a training module 705; this training module 705 is used to acquire migrated seismic data within the exploration area, and to acquire sample velocity logging parameters of drilled wells within the exploration area, the sample velocity logging parameters representing the correspondence between the vertical depth value and the acoustic velocity of the logged section within the drilled well; determine the frequency range of the migrated seismic data, and determine the second velocity trend parameter corresponding to the sample velocity logging parameter; determine the sample relative velocity corresponding to the sample velocity logging parameter based on the frequency range and the second velocity trend parameter; determine the sample seismic trace data corresponding to the location information from multiple seismic trace data based on the location information of the drilled well; determine the attribute information of the sample seismic trace data, and determine at least one sample attribute data corresponding to the attribute information from the sample seismic trace data; normalize the sample seismic trace data, sample relative velocity, and at least one sample attribute data to obtain normalized sample seismic trace data, at least one normalized sample attribute data, and normalized sample relative velocity; and train the initial acoustic velocity prediction model based on the normalized sample seismic trace data, normalized sample relative velocity, and at least one normalized sample attribute data to obtain the acoustic velocity prediction model.

[0179] This application provides an apparatus for determining acoustic velocity. Since the acoustic velocity of the well is determined by using target seismic trace data, at least one seismic trace attribute data, and an acoustic velocity prediction model, and since the target seismic trace data and at least one seismic trace attribute data are actual data covering the entire formation of the well to be tested, that is, actual data covering the entire area of ​​the well to be tested, it can reflect the characteristics of the entire formation of the well to be tested, thus improving the accuracy of determining the acoustic velocity of the entire formation, especially for missing well logging data segments.

[0180] Figure 9 This diagram illustrates a structural block diagram of a computer device 900 provided in an exemplary embodiment of the present invention. The computer device 900 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 900 may also be referred to as a user device, portable computer device, laptop computer device, desktop computer device, or other names.

[0181] Typically, computer device 900 includes a processor 901 and a memory 902.

[0182] Processor 901 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 901 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 901 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 901 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 901 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0183] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 902 are used to store at least one instruction, which is executed by the processor 901 to implement the method for determining the speed of sound provided in the method embodiments of this application.

[0184] In some embodiments, the computer device 900 may optionally include a peripheral device interface 903 and at least one peripheral device. The processor 901, memory 902, and peripheral device interface 903 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 903 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 904, a display screen 905, a camera 906, an audio circuit 907, a positioning component 908, and a power supply 909.

[0185] Peripheral device interface 903 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 901 and memory 902. In some embodiments, processor 901, memory 902 and peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 901, memory 902 and peripheral device interface 903 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0186] The radio frequency (RF) circuit 904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 904 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 904 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 904 can communicate with other computer devices via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 904 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0187] Display screen 905 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 905 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 901 for processing. In this case, display screen 905 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 905, which is located on the front panel of the computer device 900; in other embodiments, there may be at least two display screens 905, respectively located on different surfaces of the computer device 900 or in a folded design; in still other embodiments, display screen 905 may be a flexible display screen, located on a curved or folded surface of the computer device 900. Furthermore, display screen 905 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 905 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0188] The camera assembly 906 is used to acquire images or videos. Optionally, the camera assembly 906 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the computer device, and the rear-facing camera is located on the back of the computer device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 906 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash is a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0189] The audio circuit 907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 901 for processing, or input to the radio frequency circuit 904 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located in a different part of the computer device 900. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 907 may also include a headphone jack.

[0190] Positioning component 908 is used to locate the current geographical location of computer device 900 in order to enable navigation or LBS (Location Based Service). Positioning component 908 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the European Union's Galileo system.

[0191] Power supply 909 is used to supply power to the various components in computer device 900. Power supply 909 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 909 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0192] In some embodiments, the computer device 900 further includes one or more sensors 910. The one or more sensors 910 include, but are not limited to: an accelerometer 911, a gyroscope 912, a pressure sensor 913, a fingerprint sensor 914, an optical sensor 915, and a proximity sensor 916.

[0193] Accelerometer 911 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 900. For example, accelerometer 911 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 901 can control display screen 905 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 911. Accelerometer 911 can also be used for games or for acquiring user motion data.

[0194] The gyroscope sensor 912 can detect the orientation and rotation angle of the computer device 900. The gyroscope sensor 912, in conjunction with the accelerometer sensor 911, can collect 3D motion data from the user on the computer device 900. Based on the data collected by the gyroscope sensor 912, the processor 901 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0195] The pressure sensor 913 can be disposed on the side bezel of the computer device 900 and / or on the lower layer of the display screen 905. When the pressure sensor 913 is disposed on the side bezel of the computer device 900, it can detect the user's grip signal on the computer device 900, and the processor 901 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 913. When the pressure sensor 913 is disposed on the lower layer of the display screen 905, the processor 901 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 905. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0196] The fingerprint sensor 914 is used to collect a user's fingerprint. The processor 901 identifies the user based on the fingerprint collected by the fingerprint sensor 914, or vice versa. When the user's identity is verified as trusted, the processor 901 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 914 can be located on the front, back, or side of the computer device 900. When the computer device 900 has physical buttons or a manufacturer's logo, the fingerprint sensor 914 can be integrated with the physical buttons or manufacturer's logo.

[0197] An optical sensor 915 is used to collect ambient light intensity. In one embodiment, the processor 901 can control the display brightness of the display screen 905 based on the ambient light intensity collected by the optical sensor 915. Specifically, when the ambient light intensity is high, the display brightness of the display screen 905 is increased; when the ambient light intensity is low, the display brightness of the display screen 905 is decreased. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera assembly 906 based on the ambient light intensity collected by the optical sensor 915.

[0198] A proximity sensor 916, also known as a distance sensor, is typically located on the front panel of a computer device 900. The proximity sensor 916 is used to detect the distance between the user and the front of the computer device 900. In one embodiment, when the proximity sensor 916 detects that the distance between the user and the front of the computer device 900 is gradually decreasing, the processor 901 controls the display screen 905 to switch from a screen-on state to a screen-off state; when the proximity sensor 916 detects that the distance between the user and the front of the computer device 900 is gradually increasing, the processor 901 controls the display screen 905 to switch from a screen-off state to a screen-on state.

[0199] Those skilled in the art will understand that Figure 9 The structure shown does not constitute a limitation on the computer device 900, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0200] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the method for determining the speed of sound in any of the possible implementations described above.

[0201] In an exemplary embodiment, a computer program product is also provided, the computer program product including computer program code stored in a computer-readable storage medium; a processor of a computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code, causing the computer device to load and execute the operation performed by the method for determining the speed of sound in any of the above possible implementations.

[0202] Other embodiments of this disclosure 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 disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0203] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the velocity of sound waves, characterized in that, The method includes: Acquire migration seismic data within the exploration area and location information of the wells to be measured within the exploration area, wherein the migration seismic data includes multiple seismic traces; Based on the location information, the target seismic trace data corresponding to the location information is determined from the plurality of seismic trace data; Determine the attribute information of the target seismic trace data, and determine at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data, wherein the attribute information includes at least one of amplitude information, frequency information and phase information; The target seismic trace data and the at least one seismic trace attribute data are normalized to obtain normalized seismic trace data and at least one normalized attribute data. The normalized seismic trace data and the at least one normalized attribute data are input into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well. The normalized velocity is inversely normalized to obtain the relative velocity corresponding to the entire formation during drilling. A first velocity trend parameter for the drilling is determined, which represents the trend of the acoustic velocity with depth. The acoustic velocity of the entire formation is determined based on the sum of the relative velocity corresponding to the entire formation during drilling and the first velocity trend parameter.

2. The method according to claim 1, characterized in that, The normalization process for the target seismic trace data and the at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data includes: Determine multiple first amplitude data corresponding to the target seismic trace data and multiple second amplitude data corresponding to each seismic trace attribute data; Determine the average and standard deviation of the plurality of first amplitude data and the average and standard deviation of the plurality of second amplitude data; The target seismic trace data is normalized based on the first average value and first standard deviation of the plurality of first amplitude data to obtain normalized seismic trace data. Furthermore, the seismic trace attribute data is normalized based on the second average value and second standard deviation of the plurality of second amplitude data to obtain normalized attribute data.

3. The method according to claim 1, characterized in that, The drilling includes a logged section and an unlogged section. The logged section is the formation section for which velocity logging parameters reflecting formation properties have been obtained through logging instruments. The unlogged section is the formation section for which velocity logging parameters have not been measured. Determining the first velocity trend parameter of the drilling includes: Determine the maximum and minimum drilling speeds; Obtain the acoustic velocity corresponding to multiple depth values ​​within the measured well section; The first velocity trend parameter of the drilling is determined based on the maximum velocity, the minimum velocity, and the acoustic velocity corresponding to the plurality of depth values.

4. The method according to claim 1, characterized in that, The step of determining the target seismic trace data corresponding to the location information from the plurality of seismic trace data based on the location information includes: Obtain the velocity logging parameters corresponding to the logged section within the well, wherein the velocity logging parameters are used to represent the correspondence between the vertical depth value and the sonic velocity of the logged section; Determine the convolution between the wavelet of the migrated seismic data and the reflection coefficient of the velocity logging parameters; Based on the location information, multiple first seismic trace data are determined within the target area surrounding the location information; Determine the correlation coefficient between each first seismic trace data and the convolution; The target seismic trace data with the highest correlation coefficient is determined from the plurality of first seismic trace data.

5. The method according to claim 1, characterized in that, The process of training the sound wave velocity prediction model includes: Acquire migration seismic data within the exploration area, and acquire sample velocity logging parameters of drilled wells within the exploration area. The sample velocity logging parameters are used to represent the correspondence between the vertical depth value and the sonic velocity of the logged section within the drilled well. Determine the frequency range of the migrated seismic data, and determine the second velocity trend parameter corresponding to the sample velocity logging parameters; Based on the frequency range and the second velocity trend parameter, determine the sample relative velocity corresponding to the sample velocity logging parameter; Based on the location information of the drilled wells, sample seismic trace data corresponding to the location information are determined from the multiple seismic trace data; Determine the attribute information of the sample seismic trace data, and determine at least one sample attribute data corresponding to the attribute information from the sample seismic trace data; The sample seismic trace data, the sample relative velocity, and the at least one sample attribute data are normalized to obtain normalized sample seismic trace data, at least one normalized sample attribute data, and normalized sample relative velocity. Based on the normalized sample seismic trace data, the normalized sample relative velocity, and the at least one normalized sample attribute data, the initial acoustic velocity prediction model is trained to obtain the acoustic velocity prediction model.

6. A device for determining the velocity of sound waves, characterized in that, The device includes: The acquisition module is used to acquire the migration seismic data within the exploration area and the location information of the wells to be measured within the exploration area. The migration seismic data includes multiple seismic traces. The first determining module is used to determine the target seismic trace data corresponding to the location information from the plurality of seismic trace data based on the location information; The second determining module is used to determine the attribute information of the target seismic trace data, and to determine at least one seismic trace attribute data corresponding to the attribute information from the target seismic trace data. The attribute information includes at least one of amplitude information, frequency information and phase information. The third determining module is used to normalize the target seismic trace data and the at least one seismic trace attribute data to obtain normalized seismic trace data and at least one normalized attribute data; input the normalized seismic trace data and the at least one normalized attribute data into the acoustic velocity prediction model to obtain the normalized velocity corresponding to the entire formation of the well; perform inverse normalization on the normalized velocity to obtain the relative velocity corresponding to the entire formation of the well; determine a first velocity trend parameter for the well, which represents the trend of the acoustic velocity with depth; and determine the acoustic velocity of the entire formation of the well based on the sum of the relative velocity corresponding to the entire formation of the well and the first velocity trend parameter.

7. A computer device, characterized in that, The computer device includes: A processor and a memory, wherein the memory stores at least one line of program code, which is loaded and executed by the processor to implement the method for determining the speed of sound as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the method for determining the speed of sound as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes at least one piece of program code, which is loaded and executed by a processor to implement the method for determining the speed of sound as described in any one of claims 1 to 5.

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