Threshold reconstruction micro-amplitude structure identification method and device based on wavelet transform

Abstract

According to the wavelet transform-based threshold reconstruction micro-amplitude structure identification method and device provided by the invention, equal-interval sampling is carried out on equal-interval sampling two-dimensional data through wavelet transform, and a'scale-frequency 'wavelet function is established. Wavelet transformation is carried out based on equally-spaced sampled two-dimensional data, structural fluctuation is decomposed in a multi-scale mode, the identification weight of signal high-frequency components on microstructures is improved, wavelet coefficients are optimized through threshold value control to obtain a low-frequency structure background and high-frequency microstructure fluctuation, the shielding effect of the low-frequency components of the structures on microstructure identification is eliminated, and the identification accuracy of the microstructures is improved. Medium-high frequency fluctuation information is better reserved, and the micro-structure recognition precision and recognition efficiency are improved.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration, and in particular to a threshold reconstruction micro-structure identification method and device based on wavelet transform. Background Art

[0002] In recent years, with the continuous improvement of basin exploration and the continuous reduction of large structural reservoirs, the requirements for exploration accuracy have become increasingly higher. Therefore, it is particularly important to find small micro-structural reservoirs, study micro-structural identification and analysis methods, and provide favorable drilling targets for oil and gas exploration deployment, which has considerable practical significance and application value.

[0003] Microstructures often refer to a type of geological body with relatively gentle structures, small closure amplitude and area, and strong concealment. Its general structural amplitude is less than 15m or 10ms. Microstructures are divided into two types: tectonic and sedimentary. Tectonic structures are formed by weak compression of microtectonic activities, and sedimentary structures are affected by differential compaction of sedimentary bodies and are generally distributed at paleo-geomorphic high points.

[0004] At present, the main technologies used to identify or track micro-structures are well-seismic joint construction of velocity field, pre-stack migration, coherence volume, feature points, horizontal slices, mean filtering, and S transform. These technologies mainly focus on improving the accuracy of micro-structure imaging or layer interpretation, and conventional mapping analysis and characterization methods are difficult to accurately display the detailed characteristics of micro-structures, which is not conducive to oil and gas exploration. Summary of the invention

[0005] The present invention provides a threshold reconstruction micro-structure recognition method and device based on wavelet transform, which are used to reduce the shielding effect of low-frequency components of the structure on micro-structure recognition and improve the micro-structure recognition accuracy and recognition efficiency.

[0006] In a first aspect, the present invention provides a method for identifying micro-structures by threshold reconstruction based on wavelet transform, comprising:

[0007] Obtain equally spaced sampled two-dimensional data corresponding to equal depth data;

[0008] Performing a two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced two-dimensional data according to a two-dimensional wavelet transform formula to obtain wavelet coefficients of scales corresponding to different frequencies;

[0009] Selecting wavelet coefficients from the wavelet coefficients of different frequency corresponding scales by using threshold control in turn to obtain preferred wavelet coefficients of different frequency corresponding scales;

[0010] Determine the target signal corresponding to the preferred wavelet coefficient, and reconstruct the target signal through an optimization function to obtain target data;

[0011] The target data is identified to obtain corresponding micro-structural features.

[0012] Optionally, obtaining equally spaced sampled two-dimensional data corresponding to the equal depth data includes:

[0013] Acquire the bathymetric data and convert the bathymetric data into a two-dimensional data set;

[0014] In the two-dimensional data set, sampling is performed at equal intervals according to a linear interpolation formula to obtain two-dimensional data with equal intervals.

[0015] Optionally, selecting wavelet coefficients from the wavelet coefficients of different frequency corresponding scales by using threshold control in turn to obtain preferred wavelet coefficients of different frequency corresponding scales includes:

[0016] By means of a preset amplitude function, the amplitudes of the scale factors corresponding to different frequencies are determined;

[0017] Based on the amplitude and in combination with a preset threshold function expression, the preferred wavelet coefficients corresponding to different frequency scales are extracted in sequence.

[0018] Optionally, determining a target signal corresponding to the preferred wavelet coefficients, and reconstructing the target signal through an optimization function to obtain target data includes:

[0019] Determining the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient;

[0020] The target data is obtained by reconstructing the microstructure major axis and microstructure minor axis identified initially.

[0021] In a second aspect, the present invention provides a threshold reconstruction micro-structure identification device based on wavelet transform, comprising:

[0022] An acquisition module is used to acquire equally spaced sampled two-dimensional data corresponding to equal depth data;

[0023] A transformation module is used to perform a two-dimensional wavelet transformation along the horizontal and vertical coordinate directions of the two-dimensional data sampled at equal intervals according to a two-dimensional wavelet transformation formula to obtain wavelet coefficients of scales corresponding to different frequencies;

[0024] A screening module is used to sequentially screen the wavelet coefficients of the wavelet coefficients of different frequency corresponding scales by using threshold control to obtain the preferred wavelet coefficients of the wavelet coefficients of different frequency corresponding scales;

[0025] A reconstruction module, used to determine the target signal corresponding to the preferred wavelet coefficients, and reconstruct the target signal through an optimization function to obtain target data;

[0026] The recognition module is used to recognize the target data and obtain the corresponding micro-structural features.

[0027] Optionally, the acquisition module includes:

[0028] An acquisition submodule, used for acquiring the isobath data and converting the isobath data into a two-dimensional data set;

[0029] The equally spaced sampling two-dimensional data sampling submodule is used to equally spaced sample the two-dimensional data set according to a linear interpolation formula to obtain equally spaced sampled two-dimensional data.

[0030] Optionally, the screening module includes:

[0031] The amplitude determination submodule is used to determine the amplitude of the scale factor corresponding to different frequencies through a preset amplitude function;

[0032] The extraction submodule is used to extract the preferred wavelet coefficients of different frequency corresponding scales in sequence based on the amplitude and in combination with a preset threshold function expression.

[0033] Optionally, the reconstruction module includes:

[0034] A function determination submodule, used to determine the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient;

[0035] The reconstruction submodule is used to reconstruct the target data by using the microstructure major axis and microstructure minor axis identified initially.

[0036] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are performed.

[0037] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in the method provided in the first aspect are performed.

[0038] It can be seen from the above technical solutions that the present invention has the following advantages:

[0039] The present invention provides a method and device for identifying micro-structures based on threshold reconstruction of wavelet transform, the method comprising: obtaining equally spaced sampled two-dimensional data corresponding to equal depth data; performing two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced sampled two-dimensional data according to the two-dimensional wavelet transform formula to obtain wavelet coefficients corresponding to scales of different frequencies; sequentially screening wavelet coefficients from the wavelet coefficients corresponding to scales of different frequencies by using threshold control to obtain preferred wavelet coefficients corresponding to scales of different frequencies; determining the target signal corresponding to the preferred wavelet coefficient, and reconstructing the target signal by using an optimization function to obtain target data; identifying the target data to obtain the corresponding micro-structure feature. The equally spaced sampled two-dimensional data is equally spaced sampled by using wavelet transform to establish a "scale-frequency" wavelet function. Based on the equally spaced sampled two-dimensional data, wavelet transform is performed to decompose the structural undulation in multiple scales, improve the recognition weight of the high-frequency component of the signal to the microstructure, and then the low-frequency structural background and high-frequency micro-structure undulation are obtained by threshold control of the preferred wavelet coefficient, the shielding effect of the low-frequency component of the structure on the microstructure recognition is eliminated, the medium and high frequency undulation information is better retained, and the microstructure recognition accuracy and recognition efficiency are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 This is a flowchart of a first embodiment of a method for identifying micro-structures by threshold reconstruction based on wavelet transform according to the present invention;

[0042] Figure 2 This is a flowchart of a second embodiment of a method for identifying micro-structures by threshold reconstruction based on wavelet transform according to the present invention;

[0043] Figure 3 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 1000;

[0044] Figure 4 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 800;

[0045] Figure 5 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 600;

[0046] Figure 6 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 400;

[0047] Figure 7 This is a schematic diagram of the frequency fluctuation trend with a search step of 400;

[0048] Figure 8 This is a schematic diagram of the frequency fluctuation trend with a search step of 200;

[0049] Fig. 9 It is a schematic diagram of the reconstructed medium and low frequency fluctuation trend;

[0050] Fig.10 It is a schematic diagram of reconstructing medium and high frequency slight fluctuations;

[0051] Fig.11 It is a micro-structural plane in the slope area;

[0052] Fig.12 This is a characteristic diagram of the micro-structure profile in the slope area;

[0053] Fig.13 This is a schematic diagram of the depth data of the M1 layer before reconstruction;

[0054] Fig.14 This is a schematic diagram of the reconstructed M1 layer depth data;

[0055] Fig.15 It is a schematic diagram of small and medium-scale local microstructures;

[0056] Fig.16 It is a multi-scale wavelet reconstruction map;

[0057] Fig.17 It is a schematic diagram of the northeast-trending microstructural section;

[0058] Fig.18 This is a schematic diagram of the southeast-trending microstructural section;

[0059] Fig.19 It is a structural block diagram of an embodiment of a device for identifying micro-structures by threshold reconstruction based on wavelet transform according to the present invention. DETAILED DESCRIPTION

[0060] The embodiment of the present invention provides a threshold reconstruction micro-structure identification method and device based on wavelet transform, which is used to reduce the masking effect of low-frequency components of the structure on micro-structure identification and improve the micro-structure identification accuracy and efficiency.

[0061] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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.

[0062] For example, see Figure 1 , Figure 1 This is a flowchart of a first embodiment of a method for identifying micro-structures by threshold reconstruction based on wavelet transform according to the present invention, which specifically includes:

[0063] Step S101, obtaining equally spaced sampling two-dimensional data corresponding to equal depth data;

[0064] It should be noted that bathymetric data refers to the isobath data of sediments or strata at a specific water depth. These data can be obtained through a series of technical means and tools, such as measurement and modeling techniques. Bathymetric data are usually presented in graphical or numerical form and are widely used in the fields of earth science, oceanography, geology, hydrology, etc.

[0065] Step S102, performing a two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced sampled two-dimensional data according to a two-dimensional wavelet transform formula to obtain wavelet coefficients of scales corresponding to different frequencies;

[0066] It should be noted that in the short-time Fourier transform, the wavelet transform adds a "time-frequency" window that changes with the frequency according to the local characteristics of the signal, splits the signal and noise into different frequency bands, and obtains nonlinear denoising with different scale coefficients, thereby highlighting the details of signals in different frequency bands or eliminating high-frequency noise and identifying high-frequency effective signals.

[0067] Step S103, sequentially selecting wavelet coefficients from the wavelet coefficients of different frequency corresponding scales by using threshold control to obtain preferred wavelet coefficients of different frequency corresponding scales;

[0068] It should be noted that threshold control refers to constraining wavelet coefficients by setting a certain threshold to achieve the purpose of signal compression and denoising. Usually, wavelet transform decomposes the signal into coefficients of multiple scales and directions, and threshold control can screen and adjust these coefficients to remove noise and retain signal information.

[0069] Step S104, determining the target signal corresponding to the preferred wavelet coefficients, and reconstructing the target signal through an optimization function to obtain target data;

[0070] In the embodiment of the present invention, the low-frequency, medium-frequency and high-frequency components of the structure are reorganized by optimizing the wavelet coefficients, thereby increasing the recognition weight of the high-frequency components of the structure on the microstructure and reducing the masking effect of the low-frequency components of the structure on the microstructure identification.

[0071] Step S105, identifying the target data to obtain corresponding micro-structural features.

[0072] A threshold reconstruction micro-structure identification method based on wavelet transform provided in an embodiment of the present invention includes: obtaining equally spaced sampled two-dimensional data corresponding to equal depth data; performing two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced sampled two-dimensional data according to the two-dimensional wavelet transform formula to obtain wavelet coefficients corresponding to scales of different frequencies; sequentially screening wavelet coefficients from the wavelet coefficients corresponding to scales of different frequencies using threshold control to obtain preferred wavelet coefficients corresponding to scales of different frequencies; determining the target signal corresponding to the preferred wavelet coefficient, and reconstructing the target signal through an optimization function to obtain target data; identifying the target data to obtain the corresponding micro-structure feature. The equally spaced sampled two-dimensional data is equally spaced sampled using wavelet transform to establish a "scale-frequency" wavelet function. Based on the equally spaced sampled two-dimensional data, wavelet transform is performed to decompose the structural undulation in multiple scales, improve the recognition weight of the high-frequency component of the signal for the microstructure, and then the low-frequency structural background and high-frequency micro-structure undulation are obtained by threshold control of the preferred wavelet coefficient, eliminating the shielding effect of the low-frequency component of the structure on the microstructure identification, better retaining the medium and high frequency undulation information, and improving the microstructure identification accuracy and recognition efficiency.

[0073] For example 2, please refer to Figure 2 , Figure 2 This is a flow chart of a second embodiment of a method for identifying micro-structures by threshold reconstruction based on wavelet transform of the present invention, the method comprising:

[0074] Step S201, acquiring the bathymetric data and converting the bathymetric data into a two-dimensional data set;

[0075] Step S202, sampling the two-dimensional data set at equal intervals according to a linear interpolation formula to obtain two-dimensional data with equal intervals;

[0076] Let the two-dimensional data set be {f 1 (x 1 ,y 1 ), f 2 (x 2 ,y 2 ), f 3 (x 3 ,y 3 ).......f n (x n ,y n )}, let the interpolation point f i (x i ,y i ), and the amplitude and coordinates of the adjacent points in the set are f 1 (x 1 ,y 1 ), f 2 (x 2 ,y2 ), then the linear interpolation formula is:

[0077]

[0078] Suppose the equally spaced sampled two-dimensional data after equally spaced sampling is f(x,y)∈(R), where x and y represent the horizontal and vertical coordinates, f(x,y) has equal depth values, and R represents the real number space.

[0079] Step S203, performing a two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced sampled two-dimensional data according to a two-dimensional wavelet transform formula to obtain wavelet coefficients of scales corresponding to different frequencies;

[0080] In the embodiment of the present invention, the scale-frequency characteristics of the width and amplitude of the structural axis, that is, the two-dimensional data sampled at equal intervals along the horizontal and vertical coordinate directions are subjected to wavelet transform to obtain wavelet coefficients at the corresponding scales of high, medium and low frequencies.

[0081] In the specific implementation, let ψ(x) and ψ(y) be the same wavelet function along the horizontal and vertical coordinate directions, then the two-dimensional wavelet transform formula is:

[0082]

[0083] Among them, a∈(0,+∞) is the scale adjustment parameter, b x 、b y ∈R is the translation adjustment parameter, is the wavelet function along the x direction, is the wavelet function along the y direction.

[0084] The inverse transform of the two-dimensional wavelet is:

[0085]

[0086]

[0087] Among them, w x With w y is the wavelet coefficient.

[0088] Step S204, determining the amplitudes of scale factors corresponding to different frequencies through a preset amplitude function;

[0089] Step S205, based on the amplitude and in combination with a preset threshold function expression, sequentially extracting the preferred wavelet coefficients corresponding to different frequency scales;

[0090] In the embodiment of the present invention, since different frequencies correspond to different scale factors, a suitable threshold is crucial to separate low-frequency and high-frequency signals of different scales. Usually, the frequency of the micro-structure is higher than the frequency of the background data, so a threshold function can be introduced to filter and limit the wavelet coefficients of the low frequency, medium frequency, and high frequency, and the wavelet coefficients of the low frequency threshold, the medium frequency, and the high frequency are extracted in turn.

[0091] In the specific implementation, it is assumed that f(x,y) is constructed by low-frequency undulating trend surface f L (x,y), medium and high frequency fluctuations f M (x,y),f H (x, y) consists of three parts, then:

[0092] f(x,y)=f L (x,y)+f M (x,y)+f H (x,y);

[0093] Based on the inherent correlation between wavelet coefficients, we know that due to the low-frequency structural fluctuation trend surface f L (x,y) is mainly distributed in the larger wavelet coefficients, with medium and high frequency fluctuations f MH (x,y) is mainly distributed in the smaller wavelet coefficients.

[0094] In order to better reflect the frequency domain characteristics of the data, the expression of converting the wavelet scale into frequency in different directions is:

[0095]

[0096]

[0097] Among them, a is the scale factor, f x With f y is the actual frequency corresponding to the scale factor a, f s is the sampling interval of the equal depth data, f c is the center frequency of the wavelet function.

[0098] At the same time, since the high, medium and low frequency signals corresponding to structural fluctuations of different scales have different amplitudes, for scale factors of different values, the amplitude function related to frequency and wavelet coefficients is:

[0099]

[0100] Among them, f xi With f yi is the actual frequency corresponding to the scale factor a, w xi With w yi are the wavelet coefficients along different coordinate axes, i∈(1, 2, 3...N).

[0101] The threshold function expression for separating low-frequency, medium-frequency, and high-frequency structural trends is:

[0102]

[0103] in, and is the threshold function. Taking the value of a as a reference, by comparing the two constraints, the extraction threshold values ​​δ of low-frequency, medium-frequency, and high-frequency microstructures are determined, and the wavelet coefficients corresponding to high, medium, and low frequencies are calculated and retained.

[0104] Step S206, determining the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient;

[0105] Step S207, reconstructing using the initially identified microstructure major axis and microstructure minor axis to obtain the target data;

[0106] In the embodiment of the present invention, the low-frequency, medium-frequency and high-frequency components are reorganized by using the optimal wavelet coefficients.

[0107] In the specific implementation, the low, medium and high frequency functions corresponding to the optimal wavelet coefficients are:

[0108]

[0109]

[0110]

[0111] The optimized mid-low frequency and mid-high frequency functions corresponding to different scale factors are:

[0112]

[0113]

[0114] Among them, i∈(1, 2, 3...N), L, M, H are natural numbers.

[0115] Subsequently, the long and short axes of the initially identified microstructures are used to determine the optimal reconstruction of low-frequency and high-frequency functions.

[0116] Step S208, identifying the target data to obtain corresponding micro-structural features.

[0117] A threshold reconstruction micro-structure identification method based on wavelet transform provided in an embodiment of the present invention includes: obtaining equally spaced sampled two-dimensional data corresponding to equal depth data; performing two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced sampled two-dimensional data according to the two-dimensional wavelet transform formula to obtain wavelet coefficients corresponding to scales of different frequencies; sequentially screening wavelet coefficients from the wavelet coefficients corresponding to scales of different frequencies using threshold control to obtain preferred wavelet coefficients corresponding to scales of different frequencies; determining the target signal corresponding to the preferred wavelet coefficient, and reconstructing the target signal through an optimization function to obtain target data; identifying the target data to obtain the corresponding micro-structure feature. The equally spaced sampled two-dimensional data is equally spaced sampled using wavelet transform to establish a "scale-frequency" wavelet function. Based on the equally spaced sampled two-dimensional data, wavelet transform is performed to decompose the structural undulation in multiple scales, improve the recognition weight of the high-frequency component of the signal for the microstructure, and then the low-frequency structural background and high-frequency micro-structure undulation are obtained by threshold control of the preferred wavelet coefficient, eliminating the shielding effect of the low-frequency component of the structure on the microstructure identification, better retaining the medium and high frequency undulation information, and improving the microstructure identification accuracy and recognition efficiency.

[0118] To facilitate the understanding of the reliability of the micro-structure simulation data for verification of the present invention by those skilled in the art, a simulated structure line with 20m equal-spaced sampling on the horizontal axis and depth on the vertical axis is used to perform wavelet threshold micro-structure recognition analysis.

[0119] Wavelet transform is performed on it, and the wavelet coefficients are calculated using threshold control. After experimental comparison of the recognition effects of wavelet coefficients of different scales on low-frequency, medium-frequency and high-frequency fluctuations, further analysis is performed to determine the optimal recognition scale of structural fluctuations, and the optimal wavelet coefficients for micro-structure reconstruction are selected.

[0120] See also Figure 3 to Figure 6 , Figure 3 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 1000. Figure 4 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 800. Figure 5 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 600. Figure 6 This is a schematic diagram of the low-frequency fluctuation trend with a search step of 400. According to the terrain fluctuation range of the simulation data, that is, Figure 3 to Figure 6 The original data in the image is used to determine the corresponding search radius, and the corresponding low-frequency trend line is obtained by simulation using the radius, that is, Figure 3 to Figure 6The low-frequency trend in the slope. The comparison shows that the processing results corresponding to the search radius of 1000m and 800m can better reflect the low-frequency trend, among which the search radius of 800m has a better effect; the trend data corresponding to the search radius of 600m and 400m contain some medium-frequency information, resulting in the wide and gentle micro-structure information with medium-frequency characteristics being compounded in the low-frequency trend, which cannot better represent the overall slope trend. Comprehensive experimental analysis determines that the wavelet scale parameter of about 800m is suitable for the low-frequency information extraction of the model data, and the corresponding wavelet coefficients obtained can well reflect the slope trend.

[0121] See also Figure 7-8 , Figure 7 This is a schematic diagram of the frequency fluctuation trend with a search step of 400. Figure 8 This is a schematic diagram of the mid-frequency fluctuation trend with a search step of 200. Repeat the above ideas and processes to continuously separate and determine the mid-frequency and high-frequency wavelet coefficients. Figure 7 and Figure 8 The range of medium and high frequency terrain fluctuations, that is, the original data is processed with a test radius of 400m and 200m respectively. Figure 7 It can reflect the trend of medium frequency better and highlight the micro-structure with an axial width of about 400m; Figure 8 Since the search radius contains a lot of intermediate frequency information, it is not conducive to characterizing the micro-structure with wide and gentle axial direction. The experimental analysis determined that the wavelet scale parameter of about 400m is suitable for the extraction of intermediate frequency information of the simulation data, which can better retain the intermediate frequency and effectively highlight the micro-structure information with wide and gentle axial direction.

[0122] Finally, according to the analysis of the selected low-frequency, medium-frequency and high-frequency wavelet coefficients, the medium-low frequency and medium-high frequency signals are reconstructed using the optimization function. Fig. 9 and Fig.10 , Fig. 9 This is a schematic diagram of the reconstructed medium and low frequency fluctuation trend. Fig.10 This is a schematic diagram of reconstructing medium and high frequency slight fluctuations. Compare the original signal with the reconstructed result. Fig. 9 The original data has obvious peak and valley fluctuation information, while the medium and low frequency trend lines mainly reflect the slope trend and medium frequency trough information; and Fig.10 The data reconstructed using medium and high frequency wavelet coefficients show that different types of micro-amplitude structures with axial width, relatively wide, relatively short and amplitudes not exceeding 3 m are quantitatively identified.

[0123] By testing and analyzing the simulation data, and reconstructing the structural signal using wavelet transform and threshold-optimized wavelet coefficients, it is possible to extract characteristic information of micro-structural fluctuations of different scales, and separate the information of low-frequency slope background and micro-structure. The threshold function can effectively prevent the loss of micro-structures with low amplitude and axial width, which is an effective technical means to quantitatively describe and analyze micro-structures.

[0124] Embodiment 3: The embodiment of the present invention is applied to the identification and analysis of microstructures in the slope area of ​​the Oriente Basin in South America.

[0125] The slope belt of the Oriente Basin in South America has weak structural deformation, and a series of large structural traps and small micro-structural traps are developed on it. Micro-structural traps are one of the current exploration targets. The study area has developed micro-structures with 3-5m thin layers of sandstone. The structural amplitude is as follows: Fig.11 The micro-structural plane of the slope area and Fig.12 As shown in the characteristic map of the micro-structural profile in the slope area, the structural amplitude does not exceed 5ms. Since its amplitude is smaller than the accuracy of geological structure interpretation, conventional mapping and analysis methods are difficult to effectively highlight the micro-structures related to thin layers.

[0126] A series of micro-structures with low amplitude and small closure area are developed in this block. The closure height generally does not exceed 12m (8ms) of micro-structures, and the amplitude in some areas is less than 6m (4ms). Please refer to Fig.13 , Fig.13 This is a schematic diagram of the depth data of the M1 layer before reconstruction. It can be seen that its micro-structure characteristics are not obvious. Based on this, the wavelet transform micro-structure recognition technology is used to determine the amplitude of the scale factor corresponding to different frequencies through the pre-set amplitude function, and then combined with the pre-set threshold function expression, the medium and large scale structural fluctuation information is selected in turn, and the reconstruction is obtained. Fig.14 The schematic diagram of the reconstructed M1 layer depth data is shown in the figure. The medium and small scale wavelet transform optimal functions are repeatedly used to separate and reconstruct the medium and high frequency information, and the following is obtained: Fig.15 The schematic diagram of small and medium-scale local micro-structures is shown in Figure 1. Based on the results of two wavelet transforms and reconstructions, the large-scale slope background and local micro-structures are combined to obtain the following Fig.16 The multi-scale wavelet reconstruction shown. Fig.15 In the local micro-amplitude fluctuations, micro-structure information of different scales was identified, including 1.5-3m, 1.5-6m, 1.5-9m, 1.5-15m, etc., which can effectively characterize the micro-structure characteristics of less than 5m and quickly show the details of the micro-structure. The multiple micro-structures identified in the western part of the area are mainly small fluctuations with a wide and gentle shape less than 5m; while the amplitude identified in the eastern part is about 9m, which is a micro-structure reservoir that has been confirmed by drilling. Please refer to Fig.16 The number, amplitude and range of microstructures identified are basically consistent with the quantitative identification results. Therefore, the multi-scale wavelet reconstruction method can not only eliminate the influence of the macrostructural background, but also quickly display the details of microstructures, and efficiently realize the quantitative identification of microstructures and the mapping analysis of microstructures.

[0127] Based on the geological achievements and knowledge on reservoir prediction and accumulation, it is believed that low-amplitude structural reservoirs are developed in this slope area. The micro-amplitude structural circle where Well X01 is located is selected as the drilling target. The cross section of the micro-amplitude structural high point is shown in the figure below. Fig.17 Schematic diagram of the northeast-trending microstructural section and Fig.18 The well encountered 4m thin sandstone in the M1 layer and obtained industrial oil flow. The drilling confirmed that the trap is a thin sandstone reservoir with a gentle oil-water transition zone and a micro-structural reservoir.

[0128] Example 4, please refer to Fig.19 , Fig.19 The structural block diagram of an embodiment of a threshold reconstruction micro-structure identification device based on wavelet transform of the present invention comprises:

[0129] An acquisition module 501 is used to acquire equally spaced sampled two-dimensional data corresponding to equal depth data;

[0130] Transformation module 502, used for performing two-dimensional wavelet transformation along the horizontal and vertical coordinate directions of the equally spaced two-dimensional data according to the two-dimensional wavelet transformation formula to obtain wavelet coefficients corresponding to scales of different frequencies;

[0131] A screening module 503 is used to sequentially screen the wavelet coefficients of the wavelet coefficients of different frequency corresponding scales by using threshold control to obtain the preferred wavelet coefficients of the wavelet coefficients of different frequency corresponding scales;

[0132] A reconstruction module 504 is used to determine the target signal corresponding to the preferred wavelet coefficients, and reconstruct the target signal through an optimization function to obtain target data;

[0133] The identification module 505 is used to identify the target data and obtain corresponding micro-structural features.

[0134] In an optional embodiment, the acquisition module 501 includes:

[0135] An acquisition submodule, used for acquiring the isobath data and converting the isobath data into a two-dimensional data set;

[0136] The equally spaced sampling two-dimensional data sampling submodule is used to equally spaced sample the two-dimensional data set according to a linear interpolation formula to obtain equally spaced sampled two-dimensional data.

[0137] In an optional embodiment, the screening module 503 includes:

[0138] The amplitude determination submodule is used to determine the amplitude of the scale factor corresponding to different frequencies through a preset amplitude function;

[0139] The extraction submodule is used to extract the preferred wavelet coefficients of different frequency corresponding scales in sequence based on the amplitude and in combination with a preset threshold function expression.

[0140] In an optional embodiment, the reconstruction module 504 includes:

[0141] A function determination submodule, used to determine the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient;

[0142] The reconstruction submodule is used to reconstruct the target data by using the microstructure major axis and microstructure minor axis identified initially.

[0143] An embodiment of the present invention also provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the wavelet transform-based threshold reconstruction micro-structure identification method as described in any of the above embodiments.

[0144] An embodiment of the present invention further provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by the processor, the steps of the method for identifying micro-structures by threshold reconstruction based on wavelet transform as described in any of the above embodiments are implemented.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] In the several embodiments provided in the present application, it should be understood that the methods, devices, electronic devices and storage media disclosed in the present invention can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a readable storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0150] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A threshold reconstruction micro-structure recognition method based on wavelet transform, characterized in that: include: Obtain equally spaced sampled two-dimensional data corresponding to equal depth data; Performing a two-dimensional wavelet transform along the horizontal and vertical coordinate directions of the equally spaced two-dimensional data according to a two-dimensional wavelet transform formula to obtain wavelet coefficients of scales corresponding to different frequencies; Selecting wavelet coefficients from the wavelet coefficients of different frequency corresponding scales by using threshold control in turn to obtain preferred wavelet coefficients of different frequency corresponding scales; Determine the target signal corresponding to the preferred wavelet coefficient, and reconstruct the target signal through an optimization function to obtain target data; The target data is identified to obtain corresponding micro-structural features.

2. The method for identifying micro-structures by threshold reconstruction based on wavelet transform according to claim 1 is characterized in that: Obtain equally spaced sampled two-dimensional data corresponding to equal depth data, including: Acquire the bathymetric data and convert the bathymetric data into a two-dimensional data set; In the two-dimensional data set, sampling is performed at equal intervals according to a linear interpolation formula to obtain two-dimensional data with equal intervals.

3. The method for identifying micro-structures by threshold reconstruction based on wavelet transform according to claim 1, characterized in that: The wavelet coefficients of different frequency corresponding scales are screened by using threshold control in turn to obtain the preferred wavelet coefficients of different frequency corresponding scales, including: By means of a preset amplitude function, the amplitudes of the scale factors corresponding to different frequencies are determined; Based on the amplitude and in combination with a preset threshold function expression, the preferred wavelet coefficients corresponding to different frequency scales are extracted in sequence.

4. The method for identifying micro-structures by threshold reconstruction based on wavelet transform according to claim 3 is characterized in that: Determining a target signal corresponding to the preferred wavelet coefficients, and reconstructing the target signal through an optimization function to obtain target data, including: Determining the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient; The target data is obtained by reconstructing the microstructure major axis and microstructure minor axis identified initially.

5. A threshold reconstruction micro-structure identification device based on wavelet transform, characterized in that: include: An acquisition module is used to acquire equally spaced sampled two-dimensional data corresponding to equal depth data; A transformation module is used to perform a two-dimensional wavelet transformation along the horizontal and vertical coordinate directions of the two-dimensional data sampled at equal intervals according to a two-dimensional wavelet transformation formula to obtain wavelet coefficients of scales corresponding to different frequencies; A screening module is used to sequentially screen the wavelet coefficients of the wavelet coefficients of different frequency corresponding scales by using threshold control to obtain the preferred wavelet coefficients of the wavelet coefficients of different frequency corresponding scales; A reconstruction module, used to determine the target signal corresponding to the preferred wavelet coefficients, and reconstruct the target signal through an optimization function to obtain target data; The recognition module is used to recognize the target data and obtain the corresponding micro-structural features.

6. The threshold reconstruction micro-structure identification device based on wavelet transform according to claim 5 is characterized in that: The acquisition module comprises: An acquisition submodule, used for acquiring the isobath data and converting the isobath data into a two-dimensional data set; The equally spaced sampling two-dimensional data sampling submodule is used to equally spaced sample the two-dimensional data set according to a linear interpolation formula to obtain equally spaced sampled two-dimensional data.

7. The threshold reconstruction micro-structure identification device based on wavelet transform according to claim 5 is characterized in that: The screening module comprises: The amplitude determination submodule is used to determine the amplitude of the scale factor corresponding to different frequencies through a preset amplitude function; The extraction submodule is used to extract the preferred wavelet coefficients of different frequency corresponding scales in sequence based on the amplitude and in combination with a preset threshold function expression.

8. The threshold reconstruction micro-structure identification device based on wavelet transform according to claim 7 is characterized in that: The reconstruction module includes: A function determination submodule, used to determine the optimized low-mid frequency function and high-mid frequency function based on the scale factor and the preferred wavelet coefficient; The reconstruction submodule is used to reconstruct the target data by using the microstructure major axis and microstructure minor axis identified initially.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 4 is executed.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is executed.

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