Full aperture characterization method based on dynamic time warping

Abstract

The invention provides a full aperture characterization method based on dynamic time warping, and belongs to the technical field of aperture characterization, and the method comprises the steps: collecting nuclear magnetic resonance T2 spectrum data and aperture distribution data from different experiments, and carrying out the preprocessing and standardization processing of the collected data; performing nonlinear matching processing on the processed nuclear magnetic resonance T2 spectrum data and aperture distribution experimental data by using a dynamic time warping algorithm; and based on a matching result, integrating a gas adsorption experiment, a high-pressure mercury injection experiment and a nuclear magnetic resonance T2 spectrum result, and modeling pore size distribution covering micropores, mesopores and macropores to complete the characterization of the full pore size. The problems that an existing shale oil reservoir pore structure characterization method is not high in resolution within the range of micropores, mesopores and macropores, and experimental data are difficult to integrate are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of aperture characterization, and in particular relates to a full aperture characterization method based on dynamic time warping. Background Art

[0002] The pore size distribution characteristics of shale oil reservoirs have an important impact on the reservoir storage capacity and fluid migration characteristics. Traditional pore size characterization methods include nuclear magnetic resonance pore size spectroscopy, gas adsorption and high-pressure mercury injection. These methods are based on different physical principles and have certain scopes of application and limitations: Nuclear magnetic resonance pore size spectroscopy: The pore size distribution is deduced using the NMRT2 spectrum, but the diffusion relaxation effect and the influence of the complex pore structure on the relaxation time are ignored, resulting in deviations in the characterization results.

[0003] Gas adsorption method: Nitrogen adsorption or carbon dioxide adsorption is used to characterize micropores and mesopores, but the ability to characterize macropore distribution is limited.

[0004] High-pressure mercury injection method: It is suitable for the characterization of macropore distribution, but the destructive effect of high pressure on shale structure may lead to errors in the measurement results.

[0005] Although the nuclear magnetic resonance method, gas adsorption method and high-pressure mercury injection method are theoretically feasible for pore size characterization, when they are actually applied to shale oil reservoirs, due to the complex pore structure and the presence of multiphase fluids, these methods face significant challenges in parameter setting and data fusion. Specifically, they are as follows: (1) Limitations of a single method: A single pore size characterization method is difficult to cover pores of all scales in shale reservoirs, and there is a problem of incomplete coverage of the pore size range. (2) Difficulty in data fusion: Data from different methods have overlapping and complementary pore size coverage, and the heterogeneity of data sources increases the difficulty of data fusion. Existing data fusion methods often rely on idealized assumptions and simplified models, and it is difficult to make full use of limited experimental data for accurate characterization of the full pore size range.

[0006] Traditional nuclear magnetic resonance T2 spectroscopy, gas adsorption and high-pressure mercury injection methods are all theoretically feasible for pore size characterization, but when applied to shale oil reservoirs, these methods face significant challenges in parameter setting and data fusion due to the complex pore structure and the presence of multiphase fluids. Existing technologies make it difficult to fully and accurately characterize the full pore size range of shale oil reservoirs from micropores to macropores. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the present invention provides a full-pore characterization method based on dynamic time warping, which solves the problems of low resolution of existing shale oil reservoir pore structure characterization methods in the micropore, mesopore and macropore ranges and difficulty in integrating experimental data.

[0008] In order to achieve the above objectives, the technical solution adopted by the present invention is: a full aperture characterization method based on dynamic time warping, comprising the following steps: S1. Data preparation: Collect NMR T2 spectrum data and pore size distribution data from different experiments, and preprocess and standardize the collected data; S2, dynamic time warping matching: using the dynamic time warping algorithm, nonlinear matching processing is performed on the NMR T2 spectrum data processed by S1 and the pore size distribution experimental data; S3. Characterization of full pore size distribution: Based on the matching results, the gas adsorption experiment, high-pressure mercury injection experiment and nuclear magnetic resonance T2 spectrum results are integrated to model the pore size distribution covering micropores, mesopores and macropores to complete the characterization of the full pore size.

[0009] The beneficial effects of the present invention are as follows: based on the dynamic time warping (DTW) algorithm, the present invention obtains the optimal correspondence between the nuclear magnetic resonance T2 spectrum and the experimental pore size distribution data by standardizing and matching the two, and fits the mathematical conversion model to convert the nuclear magnetic resonance T2 spectrum into a full pore size distribution curve, thereby achieving accurate characterization and correction of the pore structure of the shale oil reservoir, achieving a comprehensive characterization of the reservoir pore structure, and providing a high-precision data fusion solution.

[0010] Furthermore, the S1 comprises the following steps: S101, collecting nuclear magnetic resonance T2 spectrum data, gas adsorption experimental data and high-pressure mercury injection experimental data; S102, performing Gaussian smoothing and normalization processing on the gas adsorption experimental data and the high-pressure mercury injection experimental data to obtain normalized pore size distribution experimental data; S103, performing normalization processing on the collected nuclear magnetic resonance T2 spectrum data; S104: The data processed by S102 and S103 are fused according to the same scale interval to complete the preprocessing and standardization processing.

[0011] The beneficial effect of the above further scheme is: in order to eliminate the scale differences in pore data of different experimental methods (such as carbon dioxide adsorption method, nitrogen adsorption method and high-pressure mercury injection method), the present invention adopts standardization and normalization processing technology to unify all pore distribution data into the same scale interval (0~1). This processing step improves the comparability between data while ensuring data consistency, which is a prerequisite for ensuring the effective application of the dynamic time warping algorithm DTW. At the same time, the present invention also comprehensively considers the data characteristics of different pore size ranges through the fusion of multi-source data, and solves the problem that traditional methods cannot uniformly process large-scale pore data. This technology can extract fine pore characteristics of complex shale reservoirs, thereby providing support for subsequent reservoir evaluation and fluid identification.

[0012] Furthermore, the S2 comprises the following steps: S201, calculating the Euclidean distance between the nuclear magnetic resonance T2 spectrum data sequence T processed by S1 and the pore size distribution experimental data P; S202, based on the Euclidean distance, using the recursive formula of the dynamic time warping algorithm to calculate the cumulative distance matrix C; S203, based on the cumulative distance matrix C, the optimal path W is traced back, and based on the optimal path W, a conversion relationship between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P is constructed to complete the nonlinear matching process.

[0013] The beneficial effect of the above further solution is that the present invention extracts the best matching point pairs in different aperture ranges by backtracking the optimal path of the dynamic time warping algorithm DTW, and establishes the conversion relationship between the T2 spectrum and the aperture size based on these point pairs. This step not only optimizes the matching process, but also establishes an accurate mathematical model by minimizing the fitting error, thereby further improving the accuracy of pore characterization.

[0014] Furthermore, the S203 is specifically as follows: Assume the end point of the optimal path is , assuming the backtracking starting point is ,from Start by tracing back in the direction of the minimum cumulative distance to , and the optimal path W is obtained, where the next point of the backtracking path is obtained using the following formula:

[0015] in, Indicates current location The previous point, Indicates current location Above, Indicates current location On the left side, Indicates the current The upper left corner; Based on the optimal path W, a conversion relationship is established between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P to complete the nonlinear matching process.

[0016] Furthermore, the expression of the optimal path W is as follows:

[0017]

[0018] in, represents the last coordinate in the optimal path W, represents the first The coordinates of the path points, Indicates the current optimal path point The next backtracking path point of Represents the index number of the point on the optimal path W, represents the total number of steps in the optimal path W. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of the method.

[0020] Figure 2 Schematic diagram of normalized full pore size distribution characteristics for different experiments.

[0021] Figure 3 This is a schematic diagram of the matching results.

[0022] Figure 4 It is the integrated cross-plot of the sample pore size distribution and T2 spectrum matching results.

[0023] Figure 5 This is a fitting diagram of the functional relationship between the NMR T2 spectrum and the pore size distribution.

[0024] Figure 6 Schematic diagram of the calculated pore size spectrum of HQ1H.

[0025] Figure 7 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0026] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0027] Example 1 like Figure 1 As shown, the present invention provides a full aperture characterization method based on dynamic time warping, and its implementation method is as follows: S1. Data preparation: Collect NMR T2 spectrum data and pore size distribution data from different experiments, and preprocess and standardize the collected data. The implementation method is as follows: S101, collecting nuclear magnetic resonance T2 spectrum data, gas adsorption experimental data and high-pressure mercury injection experimental data; S102, performing Gaussian smoothing and normalization processing on the gas adsorption experimental data and the high-pressure mercury injection experimental data to obtain normalized pore size distribution experimental data; S103, performing normalization processing on the collected nuclear magnetic resonance T2 spectrum data; S104: The data processed by S102 and S103 are fused according to the same scale interval to complete the preprocessing and standardization processing.

[0028] In this embodiment, the pore size distribution data obtained by carbon dioxide adsorption, nitrogen adsorption and high pressure mercury injection method need to be Gaussian smoothed due to their large fluctuations. (corresponding to the first points), the smoothing process can be performed through the Gaussian function, the specific formula is as follows:

[0029] in, represents the standard deviation of the Gaussian kernel, N represents the number of data points, represents the smoothed pore size distribution data, j Indicates the data point that currently needs to be smoothed.

[0030] In this example, in order to make the data of different experimental methods (such as carbon dioxide adsorption, nitrogen adsorption and high pressure mercury injection) and nuclear magnetic resonance T2 spectrum comparable, all data need to be normalized. Suppose the pore size distribution data obtained by a certain experimental method is , whose minimum and maximum values ​​are and , the normalized data It can be calculated by the following formula:

[0031] like Figure 2 As shown, all data will be normalized to the interval [0,1] to eliminate the differences in data scales between different experimental methods. Figure 2 The pore size distributions obtained by different experimental methods are integrated into the full pore size distribution data. The X-axis represents the average pore size of the pores, in nanometers (nm), with a pore size range from 1 nm to 1000 nm, covering the range of micropores (<2 nm), mesopores (2-50 nm) and macropores (>50 nm). The Y-axis represents the pore distribution characteristics of different samples in each pore size range, and the values ​​are normalized (standardized to the range of 0-1). The pore size is divided into three adsorption / measurement intervals by three red dotted lines, the leftmost is Adsorption pore size range (<10 nm), which mainly reflects the distribution of micropores (usually pores with a pore size less than 2 nm). The adsorption pore size range (10-100 nm) reflects the mesopore distribution. Adsorption measurements are applied to the analysis of mesopores from 2 to 50 nm. The rightmost side shows the high pressure mercury injection pore size range (>100 nm): This range is mainly used to measure macropores, usually using high pressure mercury injection. The legend shows the numbers of different samples (such as 22CJ26-1, 22CJ26-2, etc.), and each curve represents the pore distribution characteristics of a specific sample in different pore size ranges.

[0032] After completing the above data smoothing and normalization processing, the pore size distribution data obtained from different experimental methods (such as carbon dioxide adsorption, nitrogen adsorption, and high-pressure mercury injection) are integrated according to the same scale interval (0~1). The integrated data will be used for subsequent full pore size characterization and DTW algorithm-driven analysis.

[0033] S2, dynamic time warping matching: using the dynamic time warping algorithm, the NMR T2 spectrum data processed by S1 and the pore size distribution experimental data are nonlinearly matched. The implementation method is as follows: S201, calculating the Euclidean distance between the nuclear magnetic resonance T2 spectrum data sequence T processed by S1 and the pore size distribution experimental data P; S202, based on the Euclidean distance, using the recursive formula of the dynamic time warping algorithm to calculate the cumulative distance matrix C; S203, based on the cumulative distance matrix C, the optimal path W is traced back, and based on the optimal path W, a conversion relationship between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P is constructed to complete the nonlinear matching, which is specifically: Assume the end point of the optimal path is , assuming the backtracking starting point is ,from Start by tracing back in the direction of the minimum cumulative distance to , get the optimal path W; Based on the optimal path W, a conversion relationship is established between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P to complete the nonlinear matching process.

[0034] In this embodiment, the normalized data of the nuclear magnetic resonance T2 spectrum is assumed to be the sequence , the pore size distribution experimental data is normalized to the sequence , represents the first m data, Indicates the first nThe purpose of the dynamic time warping algorithm is to minimize the matching error between sequence T and sequence P by finding the optimal path W. The specific steps are as follows: 1) Distance matrix construction Calculate the Euclidean distance between sequence T and sequence P and construct a distance matrix ,in, Indicates i The T point and j The Euclidean distance between points P:

[0035] in, represents the normalized value of the pore size distribution experimental data, Represents the normalized data of NMR T2 spectrum.

[0036] 2) Cumulative distance matrix calculation The cumulative distance matrix C is calculated according to the recursive formula of DTW, and its recursive formula is:

[0037] in, Represents the cumulative distance matrix C at position The value of the current point The cumulative distance when taking one step up (from the previous row), Represents the cumulative distance matrix C at position The value of the current point The cumulative distance when taking one step to the left (from the previous column), Represents the cumulative distance matrix C at position The value of the current point The cumulative distance transferred in the diagonal (upper left) direction.

[0038] The boundary conditions are:

[0039] in, represents the starting point (boundary condition) of the cumulative distance matrix, Represents the original distance matrix 𝐷 at point The value at , that is, the Euclidean distance between the two sequences at the starting point.

[0040] for and :

[0041] in, Represents the cumulative distance matrix in The value of the row and column 1, that is, the cumulative distance when recursively going down the first column, Represents the original distance matrix 𝐷 at position The value at , represents the value of The local distance between a point and the first point in the sequence P, Represents the cumulative distance matrix in The value of the row and column 1, that is, the previous cumulative value when calculating step by step to the right along the first row, Indicates that the cumulative distance matrix is ​​in the first row and the The value of the column, that is, the cumulative distance when recursively going right along the first row, Represents the original distance matrix 𝐷 at position The value at , which indicates the difference between the first point in sequence T and the first point in sequence P The local distance between points, Indicates that the cumulative distance matrix is ​​in the first row and the The value of the column, that is, the previous cumulative value as you progress right along the first row.

[0042] 3) Optimal path backtracking The purpose of optimal path backtracking is to find arrive The path that minimizes the cumulative distance on the entire path.

[0043] The end point of the optimal path is , the starting point is The backtracking process starts from Start by tracing back in the direction of the minimum cumulative distance to .

[0044] The backtracking process follows the following rules: For each position , select the source point with the minimum cumulative distance to the current position. Specifically, the next point on the backtracking path is the one with the minimum distance from the three possible directions:

[0045] in Indicates current location The previous point may be from above , left side , or upper left point.

[0046] from Start by going back to :

[0047]

[0048] Until arrival So far, the backtracking path This is the optimal matching path.

[0049] in, represents the last coordinate in the optimal path W, represents the first The coordinates of the path points, Indicates the current optimal path point The next backtracking path point of Represents the index number of the point on the optimal path W, Represents the total number of steps in the optimal path W, or the total number of points on the path.

[0050] like Figure 3 As shown, by performing dynamic time warping algorithm on the nuclear magnetic resonance T2 spectrum sequence and the pore size distribution sequence to find the optimal path, the following is obtained: Figure 4 The matching relationship between the values ​​of the two sequences is shown. Figure 3 This is the result of DTW matching of NMR T2 spectrum and full pore size distribution data. The X-axis uses Index as the coordinate, which represents the index or classification index value of the pore size distribution and T2 distribution data. The Y-axis shows the normalized value of the pore size distribution and T2 distribution. The data has been standardized (range 0-1). The blue pore size distribution curve represents the change in the normalized value of the sample "22CJ26-22" in the pore size distribution. The yellow T2 distribution curve represents the change in the normalized value of the sample "22CJ26-22" in the T2 distribution data. The flexible matching line is a flexible connection line that matches the yellow T2 distribution curve to the blue pore size distribution curve according to the optimal path in the figure. The figure intuitively shows the correlation between the pore size distribution and T2 distribution of the sample "22CJ26-22". Through the flexible matching technology, the correlation between the NMR pore structure characteristics and the actual pore size distribution data can be deeply analyzed.

[0051] Figure 4 The matching relationship between the NMR T2 spectra and pore size distribution of multiple samples. The X-axis shows the NMR T2 spectrum time in milliseconds (ms), and the Y-axis shows the pore size in nanometers (nm), ranging from 0 nm to 2000 nm, including micropores, mesopores and macropores. The legend shows multiple samples, including "22CJ26-1", "22CJ26-2", "22CJ26-3", etc. Each curve represents the relationship between T2 and pore size of a specific sample. The figure intuitively shows the relationship characteristics between the NMR T2 regression time and pore size of the sample.

[0052] The present invention generates a complete full pore size distribution curve by analyzing data derivatives and extreme points and fitting the conversion relationship of different experimental data using a mathematical model.

[0053] Based on the matching points in the optimal path W, a conversion relationship between the magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P is established. Assume that the conversion relationship is a linear relationship:

[0054] in, and All represent the parameters to be fitted. Solve by minimizing the following objective function and :

[0055] in, Represents the total number of matching point pairs, represents the first position of the pore size distribution experimental data in the optimal matching path The value of the matching point, Indicates the index number of the matching point pair, the value range is ,in is the total number of matching points, Represents the first position of the NMR T2 spectrum data in the optimal matching path The value of the matching point. Solving this optimization problem can obtain the optimal conversion parameters and .

[0056] like Figure 5 As shown in the figure, the functional relationship between the NMR T2 spectrum and the pore size is obtained by solving the conversion parameters. The figure is the functional relationship between the NMR T2 spectrum and the pore size distribution obtained by taking the average of the matching results of multiple samples. The X-axis shows the NMR T2 spectrum time in milliseconds (ms), and the Y-axis shows the pore size in nanometers (nm). The range is from 0 nm to 2500 nm, including micropores, mesopores and macropores. The yellow data points in the figure represent the corresponding relationship between the actual pore size and the T2 regression time. The red dotted line is the data fitting curve, which represents the reasonable relationship between the NMR T2 time and the pore size. The fitting equation given in the figure is , indicating that there is a functional relationship between pore size and T2 time. Through this model, the T2 measurement results can be converted into pore size distribution characteristics.

[0057] S3. Characterization of full pore size distribution: Based on the matching results, the gas adsorption experiment, high-pressure mercury injection experiment and nuclear magnetic resonance T2 spectrum results are integrated to model the pore size distribution covering micropores, mesopores and macropores to complete the characterization of the full pore size.

[0058] like Figure 6 As shown in the figure, the characterization of the T2 spectrum at a certain depth point of the HQ1H well to the full pore size distribution is completed. This figure is the conversion of the nuclear magnetic T2 spectrum at multiple depths into a pore size distribution diagram. The X-axis shows the pore size in microns (μm). The Y-axis represents the pore component. The pore component value is used to represent the density and distribution characteristics of the pore structure within each pore size range. Figure 6 The data distribution curves of samples at different depths are displayed, and each curve is displayed in a different color to facilitate intuitive comparison of pore structure characteristics at different depths.

[0059] In this embodiment, the above method can achieve accurate characterization of different pore size ranges in shale oil reservoirs and extraction of fluid pore characteristics. The full pore size characterization technology proposed in the present invention significantly improves the automation level of data processing, reduces the need for human intervention, and enhances the ability to identify and evaluate complex reservoir pore structures.

[0060] The present invention combines data fusion with mathematical modeling to not only accurately characterize the complex pore structure of shale oil reservoirs, but also provide reliable technical support for reservoir analysis and development. Through practical verification, the method has high distribution characterization accuracy and strong applicability in the full pore size range, and can be widely used in the characterization and evaluation of shale oil reservoirs and other unconventional oil and gas reservoirs.

[0061] Example 2 like Figure 7 As shown, the present invention provides a full-aperture characterization system based on dynamic time warping, and the full-aperture characterization system is used to execute the full-aperture characterization method described in Example 1, including: The first processing module is used to collect NMR T2 spectrum data and pore size distribution data from different experiments, and perform preprocessing and standardization on the collected data; The second processing module is used to perform nonlinear matching processing on the nuclear magnetic resonance T2 spectrum data processed by the first processing module and the pore size distribution experimental data using a dynamic time warping algorithm; The third module is used to integrate the gas adsorption experiment, high-pressure mercury injection experiment and nuclear magnetic resonance T2 spectrum results based on the matching results, to model the pore size distribution covering micropores, mesopores and macropores, and complete the characterization of the full pore size.

[0062] like Figure 7 The full-aperture characterization system provided in the illustrated embodiment can execute the technical solution shown in the full-aperture characterization method in the above-mentioned method embodiment 1, and its implementation principle and beneficial effects are similar and will not be repeated here.

[0063] In this embodiment, the present application can divide the functional units according to the full aperture characterization method. For example, each function can be divided into each functional unit, or two or more functions can be integrated into one processing unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the present invention is schematic and is only a logical division. There may be other division methods in actual implementation.

[0064] In this embodiment, in order to realize the principles and beneficial effects of the full-aperture characterization method, the full-aperture characterization system includes hardware structures and / or software modules corresponding to the execution of various functions. Those skilled in the art should easily realize that, in combination with the schematic units and algorithm steps described in the embodiments disclosed in the present invention, the present invention can be implemented in the form of hardware and / or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven manner depends on the specific application and design constraints of the technical solution. Different methods can be used for each specific application to implement the described function, but such implementation should not be considered to exceed the scope of this application.

Claims

1. A full-aperture characterization method based on dynamic time warping, characterized in that: The following steps are involved: S1. Data preparation: Collect NMR T2 spectrum data and pore size distribution data from different experiments, and preprocess and standardize the collected data; S2, dynamic time warping matching: using the dynamic time warping algorithm, nonlinear matching processing is performed on the NMR T2 spectrum data processed by S1 and the pore size distribution experimental data; S3. Characterization of full pore size distribution: Based on the matching results, the gas adsorption experiment, high-pressure mercury injection experiment and nuclear magnetic resonance T2 spectrum results are integrated to model the pore size distribution covering micropores, mesopores and macropores to complete the characterization of the full pore size.

2. The full-aperture characterization method based on dynamic time warping according to claim 1, characterized in that: The S1 comprises the following steps: S101, collecting nuclear magnetic resonance T2 spectrum data, gas adsorption experimental data and high-pressure mercury injection experimental data; S102, performing Gaussian smoothing and normalization processing on the gas adsorption experimental data and the high-pressure mercury injection experimental data to obtain normalized pore size distribution experimental data; S103, performing normalization processing on the collected nuclear magnetic resonance T2 spectrum data; S104: The data processed by S102 and S103 are fused according to the same scale interval to complete the preprocessing and standardization processing.

3. The full-aperture characterization method based on dynamic time warping according to claim 1, characterized in that: The S2 comprises the following steps: S201, calculating the Euclidean distance between the nuclear magnetic resonance T2 spectrum data sequence T processed by S1 and the pore size distribution experimental data P; S202, based on the Euclidean distance, using the recursive formula of the dynamic time warping algorithm to calculate the cumulative distance matrix C; S203, based on the cumulative distance matrix C, the optimal path W is traced back, and based on the optimal path W, a conversion relationship between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P is constructed to complete the nonlinear matching process.

4. The full-aperture characterization method based on dynamic time warping according to claim 3, characterized in that: The S203 is specifically as follows: Assume the end point of the optimal path is , assuming the backtracking starting point is ,from Start by tracing back in the direction of the minimum cumulative distance to , and the optimal path W is obtained, where the next point of the backtracking path is obtained using the following formula: in, Indicates current location The previous point, Indicates current location Above, Indicates current location On the left side, Indicates the current 's upper left; Based on the optimal path W, a conversion relationship is established between the nuclear magnetic resonance T2 spectrum data sequence T and the pore size distribution experimental data P to complete the nonlinear matching process.

5. The full aperture characterization method based on dynamic time warping according to claim 4, characterized in that: The expression of the optimal path W is as follows: in, represents the last coordinate in the optimal path W, represents the first The coordinates of the path points, Indicates the current optimal path point The next backtracking path point of Represents the index number of the point on the optimal path W, represents the total number of steps in the optimal path W.

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