Reservoir fracture identification method based on conventional logging curve

Abstract

The invention discloses a reservoir fracture identification method based on a conventional logging curve. The reservoir fracture identification method comprises the following specific steps: step 1, fracture single-factor identification; 2, crack comprehensive identification is carried out through an entropy weight method; and 3, quantitatively predicting the fracture-developed zone. The method is aimed at old oil field logging series and equal logging types without inclination angles. According to the method, on the basis of determining the number and quality of each logging curve, a three-porosity ratio method and other conventional logging methods are fused, and a set of method for identifying cracks in an old oil field and a water injection oil reservoir is established; according to the method, a three-porosity ratio method, a secondary porosity index method, an elastic modulus difference ratio method, a double-induction amplitude difference index method and a cracking coefficient and borehole diameter phase anomaly method are fused through an entropy weight method, a crack development comprehensive index is calculated, the higher the value of the comprehensive index is, the higher the crack development degree is, and otherwise, the lower the crack development degree is.

Description

Technical Field

[0001] The present invention relates to a method for identifying reservoir fractures, in particular to a method for identifying reservoir fractures based on conventional logging curves. Background Art

[0002] With the continuous expansion of the oil and gas exploration and development fields in China, the research status of fractured tight sandstone reservoirs has been rising day by day, and the oil and gas reserves and production capacity of deep tight reservoirs play a crucial role. The accurate identification of reservoir fractures is the basis for studying the development, distribution, laws and their influence on production capacity of fractures.

[0003] At present, the identification methods for reservoir fractures at home and abroad mainly include three categories.

[0004] The first category of reservoir fracture identification methods is conventional identification methods, mainly including field outcrop identification methods, core analysis methods and microscopic observation methods, etc. Rock fractures in the field outcrop area are fractures generated when the rock is subjected to geological tectonic actions. The field outcrop identification method is of great significance in field geological investigations. At present, the methods for identifying fractures in the outcrop area mainly include traditional manual description and processing methods and traditional image processing methods such as principal component analysis method, Laplace eigenmap method, etc., and field outcrop identification methods based on deep learning such as those based on the Tensor Flow architecture, which will input the processed outcrop pictures into a convolutional neural network model to identify rock fractures and record the fracture position information; Cores contain the most intuitive fracture information, so the core analysis method is the basis for fracture characterization and prediction and the most intuitive means for testing characterization. By statistically recording the fracture parameters of the core, including data such as direction, length, aperture, etc. and analyzing them with the corresponding formation physical properties, the correlation between fracture development and formation physical properties can be summarized; The microscopic observation methods mainly include the cast thin section method, scanning electron microscope method, etc., which can most intuitively see the morphology, occurrence, filling property of fracture development and its contact relationship with surrounding minerals, etc.

[0005] The identification method of the second type of reservoir fractures is to identify fractures based on logging data, mainly including dip logging technology and imaging logging technology, etc. Both dip logging and dipmeter detect fractures by the conductivity generated when the microfocus electrodes on the borehole wall plates contact fractures, and identify the occurrence and strike of fractures. Generally, it is divided into conductivity anomaly detection DCA and fracture identification logging FIL. For example, Chinese Patent Application CN116184513A discloses an evaluation method for formation fracture parameters of array laterolog, including: S1: Qualitatively identify the fracture position based on the variation characteristics of the resistivity measurement values in the deep detection mode of array laterolog; S2: At the identified fracture position, extract the resistivity measurement values of each detection mode of array laterolog, and calculate the fracture dip information based on the resistivity measurement values; S3: According to the calculated fracture dip, construct an array laterolog fracture porosity response chart based on different mud resistivity conditions using numerical simulation technology; S4: Extract the deep detection apparent resistivity and mud resistivity data of the actual oil and gas reservoir array laterolog, and interpolate the reservoir fracture porosity information based on the array laterolog fracture porosity response chart. The imaging logging method can intuitively, vividly and clearly display the geological characteristics of the two-dimensional space of the borehole wall. There are mainly four types of imaging logging downhole instruments: electrical imaging, acoustic imaging, nuclear magnetic imaging and downhole optical photography. For example, Chinese Patent Application CN

[0006] 108181665A discloses a method and device for determining fractures, wherein the method includes: obtaining the electrical imaging logging data of the target area; determining the electrical imaging logging image of the target area according to the electrical imaging logging data; performing morphological processing on the electrical imaging logging image in the vertical direction to obtain a first-angle fracture image; performing morphological processing on the electrical imaging logging image in the horizontal direction to obtain a second-angle fracture image; determining the fractures in the target area according to the first-angle fracture image and the second-angle fracture image.

[0007] The identification method for the third type of reservoir fractures is to use fracture parameters for simulation or modeling, such as seismic physical simulation, 3D modeling and other methods. As a forward modeling means of seismic exploration, seismic physical simulation plays a crucial role in the identification and quantitative prediction of fractured reservoirs. It can conduct single fracture parameter research, solve the problem that it is difficult to quantitatively predict single fracture parameters in the field, and its simulation results are real and effective. For example, Chinese Patent Application CN 102253415A discloses a method for establishing a seismic response pattern based on a fracture equivalent medium model, belonging to the field of exploration geophysics. The feature is to quantitatively represent the relationship between fracture characteristic parameters and equivalent medium elastic parameters using the fracture equivalent medium theory, establish the constitutive relationship of the anisotropic equivalent medium model of the fracture network based on the Bond transformation and superposition principle, and place the constructed fracture network equivalent medium model in the actual spatial position of the formation to establish the actual formation fracture equivalent medium model; use seismic wave field numerical simulation means to establish the seismic response patterns of fractured reservoirs with different structural parts and different lithologies. Currently, the 3D modeling methods for reservoir fractures at home and abroad are mainly divided into two categories: equivalent continuous model and discrete fracture network model (DFN). The establishment of the equivalent continuous model is based on grids, and certain average fracture attribute values are assigned to each grid; the modeling method of the discrete fracture network model is to spread and arrange various types of fracture slices in 3D space to combine them into a complete fracture network, thereby constructing a fracture model.

[0008] Currently, the fracture research methods at home and abroad are relatively rich. However, for some old oilfields or reservoirs developed by water injection, there is a lack of data such as seismic data. There is less research on how to use conventional logging data for fracture identification and the impact of fractures on later development. Reservoir fractures cannot be accurately identified. Summary of the Invention

[0009] Object of the Invention: Aiming at the deficiencies of the above-mentioned existing technologies, the present invention discloses a method for identifying reservoir fractures based on conventional logging curves. The present invention aims to combine core and multiple single-factor conventional logging curves, find effective combination points among them by analyzing the characteristics and deficiencies of each conventional logging curve method, and apply the combined method to the fracture distribution and spatial prediction of old wells for application verification and improvement, and finally establish an effective fracture prediction method based on conventional logging curves.

[0010] Well logging technology, as a key technical means for oil and gas resource evaluation, plays an irreplaceable and crucial role. Due to the characteristics of fractured reservoirs such as severe heterogeneity, traditional well logging interpretation techniques face many challenges. Well logging curves have been applied in reservoir fracture evaluation for decades. Logging scientists have constructed a series of basic fracture identification parameters using various well logging response characteristics of fractures, such as the triple porosity ratio method, secondary porosity index method, elastic modulus difference ratio method, dual induction amplitude difference index method, turtle cracking coefficient, well diameter relative anomaly method, etc. This invention has established a method that integrates multiple conventional well logging methods: the entropy weight method. Determine the wells and the set of their respective attributes in each block to be evaluated, perform normalization processing on each attribute, use the entropy weight method to determine its weight, and finally calculate the comprehensive fracture development index on this basis.

[0011] Technical solution: A method for identifying reservoir fractures based on conventional well logging curves, the specific steps are as follows:

[0012] Step 1: Single-factor fracture identification:

[0013] Respectively conduct single-factor fracture identification of reservoir fractures through the triple porosity ratio method, secondary porosity index method, equivalent elastic modulus difference ratio method, dual induction amplitude difference index method, turtle cracking coefficient method, and well diameter relative anomaly method, and respectively obtain the triple porosity ratio, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual laterolog amplitude difference, and well diameter relative anomaly value;

[0014] Step 2: Comprehensive fracture identification through the entropy weight method:

[0015] On the basis of obtaining single parameters, normalize the triple porosity ratio, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual laterolog amplitude difference, and well diameter relative anomaly value, and use the entropy weight method to obtain the comprehensive fracture development index to comprehensively evaluate the fracture development degree;

[0016] Step 3: Quantitative prediction of fracture development zones:

[0017] Calculate the comprehensive fracture development index for the drilled wells in the study area to form a comprehensive curve. On this basis, use reservoir stochastic modeling technology to predict the spatial distribution of the comprehensive fracture development index.

[0018] Furthermore, in Step 1, the single-factor fracture identification of reservoir fractures through the triple porosity ratio method is carried out according to the following formula:

[0019] Where:

[0020] R p Represents the triple porosity ratio;

[0021] Фt represents the total porosity;

[0022] Ф s represents the acoustic porosity, where:

[0023] When the ratio R of the three porosities p has a larger value, it indicates that the secondary porosity is more developed, that is, the fractures and solution pores are more developed.

[0024] Furthermore, in step one, the single-factor identification of reservoir fractures by the secondary porosity index method is carried out according to the following formula:

[0025] C sh = |φ t - φ s |, where:

[0026] C sh is the secondary porosity index;

[0027] Ф t is the total porosity;

[0028] Ф s is the acoustic porosity.

[0029] Furthermore, in step one, the single-factor identification of reservoir fractures by the equivalent elastic modulus difference ratio method is carried out according to the following formula:

[0030] where:

[0031] DR is the equivalent elastic modulus difference ratio;

[0032] ρ bw is the density of the rock when saturated with water;

[0033] Δt w is the acoustic travel time of the rock when saturated with water;

[0034] ρ b is the measured density log reading;

[0035] Δt is the measured acoustic travel time log reading;

[0036] When the formation is a fractured formation, the acoustic travel time increases and the density decreases, then DR > 0; when the formation is a tight formation, DR approaches 0.

[0037] Furthermore, in step one, the single-factor identification of reservoir fractures by the dual induction amplitude difference index method is carried out according to the following formula:

[0038]

[0039] In the formula: F chDenote the resistivity invasion correction difference ratio;

[0040] R ILM 、R ILD are the medium and deep induction logging readings respectively;

[0041] R mf and R w are the resistivity of mud filtrate and formation water respectively;

[0042] m f is the cementation exponent of the fractured formation;

[0043] F ch The larger the value, the higher the degree of fracture development.

[0044] Furthermore, in step one, the single factor identification of reservoir fractures by the crack coefficient method is carried out according to the following formula:

[0045]

[0046] In the formula: S represents the dual laterolog amplitude difference;

[0047] Δt ma is the matrix acoustic travel time difference;

[0048] Δt is the measured acoustic travel time difference;

[0049] V p 、V pma are the wave velocity and the longitudinal velocity of the matrix respectively;

[0050] Among them: The smaller S is, the closer the acoustic travel time difference of the rock is to the acoustic travel time difference of the rock matrix, indicating that the integrity of the rock is better, the fracture development gap is larger, and the fracture development frequency is smaller;

[0051] The larger S is, the more serious the rock damage is, the smaller the fracture development gap is, and the larger the fracture development frequency is.

[0052] Furthermore, in step one, the single factor identification of reservoir fractures by the relative wellbore diameter anomaly method is carried out according to the following formula:

[0053]

[0054] In the formula: A CAL is the relative wellbore diameter anomaly value;

[0055] D CAL is the measured wellbore diameter value;

[0056] D BIT is the bit diameter.

[0057] Furthermore, the specific steps for obtaining the comprehensive index of fracture development degree by using the entropy weight method in step (2) are as follows:

[0058] 21). Determine the set of objects, and determine the set of each well to be calculated in the block to be evaluated, denoted as:

[0059] A = (A 1 , A 2 , A 3 ,...... A n )

[0060] 22). Determine the set of index factors, that is, the set of each attribute of the object, where: each attribute of the object respectively refers to the triple porosity ratio, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual laterolog amplitude difference, and relative abnormal well diameter value obtained in step (1), denoted as:

[0061] P = (P 1 , P 2 , P 3 ,...... P m );

[0062] 23). Determine the index matrix, and the matrix elements are attribute parameters:

[0063]

[0064] 24). Normalize the index values:

[0065] Element a ij is normalized as b according to the following formula ij ,

[0066]

[0067]

[0068] For the values of the parameters, after normalization, the matrix is:

[0069]

[0070] 25). Use the entropy weight method to determine the weights of the indicators and calculate the entropy values of each indicator:

[0071]

[0072] When b ij = 0, let From the extreme value of entropy, it can be seen that the closer the level values of each indicator are, the greater its entropy value;

[0073] When are equal, the entropy takes the maximum value, that is, H(Pj ) MAX = log(n), and the obtained entropy value is normalized to obtain the characterization index P j of the relative importance entropy E(P j ):

[0074]

[0075] According to the properties of entropy, it can be judged that the larger E(P j ), the smaller the relative importance degree of P j ;

[0076] The weights of each index are as follows:

[0077]

[0078] Calculate the weight value corresponding to each index to obtain the weight vector

[0079] W = (w 1 , w 2 , w 3 , …, w m );

[0080] 6) Calculate the comprehensive fracture development index I:

[0081] I = W · B

[0082] The larger I is, the higher the fracture development degree; the smaller I is, the lower the fracture development degree.

[0083] Furthermore, the specific steps of step three are: construct a three-dimensional stratigraphic-structure model for the study area, on this basis, discretize the comprehensive fracture development index of the drilled wells into grids, and then use stochastic modeling technology to predict its spatial distribution.

[0084] Even further, during the simulation, first perform data analysis on the parameters. The main content of the analysis mainly includes data transformation, histogram statistical analysis, and variogram fitting on the fracture development index of each layer.

[0085] A reservoir fracture identification method based on conventional logging curves disclosed by the present invention has the following beneficial effects:

[0086] For some old oilfields, the logging series are relatively old, and the logging curves of exploration wells are all conventional curves, without logging types such as dip angle and formation facies. In view of this, on the basis of clarifying the quantity and quality of each logging curve, the present invention establishes a set of fracture identification methods for old oilfields and waterflooded reservoirs by integrating multiple conventional logging methods such as the three-porosity ratio method. Taking core data as the inspection means and using conventional logging data to study the fracture identification method, after comparing and screening various identification methods, a comprehensive fracture identification parameter is constructed, which can effectively identify fractures. The entropy weight method is used to integrate the three-porosity ratio method, the secondary porosity index method, the elastic modulus difference ratio method, the dual induction amplitude difference index method, the crack coefficient and the abnormal well diameter method to calculate the comprehensive fracture development index. The higher the value, the higher the fracture development degree; on the contrary, the lower the fracture development degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 The flowchart of a reservoir fracture identification method based on conventional logging curves disclosed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] The following details the specific embodiments of the present invention.

[0089] Embodiment 1

[0090] As Figure 1 shown, Figure 1 The flowchart of a reservoir fracture identification method based on conventional logging curves disclosed by the present invention.

[0091] A reservoir fracture identification method based on conventional logging curves, the specific steps are as follows:

[0092] Step 1: Single-factor fracture identification

[0093] This technical invention combines the actual logging curve types, quantities and qualities in the Daniudi area, selects 6 methods for calculation, and uses targeted methods for comprehensive discrimination.

[0094] 11) Three-porosity ratio method

[0095] From the measurement principle of three-porosity logging, it is known that neutron logging and density logging reflect the total porosity of the formation, and acoustic velocity logging mainly reflects the primary intergranular porosity and horizontal fractures. Therefore, in fractured formations, by obtaining the total porosity Ф t , acoustic porosity Ф s , the three-porosity ratio R p is obtained:

[0096]

[0097] The above formula indicates that when Rp is larger, it shows that the secondary porosity is more developed, that is, the fractures and solution pores are more developed.

[0098] 12) Secondary porosity index method

[0099] Acoustic porosity Ф s measures the matrix porosity Ф h , then the total porosity Ф t The difference between the total porosity and the acoustic porosity is the secondary porosity Ф z = Ф t - Ф s . Ф z The larger it is, the more developed the fractures are. However, when there are horizontal fractures, acoustic wave period jumps may occur. If it is a horizontal fracture zone, continuous period jumps may occur, making Ф s > Ф t , that is, Ф z > 0 or Ф z < 0 may both be indications of fractures. Therefore, a fracture index can be defined according to the following formula: Secondary porosity index C sh

[0100] C sh = |φ t - φ s |

[0101] 13) Equivalent elastic modulus difference ratio method

[0102] Construct the equivalent modulus difference ratio using the acoustic wave travel time and density measurement values as follows:

[0103]

[0104] In the formula: DR is the equivalent elastic modulus difference ratio; ρ bw is the density of the rock when saturated with water; Δt w is the acoustic wave travel time of the rock when saturated with water; ρ b is the measured density logging reading; Δt is the measured acoustic wave travel time logging reading. When the formation is a fractured formation, the acoustic wave travel time increases and the density decreases, then DR > 0; when the formation is a tight formation, DR is close to 0.

[0105] 14) Dual induction amplitude difference index method

[0106] In the tight carbonate rock section, there is generally no amplitude difference between the deep induction and medium induction resistivity curves. When encountering a fracture zone, due to the influence of mud invasion, an amplitude difference will appear between the deep and shallow lateral curves. The magnitude of this amplitude difference can be used as a fracture index:

[0107]

[0108] In the formula: Fch is the resistivity invasion correction difference ratio; R ILM and R ILD are the medium and deep induction logging readings respectively; R mf and R w are the resistivity of the mud filtrate and formation water respectively; m f is the cementation index of the fractured formation. Obviously, the larger the F ch value, the higher the degree of fracture development.

[0109] 15) Cracking coefficient method

[0110] According to the relationship between the longitudinal wave velocity of the rock and the rock integrity, the following cracking coefficient sensitive to rock fractures is defined:

[0111]

[0112] In the formula: S is the dual laterolog amplitude difference; Δt ma is the matrix acoustic time difference; Δ t is the measured acoustic time difference, V p and V pma are the wave velocity and the longitudinal velocity of the matrix respectively.

[0113] The smaller S is, the closer the acoustic time difference of the rock is to the acoustic time difference of the rock matrix, indicating that the integrity of the rock is better, the fracture development gap is larger, and the fracture development frequency is smaller; the larger S is, the more serious the rock damage is, the smaller the fracture development gap is, and the larger the fracture development frequency is. Therefore, the cracking coefficient is used to identify the degree of formation fracture development. When fractures develop, S increases.

[0114] 16) Relative wellbore diameter anomaly method

[0115] The following parameters are constructed based on the wellbore diameter measurement value:

[0116]

[0117] In the formula: A CAL is the relative wellbore diameter anomaly value; D CAL is the measured wellbore diameter value; D BIT is the bit diameter.

[0118] Step 2: Comprehensive identification of fractures by entropy weight method

[0119] On the basis of obtaining single parameters, the triple porosity ratio, equivalent elastic modulus difference ratio, secondary porosity index, relative wellbore diameter anomaly value, dual laterolog amplitude difference, and resistivity invasion correction difference ratio are normalized, and the entropy weight method is used to obtain the comprehensive index of fracture development degree to comprehensively evaluate the fracture development degree.

[0120] The specific steps to calculate the comprehensive index of fracture development degree using the entropy weight method are as follows:

[0121] 21) Determine the set of objects, and determine the set of each well to be calculated in the block to be evaluated, denoted as:

[0122] A = (A 1 , A 2 , A 3 ,...... A n )

[0123] 22) Determine the set of index factors, that is, the set of each attribute of the object. Here, the attribute set refers to the calculation results of the above six individual methods, such as the set of parameters such as the triple porosity ratio, denoted as:

[0124] P = (P 1 , P 2 , P 3 ,...... P m )

[0125] 23) Determine the index matrix, and the matrix elements are attribute parameters.

[0126]

[0127] 24) Normalize the index values. The element a ij can be normalized according to the following formula to b ij ,

[0128]

[0129]

[0130] For the values of the parameters, perform normalization, and the normalized matrix is:

[0131]

[0132] 25) Use the entropy weight method to determine the weights of the indicators. Calculate the entropy values of each indicator:

[0133]

[0134] When b ij = 0, let From the extreme value property of entropy, it can be seen that the closer the level values of each indicator are, the larger its entropy value. When are equal, the entropy takes the maximum value, that is, H(P j ) MAX = log(n), and use the obtained entropy value to perform normalization to obtain the entropy E(P j ) representing the relative importance of the indicator P j :

[0135]

[0136] According to the properties of entropy, it can be judged that the larger E(P j ), the smaller the relative importance of P j . The weights of each index are as follows:

[0137]

[0138] Calculate the weight value corresponding to each index to obtain the weight vector

[0139] W = (w 1 , w 2 , w 3 , …, w m )

[0140] 26) Calculate the comprehensive fracture development index I:

[0141] I = W · B

[0142] The larger I is, the higher the fracture development degree; the smaller I is, the lower the fracture development degree.

[0143] By sorting out and organically integrating the above two steps, an effective fracture prediction technology based on conventional logging curves is established, and good results have been obtained through its application in the Daniudi Gas Field in the study area.

[0144] Step 3: Quantitative prediction of fracture development zones

[0145] Calculate the comprehensive fracture development index for the drilled wells in the study area to form a comprehensive curve. On this basis, use reservoir stochastic modeling technology to predict the spatial distribution of the comprehensive fracture development index. Build a three-dimensional stratigraphic-structural model for the study area. On this basis, discretize the comprehensive fracture development index of the drilled wells into grids, and then use stochastic modeling technology to predict its spatial distribution. When simulating, first perform data analysis on the parameters. The main contents of the analysis include data transformation, histogram statistical analysis, and variogram fitting of the fracture development index for each layer.

[0146] Example 2

[0147] It is roughly the same as Example 1, except that: the index matrix A in step 23) can also be expressed as A T = [A 1 , A 2 , A 3 , …, A n , where

[0148]

[0149] The above has described the embodiments of the present invention in detail. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the gist of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A reservoir fracture identification method based on conventional logging curves, characterized in that: The specific steps are as follows: Step 1: Single factor identification of cracks: The single factor identification of reservoir fractures was carried out by three porosity ratio method, secondary porosity index method, equivalent elastic modulus difference ratio method, dual induction amplitude difference index method, crack coefficient method and well diameter relative anomaly method, and the three porosity ratio, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual lateral amplitude difference and well diameter relative anomaly value were obtained respectively; Step 2: Comprehensive identification of cracks using entropy weight method: On the basis of obtaining single parameters, the three porosity ratios, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual lateral amplitude difference, and wellbore relative abnormal value are normalized, and the comprehensive index of fracture development is obtained by using the entropy weight method to comprehensively evaluate the fracture development degree. Step 3: Quantitative prediction of fracture development zones: The comprehensive index of fracture development in the wells drilled in the study area is obtained to form a comprehensive curve. On this basis, the reservoir stochastic modeling technology is used to predict the spatial distribution of the comprehensive index of fracture development.

2. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by the three-porosity ratio method according to the following formula: in: R p It represents the three porosity ratios; Ф t represents the total porosity; Ф s represents the acoustic porosity, where: When the three porosity ratio R p The larger the value, the more developed the secondary porosity, that is, the more developed the cracks and solution pores.

3. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by the secondary porosity index method according to the following formula: C sh =|φ t -φ s |, where: C sh It is an indicator of secondary porosity; Ф t is the total porosity; Ф s is the acoustic porosity.

4. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by using the equivalent elastic modulus difference ratio method according to the following formula: in: DR is the equivalent elastic modulus difference ratio; ρ bw is the density of rock when it is saturated with water; Δt w It is the time difference of sound waves when the rock is saturated with water; ρ b It is the measured density logging reading value; Δt is the measured acoustic time difference logging reading; When the stratum is a fractured stratum, the acoustic time difference increases and the density decreases, then DR>0; when the stratum is a dense stratum, DR is close to 0.

5. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by the dual induction amplitude difference index method according to the following formula: Where: F ch It indicates the ratio of resistivity intrusion correction difference; R ILM , R ILD These are the medium and deep induction logging readings, respectively; R mf and R w are the resistivities of mud filtrate and formation water, respectively; m f is the cementation index of fractured formations; Resistivity intrusion correction difference ratio F ch The larger it is, the more developed the cracks are.

6. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by the crack coefficient method according to the following formula: Where: S represents the bilateral lateral amplitude difference; Δt ma is the time difference of skeleton sound wave; Δt is the measured acoustic time difference; V p 、V pma are the wave velocity and the longitudinal velocity of the skeleton respectively; Among them: the smaller S is, the closer the acoustic time difference of the rock is to the acoustic time difference of the rock skeleton, which means the integrity of the rock is better, the gap between the cracks is larger, and the frequency of crack development is smaller; The larger the S is, the more serious the rock damage is, the smaller the crack development gap is, and the greater the crack development frequency is.

7. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: In step 1, the single factor identification of reservoir fractures is performed by the wellbore relative anomaly method according to the following formula: Where: A CAL is the relative abnormal value of the well diameter; D CAL is the measured well diameter value; D BIT is the drill bit diameter.

8. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: The specific steps of using the entropy weight method to obtain the comprehensive index of fracture development degree in step (2) are as follows: 21) Determine the set of objects. Determine the set of wells to be calculated in the block to be evaluated, which is recorded as: <h2 style=";text-align:left;direction:ltr">A=(A1,A2,A3,......A<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) 22) Determine a set of index factors, that is, a set of various attributes of the object, wherein the various attributes of the object refer to the three porosity ratios, secondary porosity index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, dual lateral amplitude difference, and wellbore relative abnormal value obtained in step (1), respectively, and are recorded as: P=(P1,P2,P3,......P m ); 23) Determine the indicator matrix, where the matrix elements are attribute parameters: 24) Normalized index value: elementa ij The normalization is performed as follows: ij , Normalize the parameter values, and the normalized matrix is: 25) Use the entropy weight method to determine the weight of the indicator and calculate the entropy value of each indicator: When b ij = 0, let From the extreme value of entropy, we can know that the closer the level values ​​of each indicator are, the greater the entropy value; when When they are equal, entropy takes the maximum value, that is, H(P j ) MAX =log(n), and the obtained entropy value is normalized to obtain the characterization index P j The entropy E(P j ): According to the properties of entropy, it can be judged that E(P j ) is larger, P j The smaller the relative importance of The weight of each indicator is: Find the weight corresponding to each indicator and get the weight vector <h2 style=";text-align:left;direction:ltr">W=(w1,w2,w3,…,w<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr"> ); 6) Calculate the comprehensive index of crack development I: I=W·B The larger I is, the higher the degree of crack development is; the smaller I is, the lower the degree of crack development is.

9. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 1, characterized in that: The specific steps of step three are as follows: construct a three-dimensional stratigraphic-structural model for the study area, discretize the comprehensive index of fracture development in the drilled wells on this basis, and then use stochastic modeling technology to predict its spatial distribution.

10. A reservoir fracture identification method based on conventional well logging curves as claimed in claim 9, characterized in that: In step three, during simulation, the parameters are first analyzed. The analysis includes data transformation, histogram statistical analysis and variogram fitting of the fracture development index of each layer.

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