A method for fluid probability prediction by combining well and seismic data with an epanechnikov kernel function

Abstract

The application discloses a kind of Epanechnikov kernel function well seismic combined prediction fluid probability method. Including: S1, obtaining drilling coring data, geologic stratification data, well logging curve data, well logging interpretation data and the longitudinal wave impedance, shear wave impedance and density data volume data obtained by prestack seismic three parameter inversion;S2, identify fluid property;S3, calculate elastic parameter and fluid sensitive factor curve data;S4, select the oil and gas sensitive attribute in the purpose layer;S5, make label;S6, make fluid probability volume;S7, calculate corresponding sensitive elastic parameter attribute data volume;S8, calculate fluid probability volume;S9, determine the probability threshold value of distinguishing fluid property;S10, predict fluid probability.The beneficial effects of the application are: the mode of probability predicts fluid property.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of petroleum chemical industry, and particularly relates to a method for predicting fluid probability by Epanechnikov kernel function well-seismic joint. BACKGROUND

[0002] In modern oil and gas exploration, reservoir prediction and hydrocarbon detection are mainly based on geophysical technology, that is, taking seismic information as the main basis, comprehensively using other data (geology, logging, reservoir, etc.) as constraints, and predicting quality parameters of oil and gas reservoirs such as geometric characteristics, geological characteristics, and reservoir physical characteristics. The main research contents are generally divided into four aspects: (1) lithofacies: predicting facies belts that control the development of reservoirs; (2) lithology: predicting the lithology, thickness, and top structure of the reservoir; (3) physical property: predicting parameters such as porosity and permeability; and (4) oil and gas bearing property: comprehensively analyzing and studying the properties and distribution of fluids contained in the reservoir and the saturation thereof. Among them, the oil and gas bearing property prediction, i.e., hydrocarbon detection, is the most important but also the most difficult part, and the reservoir containing oil and gas is the valuable reservoir in industry.

[0003] Hydrocarbon detection using seismic data has been continuously concerned and developed from the initial seismic profile highlight technology, to the parameter inversion, low-frequency shadow, and spectrum analysis technology based on post-stack seismic data, to the elastic parameter simultaneous inversion, various AVO analysis, and various fluid sensitive factor construction technology based on pre-stack seismic data. A systematic and comprehensive research method has been formed, in which the pre-stack seismic data detection technology is the main part, and the post-stack seismic data detection technology is the auxiliary part.

[0004] The existing hydrocarbon detection technology based on seismic data mainly has the following problems: 1) Most of the existing technical solutions based on post-stack seismic data or pre-stack seismic data are predicted by a single seismic attribute sensitive to oil and gas. Affected by the quality of seismic data and the nature of sensitive attributes, there is a multi-solution problem; 2) Most of the existing hydrocarbon detection technologies are mainly qualitative interpretation, and it is difficult to achieve quantification. With the deepening of exploration and the needs of production, the quantification of oil and gas bearing property has become one of the basic requirements of current hydrocarbon detection. SUMMARY

[0005] The present application relates to the technical field of petroleum chemical industry, and particularly relates to a method for predicting fluid probability by Epanechnikov kernel function well-seismic joint.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solution, comprising the following steps:

[0007] S1. Acquire drilling coring data, geological layering data, well logging curve data, well logging interpretation data, and P-wave impedance, S-wave impedance, and density data obtained by pre-stack seismic three-parameter inversion, wherein the well logging interpretation data includes well logging oil and gas interpretation data and well logging lithology interpretation data;

[0008] S2. Identify the fluid properties in the target layer on the well based on the drilling coring data, geological layering data, well logging curve data, and well logging interpretation data;

[0009] S3. Calculating elastic parameters or constructing a fluid indicator factor curve based on the P-wave velocity, S-wave velocity, and density data in the well logging curve data;

[0010] S4. Selecting oil and gas sensitive properties in the target layer by performing logging rock physical analysis on elastic parameters or fluid indicator factor curves;

[0011] S5, calibrate the oil and gas sensitive attributes in S4 and the fluid properties in S2 in the well section and create labels;

[0012] S6. At the well point, multiple oil and gas sensitive attributes are estimated using a high-dimensional Epanechnikov kernel function for high-dimensional kernel density estimation. The label data prepared in S5 are used to prepare a fluid probability scale based on the Bayesian probability classification method.

[0013] S7, calculating the corresponding sensitive elastic parameter attribute data volume from the P-wave impedance, S-wave impedance and density data volume obtained by pre-stack seismic three-parameter inversion;

[0014] S8, projecting the sensitive elastic parameter attribute data volume calculated in S7 onto the fluid probability volume plate prepared in S6 to calculate the fluid probability volume;

[0015] S9, by analyzing the response characteristics of the fluid properties in the target layer section on the well in S2 to the fluid probability body, determining the probability threshold value for distinguishing the fluid properties;

[0016] S10. Quantify the fluid probability data volume obtained in S8 according to the probability threshold value for distinguishing fluid properties determined in S9, thereby predicting the fluid properties of the reservoir in the target layer.

[0017] Preferably, in step S2, the top and bottom ranges of the target layer study section on the well are determined by geological stratification data; the lithologic characteristics of different depth sections within the target layer are determined by drilling coring data, logging curve data and logging lithologic interpretation data; and the fluid properties of the favorable lithologic sections are determined by logging oil and gas interpretation data.

[0018] Preferably, in step S3, the elastic parameters include longitudinal wave impedance, shear wave impedance, longitudinal and shear wave velocity ratio, Poisson's ratio, Poisson's damping factor, Lame constant, shear modulus, bulk modulus, and Young's modulus.

[0019] Preferably, in step S3, the fluid sensitivity factor curve is constructed by constructing the fluid indicator factor Y with the help of the longitudinal wave impedance and the shear wave impedance in the well, and its expression is:

[0020] Y=AIcosθ+SIsinθ (1)

[0021] In formula (1), θ (0°≤θ≤360°) is the factor angle, AI is the longitudinal wave impedance, and SI is the shear wave impedance. Under given longitudinal wave impedance AI and shear wave impedance SI, as the angle θ changes, a series of factor curves can be obtained from the above formula. The logging curve related to the fluid is selected as the target curve, and a correlation scan is performed with the series of factor curves to obtain the factor curve with the highest correlation. This factor curve is the constructed fluid indication factor curve, and the corresponding factor angle is called the fluid indication factor angle.

[0022] Preferably, in step S4, the method of the well logging petrophysical analysis is to select two well logging curves for cross-plot display, and select the oil and gas sensitive attributes in the target layer from a large number of well logging curve data through the clustering characteristics of the cross-plot data.

[0023] Preferably, in step S6, the Bayesian probability classification method is:

[0024] Using probability to represent all types of uncertainty, for G categories, the Bayesian rule for category "i" is:

[0025]

[0026] In formula (2), X represents the sample point data, c i is a fluid property, p(c i |X) A sample point is classified as fluid property c i The posterior probability density, p(X|c i ) is the fluid property c i The conditional probability density of p(c i ) is c i The prior probability density of , which is equal to the proportion of this fluid property in all fluid types; p(X) is the marginal probability density.

[0027] Preferably, in step S6, the fluid property c is calculated by a non-parametric kernel density estimation method. i The conditional probability density p(X|c i );

[0028] Let x1, x2, ..., x n There are n independent and identically distributed sample points with a probability density of f. The kernel density estimation formula of the one-dimensional probability distribution function is:

[0029]

[0030] Where n is the number of sample points, x i is the observation value of a sample, K is the selected kernel function (non-negative, integral is 1, conforms to the probability density property, and has a mean of 0), h is the smoothing bandwidth that controls the smoothing amount (h>0); the kernel with the subscript h is called the scaling kernel, which is defined as

[0031] Select multiple fluid sensitive property parameters and use high-dimensional kernel density estimation. The formula is:

[0032]

[0033] Where D is the number of selected parameters, d(xi, X i ) is x and sample X in D-dimensional space i distance, h is the smoothing bandwidth parameter, and K is the kernel function;

[0034] The Epanechnikov kernel function is selected, and its formula is as follows;

[0035]

[0036] Substituting the Epanechnikov kernel function into equation (3), we obtain the high-dimensional kernel density estimation formula:

[0037]

[0038] Among them, C D is the volume of the D-dimensional hypersphere, which is expressed as;

[0039]

[0040] here,

[0041] Preferably, in step S6, the observation value X is determined to belong to fluid c according to the following principle: i or fluid c j ;

[0042] (1) If p(X|c i )p(c i )>p(X|c j )p(c j ), then X belongs to fluid c i ;

[0043] (2) If p(X|c j )p(c j )>p(X|c i )p(ci ), then X belongs to fluid c j .

[0044] Preferably, in step S7, the corresponding elastic parameter attribute data body is calculated according to the elastic parameter definition in step S3, and the longitudinal wave impedance data body and the shear wave impedance data body can be converted into a fluid indicator factor data body according to the fluid indicator factor angle determined by the logging curve in step S3.

[0045] The beneficial effects of the present invention are:

[0046] 1. Based on the high-dimensional Epanechnikov kernel function, multiple fluid sensitive attributes are subjected to high-dimensional kernel density estimation. The fluid probability density estimation is obtained through the Bayesian probability classification method, and the fluid properties are predicted in a probabilistic way.

[0047] 2. The fluid property data predicted by combining well and seismic data with multiple fluid sensitive attributes is highly reliable. This method is universal and can effectively solve the industry's difficulties in detecting fluids in carbonate, igneous, and unconventional reservoirs.

[0048] 3. By calibrating with actual drilling and logging data, the probability threshold for distinguishing oil and gas in the target reservoir is determined, and the distribution characteristics of fluids in the reservoir space are quantitatively studied, which will take hydrocarbon detection results a step further, improve the reliability of the basis for deploying exploration wells in oil and gas reservoirs in the target area, and thus increase the success rate of exploration wells in oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the process of the present invention;

[0050] Figure 2 Schematic diagram of the construction principle of fluid-sensitive indicator factors;

[0051] Figure 3 Obtain the fluid indicator factor angle correlation analysis diagram based on the target curve;

[0052] Figure 4 Fluid indicator factor curve analysis chart;

[0053] Figure 5 Optimizing fluid selection and sensitive elastic parameter attribute crosstalk using well logging rock physics analysis Figure 1 ;

[0054] Figure 6 Optimizing fluid selection and sensitive elastic parameter attribute crosstalk using well logging rock physics analysis Figure 2 ;

[0055] Figure 7 Optimizing fluid selection and sensitive elastic parameter attribute crosstalk using well logging rock physics analysis Figure 3 ;

[0056] Figure 8 Calibrate the fluid properties of the selected sensitive elastic parameter attributes and produce a label data feature map;

[0057] Figure 9 Fluid probability scale based on Epanechnikov kernel function probability prediction technology Figure 1 ;

[0058] Figure 10 Fluid probability scale based on Epanechnikov kernel function probability prediction technology Figure 2 ;

[0059] Figure 11 This is a result diagram of the fluid probability density data volume obtained based on the technical solution of the present invention. DETAILED DESCRIPTION

[0060] The invention will be described in further detail below with reference to the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0061] It should be understood that terms such as “having”, “including” and “comprising” used herein do not exclude the existence or addition of one or other elements or combinations thereof.

[0062] Example

[0063] like Figure 1 As shown, the present invention provides a method for predicting fluid probability based on Epanechnikov kernel function combined with well-seismic multi-attribute data, including the following steps:

[0064] S1. Acquire drilling coring data, geological layering data, logging curve data, logging interpretation data, and P-wave impedance, S-wave impedance, and density data obtained by pre-stack seismic three-parameter inversion, wherein the logging interpretation data includes logging oil and gas interpretation data and logging lithology interpretation data.

[0065] S2. Identify the fluid properties in the target layer on the well based on the drilling coring data, geological layering data, logging curve data, and logging interpretation data; wherein, determine the top and bottom range of the target layer on the well based on the geological layering data, such as Figure 4 As shown in the fourth column, the geological layer E3d2U at a depth of 2900 meters is used as the top boundary of the target layer, and the geological layer E3d3 at a depth of 3900 meters is used as the bottom boundary, which determines the range of the target layer, the Dongying Formation Dongsan Section (E3d2U to E3d3). The well logging curves and logging data in subsequent analysis are all taken from this target section. According to the drilling core data, well logging curve data ( Figure 4 Column 1) and well logging lithologic interpretation data ( Figure 4Column 4) determines the lithologic characteristics of different depth sections within the target layer, and uses the logging oil and gas interpretation data ( Figure 4 The filling curve in the 5th column is the oil saturation, indicating that the formation contains oil) to determine the favorable lithologic section ( Figure 4 The fluid properties of the sandstone in column 4 are oil, gas or water.

[0066] S3, according to the longitudinal wave velocity (V p ), shear wave velocity (V s ) and density (ρ) data to calculate elastic parameter curves or construct fluid indicator factor curve data. Commonly used elastic parameters include longitudinal wave impedance, shear wave impedance, longitudinal and shear wave velocity ratio, Poisson's ratio, Poisson's damping factor, Lame constant, shear modulus, bulk modulus, and Young's modulus. The definitions and geological meanings of the above elastic parameters are as follows:

[0067] Longitudinal wave impedance AI: AI = ρ-V p ;

[0068] Shear wave impedance SI: SI = ρ*V s ;

[0069] P-wave velocity ratio VP / VS: VP / VS = V p / V s

[0070] Poisson's ratio σ:

[0071] Poisson damping factor PDF:

[0072] Lamé constant λ:

[0073] Shear modulus μ:

[0074] Bulk modulus BULK:

[0075] Young's modulus E:

[0076] The construction of the fluid sensitivity factor curve is based on Connolly's theory on the relationship between elastic parameters and longitudinal wave impedance and shear wave impedance. The target correlation algorithm is used to construct the fluid indicator factor Y with the help of the longitudinal wave impedance and shear wave impedance in the well. Its expression is:

[0077] Y=AI cosθ+SI sinθ (1)

[0078] In formula (1), θ (0°≤θ≤360°) is the rotation angle, also known as the factor angle.

[0079] The construction principle of fluid indicator factors is as followsFigure 2 As shown in the figure, under given longitudinal and transverse wave impedance curves, a series of factor curves can be obtained from the above formula as the angle θ changes. Studies have shown that the cross-correlation calculation of the elastic parameters in the table as the target curve and a series of factor curves that change with angles can all be highly correlated with the factor curve of a certain factor angle, and the correlation is close to 1 (e.g. Figure 2 As shown in the table on the right). Generally, the water saturation curve, resistivity curve, or natural potential curve is selected as the target curve, and a correlation scan is performed with the series of factor curves calculated by formula (1) to obtain the factor curve with the highest correlation. This curve is called the fluid indicator factor curve, and the corresponding factor angle is called the fluid indicator factor angle. Figure 3 The fluid indicator factor angle is obtained from multiple wells using the water saturation curve as the target curve in this example. The different types of dotted lines in the figure represent the correlation curves (the horizontal axis is the angle, and the vertical axis is the correlation coefficient) of different wells (BZ2-1-2 well, BZ1-1-2 well, and BZ1-1-1 well) as the angle θ (0°≤θ≤360°) changes. The solid line is the average correlation curve of multiple wells. The fluid indicator factor angle determined in this example is 300 degrees. Figure 4 is the fluid factor curve Figure 4 ② and the measured curve Figure 4 ① and water saturation curve ( Figure 4 ⑤) The comparative analysis shows that the fluid factor curve is in good agreement with the fluid.

[0080] S4. Select oil and gas sensitive properties within the target layer by performing logging petrophysical analysis on elastic parameter or fluid indicator factor curves. Select two logging curves for cross-plot display. The grayscale of the sample point is represented by the fluid property determined in S2 (e.g., water saturation, where 1 indicates 100% water and 0 indicates 100% oil or gas with no water). The clustering characteristics of the cross-plot data are used to select the oil and gas sensitive properties within the target layer from a large number of logging curves (including measured curves, interpreted curves, elastic parameter curves, fluid indicator factor curves, etc.).

[0081] Figure 5 The cross-plot of longitudinal wave impedance (Zp) and longitudinal and shear wave velocity ratio (Vp / Vs ratio) at water saturation shows that in this example, samples with Vp / Vs values ​​less than 1.9 have low water content (<70%) and can distinguish between oil and water. The analysis shows that the longitudinal and shear wave velocity ratio is a fluid-sensitive property.

[0082] same Figure 6 This is a cross-plot of longitudinal wave impedance (Zp) and fluid indicator factor (FI) at water saturation. In this example, when the FI value is below 390, the sample contains oil and gas. Analysis shows that the fluid indicator factor is also a fluid-sensitive property.

[0083] Figure 7This is the cross-plot of the P-wave velocity ratio and the fluid indicator factor at water saturation. From the cross-plot, it is further determined that the P-wave velocity ratio and the fluid indicator factor are both fluid sensitive parameter attributes in this example.

[0084] S5, calibrate the oil and gas sensitive attributes in S4 and the fluid properties in S2 in the well section and make labels; Figure 8 As shown;

[0085] S6. In the well section, the P-wave velocity ratio and fluid indicator factor fluid sensitivity curve selected in S4 are discretized into sample points according to depth, X j (x1, x2, ..., x n ), j = 1, 2 correspond to the ratio of longitudinal and transverse wave velocities and fluid indicator factors, n is the number of sample points, x i is the observed value of a sample. First, multiple oil and gas sensitive attributes are estimated at the sample point using the high-dimensional Epanechnikov kernel function. The calculation formula of the Epanechnikov kernel function is as follows:

[0086]

[0087] Substituting formula (2) into high-dimensional kernel density estimation formula (3) yields high-dimensional kernel density estimation calculation formula (4)

[0088]

[0089] Where D is the number of selected parameters (D=2 in the embodiment), d(x, X i ) is x and sample X in D-dimensional space i distance, h is the smoothing bandwidth parameter, and K is the kernel function.

[0090]

[0091] Among them, C D is the volume of the D-dimensional hypersphere, which is expressed as

[0092]

[0093] here,

[0094] At the sample point, the ratio of longitudinal and transverse wave velocities and the oil and gas sensitive attributes of the fluid indicator factor are used to perform high-dimensional kernel density estimation using formula (4) to calculate the conditional probability p(X|c i ), where c i is a fluid property. Then, using the label data created in S5 and c i The prior probability density p(c i) and marginal probability density p(X) are used to make the fluid probability scale according to the Bayesian probability classification formula (6):

[0095]

[0096] Finally, the observation value X is determined to belong to fluid c according to the following principle: i or fluid c j ;

[0097] (1) If p(X|c i )p(c i )>p(X|c j )p(c j ), then X belongs to fluid c i ;

[0098] (2) If p(X|c j )p(c j )>p(X|c i )p(c i ), then X belongs to fluid c j .

[0099] Figure 9 and Figure 10 This is a fluid probability scale plate produced according to the present invention. The left figure is an example of a fluid (oil and water) probability scale plate related to the ratio of longitudinal and transverse wave velocities, and the right figure is an example of a fluid (oil and water) probability scale plate related to fluid indicator factors. The horizontal and vertical axes in the probability scale plate are the identified attributes. Solid points are determined to be water-containing samples, and hollow points are determined to be oil-containing samples. The curve surrounding the solid points of the water-containing samples is the water-containing probability curve, and the curve surrounding the hollow points of the oil-containing samples is the oil-containing probability curve. In this way, the fluid probability scale plate of the measured samples is produced by high-dimensional kernel density estimation using the high-dimensional Epanechnikov kernel function and based on the Bayesian probability classification method. By inputting a set of attribute values ​​into the scale plate, the oil or water probability value of the stratum where the attribute group is located can be read.

[0100] S7. Calculate the corresponding sensitive elastic parameter attribute data volume from the P-wave impedance, S-wave impedance, and density data volumes obtained from the pre-stack seismic three-parameter inversion. The pre-stack three-parameter synchronous inversion can invert the P-wave impedance, S-wave impedance, and density data volumes, and thus calculate the corresponding elastic parameter attribute data volume based on the elastic parameter attribute definition mentioned in S3. The P-wave impedance data volume and S-wave impedance data volume can be converted into a fluid indicator factor data volume based on the fluid indicator factor angle determined by the well logging curve in S3.

[0101] S8, projecting the sensitive elastic parameter attribute data volume calculated in S7 onto the fluid probability volume plate prepared in S6 to calculate the fluid probability volume;

[0102] S9, by analyzing the response characteristics of the fluid properties in the target layer section on the well in S2 to the fluid probability body, determining the probability threshold value for distinguishing the fluid properties;

[0103] S10, quantify the fluid probability data body obtained in S8 according to the probability threshold value for distinguishing fluid properties determined in S9, thereby predicting the fluid probability and the distribution characteristics of the fluid in the reservoir space, such as Figure 11 As shown, the high-probability (dark) strata on the seismic profile are oil-bearing strata.

[0104] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for joint prediction of fluid probability using Epanechnikov kernel function and well seismic analysis, characterized in that: The following steps are involved: S1. Acquire drilling coring data, geological layering data, well logging curve data, well logging interpretation data, and P-wave impedance, S-wave impedance, and density data obtained by pre-stack seismic three-parameter inversion, wherein the well logging interpretation data includes well logging oil and gas interpretation data and well logging lithology interpretation data; S2. Identify the fluid properties in the target layer on the well based on the drilling coring data, geological layering data, well logging curve data, and well logging interpretation data; S3. Calculating elastic parameters or constructing a fluid indicator factor curve based on the P-wave velocity, S-wave velocity, and density data in the well logging curve data; S4. Selecting oil and gas sensitive properties in the target layer by performing logging rock physical analysis on elastic parameters or fluid indicator factor curves; S5, calibrate the oil and gas sensitive attributes in S4 and the fluid properties in S2 in the well section and create labels; S6. At the well point, multiple oil and gas sensitive attributes are estimated using a high-dimensional Epanechnikov kernel function for high-dimensional kernel density estimation. The label data prepared in S5 are used to prepare a fluid probability scale based on the Bayesian probability classification method. S7, calculating the corresponding sensitive elastic parameter attribute data volume from the P-wave impedance, S-wave impedance and density data volume obtained by pre-stack seismic three-parameter inversion; S8, projecting the sensitive elastic parameter attribute data volume calculated in S7 onto the fluid probability volume plate prepared in S6 to calculate the fluid probability volume; S9, by analyzing the response characteristics of the fluid properties in the target layer section on the well in S2 to the fluid probability body, determining the probability threshold value for distinguishing the fluid properties; S10. Quantify the fluid probability data volume obtained in S8 according to the probability threshold value for distinguishing fluid properties determined in S9, thereby predicting the fluid properties of the reservoir in the target layer.

2. The method for joint prediction of fluid probability using Epanechnikov kernel function and well-seismic analysis according to claim 1, characterized in that: In step S2, the top and bottom ranges of the target layer study section on the well are determined by geological stratification data; the lithologic characteristics of different depth sections within the target layer are determined by drilling core data, well logging curve data, and well logging lithologic interpretation data; The fluid properties of favorable lithologic sections are determined using well logging oil and gas interpretation data.

3. The method for joint prediction of fluid probability using Epanechnikov kernel function and well-seismic analysis according to claim 1, characterized in that: In step S3, the elastic parameters include longitudinal wave impedance, shear wave impedance, longitudinal and shear wave velocity ratio, Poisson's ratio, Poisson's damping factor, Lame constant, shear modulus, bulk modulus, and Young's modulus.

4. The method for joint prediction of fluid probability using Epanechnikov kernel function and well-seismic analysis according to claim 1, characterized in that: In step S3, the fluid indicator factor curve is constructed by using the longitudinal wave impedance and shear wave impedance in the well to construct the fluid indicator factor Y, which is expressed as follows: Y=AIcosθ+SIsinθ (1) In formula (1), θ (0°≤θ≤360°) is the factor angle, AI is the longitudinal wave impedance, and SI is the shear wave impedance. Under given longitudinal wave impedance AI and shear wave impedance SI, as the angle θ changes, a series of factor curves are obtained from the above formula. The logging curve related to the fluid is selected as the target curve, and a correlation scan is performed with the series of factor curves to obtain the factor curve with the highest correlation. This factor curve is the constructed fluid indicator factor curve, and the corresponding factor angle is called the fluid indicator factor angle.

5. The method for joint prediction of fluid probability using Epanechnikov kernel function and seismic analysis according to claim 1 is characterized by: In step S4, the method of well logging petrophysical analysis is to select two well logging curves for cross-plot display, and select the oil and gas sensitive attributes in the target layer from a large number of well logging curve data through the clustering characteristics of the cross-plot data.

6. The method for joint prediction of fluid probability using Epanechnikov kernel function and well seismic analysis according to claim 1, characterized in that: In step S6, the Bayesian probability classification method is: Using probabilities to represent all types of uncertainty, for G categories, the Bayesian rule for category "i" is: in In formula (2), X represents the sample point data, c i is a fluid property, p(c i |X) A sample point is classified as fluid property c i The posterior probability density, p(X|c i ) is the fluid property c i The conditional probability density of p(c i ) is c i The prior probability density of , which is equal to the proportion of this fluid property in all fluid types; p(X) is the marginal probability density.

7. The method for joint prediction of fluid probability using Epanechnikov kernel function and seismic analysis according to claim 6, characterized in that: In step S6, the fluid property c is calculated by a nonparametric kernel density estimation method. i The conditional probability density p(X|c i ); Let x 1, x 2, …, x n There are n independent and identically distributed sample points with a probability density of f. The kernel density estimation formula of the one-dimensional probability distribution function is: Where n is the number of sample points, x i is the observation value of a sample, K is the selected kernel function (non-negative, integral is 1, conforms to the probability density property, and has a mean of 0), h is the smoothing bandwidth that controls the smoothing amount (h>0); the kernel with the subscript h is called the scaling kernel, which is defined as Select multiple fluid sensitive property parameters and use high-dimensional kernel density estimation. The formula is: Where D is the number of selected parameters, d(x, X i ) is x and sample X in D-dimensional space i distance, h is the smoothing bandwidth parameter, and K is the kernel function; The Epanechnikov kernel function is selected, and its formula is as follows: Substituting the Epanechnikov kernel function into equation (3), we obtain the high-dimensional kernel density estimation formula: when If true, it is 0 otherwise; Among them, C D is the volume of the D-dimensional hypersphere, which is expressed as: here, 8. The method for joint prediction of fluid probability using Epanechnikov kernel function and seismic analysis according to claim 7 is characterized by: In step S6, the observation value X is determined to belong to fluid c according to the following principle: i or fluid c j ; (1) If p(X|c i )p(c i )>p(X|c j )p(c j ), then X belongs to fluid c i ; (2) If p(X|c j )p(c j )>p(X|c i )p(c i ), then X belongs to fluid c j .

9. The method for joint prediction of fluid probability using Epanechnikov kernel function and well-seismic analysis according to claim 8, characterized in that: In step S7, the corresponding elastic parameter attribute data volume is calculated according to the elastic parameter definition in step S3, and the longitudinal wave impedance data volume and the shear wave impedance data volume can be converted into a fluid indicator factor data volume according to the fluid indicator factor angle determined by the logging curve in step S3.

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