Microseismic event cloud crack interpretation method based on Gaussian distribution

Abstract

The invention provides a micro-seismic event cloud crack interpretation method based on Gaussian distribution. The method comprises the following steps: step 1, fitting a straight line by using coordinates of a micro-seismic event point as the trend of a fractured well; 2, solving a covariance matrix of the two-dimensional data of the coordinate position of the microseism event point, and obtaining a covariance matrix meeting the fracture development direction according to the relationship between the fractured well and the fracture; 3, determining parameters of a two-dimensional Gaussian distribution probability function; step 4, carrying out normalization processing on the vibration intensity data of all the microseismic event points and substituting the data into the function; and step 5, superposing the probability distributions of all seismic event points to obtain a fracture distribution law diagram. The result of the microseismic event cloud crack interpretation method based on Gaussian distribution has accuracy and verifiability, probability distribution is described through a Gaussian function, a crack distribution rule conforming to the rule is obtained, the calculation efficiency is high, the result is accurate, and meanwhile the requirement of crack interpretation work can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic interpretation of microseismic event cracks, and in particular to a microseismic event cloud crack interpretation method based on Gaussian distribution. Background Art

[0002] Currently, various traditional fracture interpretation methods rely primarily on the geometric distribution of microseismic event points to estimate parameters such as fracture length, width, height, and fracture volume. These methods have significant limitations and are difficult to describe complex fractures within the fracture range. Therefore, microseismic fracture interpretation methods need to be explored in new areas.

[0003] The Chinese patent application with application number CN202010009416.5 involves a method for inverting the distribution of fractures in tight oil and gas reservoirs assisted by microseismic data. The present invention uses the Hough transform to transform microseismic data into the Hough space, obtaining the Hough space function to form the initial mean of the Hough space random field; uses the Hough inverse transform to generate a sample of the fracture distribution in the physical space; predicts the production prediction data corresponding to the sample of the fracture distribution in the physical space through numerical simulation; uses algebraic statistics to obtain the correlation matrix between the Hough space random field and the production prediction data; utilizes the correlation matrix to maximize the posterior probability of the Hough space random field using an iterative algorithm based on a sample set, thereby forming a fracture distribution inversion method; the invention can improve the accuracy of the inversion of the fracture distribution in tight oil and gas reservoirs and the production prediction of development wells.

[0004] In the Chinese patent application with application number: CN202110190969.X, a method for predicting the length of hydraulic fractures, a method and apparatus for modeling fracture networks are involved. The prediction method includes: S1, obtaining the spatiotemporal distribution of microseismic events, and determining the fracture network connection criteria based on the source mechanism information of the microseismic events, performing hydraulic fracture interpretation to generate a hydraulic fracture network model, and obtaining the position and fracture length of the main fracture and / or branch fracture; S2, on the basis of generating the hydraulic fracture network model, extracting the three-dimensional seismic attributes of the main fracture and / or branch fracture event points; and S3, on the basis of completing the three-dimensional seismic attribute extraction, using Bayesian theorem to predict the length of hydraulic fractures. This disclosure can accurately describe complex hydraulic fracture networks, and not only has a good explanation for the areas where microseismic events occur, but can also be extrapolated to other areas without microseismic events, providing substantial guidance for the next step of fracturing operations.

[0005] Chinese patent application number CN201410132524.6 discloses a method for inverting reservoir geomechanical parameters based on microseismic events. The method includes the following steps: 1. Continuing the microseismic event cloud using a Gaussian distribution function; 2. Selecting a prediction model to obtain predicted microseismic events and current reservoir geomechanical parameters; 3. Obtaining the prediction error distribution and calculating the Kalman filter factor; 4. Updating the reservoir geomechanical parameters; and 5. Updating data for all time periods to update the initial geomechanical parameters and obtain the actual reservoir geomechanical parameters. This method can establish an accurate underground reservoir geomechanical model, understand the development and distribution of underground fractures, and accurately guide fracturing operations during development, effectively reducing development costs, improving development efficiency, and increasing production.

[0006] The Chinese patent application, application number CN201710915453.0, relates to a method for real-time monitoring of the dynamic distribution of ground microseismic fractures. The method includes: acquiring continuous microseismic data, gridding the fractured area, and establishing a block-like high-precision velocity model; calculating the travel time, azimuth angle, and exit angle of the grid points; performing frequency band processing using a spectral decomposition method to calculate the three-component waveform covariance matrix and linear polarization characteristics of the three frequency band data; performing polarization filtering on the frequency band data based on the linear polarization characteristics; selecting grid points, performing vector migration imaging on the polarization-filtered microseismic data, and obtaining the imaging energy value of the grid point; and treating areas with gradient descent values ​​greater than a pre-set threshold as areas with dense fractures, thereby obtaining an image of the fracture distribution within that time period. This method for real-time monitoring of the dynamic distribution of ground microseismic fractures is highly effective for surface microseismic monitoring and can monitor the dynamic development of microcracks.

[0007] The above existing technologies are significantly different from the present invention and fail to solve the technical problem we want to solve. Therefore, we have invented a new microseismic event cloud fracture interpretation method based on Gaussian distribution. Summary of the Invention

[0008] The purpose of the present invention is to provide a microseismic event cloud fracture interpretation method based on Gaussian distribution, which has reliable method principle, innovative interpretation method, simple operation and high efficiency.

[0009] The object of the present invention can be achieved by the following technical measures: a microseismic event cloud fracture interpretation method based on Gaussian distribution, the microseismic event cloud fracture interpretation method based on Gaussian distribution includes:

[0010] Step 1: Use the coordinates of the microseismic event points to fit a straight line as the direction of the fracturing well;

[0011] Step 2: Calculate the covariance matrix of the two-dimensional data of the microseismic event point coordinates, and obtain the covariance matrix that satisfies the fracture development direction based on the relationship between the fracturing well and the fracture;

[0012] Step 3, determining the parameters of the two-dimensional Gaussian distribution probability function;

[0013] Step 4: normalize the vibration intensity data of all microseismic event points and substitute them into the function;

[0014] Step 5: Superimpose the probability distributions of all earthquake event points to obtain a crack distribution pattern map.

[0015] The purpose of the present invention can also be achieved by the following technical measures:

[0016] In step 1, a linear fit is performed using the two-dimensional data of the known coordinate positions of the microseismic event points, and the direction of the fitted line is considered to be the direction of the fracturing well.

[0017] In step 1, select the coordinate position of the microseismic event point (x i ,y i ) as input, in order to make the curve fitting effect the best, the least squares curve fitting method is used to make the error r in the given function class Φ i =p(x i )-y i The sum of squares of (i=0,1...,m) is the smallest, that is,

[0018]

[0019] The function p(x) is called the fitting function, and the polynomial expression of the fitting function is:

[0020] p(x)=p1x n +p2x n-1 +...+p n x+p n+1 .

[0021] In step 1, the first-order least squares method, i.e., linear fitting algorithm, is used, and the expression of the fitting function is:

[0022] p(x)=ax+b

[0023] Where a is the slope of the fitting function and b is the intercept of the fitting function.

[0024] The final fitting straight line is used as the trend of the fracturing well and is represented in the microseismic event point location and vibration intensity distribution diagram.

[0025] In step 2, the covariance matrix of the two-dimensional data of the microseismic event point coordinate position is obtained, and the covariance matrix that satisfies the fracture development direction is obtained according to the positional relationship between the strike of the fracturing well and the fracture development direction.

[0026] In step 2, each element of the covariance matrix is ​​the covariance between the elements of each vector. The covariance is defined as a measure of the degree to which each dimension deviates from its mean:

[0027]

[0028] Where (x i ,y i ) is the coordinate position of the microseismic event point, is the average value of the horizontal coordinates of the microseismic event points, is the average value of the vertical coordinates of the microseismic event points, and n represents the number of microseismic event points.

[0029] Select the two-dimensional coordinate position of the microseismic event point (x i ,y i ) as input to obtain its covariance matrix C:

[0030]

[0031] Among them C 11 =E[x i -E(x i )] 2 , C 12 =E[x i -E(x i )][y i -E(y i )],

[0032] C 21 =E[y i -E(y i )][x i -E(y i )],C 22 =E[y i -E(y i )] 2 , E is the identity matrix.

[0033] In step 2, the strike of the fracturing well and the normal direction of the fracture development satisfy a perpendicular relationship. Therefore, the covariance matrix C describing the strike of the fracturing well and the perpendicular relative relationship can be used to obtain the covariance matrix C' that satisfies the fracture development:

[0034]

[0035] In step 3, the obtained covariance matrix is ​​used to determine the variance and correlation coefficient of the corresponding two-dimensional Gaussian distribution; the coordinate position of the microseismic event point determines the mean of the two-dimensional Gaussian distribution.

[0036] In step 3, the relationship between the covariance matrix of the two-dimensional random variable and the variance and correlation coefficient of the corresponding Gaussian distribution is:

[0037]

[0038] Where μ1 and μ2 are the means of the two-dimensional Gaussian distribution, σ1 and σ2 are the variances of the two-dimensional Gaussian distribution, and ρ is the correlation coefficient of the two-dimensional Gaussian distribution.

[0039] In this way, a two-dimensional Gaussian distribution density function that satisfies the crack development conditions is obtained:

[0040]

[0041] In step 4, the vibration intensity data of all microseismic event points are normalized, and the processed data is substituted into the equation as the maximum value of the two-dimensional Gaussian distribution to obtain a Gaussian function description of the vibration intensity around the event point.

[0042] In step 4, normalization is to change a column of data to a fixed interval (range), which is a decimal between [0, 1] or (-1, 1). For the convenience of data processing, mapping the data to the range of 0 to 1 is more convenient and fast. The normalization formula can be expressed as:

[0043]

[0044] Where (x i ,y i ) is the coordinate position of the microseismic event point, x min is the maximum value of the horizontal coordinate of the microseismic event point, y min is the minimum value of the vertical coordinate of the microseismic event point.

[0045] The output range of the normalized vibration intensity data of the microseismic event point is controlled between 0 and 1.

[0046] In step 5, the coordinate positions of the microseismic event points (x i ,y i ) Input the two-dimensional Gaussian distribution density function, superimpose the probability distribution of all earthquake event points, and finally form a crack distribution law map.

[0047] The purpose of the present invention can also be achieved through the following technical measures: a microseismic event cloud fracture interpretation system based on Gaussian distribution, which uses a microseismic event cloud fracture interpretation method based on Gaussian distribution to describe complex fractures within the fracturing range.

[0048] The microseismic event cloud crack interpretation method based on Gaussian distribution in the present invention uses the coordinate position of the known microseismic event point to perform data fitting to determine the direction of the fracturing well, and combines the corresponding fracturing information to determine the direction of the crack development; for the vibration intensity of different microseismic event points, the probability density function of the two-dimensional Gaussian distribution is used, and the vibration intensity of the event point is normalized to meet the corresponding probability density function (wherein the parameters such as the distribution, shape, mean, variance, and correlation coefficient of the probability density function of the two-dimensional Gaussian distribution are determined by the location and direction of the crack development). The vibration intensity of all seismic event points is described by the Gaussian function in turn, and the overall superposition effect of the probability distribution description can highlight the crack distribution law. Finally, a microseismic event cloud crack interpretation technology based on Gaussian distribution is formed, and a crack distribution law map is obtained.

[0049] The microseismic event cloud fracture interpretation technology based on Gaussian distribution has incomparable advantages over traditional microseismic fracture interpretation technology. Its specific advantages and characteristics are reflected in the following aspects:

[0050] First, the reliability of the method's principles. The two-dimensional Gaussian distribution used is a highly important probability distribution in mathematics, physics, and engineering. Because of its many beautiful properties, this distribution function has a significant impact on many areas of statistical science and discrete science. Using this method for data analysis ensures that the principles are known, and the results are accurate and verifiable.

[0051] Second, the interpretation method is innovative. Traditional microseismic fracture interpretation techniques only interpret each individual microseismic event point. Each event point represents only one individual fracture, and the connections between different event points are poorly described. This method leverages the properties of a two-dimensional Gaussian distribution to innovatively treat microseismic event points as probability distribution events. The probability of fractures around an event point decreases with distance from the event point. This probability distribution is described by a Gaussian function, resulting in a consistent fracture distribution pattern.

[0052] Third, the operation is simple and efficient. Conventional calculation formulas are used in the processing and calculation of experimental data, which has high calculation efficiency, accurate results and can meet the requirements of fracture interpretation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1Flow chart of a specific embodiment of the microseismic event cloud fracture interpretation method based on Gaussian distribution of the present application;

[0054] Figure 2 Microseismic event point position and vibration intensity size distribution graph (work area A) in a specific embodiment of the present application;

[0055] Figure 3 Two-dimensional Gaussian distribution conditional probability density function curve three-dimensional graph (work area A) in a specific embodiment of the present application

[0056] Figure 4 Two-dimensional Gaussian distribution conditional probability density function curve plane graph (work area A) in a specific embodiment of the present application;

[0057] Figure 5 Fracture distribution law graph (work area A) based on Gaussian distribution in a specific embodiment of the present application;

[0058] Figure 6 Microseismic event point position and vibration intensity size distribution graph (work area B) in a specific embodiment of the present application;

[0059] Figure 7 Two-dimensional Gaussian distribution conditional probability density function curve three-dimensional graph (work area B) in a specific embodiment of the present application;

[0060] Figure 8 Two-dimensional Gaussian distribution conditional probability density function curve plane graph (work area B) in a specific embodiment of the present application;

[0061] Figure 9 Fracture distribution law graph (work area B) based on Gaussian distribution in a specific embodiment of the present application.

[0062] Figure 10 Microseismic event point position and vibration intensity size distribution graph (work area C) in a specific embodiment of the present application;

[0063] Figure 11 Two-dimensional Gaussian distribution conditional probability density function curve three-dimensional graph (work area C) in a specific embodiment of the present application;

[0064] Figure 12 Two-dimensional Gaussian distribution conditional probability density function curve plane graph (work area C) in a specific embodiment of the present application;

[0065] Figure 13 Fracture distribution law graph (work area C) based on Gaussian distribution in a specific embodiment of the present application.

[0066] Figure 14A microseismic event point position and vibration intensity size distribution graph (work area D) in a specific embodiment of the present application;

[0067] Figure 15 A three-dimensional graph of a two-dimensional Gaussian distribution conditional probability density function curve (work area D) in a specific embodiment of the present application;

[0068] Figure 16 A planar graph of a two-dimensional Gaussian distribution conditional probability density function curve (work area D) in a specific embodiment of the present application;

[0069] Figure 17 A fracture distribution law graph based on Gaussian distribution (work area D) in a specific embodiment of the present application. DETAILED DESCRIPTION

[0070] It should be noted that the following detailed description is merely exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0071] It is also important to note that the terms used herein are not intended to limit the exemplary embodiments of the present application, unless otherwise explicitly provided. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, and / or groups thereof.

[0072] The distribution position and vibration energy of microseismic event points have certain relations with cracks, and reservoir properties also have great influences on the properties of microseismic event points. The observed microseismic event points have certain probability distribution rules, and the Gaussian function has certain probability distribution description functions. Therefore, the microseismic event cloud crack interpretation method based on Gaussian distribution is proposed to solve the problems of low precision and human interference of the microseismic event crack automatic interpretation method, and the precision of crack description can be effectively improved, and the microseismic crack interpretation work is more suitable. Under the same reservoir geological conditions, the crack probability represented by the microseismic event is equivalent, and an event point does not only represent an independent crack, but represents the position of the crack with strong vibration. Therefore, the microseismic event point is a probability distribution event, and the event point position is the position of the existing crack, and the probability of the crack around the event point gradually decreases with the distance from the event point position. The probability distribution can be described by the Gaussian function, the overall superposition effect of the probability distribution description can highlight the crack distribution rule, so that the crack length, width, height and crack azimuth information can be described, and the calculation of the fracturing volume is also easier to be statistically processed. In addition to the description of the overall information of the fracturing section, the responsible cracks in the fracturing range can be more clearly described, and the method is suitable for the industrial production of microseismic event point crack interpretation.

[0073] The hydraulic fracturing method forms a fracturing crack, improves the reservoir seepage condition, effectively solves the reservoir plugging problem, is an important measure to optimize the well deployment scheme, and therefore has been widely concerned and applied at home and abroad. The research on the crack is always accompanied by the development process of the microseismic monitoring, and is an important research direction of the microseismic monitoring technology.

[0074] Figure 1 The flow chart of the specific embodiment of the microseismic event cloud crack interpretation method based on Gaussian distribution; the microseismic event cloud crack interpretation method based on Gaussian distribution comprises:

[0075] Step one, linear fitting is carried out by using the two-dimensional data of the known microseismic event point coordinate position, and the direction of the obtained fitting straight line is considered as the strike of the fracturing well: the coordinate position (x i ,y i ) of the microseismic event point is selected as the input, in order to make the curve fitting effect best, the least square curve fitting method is used, and the square sum of the error r i =p(x i )-y i (i=0,1...,m) is minimum in the determined function class Φ, that is

[0076]

[0077] The function p(x) is called the fitting function, and the polynomial expression of the fitting function is:

[0078] p(x)=p1x n +p2x n-1 +...+p n x+p n+1

[0079] All examples in this patent use the first-order least squares method, i.e., the linear fitting algorithm, and the fitting function is expressed as:

[0080] p(x)=ax+b

[0081] The final fitting straight line is used as the trend of the fracturing well and is represented in the microseismic event point location and vibration intensity distribution diagram.

[0082] Step 2: Obtain the covariance matrix of the two-dimensional data of the microseismic event point coordinates. Based on the positional relationship between the strike of the fracturing well and the direction of fracture development, obtain the covariance matrix that satisfies the fracture development direction: In statistics and probability theory, each element of the covariance matrix is ​​the covariance between each vector element. Covariance is defined as a measure of the degree to which each dimension deviates from its mean:

[0083]

[0084] Select the two-dimensional coordinate position of the microseismic event point (x i ,y i ) as input to obtain its covariance matrix C:

[0085]

[0086] All examples in this patent assume that the strike of the fracturing well and the normal direction of the fracture development satisfy a perpendicular relationship. Therefore, the covariance matrix C describing the strike of the fracturing well and the perpendicular relative relationship can be used to obtain the covariance matrix C' that satisfies the fracture development:

[0087]

[0088] Step 3: Use the obtained covariance matrix to determine the variance and correlation coefficient of the corresponding two-dimensional Gaussian distribution; the coordinate position of the microseismic event point determines the mean of the two-dimensional Gaussian distribution:

[0089] The relationship between the covariance matrix of a two-dimensional random variable and the variance and correlation coefficient of the corresponding Gaussian distribution is:

[0090]

[0091] In this way, a two-dimensional Gaussian distribution density function that satisfies the crack development conditions is obtained:

[0092]

[0093] Where μ1 and μ2 are the means of the two-dimensional Gaussian distribution, σ1 and σ2 are the variances of the two-dimensional Gaussian distribution, and ρ is the correlation coefficient of the two-dimensional Gaussian distribution.

[0094] Step 4: Normalize the vibration intensity data of all microseismic event points and substitute the processed data into the equation as the maximum value of the two-dimensional Gaussian distribution to obtain the Gaussian function description of the vibration intensity around the event point:

[0095] Normalization is the process of converting a column of data to a fixed interval (range), usually a decimal between [0, 1] or (-1, 1). This is primarily for the convenience of data processing. Mapping data to the range of 0 to 1 makes processing faster and more convenient. The normalization formula can be expressed as:

[0096]

[0097] All examples in this patent control the output range of the normalized vibration intensity data of the microseismic event point to be between 0 and 1.

[0098] Step 5: The coordinate positions of the microseismic event points (x i ,y i ) Input the two-dimensional Gaussian distribution density function, superimpose the probability distribution of all earthquake event points, and finally form a crack distribution pattern map.

[0099] In order to verify the microseismic event cloud fracture interpretation method based on Gaussian distribution and its processing effect, the following analysis is conducted by taking the data processing of the corresponding vibration intensity of work areas A, B, C, and D as an example.

[0100] The following are several specific embodiments of the present invention:

[0101] Example 1

[0102] First, all the microseismic event point data are sorted and the horizontal and vertical coordinate values ​​are used as two-dimensional data for linear data fitting. Figure 2 The figure shows the location distribution and vibration intensity of all microseismic event points. The vibration intensity at different locations in the figure is different. The color and size of the dots represent different vibration intensities. The straight line fitted by the coordinate data is shown in the figure and can be considered as the direction of the fracturing well.

[0103] The covariance matrix is ​​obtained using the two-dimensional data of the horizontal and vertical coordinates. According to the positional relationship between the strike of the fracturing well and the direction of fracture development, the covariance matrix A that satisfies the fracture development direction is obtained.

[0104]

[0105] The relationship between the covariance matrix of two-dimensional random variables and the variance and correlation coefficient of the corresponding Gaussian distribution is known as:

[0106]

[0107] The variance of the corresponding two-dimensional Gaussian distribution can be obtained as p = 0.7364, the mean as s1 = 1.5186, s2 = 4.8332; the position coordinates of each microseismic event point as the mean of the two-dimensional Gaussian distribution, i.e. m1 and m2, so that the Gaussian distribution probability density function for describing the fracture development can be obtained. As shown in Figure 3 and Figure 4 The three-dimensional graph and the planar graph of the two-dimensional Gaussian distribution conditional probability density function curve with the mean of 0 (the coordinate position is (0, 0)) are shown.

[0108] Since the integral of the two-dimensional Gaussian distribution conditional probability density function is 1, the higher the probability distribution curve, the smaller the distribution on the entire function plane. Therefore, the vibration intensity of all microseismic event points is normalized, and the processed data is taken as the maximum value of the two-dimensional Gaussian distribution conditional probability density function curve to be brought into the equation, and the probability distribution of each microseismic point is obtained in turn, and the overall superposition effect can highlight the fracture distribution rule, as shown in Figure 5 is the final fracture distribution rule based on the Gaussian distribution.

[0109] Example 2

[0110] The same principle is used to analyze the microseismic event points of the work area B, as shown in Figure 6 The position and vibration intensity size distribution graph of all microseismic event points of the work area B is shown. The covariance matrix is obtained by using the two-dimensional data of the horizontal and vertical coordinates, and the covariance matrix B that meets the fracture development direction is obtained according to the position relationship between the fracture development direction and the fracture development direction of the fracturing well.

[0111]

[0112] The relationship between the covariance matrix of two-dimensional random variables and the variance and correlation coefficient of the corresponding Gaussian distribution is known as:

[0113]

[0114] The variance of the corresponding two-dimensional Gaussian distribution can be obtained as p = 0.7364, the mean as s1 = 1.5186, s2 = 4.8332; the position coordinates of each microseismic event point as the mean of the two-dimensional Gaussian distribution, i.e. m1 and m2, so that the Gaussian distribution probability density function for describing the fracture development can be obtained. As shown in Figure 7 and Figure 8The two-dimensional Gaussian distribution conditional probability density function curve three-dimensional graph and plane graph when the mean is 0 (the coordinate position is (0, 0)) are shown:

[0115] The vibration intensity of all microseismic event points is normalized, and the processed data is taken as the maximum value of the two-dimensional Gaussian distribution conditional probability density function curve to bring into the equation, and the probability distribution of each microseismic point is obtained in turn, and the overall superposition effect can highlight the fracture distribution rule, Figure 9 The fracture distribution rule graph of the work area B based on the Gaussian distribution is finally obtained.

[0116] Example 3

[0117] The same principle is used to analyze the microseismic event points of the work area C, as shown in Figure 10 The two-dimensional data of the horizontal and vertical coordinates are used to obtain the covariance matrix, and the covariance matrix D that meets the fracture development direction is obtained according to the position relationship between the fracture development direction and the fracture development direction of the fracturing well.

[0118]

[0119] The relationship between the covariance matrix of a two-dimensional random variable and the variance and correlation coefficient of the corresponding Gaussian distribution is known as:

[0120]

[0121] The variance ρ corresponding to the two-dimensional Gaussian distribution can be obtained as -0.6107, and the mean σ1 and σ2 are 1.2684 and 8.2739, respectively; the position coordinates of each microseismic event point are taken as the means μ1 and μ2 of the two-dimensional Gaussian distribution, and thus the Gaussian distribution probability density function for describing the fracture development can be obtained. As shown in Figure 11 and Figure 12 The two-dimensional Gaussian distribution conditional probability density function curve three-dimensional graph and plane graph when the mean is 0 (the coordinate position is (0, 0)) are shown:

[0122] The vibration intensity of all microseismic event points is normalized, and the processed data is taken as the maximum value of the two-dimensional Gaussian distribution conditional probability density function curve to bring into the equation, and the probability distribution of each microseismic point is obtained in turn, and the overall superposition effect can highlight the fracture distribution rule, Figure 13 The fracture distribution rule graph of the work area C based on the Gaussian distribution is finally obtained.

[0123] Example 4

[0124] The same principle is used to analyze the microseismic event points of the work area D, as shown in Figure 14The figure shows the location and intensity distribution of all microseismic events in work area D. Using the two-dimensional data of the horizontal and vertical coordinates, the covariance matrix is ​​calculated. Based on the positional relationship between the strike of the fracturing well and the direction of fracture development, the covariance matrix E that satisfies the fracture development direction is obtained.

[0125]

[0126] The relationship between the covariance matrix of a known two-dimensional random variable and the variance and correlation coefficient of the corresponding Gaussian distribution is:

[0127]

[0128] The variance ρ of the corresponding two-dimensional Gaussian distribution can be obtained as 0.3597, and the mean σ1=1.0761, σ2=4.3855; the position coordinates of each microseismic event point are used as the mean μ1 and μ2 of the two-dimensional Gaussian distribution, from which the Gaussian distribution probability density function used to describe the development of fractures can be obtained. Figure 15 and Figure 16 What is shown is the three-dimensional graph and plane graph of the two-dimensional Gaussian distribution conditional probability density function curve when the mean is 0 (coordinate position is (0,0)):

[0129] The vibration intensity of all microseismic event points is normalized, and the processed data is substituted into the equation as the maximum value of the two-dimensional Gaussian distribution conditional probability density function curve. The probability distribution description of each microseismic point is obtained in turn, and the overall superposition effect can highlight the crack distribution law. Figure 17 This is the final crack distribution diagram of work area D based on Gaussian distribution.

[0130] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0131] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A microseismic event cloud crack interpretation method based on Gaussian distribution, characterized by: The microseismic event cloud crack interpretation method based on Gaussian distribution includes: Step 1: Use the coordinates of the microseismic event points to fit a straight line as the direction of the fracturing well; Step 2: Calculate the covariance matrix of the two-dimensional data of the microseismic event point coordinates, and obtain the covariance matrix that satisfies the fracture development direction based on the relationship between the fracturing well and the fracture; Step 3, determining the parameters of the two-dimensional Gaussian distribution probability function; Step 4: normalize the vibration intensity data of all microseismic event points and substitute them into the function; Step 5: Superimpose the probability distributions of all earthquake event points to obtain a crack distribution pattern map.

2. The microseismic event cloud fracture interpretation method based on Gaussian distribution according to claim 1 is characterized in that: In step 1, a linear fit is performed using the two-dimensional data of the known coordinate positions of the microseismic event points, and the direction of the fitted line is considered to be the direction of the fracturing well.

3. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 2 is characterized in that: In step 1, select the coordinate position of the microseismic event point (x i ,y i ) as input, in order to make the curve fitting effect the best, the least squares curve fitting method is used to make the error r in the given function class Φ i =p(x i )-y i The sum of squares of (i=0,1...,m) is the smallest, that is, The function p(x) is called the fitting function, and the polynomial expression of the fitting function is: p(x)=p1x n +p2x n-1 +...+p n x+p n+1 。 4. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 3 is characterized in that: In step 1, the first-order least squares method, i.e., linear fitting algorithm, is used, and the expression of the fitting function is: p(x)=ax+b Where a is the slope of the fitting function, and b is the intercept of the fitting function; the final fitting straight line is used as the trend of the fracturing well and is represented in the microseismic event point location and vibration intensity distribution diagram.

5. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 1 is characterized in that: In step 2, the covariance matrix of the two-dimensional data of the microseismic event point coordinate position is obtained, and the covariance matrix that satisfies the fracture development direction is obtained according to the positional relationship between the strike of the fracturing well and the fracture development direction.

6. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 5, characterized in that: In step 2, each element of the covariance matrix is ​​the covariance between the elements of each vector. The covariance is defined as a measure of the degree to which each dimension deviates from its mean: Where (x i ,y i ) is the coordinate position of the microseismic event point, is the average value of the horizontal coordinates of the microseismic event points, is the average value of the vertical coordinates of the microseismic event points, n represents the number of microseismic event points; the two-dimensional coordinate position (x i ,y i ) as input to obtain its covariance matrix C: Among them C 11 =E[x i -E(x i )] 2 , C 12 =E[x i -E(x i )][y i -E(y i )],C 21 =E[y i -E(y i )][x i -E(y i )],C 22 =E[y i -E(y i )] 2 , E is the identity matrix.

7. The microseismic event cloud fracture interpretation method based on Gaussian distribution according to claim 6, characterized in that: In step 2, the strike of the fracturing well and the normal direction of the fracture development satisfy a perpendicular relationship. Therefore, the covariance matrix C describing the strike of the fracturing well and the perpendicular relative relationship can be used to obtain the covariance matrix C' that satisfies the fracture development:

8. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 7 is characterized in that: In step 3, the obtained covariance matrix is ​​used to determine the variance and correlation coefficient of the corresponding two-dimensional Gaussian distribution; the coordinate position of the microseismic event point determines the mean of the two-dimensional Gaussian distribution.

9. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 8, characterized in that: In step 3, the relationship between the covariance matrix of the two-dimensional random variable and the variance and correlation coefficient of the corresponding Gaussian distribution is: Where μ1 and μ2 are the means of the two-dimensional Gaussian distribution, σ1 and σ2 are the variances of the two-dimensional Gaussian distribution, and ρ is the correlation coefficient of the two-dimensional Gaussian distribution; In this way, a two-dimensional Gaussian distribution density function that satisfies the crack development conditions is obtained:

10. The microseismic event cloud fracture interpretation method based on Gaussian distribution according to claim 1, characterized in that: In step 4, the vibration intensity data of all microseismic event points are normalized, and the processed data is substituted into the equation as the maximum value of the two-dimensional Gaussian distribution to obtain a Gaussian function description of the vibration intensity around the event point.

11. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 10, characterized in that: In step 4, normalization is to change a column of data to a fixed interval (range), which is a decimal between [0, 1] or (-1, 1). For the convenience of data processing, mapping the data to the range of 0 to 1 is more convenient and fast. The normalization formula can be expressed as: Where (x i ,y i ) is the coordinate position of the microseismic event point, x min is the maximum value of the horizontal coordinate of the microseismic event point, y min is the minimum value of the vertical coordinate of the microseismic event point; The output range of the normalized vibration intensity data of the microseismic event point is controlled between 0 and 1.

12. The microseismic event cloud crack interpretation method based on Gaussian distribution according to claim 1, characterized in that: In step 5, the coordinate positions of the microseismic event points (x i ,y i ) Input the two-dimensional Gaussian distribution density function, superimpose the probability distribution of all earthquake event points, and finally form a crack distribution law map.

13. A microseismic event cloud crack interpretation system based on Gaussian distribution, characterized by: The microseismic event cloud fracture interpretation system based on Gaussian distribution adopts the microseismic event cloud fracture interpretation method based on Gaussian distribution described in any one of claims 1 to 12 to describe complex fractures within the fracturing range.

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