Ground temperature prediction method, device, equipment and medium
Through a ground temperature prediction method, the birth rate and thermal conductivity parameters are calculated using cross-variance processing and preset equations, the problems of low accuracy of ground temperature prediction and cumbersome acquisition parameters in the prior art are solved, and more efficient and accurate ground temperature prediction.
Patent Information
- Application Number
- CN202411545624.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-02-11
AI Technical Summary
When calculating ground temperature, the thermal conductivity and thermal generation rate parameters need to be obtained through actual measurement. The process is cumbersome and costly, resulting in low accuracy of ground temperature prediction.
A ground temperature prediction method is adopted to obtain initial population iteration information, initial chromosomes, actual system temperature measurement data and formation stratification data of completion reports, cross-mutation processing is performed, preset equations are called for calculations, and the sum of squares of target errors is screened. Determine the target chromosome parameters, and then calculate the birth heat rate and thermal conductivity parameters, and perform ground temperature calculations.
The process of collecting rock samples to measure thermal rate and thermal conductivity parameters is simplified, reducing costs and improving the accuracy and efficiency of ground temperature prediction.
Smart Images
Figure CN119415803A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of oil and gas resource big data and intelligent engineering, and in particular to a geothermal prediction method, device, equipment and medium. Background Art
[0002] At present, when the existing software uses the steady-state heat conduction equation algorithm to calculate the ground temperature, thermal conductivity and heat generation rate parameters are required. The thermal conductivity and heat generation rate parameters are generally obtained after statistical analysis of the measured thermal conductivity and heat generation rate values of the rock. Using this method requires collecting rock samples in the study area and sending them to professional institutions for measurement, which is relatively costly. Secondly, the heat generation rate and thermal conductivity parameters obtained by statistical methods are substituted into the one-dimensional steady-state heat conduction equation algorithm to obtain the predicted results and the measured system temperature measurement data. The deviation is relatively large, which leads to low accuracy of ground temperature prediction.
[0003] As can be seen from the above, how to improve the accuracy of geothermal prediction, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and improve the efficiency of geothermal prediction are problems to be solved in this field. Summary of the invention
[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for predicting geothermal temperature, which can improve the accuracy of geothermal temperature prediction, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs and improve the efficiency of geothermal temperature prediction. The specific scheme is as follows:
[0005] In a first aspect, the present application discloses a method for predicting ground temperature, comprising:
[0006] Acquire initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and formation stratification data, and perform crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters;
[0007] Calling a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and performing a square sum of errors calculation on the first predicted ground temperature and the actual system temperature measurement data to obtain each square sum of errors;
[0008] Sorting the square error sums, selecting a target square error sum from the sorted square error sums, and determining a target chromosome parameter corresponding to the target square error sum;
[0009] Determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information; if the current number of iterations is not less than the initial number of iterations in the initial population iteration information, determine the minimum target chromosome parameter corresponding to the target error sum of squares with the minimum value;
[0010] The heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameters, and then the ground temperature is calculated for the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
[0011] Optionally, performing crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters includes:
[0012] Determine a crossover point value from a preset crossover range, and then perform parameter exchange on the parameters of the adjacent initial chromosomes that are smaller than the crossover point value, so as to obtain each crossover chromosome;
[0013] The chromosomes after each crossover are subjected to mutation processing to obtain chromosome parameters.
[0014] Optionally, performing mutation processing on each of the crossover chromosomes to obtain chromosome parameters includes:
[0015] Determine the mutation position information from the preset mutation range;
[0016] According to the variation position information, each parameter to be varied is determined from each of the chromosomes after the crossover, and each parameter to be varied is initialized to obtain a chromosome parameter.
[0017] Optionally, the calling of a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, and the stratum layer data to obtain a first predicted ground temperature includes:
[0018] Calculating the terrestrial heat flow based on the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, and the stratum layer data to obtain the terrestrial heat flow;
[0019] The preset one-dimensional steady-state heat conduction equation is called to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, the stratum layer data and the earth heat flow to obtain the first predicted ground temperature.
[0020] Optionally, the step of sorting the sums of squared errors and selecting a target sum of squared errors from the sorted sums of squared errors comprises:
[0021] Sorting the sums of squared errors according to their numerical values to obtain sorted sums of squared errors;
[0022] The error sum of squares with the smallest value is selected from the sorted error sums of squares as the target error sum of squares.
[0023] Optionally, after determining whether the current number of iterations is less than the initial number of iterations in the initial population iteration information, the method further includes:
[0024] If the current number of iterations is less than the initial number of iterations in the initial algorithm iteration parameter information, the initial population information in the initial population iteration information is updated based on the target chromosome parameters to obtain updated population iteration information, and the current number of iterations is updated, and then the process jumps to the step of performing crossover mutation processing on the parameters in each of the initial chromosomes until the current number of iterations is not less than the initial number of iterations in the initial population iteration information.
[0025] Optionally, performing geothermal calculation on the heat generation rate and the thermal conductivity parameter to obtain a second predicted geothermal temperature includes:
[0026] The local ground temperature prediction module is used to perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters based on the actual system temperature measurement data and the stratum stratification data to obtain a second predicted ground temperature.
[0027] In a second aspect, the present application discloses a ground temperature prediction device, comprising:
[0028] A crossover mutation module is used to obtain initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and formation stratification data, and perform crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters;
[0029] The square error sum determination module is used to call a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and to perform square error sum calculation on the first predicted ground temperature and the actual system temperature measurement data to obtain each square error sum;
[0030] A screening module is used to sort the square error sums, screen out a target square error sum from the sorted square error sums, and determine a target chromosome parameter corresponding to the target square error sum;
[0031] A judgment module is used to determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, then determine the minimum target chromosome parameter corresponding to the target error square sum with the minimum value;
[0032] The ground temperature calculation module is used to calculate the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, and then perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
[0033] In a third aspect, the present application discloses an electronic device, comprising:
[0034] Memory, used to store computer programs;
[0035] A processor is used to execute the computer program to implement the aforementioned ground temperature prediction method.
[0036] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned ground temperature prediction method disclosed above are implemented.
[0037] It can be seen that the present application provides a geothermal prediction method, including obtaining initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and formation stratification data, performing cross-variation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters; calling a preset equation to calculate the initial population iteration information, the chromosome parameters, each actual system temperature measurement data, and the formation stratification data to obtain a first predicted geothermal temperature, performing a sum of squared errors on the first predicted geothermal temperature and each actual system temperature measurement data to obtain each sum of squared errors; sorting each sum of squared errors, and sorting them from the sorted order. The target sum of squares of the errors after sequencing is screened out, and the target chromosome parameter corresponding to the target sum of squares of the errors is determined; the current number of iterations is determined, and it is judged whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, the minimum target chromosome parameter corresponding to the target sum of squares of the errors with the smallest value is determined; the heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameter, and then the heat generation rate and the thermal conductivity parameters are used for geothermal calculation to obtain the second predicted geothermal temperature. The present application can directly obtain formation stratification data and system temperature measurement data from the completion report, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and calculate the heat generation rate and thermal conductivity parameters based on the heat generation rate and thermal conductivity parameters to obtain the second predicted geothermal temperature, which can improve the accuracy and efficiency of geothermal prediction. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0039] Figure 1 A flow chart of a ground temperature prediction method disclosed in this application;
[0040] Figure 2 A flow chart of a ground temperature prediction method disclosed in this application;
[0041] Figure 3 A specific flow chart of a ground temperature prediction method disclosed in this application;
[0042] Figure 4 This is a schematic diagram of the structure of a ground temperature prediction device disclosed in this application;
[0043] Figure 5 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION
[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0045] At present, when the existing software uses the steady-state heat conduction equation algorithm to calculate the ground temperature, thermal conductivity and heat generation rate parameters are required. The thermal conductivity and heat generation rate parameters are generally obtained after statistical analysis of the measured thermal conductivity and heat generation rate values of rocks. This method requires collecting rock samples in the study area and sending them to professional institutions for measurement, which is relatively costly. Secondly, the heat generation rate and thermal conductivity parameters obtained by statistical methods are substituted into the one-dimensional steady-state heat conduction equation algorithm to obtain the prediction results and the measured system temperature measurement data. The deviation is relatively large, resulting in low accuracy of ground temperature prediction. As can be seen from the above, how to improve the accuracy of ground temperature prediction, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and improve the efficiency of ground temperature prediction are problems to be solved in this field.
[0046] See also Figure 1 As shown, the embodiment of the present invention discloses a ground temperature prediction method, which may specifically include:
[0047] Step S11: obtaining initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and formation stratification data, and performing crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters.
[0048] In this embodiment, the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and the formation stratification data are obtained, and then the intersection value is determined from the preset intersection range, and the parameters of the adjacent initial chromosomes that are smaller than the intersection value are exchanged to obtain each crossover chromosome, and each crossover chromosome is subjected to mutation processing to obtain chromosome parameters. The specific mutation processing process is: determine the mutation position information from the preset mutation range, determine each parameter to be mutated from each crossover chromosome according to the mutation position information, and initialize the parameters of each parameter to be mutated to obtain chromosome parameters.
[0049] In this embodiment, the initial population iteration information includes the initial population size N, the heat generation rate and thermal conductivity parameter population G, and the number of iterations K. The initial population size N is used to limit the number of randomly generated heat generation rate and thermal conductivity parameter populations. There are 13 heat generation rate parameters and thermal conductivity parameters for each initial chromosome in the population G, which correspond to the thermal conductivity and heat generation rate parameters of each formation, respectively. The number of iterations K is used to limit the number of iterations of the algorithm. Each parameter of each initial chromosome is randomly generated within a certain range. For example, given a parameter generation range of 0 to 5, an example of generating an initial chromosome may be: (3.411, 4.352, 4.748, 3.379, 1.49, 0.412, 1.669, 2.538,3.626, 2.857, 3.421, 2.357, 3.472, 0.484, 1.93, 1.481, 4.817, 3.176, 4.87,2.543, 4.377, 0.173, 2.911, 2.883, 0.725, 3.499).
[0050] Taking crossover as an example, crossover first selects two adjacent initial chromosomes from each initial chromosome, randomly selects a crossover point value from a preset crossover range (for example, 2 to 25), and then exchanges the parameters of the two initial chromosomes that are smaller than the crossover point value. For example, if the parameters of the first initial chromosome g1 in population G are (3.443, 0.935, 3.427, 1.255, 4.927, 0.105, 4.603, 1.571, 0.777, 3.654, 1.598, 4.228,4.684, 3.842, 3.51, 2.234, 2.769, 3.642, 2.781, 0.872, 0.536, 4.206, 0.14,2.936, 2.319, 2.365) and the parameters of the second initial chromosome g2 are (3.517, 1.706, 2.062, 2.886,2.734, 1.793, 3.261, 4.684, 4.943, 4.37, 0.898, 3.122, 4.343, 0.607, 1.323,2.158, 3.484, 3.326, 3.927, 2.401, 2.134, 2.875, 2.531, 1.594, 0.581, 0.435). If the randomly obtained crossover point value is 4, then the two chromosomes obtained by crossover are (3.517, 1.706,2.062, 2.886, 4.927, 0.105, 4.603, 1.571, 0.777, 3.654, 1.598, 4.228, 4.684,3.842, 3.51, 2.234, 2.769, 3.642, 2.781, 0.872, 0.536, 4.206, 0.14, 2.936,2.319, 2.365) and (3.443, 0.935, 3.427, 1.255, 2.734, 1.793, 3.261, 4.684,4.943, 4.37, 0.898, 3.122, 4.343, 0.607, 1.323, 2.158, 3.484, 3.326, 3.927,2.401, 2.134, 2.875, 2.531, 1.594, 0.581, 0.435), and save the two newly generated chromosomes as the chromosomes after crossover.
[0051] Taking mutation as an example, for each chromosome after crossover in the population, a value is randomly determined from the preset mutation range (taking 1 to 26 as an example), and this value is used as the mutation position information. Then, based on the mutation position information, each parameter to be mutated is determined from the chromosome after crossover, and then the parameters to be mutated are initialized according to the preset initialization rules to obtain the chromosome parameters. For example, the parameters of the chromosome after crossover are (3.443, 0.935, 3.427, 1.255,4.927, 0.105, 4.603, 1.571, 0.777, 3.654, 1.598, 4.228, 4.684, 3.842, 3.51,2.234, 2.769, 3.642, 2.781, 0.872, 0.536, 4.206, 0.14, 2.936, 2.319, 2.365), and the randomly generated value is 4, then 4 is used as the mutation position information, that is, replacing the fourth parameter in the chromosome after crossover. If the parameter to be mutated is 4, then the chromosome parameter obtained after mutation is (3.443, 0.935, 4.927, 0.105, 4.603, 1.571, 0.777, 3.654, 1.598, 4.228, 4.684, 3.842, 3.51,2.234, 2.769, 3.642, 2.781, 0.872, 0.536, 4.206, 0.14, 2.936, 2.319, 2.365).
[0052] Step S12: Call the preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and calculate the sum of squares of errors for the first predicted ground temperature and the actual system temperature measurement data to obtain each sum of squares of errors.
[0053] Step S13: sorting the square error sums, selecting a target square error sum from the sorted square error sums, and determining a target chromosome parameter corresponding to the target square error sum.
[0054] Step S14: Determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, determine the minimum target chromosome parameter corresponding to the target sum of squares with the smallest value.
[0055] In this embodiment, after determining whether the current number of iterations is less than the initial number of iterations in the initial population iteration information, it also includes: calculating the geothermal heat flow based on the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, and the stratum stratification data to obtain the geothermal heat flow; calling the preset one-dimensional steady-state heat conduction equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, the stratum stratification data, and the geothermal heat flow to obtain the first predicted ground temperature.
[0056] Step S15: Calculate the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, and then perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
[0057] In this embodiment, after calculating the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, a local geothermal prediction module is used to perform geothermal calculations on the heat generation rate and thermal conductivity parameters based on the actual system temperature measurement data and the stratum stratification data to obtain a second predicted geothermal temperature.
[0058] The present application is applied to a geothermal prediction system, and the system specifically includes a parameter acquisition module, a geothermal prediction module, and a heat generation rate and thermal conductivity parameter estimation module. The parameter acquisition module is used to acquire stratum data, actual system temperature measurement data, initial population iteration information, and each initial chromosome; the heat generation rate and thermal conductivity parameter estimation module estimates the thermal conductivity parameters and heat generation rate of the stratum using stratum data and system temperature measurement data; the geothermal prediction module calculates the predicted geothermal temperature, i.e., the second predicted geothermal temperature, based on the parameters acquired by the parameter acquisition module and the heat generation rate and thermal conductivity parameters provided by the heat generation rate and thermal conductivity parameter estimation module. The specific steps are as follows: (1) The parameter acquisition module provides the acquired parameters to the geothermal prediction module, which mainly include stratum data, actual system temperature measurement data, initial population iteration information and each initial chromosome; (2) The heat generation rate and thermal conductivity parameter estimation module obtains data from the parameter acquisition module, uses the acquired data and calls the preset equation to calculate the heat generation rate and thermal conductivity parameters; (3) The geothermal prediction module uses the data obtained from the parameter acquisition module and the heat generation rate and thermal conductivity parameters calculated by the heat generation rate and thermal conductivity parameter estimation module to calculate the geothermal temperature, and finally obtains the predicted geothermal temperature, that is, the second predicted geothermal temperature.
[0059] In this embodiment, the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and the formation stratification data are obtained, and the parameters in each of the initial chromosomes are subjected to cross-variation processing to obtain the chromosome parameters; the preset equation is called to calculate the initial population iteration information, the chromosome parameters, each actual system temperature measurement data and the formation stratification data to obtain the first predicted ground temperature, and the square sum of errors is calculated for the first predicted ground temperature and each actual system temperature measurement data to obtain each square sum of errors; the square sums of the errors are sorted, and the sorted square sums of the errors are calculated from the The target error sum of squares is screened out from the difference sum of squares, and the target chromosome parameter corresponding to the target error sum of squares is determined; the current number of iterations is determined, and it is judged whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, the minimum target chromosome parameter corresponding to the target error sum of squares with the smallest value is determined; the heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameters, and then the heat generation rate and the thermal conductivity parameters are used for geothermal calculation to obtain the second predicted geothermal temperature. The present application can directly obtain formation stratification data and system temperature measurement data from the completion report, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and calculate the heat generation rate and thermal conductivity parameters based on the heat generation rate and thermal conductivity parameters to obtain the second predicted geothermal temperature, which can improve the accuracy and efficiency of geothermal prediction.
[0060] See also Figure 2 As shown, the embodiment of the present invention discloses a ground temperature prediction method, which may specifically include:
[0061] Step S21: obtaining initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and formation stratification data, and performing crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters.
[0062] Step S22: Calculate the geothermal heat flow based on the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain the geothermal heat flow, call the preset one-dimensional steady-state heat conduction equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, the stratum stratification data and the geothermal heat flow to obtain a first predicted ground temperature, and calculate the sum of squares of errors for the first predicted ground temperature and the actual system temperature measurement data to obtain each sum of squares of errors.
[0063] In this embodiment, for a system temperature measurement data (depth Z, temperature t) of a well in an embodiment, if the chromosome parameter is ( ), the lower boundary depth of each stratum is (Quaternary , Neogene …,Sinian ), the ground temperature is , the heat flux at the bottom interface of the nth layer is First, calculate the layer where the depth Z is located. If , then Z is in the nth layer, and the formula for calculating its first predicted ground temperature using the one-dimensional steady-state heat conduction equation is as follows:
[0064] ;
[0065] ;
[0066] ;
[0067] Specifically, the chromosome parameters (3.443, 0.935, 3.427, 1.255, 4.927, 0.105, 4.603,1.571, 0.777, 3.654, 1.598, 4.228, 4.684, 3.842, 3.51, 2.234, 2.769, 3.642,2.781, 0.872, 0.536, 4.206, 0.14, 2.936, 2.319, 2.365) as an example, the corresponding bottom depths of each stratum (Quaternary 0.2km, Neogene 4.373km, Paleogene 4.510km, Cretaceous 5km, Jurassic 5.56km, Triassic 5.95km, Carboniferous 6.2km), ground temperature =10, the system temperature corresponding to Z=4km is 104℃. If we want to calculate the first predicted ground temperature at depth Z=4km, we need to calculate the ground heat flow first. :
[0068] .
[0069] Step S23: Sort the error sums of squares according to their numerical values to obtain sorted error sums of squares, select the error sum of squares with the smallest numerical value from the sorted error sums of squares as the target error sum of squares, and determine the target chromosome parameter corresponding to the target error sum of squares.
[0070] According to the above formula, the predicted ground temperature at the corresponding depth of each system temperature measurement data can be calculated. If the embodiment includes two system temperature measurement data (4km, 104°C) and (6km, 150°C) in total, and the first predicted ground temperatures corresponding to the two system temperature measurement data are calculated by the above formula to be (4km, 73.38°C) and (6km, 100°C), the square sum of the error F is calculated:
[0071] ;
[0072] in, is the predicted ground temperature of the temperature measurement data of the ith system, is the measured ground temperature of the ith system temperature measurement data, and n is the number of strata.
[0073] Step S24: Determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, determine the minimum target chromosome parameter corresponding to the target sum of squares with the smallest value.
[0074] Step S25: Calculate the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, and then perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
[0075] In this embodiment, the specific process of this application is as follows Figure 3 As shown, (1) obtain the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and the formation stratification data: including the initial population size N, the initial heat generation rate and thermal conductivity parameter population G, the initial iteration number K, and the current iteration number k; (2) crossover: exchange the parameters of the adjacent initial chromosomes that are less than the crossover point value to obtain the chromosomes after the crossover; (3) mutation: determine the parameters to be mutated from the chromosomes after the crossover according to the mutation position information, initialize the parameters of each parameter to be mutated, and obtain the chromosome parameters; (4) calculate the sum of squared errors: call the one-dimensional steady-state heat conduction equation for calculation , get the first predicted ground temperature, calculate the sum of squares of the errors of the first predicted ground temperature and the actual system temperature measurement data, and get the sum of squares of the errors; (5) Screening: sort the sum of squares of the errors, and select the sum of squares of the errors with the smallest value from the sorted sum of squares of the errors as the target sum of squares of the errors; (6) Determine whether k is less than K. If not, calculate the heat rate and thermal conductivity parameters based on the minimum target chromosome parameters corresponding to the minimum target sum of squares of the errors, and then calculate the ground temperature to get the second predicted ground temperature; if less than, execute k=k+1, update G in the initial population iteration information, and jump to the crossover mutation processing step until k is not less than K.
[0076] In this embodiment, the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and the formation stratification data are obtained, and the parameters in each of the initial chromosomes are subjected to cross-variation processing to obtain the chromosome parameters; the preset equation is called to calculate the initial population iteration information, the chromosome parameters, each actual system temperature measurement data and the formation stratification data to obtain the first predicted ground temperature, and the square sum of errors is calculated for the first predicted ground temperature and each actual system temperature measurement data to obtain each square sum of errors; the square sums of the errors are sorted, and the sorted square sums of the errors are calculated from the The target error sum of squares is screened out from the difference sum of squares, and the target chromosome parameter corresponding to the target error sum of squares is determined; the current number of iterations is determined, and it is judged whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, the minimum target chromosome parameter corresponding to the target error sum of squares with the smallest value is determined; the heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameters, and then the heat generation rate and the thermal conductivity parameters are used for geothermal calculation to obtain the second predicted geothermal temperature. The present application can directly obtain formation stratification data and system temperature measurement data from the completion report, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and calculate the heat generation rate and thermal conductivity parameters based on the heat generation rate and thermal conductivity parameters to obtain the second predicted geothermal temperature, which can improve the accuracy and efficiency of geothermal prediction.
[0077] See also Figure 4 As shown, the embodiment of the present invention discloses a ground temperature prediction device, which may specifically include:
[0078] The crossover and mutation module 11 is used to obtain the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and the formation layer data, and perform crossover and mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters;
[0079] The square error sum determination module 12 is used to call a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and to calculate the square error sum of the first predicted ground temperature and the actual system temperature measurement data to obtain each square error sum;
[0080] A screening module 13 is used to sort the square error sums, screen out a target square error sum from the sorted square error sums, and determine a target chromosome parameter corresponding to the target square error sum;
[0081] The judgment module 14 is used to determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, the minimum target chromosome parameter corresponding to the target error square sum with the minimum value is determined;
[0082] The ground temperature calculation module 15 is used to calculate the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, and then perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
[0083] In this embodiment, the initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report and the formation stratification data are obtained, and the parameters in each of the initial chromosomes are subjected to cross-variation processing to obtain the chromosome parameters; the preset equation is called to calculate the initial population iteration information, the chromosome parameters, each actual system temperature measurement data and the formation stratification data to obtain the first predicted ground temperature, and the square sum of errors is calculated for the first predicted ground temperature and each actual system temperature measurement data to obtain each square sum of errors; the square sums of the errors are sorted, and the sorted square sums of the errors are calculated from the The target error sum of squares is screened out from the difference sum of squares, and the target chromosome parameter corresponding to the target error sum of squares is determined; the current number of iterations is determined, and it is judged whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, the minimum target chromosome parameter corresponding to the target error sum of squares with the smallest value is determined; the heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameters, and then the heat generation rate and the thermal conductivity parameters are used for geothermal calculation to obtain the second predicted geothermal temperature. The present application can directly obtain formation stratification data and system temperature measurement data from the completion report, simplify the process of collecting rock samples to measure heat generation rate and thermal conductivity parameters, reduce costs, and calculate the heat generation rate and thermal conductivity parameters based on the heat generation rate and thermal conductivity parameters to obtain the second predicted geothermal temperature, which can improve the accuracy and efficiency of geothermal prediction.
[0084] In some specific embodiments, the crossover mutation module 11 may specifically include:
[0085] A crossover module, used to determine a crossover point value from a preset crossover range, and then exchange parameters of adjacent initial chromosomes with values smaller than the crossover point value to obtain chromosomes after crossover;
[0086] The mutation module is used to perform mutation processing on each of the crossover chromosomes to obtain chromosome parameters.
[0087] In some specific embodiments, the crossover mutation module 11 may specifically include:
[0088] A variation position information determination module, used to determine variation position information from a preset variation range;
[0089] The parameter initialization module is used to determine each parameter to be mutated from each of the chromosomes after the crossover according to the variation position information, and to initialize each parameter to be mutated to obtain chromosome parameters.
[0090] In some specific embodiments, the square error sum determination module 12 may specifically include:
[0091] A terrestrial heat flow calculation module, used for calculating the terrestrial heat flow based on the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum layer data to obtain the terrestrial heat flow;
[0092] The first predicted ground temperature determination module is used to call the preset one-dimensional steady-state heat conduction equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, the stratum stratification data and the earth heat flow to obtain the first predicted ground temperature.
[0093] In some specific embodiments, the screening module 13 may specifically include:
[0094] An error sum of squares sorting module, used for sorting the error sums of squares according to the numerical values of the error sums of squares to obtain sorted error sums of squares;
[0095] The target error sum of squares determination module is used to select the error sum of squares with the smallest value from the sorted error sums of squares as the target error sum of squares.
[0096] In some specific embodiments, the determination module 14 may specifically include:
[0097] An updating module is used for updating the initial population information in the initial population iteration information based on the target chromosome parameters to obtain updated population iteration information if the current iteration number is less than the initial iteration number in the initial algorithm iteration parameter information, and updating the current iteration number, and then jumping to the step of performing crossover mutation processing on the parameters in each of the initial chromosomes until the current iteration number is not less than the initial iteration number in the initial population iteration information.
[0098] In some specific embodiments, the ground temperature calculation module 15 may specifically include:
[0099] The ground temperature calculation module is used to use the local ground temperature prediction module to perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters based on the actual system temperature measurement data and the stratum stratification data to obtain a second predicted ground temperature.
[0100] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the ground temperature prediction method performed by the electronic device disclosed in any of the aforementioned embodiments.
[0101] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0102] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.
[0103] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the geothermal prediction method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the geothermal prediction device, the data 223 can also include data collected by its own input and output interface 25, etc.
[0104] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0105] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the ground temperature prediction method disclosed in any of the aforementioned embodiments are implemented.
[0106] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0107] The above is a detailed introduction to the geothermal prediction method, device, equipment and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for predicting ground temperature, characterized in that: include: Acquire initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and formation stratification data, and perform crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters; Calling a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and performing a square sum of errors calculation on the first predicted ground temperature and the actual system temperature measurement data to obtain each square sum of errors; Sorting the error sums of squares, selecting a target error sum of squares from the sorted error sums of squares, and determining a target chromosome parameter corresponding to the target error sum of squares; Determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information; if the current number of iterations is not less than the initial number of iterations in the initial population iteration information, determine the minimum target chromosome parameter corresponding to the target error sum of squares with the minimum value; The heat generation rate and thermal conductivity parameters are calculated based on the minimum target chromosome parameters, and then the ground temperature is calculated for the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
2. The ground temperature prediction method according to claim 1, characterized in that: The performing crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters includes: Determine a crossover point value from a preset crossover range, and then perform parameter exchange on the parameters of the adjacent initial chromosomes that are smaller than the crossover point value, so as to obtain each crossover chromosome; The chromosomes after each crossover are subjected to mutation processing to obtain chromosome parameters.
3. The ground temperature prediction method according to claim 2, characterized in that: The performing mutation processing on each of the crossover chromosomes to obtain chromosome parameters includes: Determine the mutation position information from the preset mutation range; According to the variation position information, each parameter to be varied is determined from each of the chromosomes after the crossover, and each parameter to be varied is initialized to obtain a chromosome parameter.
4. The ground temperature prediction method according to claim 1, characterized in that: The calling of a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum layer data to obtain a first predicted ground temperature includes: Calculating the terrestrial heat flow based on the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, and the stratum layer data to obtain the terrestrial heat flow; The preset one-dimensional steady-state heat conduction equation is called to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data, the stratum layer data and the earth heat flow to obtain the first predicted ground temperature.
5. The ground temperature prediction method according to claim 1, characterized in that: The step of sorting the square error sums and selecting a target square error sum from the sorted square error sums includes: Sorting the sums of squared errors according to their numerical values to obtain sorted sums of squared errors; The error sum of squares with the smallest value is selected from the sorted error sums of squares as the target error sum of squares.
6. The ground temperature prediction method according to claim 1, characterized in that: After determining whether the current number of iterations is less than the initial number of iterations in the initial population iteration information, the method further includes: If the current number of iterations is less than the initial number of iterations in the initial algorithm iteration parameter information, the initial population information in the initial population iteration information is updated based on the target chromosome parameters to obtain updated population iteration information, and the current number of iterations is updated, and then the process jumps to the step of performing crossover mutation processing on the parameters in each of the initial chromosomes until the current number of iterations is not less than the initial number of iterations in the initial population iteration information.
7. The ground temperature prediction method according to any one of claims 1 to 6, characterized in that: The performing ground temperature calculation on the heat generation rate and the thermal conductivity parameter to obtain a second predicted ground temperature includes: The local ground temperature prediction module is used to perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters based on the actual system temperature measurement data and the stratum stratification data to obtain a second predicted ground temperature.
8. A ground temperature prediction device, characterized in that: include: A crossover mutation module is used to obtain initial population iteration information, each initial chromosome, each actual system temperature measurement data of the completion report, and formation stratification data, and perform crossover mutation processing on the parameters in each of the initial chromosomes to obtain chromosome parameters; The square error sum determination module is used to call a preset equation to calculate the initial population iteration information, the chromosome parameters, the actual system temperature measurement data and the stratum stratification data to obtain a first predicted ground temperature, and to perform square error sum calculation on the first predicted ground temperature and the actual system temperature measurement data to obtain each square error sum; A screening module is used to sort the square error sums, screen out a target square error sum from the sorted square error sums, and determine a target chromosome parameter corresponding to the target square error sum; A judgment module is used to determine the current number of iterations, and judge whether the current number of iterations is less than the initial number of iterations in the initial population iteration information. If the current number of iterations is not less than the initial number of iterations in the initial population iteration information, then determine the minimum target chromosome parameter corresponding to the target error square sum with the minimum value; The ground temperature calculation module is used to calculate the heat generation rate and thermal conductivity parameters based on the minimum target chromosome parameters, and then perform ground temperature calculation on the heat generation rate and the thermal conductivity parameters to obtain a second predicted ground temperature.
9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the ground temperature prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the ground temperature prediction method according to any one of claims 1 to 7 is implemented.