Elastic Parameter Determination Method, Device, Computer Equipment and Storage Medium

Abstract

The present application discloses a method, device, computer equipment and storage medium for determining elastic parameters, which relates to the field of oil and gas field exploration. The method includes: for each seismic trace gather in the exploration work area, determining the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather; determining a set of parameter values according to the logging data and the initial elastic parameters of the exploration work area; determining the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for cooling; based on the initial elastic parameters, the set of parameter values, the initial temperature and the cooling function, performing parameter iteration on the seismic trace gather to obtain the elastic parameters corresponding to the seismic trace gather. Through the above scheme, the initial value of the iteration is closer to the true value, a large number of perturbation numbers are reduced, thereby improving the iteration efficiency. Moreover, since the perturbation range is reduced, the cooling function matches the parameter value range of the elastic parameters, further reducing the number of iterations and improving the iteration efficiency.

Description

Technical Field

[0001] The present application relates to the field of oil and gas field exploration, and particularly to a method, apparatus, computer device and storage medium for determining elastic parameters. Background Art

[0002] The simulated annealing algorithm, also known as the Monte Carlo algorithm, has been increasingly applied in seismic exploration data processing and seismic inversion due to its unique advantages. The simulated annealing algorithm is obtained by analogy between the global optimization problem and the thermal equilibrium problem of a substance crystallizing during the cooling process. It has also been increasingly applied in oil exploration. For example, during oil exploration, AVO (Amplitude variation with offset) elastic parameter inversion is carried out through the simulated annealing algorithm to identify the lithology and fluid-bearing properties of reservoirs.

[0003] In related technologies, under a given temperature control, the control temperature of annealing is corrected according to the change of the objective function. As the temperature decreases, an elastic parameter in the seismic trace set is randomly determined from the probability distribution as the solution of the objective function. By traversing multiple elastic parameters, the objective function is gradually searched in the decreasing direction, and finally the target elastic parameter corresponding to the minimum of the objective function is obtained, and this target elastic parameter is determined as the elastic parameter of the seismic trace set.

[0004] Although the optimization algorithm does not need to traverse all possible solutions, which greatly improves the efficiency, in seismic inversion, due to the large amount of basic data, especially in recent years, with the implementation of high-density and wide-azimuth seismic exploration methods, the data volume of pre-stack seismic inversion has increased sharply, which has a greater impact on the timeliness of seismic inversion, and the inversion efficiency is still low. Summary of the Invention

[0005] The present application provides a method, apparatus, computer device and storage medium for determining elastic parameters. The technical solutions are as follows:

[0006] According to one aspect of the present application, a method for determining elastic parameters is provided, and the method includes:

[0007] For each seismic trace set in the exploration work area, determine the initial elastic parameter of the seismic trace set according to the position of the seismic trace set;

[0008] Determine a parameter value set according to the logging data of the exploration work area and the initial elastic parameter;

[0009] Determine the sum of the amplitude values of one waveform period of the target layer in the seismic trace set as the initial temperature for cooling;

[0010] Based on the initial elastic parameters, the parameter value set, the initial temperature, and the temperature reduction function, perform parameter iteration on the seismic trace gather to obtain the elastic parameters corresponding to the seismic trace gather.

[0011] In some embodiments, the determining the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather includes:

[0012] In response to the position of the seismic trace gather coinciding with the position where the well logging is located, obtain the first well logging data of the well logging at the position of the seismic trace gather; extract the first elastic parameter from the first well logging data, and determine the first elastic parameter as the initial elastic parameter; or,

[0013] In response to the position of the seismic trace gather not coinciding with the position where the well logging is located, obtain the second well logging data of the well logging in the target area where the seismic trace gather is located, extract the second elastic parameter of the well logging from the second well logging data, perform interpolation processing on the second elastic parameter to obtain an interpolation processing result, and determine the interpolation processing result as the initial elastic parameter.

[0014] In some embodiments, the performing interpolation processing on the second elastic parameter to obtain an interpolation processing result includes:

[0015] In response to the well logging data being the well logging data corresponding to a two-dimensional seismic line, perform interpolation processing using a linear interpolation method to obtain the interpolation processing result;

[0016] In response to the well logging data being the well logging data corresponding to a three-dimensional seismic block, perform interpolation processing using a distance weighted interpolation method to obtain the interpolation processing result.

[0017] In some embodiments, the determining the parameter value set according to the well logging data of the exploration area and the initial elastic parameters includes:

[0018] According to the quality of the well logging data of the exploration area and the complexity of the reservoir, with the initial elastic parameter value as the center, determine the parameter value percentage in the decreasing and increasing directions;

[0019] Based on the initial elastic parameters and the parameter value percentage, and according to the final elastic parameter inversion accuracy requirement, determine the elastic parameter value interval and obtain the number of values;

[0020] Based on the number of values, determine the parameter values of the number of values from the parameter value range;

[0021] Determine the parameter values of the number of values as the parameter value set.

[0022] In some embodiments, determining the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for temperature reduction includes:

[0023] Based on the following formula, sum the amplitude values of one waveform period of the target layer in the seismic trace gather to obtain the initial temperature:

[0024]

[0025] where T0 is the initial temperature, c is a proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the start time of one waveform period, t2 is the end time of one waveform period, j is the amplitude value of the j-th sampling point, and abs( ) is the absolute value function.

[0026] In some embodiments, the parameter iteration of the seismic trace gather based on the initial elastic parameter, the parameter value set, the initial temperature, and the temperature reduction function to obtain the elastic parameter corresponding to the seismic trace gather includes:

[0027] Take the initial elastic parameter and the initial temperature as the initial parameters in the iteration process. In the iteration process, randomly obtain an elastic parameter from the parameter value set as the solution of the objective function;

[0028] In response to the value of the objective function under the elastic parameter being not less than the first preset threshold, determine the increment of the objective function corresponding to the elastic parameter; in response to the increment of the objective function satisfying the preset condition, determine the elastic parameter as the current solution of the objective function; where the preset condition includes that the increment of the objective function is less than the second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than the third preset threshold;

[0029] In response to the value of the objective function under the elastic parameter being less than the first preset threshold, end the iteration and determine the elastic parameter as the elastic parameter corresponding to the seismic trace gather.

[0030] In some embodiments, the method further includes:

[0031] In response to the increment of the objective function not satisfying the preset condition, reselect an elastic parameter from the parameter value set, and execute the step of taking the elastic parameter and the initial temperature as the initial parameters in the iteration process, and randomly obtaining an elastic parameter from the parameter value set as the solution of the objective function in the iteration process.

[0032] According to another aspect of the present application, there is provided an elastic parameter determination device, and the device includes:

[0033] The first determination module is configured to determine an initial elastic parameter of each seismic trace gather in the exploration work area according to the position of the seismic trace gather.

[0034] The second determination module is configured to determine a parameter value set according to the well logging data of the exploration work area and the initial elastic parameter.

[0035] The third determination module is configured to determine the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for temperature reduction.

[0036] The iteration module is configured to perform parameter iteration on the seismic trace gather based on the initial elastic parameter, the parameter value set, the initial temperature, and a temperature reduction function, to obtain the elastic parameter corresponding to the seismic trace gather.

[0037] In some embodiments, the first determination module includes:

[0038] The first determination unit is configured to, in response to the position of the seismic trace gather coinciding with the position where the well logging is located, obtain first well logging data of the well logging at the position of the seismic trace gather; extract a first elastic parameter from the first well logging data, and determine the first elastic parameter as the initial elastic parameter; or,

[0039] The second determination unit is configured to, in response to the position of the seismic trace gather not coinciding with the position where the well logging is located, obtain second well logging data of the well logging in the target area where the seismic trace gather is located, extract a second elastic parameter of the well logging from the second well logging data, perform interpolation processing on the second elastic parameter to obtain an interpolation processing result, and determine the interpolation processing result as the initial elastic parameter.

[0040] In some embodiments, the second determination unit is configured to, in response to the well logging data being the well logging data corresponding to a two-dimensional seismic line, perform interpolation processing by using a linear interpolation method to obtain the interpolation processing result; in response to the well logging data being the well logging data corresponding to a three-dimensional seismic block, perform interpolation processing by using a distance weighted interpolation method to obtain the interpolation processing result.

[0041] In some embodiments, the second determination module includes:

[0042] The third determination unit is configured to determine a parameter value percentage according to multiple well logging data of the exploration work area.

[0043] The fourth determination unit is configured to determine a parameter value range and a number of values based on the initial elastic parameter and the parameter value percentage.

[0044] A fifth determination unit, configured to determine parameter values of the number of values from the parameter value range based on the number of values.

[0045] A sixth determination unit, configured to determine the parameter values of the number of values as the parameter value set.

[0046] In some embodiments, the third determination module is configured to sum the amplitude values of one waveform period of the target layer in the seismic trace gather based on the following formula to obtain the initial temperature:

[0047]

[0048] where T0 is the initial temperature, c is a proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the start time of one waveform period, t2 is the end time of one waveform period, and S j is the amplitude value of the jth sampling point, and abs( ) is the absolute value function.

[0049] In some embodiments, the iteration module is configured to use the initial elastic parameter and the initial temperature as initial parameters in the iteration process. During the iteration process, randomly obtain an elastic parameter from the parameter value set as the solution of the objective function; in response to the value of the objective function under the elastic parameter being not less than a first preset threshold, determine the increment of the objective function corresponding to the elastic parameter; in response to the increment of the objective function satisfying a preset condition, determine the elastic parameter as the current solution of the objective function; where the preset condition includes that the increment of the objective function is less than a second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than a third preset threshold; in response to the value of the objective function under the elastic parameter being less than the first preset threshold, end the iteration and determine the elastic parameter as the elastic parameter corresponding to the seismic trace gather.

[0050] In some embodiments, the iteration module is further configured to, in response to the increment of the objective function not satisfying the preset condition, reselect an elastic parameter from the parameter value set, and execute the steps of using the elastic parameter and the initial temperature as initial parameters in the iteration process, and randomly obtaining an elastic parameter from the parameter value set as the solution of the objective function during the iteration process.

[0051] According to another aspect of the present application, there is provided a computer device, including: a processor and a memory, where the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the elastic parameter determination method as described above.

[0052] According to another aspect of the present application, there is provided a computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement the elastic parameter determination method as described above.

[0053] According to another aspect of the present application, there is provided a computer program product storing a computer program, which is loaded and executed by the processor to implement the elastic parameter determination method as described above.

[0054] The beneficial effects brought by the technical solutions provided in the embodiments of the present application at least include:

[0055] In the embodiments of the present application, by determining the initial elastic parameters and the parameter value ranges of each seismic trace gather during the iteration process, the initial value of the iteration is closer to the true value, significantly reducing the number of perturbations, thereby improving the iteration efficiency. Moreover, since the perturbation range is reduced, the cooling initial value starts to cool from a lower initial value, enabling the cooling function to match the parameter value range of the elastic parameters, further reducing the number of iterations and improving the iteration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 Shows a flowchart of the elastic parameter determination method provided by another exemplary embodiment of the present application;

[0058] Figure 2 Shows a flowchart of the elastic parameter determination method provided by another exemplary embodiment of the present application;

[0059] Figure 3 Shows a block diagram of the elastic parameter determination device provided by an exemplary embodiment of the present application;

[0060] Figure 4 Shows a block diagram of a computer device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the drawings.

[0062] As a general global optimization algorithm, the simulated annealing algorithm is applied in many fields. Its application in oil exploration is also increasing. For AVO (Amplitude Variation with Offset) elastic parameter inversion, it mainly includes the following processes:

[0063] (1) Determine model perturbation

[0064] In common simulated annealing models, distribution functions such as the Cauchy-like distribution are often used as the distribution function of simulated annealing. The generation method of its solution is shown in the following formula (1):

[0065] Formula (1):

[0066] where, c i is the i-th variable of the model, rand[0, 1] is a random number uniformly distributed on [0, 1], [A i , B i is the value range of c i , and it is required that after perturbation, c i ∈[A i , B i , sgn( ) is the sign function, and T is the temperature.

[0067] The advantage of generating a new model through the temperature-dependent Cauchy-like distribution is that when the temperature is high, a large range of searches are carried out, and when the temperature is low, the search is only carried out near the current model. Moreover, due to the Cauchy-like distribution having a flat range, the search range is easily jumped out of the local extremum, which improves the convergence speed of the simulated annealing method.

[0068] (2) Acceptance probability

[0069] The calculation formula for the acceptance probability can be determined by the information entropy (Tsallis entropy) as the following formula (2):

[0070] Formula (2): P = [1 - (1 + h)ΔE / h] 1 / 1-h

[0071] where, ΔE is the interpolation between the model objective functions obtained from the two perturbations before and after. Let E(m) represent the new model objective function and E(m; 0) represent the objective function of the previous perturbation, then ΔE = E(m) - E(m0), T is the temperature, and h is a real number. Obviously, when h = 1, P = exp(-ΔE / T).

[0072] In the related technology, in the conventional AVO elastic parameter inversion algorithm of simulated annealing, the elastic parameters involved in AVO elastic parameter inversion include the longitudinal wave velocity V p , the shear wave velocity Vs , the medium density ρ.

[0073] This conditional acceptance can be achieved by means of a random number uniformly distributed between 0 and 1. According to the specific value of this random number, it is determined whether to accept or reject the solution corresponding to the perturbation. If not accepted, the solution corresponding to the original perturbation is retained. Whenever the perturbation of a certain parameter is recognized, the value of this parameter is immediately modified. Before this parameter is modified again, all subsequent energy calculations continue to use this value.

[0074] (3) Cooling method

[0075] The annealing temperature is the physical quantity that has the greatest impact on the calculation efficiency throughout the iterative process and is also the most difficult to determine. If the initial temperature is too high, the probability is approximately uniformly distributed, and the objective function will easily lose the minimum value and search in the increasing direction, reducing the operation efficiency; if the initial temperature is too low, it is difficult for the algorithm to jump out of the local minimum of the objective function and search for the global extreme value. Therefore, the annealing cooling rate is also a key parameter.

[0076] (4) Objective function

[0077] The objective function is used by the user to determine whether a new solution is accepted. In the embodiments of the present application, the following formula three is used as the objective function.

[0078] Formula three:

[0079] Wherein, R0′(θ i ) is the reflection coefficient calculated by the theoretical model, R0(θ i ) is the reflection coefficient of the actual data, θ i is the angle corresponding to the i-th seismic trace gather, and R0(θ) is the Zoeppritz equation or its simplified form.

[0080] The basis for judging whether a solution is accepted is the acceptance criterion. The most commonly used acceptance criterion is the Metropolis criterion: if the temperature increment ΔT < 0, then accept this solution as the new current solution; otherwise, accept this solution according to the probability calculation formula and use it as the new current solution. When the new solution is determined and accepted, the new solution replaces the current solution. In this way, only the transformation part corresponding to the generation of the new solution in the current solution needs to be implemented, and at the same time, the objective function value is corrected. At this time, the current solution has completed one iteration. The next round of calculation can be started on this basis. When the new solution is determined to be discarded, the next round of calculation continues based on the original current solution.

[0081] In the related art, during the inversion process, elastic parameters are determined through a large number of iterations. Due to the increasing popularity of high-density seismic exploration, the amount of seismic data has increased exponentially, posing a challenge to the timeliness of seismic data inversion.

[0082] In the embodiments of the present application, by determining the initial elastic parameters and the parameter value ranges of each seismic trace gather during the iteration process, the initial values of the iteration are made closer to the true values, significantly reducing the number of perturbations, thereby improving the iteration efficiency. Moreover, since the perturbation range is reduced, the cooling starts from a lower initial value, making the cooling function match the parameter value range of the elastic parameters, further reducing the number of iterations and improving the iteration efficiency.

[0083] Figure 1 The flowchart of the elastic parameter determination method provided by an exemplary embodiment of the present application is shown. This method can be applied to a computer device, and the method includes:

[0084] Step 101: For each seismic trace gather in the exploration work area, the computer device determines the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather.

[0085] Step 102: The computer device determines a parameter value set according to the logging data of the exploration work area and the initial elastic parameters.

[0086] Step 103: The computer device determines the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for cooling.

[0087] Step 104: The computer device performs parameter iteration on the seismic trace gather based on the initial elastic parameters, the parameter value set, the initial temperature, and the cooling function to obtain the elastic parameters corresponding to the seismic trace gather.

[0088] In some embodiments, determining the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather includes:

[0089] In response to the position of the seismic trace gather coinciding with the position where the logging is located, obtaining the first logging data of the logging at the position of the seismic trace gather; extracting the first elastic parameter from the first logging data, and determining the first elastic parameter as the initial elastic parameter; or,

[0090] In response to the position of the seismic trace gather not coinciding with the position where the logging is located, obtaining the second logging data of the logging in the target area where the seismic trace gather is located, extracting the second elastic parameter of the logging from the second logging data, performing interpolation processing on the second elastic parameter to obtain an interpolation processing result, and determining the interpolation processing result as the initial elastic parameter.

[0091] In some embodiments, performing interpolation processing on the second elastic parameter to obtain an interpolation processing result includes:

[0092] In response to the logging data being the logging data corresponding to a two-dimensional seismic line, interpolation processing is performed by means of linear interpolation to obtain the interpolation processing result;

[0093] In response to the logging data being the logging data corresponding to a three-dimensional seismic block, interpolation processing is performed by means of distance weighted interpolation to obtain the interpolation processing result.

[0094] In some embodiments, determining a set of parameter values according to the logging data of the exploration work area and the initial elastic parameters includes:

[0095] Determining a parameter value percentage according to multiple logging data of the exploration work area;

[0096] Based on the initial elastic parameter and the parameter value percentage, determining a parameter value range and the number of values;

[0097] Based on the number of values, determining the parameter values of the number of values from the parameter value range;

[0098] Determining the parameter values of the number of values as the set of parameter values.

[0099] In some embodiments, determining the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for temperature reduction includes:

[0100] Based on the following formula, summing the amplitude values of one waveform period of the target layer in the seismic trace gather to obtain the initial temperature:

[0101]

[0102] where T0 is the initial temperature, c is a proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the start time of one waveform period, t2 is the end time of one waveform period, S j is the amplitude value of the jth sampling point, and abs() is the absolute value function.

[0103] In some embodiments, performing parameter iteration on the seismic trace gather based on the initial elastic parameter, the set of parameter values, the initial temperature, and a temperature reduction function to obtain the elastic parameters corresponding to the seismic trace gather includes:

[0104] Taking the initial elastic parameter and the initial temperature as the initial parameters calculated in the iteration process, and randomly obtaining an elastic parameter from the set of parameter values as the solution of the objective function during the iteration process;

[0105] In response to the value of the objective function at the elastic parameter being not less than a first preset threshold, determine the increment of the objective function corresponding to the elastic parameter; in response to the increment of the objective function satisfying a preset condition, determine the elastic parameter as the current solution of the objective function; wherein the preset condition includes that the increment of the objective function is less than a second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than a third preset threshold;

[0106] In response to the value of the objective function at the elastic parameter being less than the first preset threshold, end the iteration and determine the elastic parameter as the elastic parameter corresponding to the seismic trace gather.

[0107] In some embodiments, the method further includes:

[0108] In response to the increment of the objective function not satisfying the preset condition, re-select an elastic parameter from the parameter value set, and perform the steps of using the elastic parameter and the initial temperature as the initial parameters in the iterative process, and randomly obtaining an elastic parameter from the parameter value set as the solution of the objective function in the iterative process.

[0109] In the embodiments of the present application, by determining the initial elastic parameter and the parameter value range of each seismic trace gather in the iterative process, the initial value of the iteration is closer to the true value, a large number of perturbation numbers are reduced, thereby improving the iterative efficiency. And because the perturbation range is reduced, the initial value of temperature reduction starts from a lower initial value, so that the temperature reduction function matches the parameter value range of the elastic parameter, further reducing the number of iterations and improving the iterative efficiency.

[0110] Figure 2 The flowchart of the elastic parameter determination method provided by an exemplary embodiment of the present application is shown. The method can be applied to a computer device. The method includes:

[0111] Step 201: For each seismic trace gather in the exploration work area, the computer device determines whether the position of the seismic trace gather coincides with the position where the well logging is located.

[0112] For each exploration work area, there are multiple well logs in the exploration work area, and the multiple well logs are evenly or unevenly distributed in the exploration work area. Therefore, the position where each seismic trace gather is located may or may not be the position where the well logging is located. Therefore, in this step, the computer device determines whether the position of the seismic trace gather coincides with the position where the well logging is located; in response to the position of the seismic trace gather coinciding with the position where the well logging is located, perform step 202; in response to the position of the seismic trace gather not coinciding with the position where the well logging is located, perform step 204.

[0113] Step 202: The computer device obtains the first logging data of the well logging at the location of the seismic trace gather.

[0114] In this step, the computer device obtains the first logging data of the well logging at the location where the seismic trace gather to be processed is located. Among them, the first logging data includes the elastic parameters of the target reservoir corresponding to the well logging.

[0115] Step 203: The computer device extracts the first elastic parameters from the first logging data and determines the first elastic parameters as the initial elastic parameters.

[0116] Among them, the first elastic parameters include the longitudinal wave velocity V p , the shear wave velocity V s , and the medium density ρ. Correspondingly, in this step, the computer device extracts V p , V s and ρ from the elastic parameters of the target reservoir in the first logging data.

[0117] It should be noted that the seismic trace gather used for inversion is usually the seismic trace gather in the time - space domain of CDP (Common Depth Point) converted to the seismic trace gather in the time - angle domain, that is, the angular seismic trace gather. Then, in this step, the computer device defines the reflection phase time of the target layer and obtains the variation relationship of the amplitude with the incident angle within each seismic trace gather according to the reflection phase time, that is, the AVO characteristic.

[0118] Step 204: The computer device obtains the second logging data of the well logging in the target area where the location of the seismic trace gather is located.

[0119] In this step, the computer device determines the second logging data of the well logging closest to this location; or, the computer device determines all the well loggings in the area where this location is located, obtains the logging data of all the well loggings, determines the average value of these logging data, and determines the average value as the second logging data.

[0120] Among them, the process of the computer device determining the logging data of each well logging is similar to Step 202 and will not be elaborated here.

[0121] Step 205: The computer device extracts the second elastic parameters of the well logging from the second logging data.

[0122] This step is similar to Step 203 and will not be elaborated here.

[0123] Step 206: The computer device performs interpolation processing on the second elastic parameters to obtain an interpolation processing result and determines the interpolation processing result as the initial elastic parameters.

[0124] In this step, different interpolation processing methods are determined based on the type of logging data. Correspondingly, in response to the logging data being the logging data corresponding to a two-dimensional seismic line, the computer device performs interpolation processing using the linear interpolation method to obtain the interpolation processing result; in response to the logging data being the logging data corresponding to a three-dimensional seismic block, the computer device performs interpolation processing using the distance-weighted interpolation method to obtain the interpolation processing result.

[0125] Step 207: The computer device determines a set of parameter values according to the logging data of the exploration work area and the initial elastic parameters.

[0126] In this step, the computer device can determine the logging data, the quality of seismic data, and the stability of the reservoir in the exploration work area based on the logging data. The set of parameter values is determined based on the quality of the exploration work area and the stability of the reservoir. This process is implemented through the following steps (1)-(4), including:

[0127] (1) The computer device determines the percentage of the parameter value range in the decreasing and increasing directions with the initial elastic parameter value as the center according to the quality of the logging data and the complexity of the reservoir in the exploration work area.

[0128] Among them, the computer device determines the quality of the exploration work area and the stability of the reservoir based on multiple logging data in the exploration work area. The higher the quality and the more stable the reservoir, the smaller the percentage of the parameter value.

[0129] (2) The computer device determines the parameter value range and the number of values based on the initial elastic parameter and the percentage of the parameter value range.

[0130] In this step, based on the initial elastic parameter, the value corresponding to the percentage increase or decrease of the initial elastic parameter is determined to obtain the parameter value range. For example, the initial elastic parameter is V p ′, V s ′ and ρ′. For V p ′, when the parameter value percentage is 20%, the parameter value range of the longitudinal wave velocity in the elastic parameter is [V p ′ - V p ′ * 20%, V p ′ + V p ′ * 20%].

[0131] The number of values is determined according to the value accuracy. For example, if the value accuracy is 20, then continuing with the above example, the number of values is

[0132] It should be noted that for other parameters in the initial elastic parameters, the process of determining the parameter value range is the same as that of determining V pThe process of determining the value range of ′ is similar, and the process of determining the number of parameter values is similar to the process of determining the number of values of V p ′, and the process of determining the number of values is similar, which will not be elaborated here.

[0133] (3) Based on the number of values, the computer device determines the parameter values of this number of values from the parameter value range.

[0134] In this step, the parameter values corresponding to each value position in the value range are determined within the value range through this number of values.

[0135] (4) The computer device determines the parameter values of this number of values as the parameter value set.

[0136] In this implementation, the value range of the elastic parameter is restricted near the true value, the range shrinks sharply, and the number of parameter values also decreases accordingly, so that the number of iterations of simulated annealing is reduced proportionally, and the convergence speed is greatly improved.

[0137] Step 208: The computer device determines the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for cooling.

[0138] Since the parameter value range shrinks, and the value of the elastic parameter is close to the true value, there is no need to start the cooling process from the high-temperature state where the probability values are relatively equal. In actual calculations, the initial temperature is the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature, that is, based on the following formula, the amplitude values of one waveform period of the target layer in the seismic trace gather are summed to obtain the initial temperature:

[0139]

[0140] where, T0 is the initial temperature, c is the proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the start time of one waveform period, t2 is the end time of one waveform period S j is the amplitude value of the jth sampling point, and abs( ) is the absolute value function.

[0141] Step 209: The computer device performs parameter iteration on the seismic trace gather based on the initial elastic parameter, the parameter value set, the initial temperature, and the cooling function, and obtains the elastic parameter corresponding to the seismic trace gather.

[0142] where, the cooling function is: T (k+1) = T0α (k-m) ;

[0143] where, T (k+1)T is the temperature corresponding to the current iteration number, T0 is the initial temperature, k is the iteration number, m is an integer constant, taking 1 / 3 of the estimated iteration number, the larger it is, the higher the starting temperature. α is a constant greater than 0 and less than 1. The smaller it is, the faster the temperature drops, depending on the quality of the AVO feature. If the quality is high, a smaller value can be selected, with a faster temperature drop; otherwise, a larger value is selected.

[0144] This step is implemented through the following steps (1)-(3), including:

[0145] (1) The computer device takes the initial elastic parameter and the initial temperature as the initial parameters in the iteration process. During the iteration process, a random elastic parameter is obtained from the parameter value set as the solution of the objective function.

[0146] (2) In response to the value of the objective function under the elastic parameter being not less than the first preset threshold, the computer device determines the increment of the objective function corresponding to the elastic parameter.

[0147] (3) In response to the increment of the objective function satisfying the preset condition, the elastic parameter is determined as the current solution of the objective function.

[0148] Among them, the preset condition includes that the increment of the objective function is less than the second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than the third preset threshold;

[0149] (4) In response to the value of the objective function under the elastic parameter being less than the first preset threshold, the iteration is ended, and the elastic parameter is determined as the elastic parameter corresponding to the seismic trace gather.

[0150] In addition, in response to the increment of the objective function not satisfying the preset condition, the computer device re-selects an elastic parameter from the parameter value set, and executes the step of taking the elastic parameter and the initial temperature as the initial parameters in the iteration process, and randomly obtaining an elastic parameter from the parameter value set as the solution of the objective function during the iteration process.

[0151] After the iteration is completed for each seismic trace gather in the exploration work area, it is determined that the elastic parameter inversion ends, and the elastic parameter corresponding to each seismic trace gather is obtained.

[0152] In the embodiment of the present application, by determining the initial elastic parameter and the parameter value range of each seismic trace gather during the iteration process, the initial value of the iteration is closer to the true value, the number of perturbations is greatly reduced, thereby improving the iteration efficiency. And because the perturbation range is reduced, the initial value of the temperature drop starts from a lower initial value, so that the temperature drop function matches the parameter value range of the elastic parameter, further reducing the iteration number and improving the iteration efficiency.

[0153] Figure 3 The block diagram of an elastic parameter determination device provided by an exemplary embodiment of the present application is shown. The device includes:

[0154] A first determination module 301, configured to determine an initial elastic parameter of each seismic trace gather in an exploration work area according to the position of the seismic trace gather.

[0155] A second determination module 302, configured to determine a parameter value set according to the well logging data of the exploration work area and the initial elastic parameter.

[0156] A third determination module 303, configured to determine the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for temperature reduction.

[0157] An iteration module 304, configured to perform parameter iteration on the seismic trace gather based on the initial elastic parameter, the parameter value set, the initial temperature, and a temperature reduction function, to obtain the elastic parameter corresponding to the seismic trace gather.

[0158] In some embodiments, the first determination module 301 includes:

[0159] A first determination unit, configured to, in response to the position of the seismic trace gather coinciding with the position where well logging is located, obtain first well logging data of the well logging at the position of the seismic trace gather; extract a first elastic parameter from the first well logging data, and determine the first elastic parameter as the initial elastic parameter; or,

[0160] A second determination unit, configured to, in response to the position of the seismic trace gather not coinciding with the position where well logging is located, obtain second well logging data of the well logging in the target area where the position of the seismic trace gather is located, extract a second elastic parameter of the well logging from the second well logging data, perform interpolation processing on the second elastic parameter to obtain an interpolation processing result, and determine the interpolation processing result as the initial elastic parameter.

[0161] In some embodiments, the second determination unit is configured to, in response to the well logging data being the well logging data corresponding to a two-dimensional seismic line, perform interpolation processing by using a linear interpolation method to obtain the interpolation processing result; in response to the well logging data being the well logging data corresponding to a three-dimensional seismic block, perform interpolation processing by using a distance weighted interpolation method to obtain the interpolation processing result.

[0162] In some embodiments, the second determination module 302 includes:

[0163] A third determination unit, configured to determine a parameter value percentage according to multiple well logging data of the exploration work area.

[0164] A fourth determination unit, configured to determine a parameter value range and the number of values based on the initial elastic parameter and the parameter value percentage.

[0165] A fifth determination unit, configured to determine parameter values of the number of values from the parameter value range based on the number of values.

[0166] A sixth determination unit, configured to determine the parameter values of the number of values as the parameter value set.

[0167] In some embodiments, the third determination module 303 is configured to sum the amplitude values of one waveform period of the target layer in the seismic trace gather based on the following formula to obtain the initial temperature:

[0168]

[0169] where, T0 is the initial temperature, c is a proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the start time of one waveform period, t2 is the end time of one waveform period, S j is the amplitude value of the j-th sampling point, and abs( ) is an absolute value function.

[0170] In some embodiments, the iteration module 304 is configured to use the initial elastic parameter and the initial temperature as initial parameters in the iteration process. During the iteration process, randomly obtain an elastic parameter from the parameter value set as the solution of the objective function; in response to the value of the objective function under the elastic parameter being not less than a first preset threshold, determine the increment of the objective function corresponding to the elastic parameter; in response to the increment of the objective function satisfying a preset condition, determine the elastic parameter as the current solution of the objective function; where the preset condition includes that the increment of the objective function is less than a second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than a third preset threshold; in response to the value of the objective function under the elastic parameter being less than the first preset threshold, end the iteration, and determine the elastic parameter as the elastic parameter corresponding to the seismic trace gather.

[0171] In some embodiments, the iteration module 304 is further configured to, in response to the increment of the objective function not satisfying the preset condition, reselect an elastic parameter from the parameter value set, and execute the steps of using the elastic parameter and the initial temperature as initial parameters in the iteration process, and randomly obtaining an elastic parameter from the parameter value set as the solution of the objective function during the iteration process.

[0172] It should be noted that: when the elastic parameter determination device provided in the above embodiment determines the elastic parameter, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the elastic parameter determination device provided in the above embodiment and the embodiment of the elastic parameter determination method belong to the same concept. For the specific implementation process, please refer to the method embodiment and will not be elaborated here.

[0173] In the embodiment of the present application, by determining the initial elastic parameter and the parameter value range of each seismic trace gather during the iteration process, the initial value of the iteration is closer to the true value, a large number of perturbation numbers are reduced, thereby improving the iteration efficiency. Moreover, since the perturbation range is reduced, the cooling initial value starts to cool from a lower initial value, so that the cooling function matches the parameter value range of the elastic parameter, further reducing the number of iterations and improving the iteration efficiency.

[0174] Figure 4 The structural block diagram of a computer device 400 provided by an exemplary embodiment of the present application is shown. The computer device 400 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a notebook computer or a desktop computer. The computer device 400 may also be referred to by other names such as a user device, a portable computer device, a laptop computer device, a desktop computer device, etc.

[0175] Generally, the computer device 400 includes: a processor 401 and a memory 402.

[0176] The processor 401 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0177] The memory 402 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 402 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 402 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 401 to implement the elastic parameter determination method provided in the method embodiments of the present application.

[0178] In some embodiments, the computer device 400 may further optionally include: a peripheral device interface 403 and at least one peripheral device. The processor 401, the memory 402, and the peripheral device interface 403 may be connected through a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 403 through a bus, signal lines, or a circuit board. Specifically, the peripheral devices include at least one of a radio frequency circuit 404, a display screen 405, a camera assembly 406, an audio circuit 407, a positioning assembly 408, and a power supply 409.

[0179] The peripheral device interface 403 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 401 and the memory 402. In some embodiments, the processor 401, the memory 402, and the peripheral device interface 403 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 401, the memory 402, and the peripheral device interface 403 can be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0180] The radio frequency circuit 404 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 404 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 404 converts an electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so on. The radio frequency circuit 404 can communicate with other computer devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, generations of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a Wi-Fi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 404 may further include a circuit related to NFC (Near Field Communication), and this application does not limit this.

[0181] The display screen 405 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 405 is a touch display screen, the display screen 405 also has the ability to collect touch signals on or above the surface of the display screen 405. The touch signals can be input to the processor 401 as control signals for processing. At this time, the display screen 405 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 405, which is provided on the front panel of the computer device 400; in other embodiments, there may be at least two display screens 405, which are respectively provided on different surfaces of the computer device 400 or are in a folding design; in still other embodiments, the display screen 405 may be a flexible display screen, which is provided on the curved surface or the folding surface of the computer device 400. Even further, the display screen 405 can also be set to an irregular non-rectangular shape, that is, an irregular-shaped screen. The display screen 405 can be prepared using materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0182] The camera module 406 is used to collect images or videos. Optionally, the camera module 406 includes a front camera and a rear camera. Generally, the front camera is provided on the front panel of the computer device, and the rear camera is provided on the back of the computer device. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth camera, a wide-angle camera, and a telephoto camera, so as to implement functions such as background blurring by fusing the main camera and the depth camera, panoramic shooting by fusing the main camera and the wide-angle camera, and VR (Virtual Reality) shooting function or other fused shooting functions. In some embodiments, the camera module 406 may also include a flash. The flash can be a single-color temperature flash or a two-color temperature flash. A two-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0183] The audio circuit 407 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals for input to the processor 401 for processing, or input to the radio frequency circuit 404 to enable voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the computer device 400. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 401 or the radio frequency circuit 404 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for uses such as ranging. In some embodiments, the audio circuit 407 may also include a headphone jack.

[0184] The positioning component 408 is used to locate the current geographical location of the computer device 400 to achieve navigation or LBS (Location Based Service). The positioning component 408 may be a positioning component based on the GPS (Global Positioning System) of the United States, the Beidou system of China, or the Galileo system of Russia.

[0185] The power supply 409 is used to supply power to each component in the computer device 400. The power supply 409 may be alternating current, direct current, a disposable battery, or a rechargeable battery. When the power supply 409 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0186] In some embodiments, the computer device 400 further includes one or more sensors 410. The one or more sensors 410 include but are not limited to: an acceleration sensor 411, a gyroscope sensor 412, a pressure sensor 413, a fingerprint sensor 414, an optical sensor 415, and a proximity sensor 416.

[0187] The acceleration sensor 411 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the computer device 400. For example, the acceleration sensor 411 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 401 can control the touch display screen 405 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 411. The acceleration sensor 411 can also be used for collecting game or user movement data.

[0188] The gyroscope sensor 412 can detect the body orientation and rotation angle of the computer device 400. The gyroscope sensor 412 can cooperate with the acceleration sensor 411 to collect the 3D actions of the user on the computer device 400. Based on the data collected by the gyroscope sensor 412, the processor 401 can implement the following functions: motion sensing (such as changing the UI according to the user's tilting operation), image stabilization during shooting, game control, and inertial navigation.

[0189] The pressure sensor 413 can be disposed on the side frame of the computer device 400 and / or the lower layer of the touch display screen 405. When the pressure sensor 413 is disposed on the side frame of the computer device 400, it can detect the holding signal of the user on the computer device 400, and the processor 401 can perform left / right hand recognition or shortcut operations based on the holding signal collected by the pressure sensor 413. When the pressure sensor 413 is disposed on the lower layer of the touch display screen 405, the processor 401 can control the operable controls on the UI interface according to the pressure operation of the user on the touch display screen 405. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0190] The fingerprint sensor 414 is used to collect the fingerprints of the user. The processor 401 can identify the user's identity based on the fingerprints collected by the fingerprint sensor 414, or the fingerprint sensor 414 can identify the user's identity based on the collected fingerprints. When the identified user identity is a trusted identity, the processor 401 authorizes the user to perform relevant sensitive operations, and the sensitive operations include unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings, etc. The fingerprint sensor 414 can be disposed on the front, back, or side of the computer device 400. When there are physical buttons or manufacturer logos on the computer device 400, the fingerprint sensor 414 can be integrated with the physical buttons or manufacturer logos.

[0191] The optical sensor 415 is used to collect the ambient light intensity. In one embodiment, the processor 401 can control the display brightness of the touch display screen 405 according to the ambient light intensity collected by the optical sensor 415. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 405 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 405 is decreased. In another embodiment, the processor 401 can also dynamically adjust the shooting parameters of the camera module 406 according to the ambient light intensity collected by the optical sensor 415.

[0192] The proximity sensor 416, also known as the distance sensor, is usually disposed on the front panel of the computer device 400. The proximity sensor 416 is used to collect the distance between the user and the front of the computer device 400. In one embodiment, when the proximity sensor 416 detects that the distance between the user and the front of the computer device 400 is gradually decreasing, the touch display screen 405 is controlled by the processor 401 to switch from the lit state to the off state; when the proximity sensor 416 detects that the distance between the user and the front of the computer device 400 is gradually increasing, the touch display screen 405 is controlled by the processor 401 to switch from the off state to the lit state.

[0193] Those skilled in the art can understand that Figure 4 the structure shown in does not constitute a limitation on the computer device 400, and may include more or fewer components than shown, or combine certain components, or adopt a different component arrangement.

[0194] This application also provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set or instruction set is loaded and executed by the processor to implement the elastic parameter determination method provided in the above method embodiment.

[0195] Optionally, this application also provides a computer program product containing instructions, which, when running on a computer device, causes the computer device to execute the elastic parameter determination method described in the above aspects.

[0196] The serial numbers of the above embodiments of this application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0197] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a disk or an optical disc, etc.

[0198] The above are only optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. A method for determining elastic parameters, characterized in that The method includes: For each seismic trace gather in the exploration work area, determine the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather; Determine a set of parameter values according to the logging data of the exploration work area and the initial elastic parameters; Determine the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for temperature reduction; Based on the initial elastic parameters, the set of parameter values, the initial temperature, and a temperature reduction function, perform parameter iteration on the seismic trace gather to obtain the elastic parameters corresponding to the seismic trace gather; wherein, performing parameter iteration on the seismic trace gather based on the initial elastic parameters, the set of parameter values, the initial temperature, and the temperature reduction function to obtain the elastic parameters corresponding to the seismic trace gather includes: Use the initial elastic parameters and the initial temperature as the initial parameters calculated during the iteration process. During the iteration process, randomly obtain elastic parameters from the set of parameter values as the solution of the objective function; In response to the value of the objective function under the elastic parameters being not less than a first preset threshold, determine the increment of the objective function corresponding to the elastic parameters; in response to the increment of the objective function satisfying a preset condition, determine the elastic parameters as the current solution of the objective function; wherein the preset condition includes that the increment of the objective function is less than a second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than a third preset threshold; In response to the value of the objective function under the elastic parameters being less than the first preset threshold, end the iteration and determine the elastic parameters as the elastic parameters corresponding to the seismic trace gather.

2. The method according to claim 1, wherein The determining the initial elastic parameters of the seismic trace gather according to the position of the seismic trace gather includes: In response to the position of the seismic trace gather coinciding with the position where the logging is located, obtain the first logging data of the logging at the position of the seismic trace gather; extract the first elastic parameters from the first logging data and determine the first elastic parameters as the initial elastic parameters; or, In response to the position of the seismic trace gather not coinciding with the position where the logging is located, obtain the second logging data of the logging in the target area where the position of the seismic trace gather is located, extract the second elastic parameters of the logging from the second logging data, perform interpolation processing on the second elastic parameters to obtain an interpolation processing result, and determine the interpolation processing result as the initial elastic parameters.

3. The method according to claim 2, characterized in that, The performing interpolation processing on the second elastic parameters to obtain an interpolation processing result includes: In response to the logging data being the logging data corresponding to a two-dimensional seismic line, perform interpolation processing using a linear interpolation method to obtain the interpolation processing result; In response to the logging data being the logging data corresponding to a three-dimensional seismic block, perform interpolation processing using a distance weighted interpolation method to obtain the interpolation processing result.

4. The method according to claim 1, wherein The determining a set of parameter values according to the logging data of the exploration work area and the initial elastic parameters includes: According to the quality of the logging data of the exploration work area and the complexity of the reservoir, determine the percentage of the parameter value range in the decreasing and increasing directions with the initial elastic parameter value as the center; Based on the initial elastic parameter and the percentage of the parameter value range, determine the elastic parameter value interval according to the final elastic parameter inversion accuracy requirement, and obtain the number of values; Based on the number of values, determine the parameter values of the number of values from the parameter value range; Determine the parameter values of the number of values as the parameter value set.

5. The method according to claim 1, wherein The determination of the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for cooling includes: Based on the following formula, sum the amplitude values of one waveform period of the target layer in the seismic trace gather to obtain the initial temperature: Among them, T0 is the initial temperature, c is the proportionality coefficient, NT is the number of traces in a seismic trace gather, t1 is the starting time of a waveform period, and t2 is the ending time of a waveform period S j is the amplitude value of the j-th sampling point, and abs() is the absolute value function.

6. The method according to claim 1, characterized in that, The method further includes: In response to the increment of the objective function not satisfying the preset condition, reselect the elastic parameter from the parameter value set, and execute the step of using the elastic parameter and the initial temperature as the initial parameters calculated in the iterative process, and randomly obtain the elastic parameter from the parameter value set as the solution of the objective function in the iterative process.

7. An elastic parameter determination device, characterized in that, The device includes: The first determination module is used to determine the initial elastic parameter of each seismic trace gather in the exploration work area according to the position of the seismic trace gather; The second determination module is used to determine the parameter value set according to the logging data of the exploration work area and the initial elastic parameter; The third determination module is used to determine the sum of the amplitude values of one waveform period of the target layer in the seismic trace gather as the initial temperature for cooling; The iteration module is used to perform parameter iteration on the seismic trace gather based on the initial elastic parameter, the parameter value set, the initial temperature, and the cooling function to obtain the elastic parameter corresponding to the seismic trace gather; The iteration module is used to use the initial elastic parameter and the initial temperature as the initial parameters calculated in the iterative process, and randomly obtain the elastic parameter from the parameter value set as the solution of the objective function in the iterative process; In response to the value of the objective function under the elastic parameter being not less than the first preset threshold, determine the increment of the objective function corresponding to the elastic parameter; in response to the increment of the objective function satisfying the preset condition, determine the elastic parameter as the current solution of the objective function; wherein, the preset condition includes that the increment of the objective function is less than the second preset threshold; or, the increment of the objective function is not less than the second preset threshold, and the exponential probability of the ratio of the increment to the initial temperature is greater than the third preset threshold; In response to the value of the objective function under the elastic parameter being less than the first preset threshold, end the iteration and determine the elastic parameter as the elastic parameter corresponding to the seismic trace gather.

8. A computer device, characterized in that, The computer device includes: a processor and a memory, the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the elastic parameter determination method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the elastic parameter determination method according to any one of claims 1 to 6.

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