Shale gas multi-layer stacked reservoir dessert evaluation and separate layer fracturing decision-making method

Abstract

The invention relates to a shale gas multi-layer stacked reservoir dessert evaluation and separate layer fracturing decision-making method, and belongs to the technical field of oil-gas field development, and the method comprises the following steps: calculating porosity, adsorbed gas content, organic carbon content and vitrinite reflectance geological parameters; calculating brittleness index and fracture toughness engineering parameters; carrying out normalization processing and weight distribution by adopting a CRITIC weight method, constructing a geological-engineering comprehensive evaluation model, and dividing four types of sweet spot areas; through a mixed method of main parameter priority and abnormal value verification, preferentially analyzing interlayer gradients of high-weight parameters, and performing local fine verification on abnormal abrupt change of low-weight parameters based on a 3 sigma principle; and in combination with a rigid-flexible layering model, a fracturing layering strategy is dynamically decided, and the complexity of the fracture network is optimized. According to the method, the reservoir identification precision is improved through multi-parameter fusion evaluation, the fracturing efficiency is enhanced through dynamic hierarchical decision, the development cost is finally reduced, and technical support is provided for efficient development of shale gas.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field development, and particularly relates to a method for evaluating sweet spots of shale gas multi-layer stacked reservoirs and making decisions on staged fracturing. Background Art

[0002] Shale gas reservoirs are characterized by low porosity and extra-low permeability, and volume fracturing technology is required for effective development. Due to strong lateral heterogeneity of the reservoir and significant differences in reservoir properties along the wellbore trajectory, it is particularly important to preferentially conduct volume fracturing transformation in the sweet spot area of the well section. The sweet spot area is a region in shale gas exploration and development that is easy to fracture and has relatively high production. The preference selection needs to be carried out from two aspects: firstly, from a geological perspective, select high-quality reservoirs with high gas content, high TOC (total organic carbon), and high porosity; secondly, from an engineering transformation perspective, select sections with small horizontal stress difference and high brittleness index to form complex fracture networks and maximize the stimulated reservoir volume (SRV).

[0003] The heterogeneity of the developed reservoir is the main reason affecting shale gas development. Therefore, it is particularly important to preferentially conduct volume fracturing transformation in the sweet spot area of the well section. At present, there are many problems in the evaluation of shale gas sweet spots. Although there are many methods for evaluating fracturability, actual operations still rely heavily on single-factor indexes, and the integrated research on geological-engineering parameters is very insufficient. It is necessary to explore a high-precision fracturing potential calculation method that can take into account multiple factors and is easy to operate. In addition, shale gas production is affected not only by geological and engineering parameters but also by fracturing construction parameters, resulting in certain errors in the establishment of sweet spot evaluation methods. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for evaluating sweet spots of shale gas multi-layer stacked reservoirs and making decisions on staged fracturing.

[0005] The technical solution of the present invention is as follows:

[0006] A method for evaluating sweet spots of shale gas multi-layer stacked reservoirs and making decisions on staged fracturing, the steps are as follows:

[0007] S1. Select four geological parameters, namely porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance, through core description and logging curves to evaluate the potential of the developed reservoir;

[0008] S11. The bulk density obtained by logging is a comprehensive response of the rock matrix and pore fluid densities. Among them, the porosity parameter model uses the rock volume model to obtain the porosity expression:

[0009]

[0010] φ N = 0.01×(CN - LOCR - 0.5×Vcl ×N cl ) (2)

[0011]

[0012] In the formula, φ D and φ N are the density porosity and neutron porosity respectively, %; ρ ma , ρ mf and ρ vl are the rock matrix density, formation fluid density and shale density respectively, g·cm -3 ; CN is the density log value of the target layer, g·cm -3 ; LOCR is the neutron value of the rock matrix, %; N cl is the neutron value of shale, %.

[0013] S12, the adsorbed gas accounts for 20% - 80% of the total gas content in shale, and is often adsorbed on the surface of shale organic matter. The BET multi-layer adsorption theory model is used to calculate the adsorbed gas content in deep shale. The formula is as follows:

[0014] According to the adsorption and desorption heat kinetics relationship between the (n - 1)th layer and the nth layer in the classical BET model, the adsorption - desorption coefficient k is defined as:

[0015]

[0016] In the formula, k is the adsorption - desorption coefficient, dimensionless; a n , b n are the adsorption coefficient and desorption coefficient of the nth layer respectively, s -1 ; E L is the liquid adsorption energy, 4.187 J / mol; R is the universal gas constant, with a value of 8.314 J / (mol·K); T is the temperature, K.

[0017] The adsorption heat correlation coefficient C in the BET model is:

[0018]

[0019] In the formula, C is the constant related to the adsorption heat in the BET model, dimensionless; a 1 , b 1 are the adsorption coefficient and desorption coefficient of the first layer respectively, s -1 ; E 1 is the adsorption energy of the first layer, 4.187 J / mol.

[0020] Referring to the classical BET theory method, the improved BET multi-layer adsorption model is obtained:

[0021]

[0022] In the formula, V aMBET is the improved BET multi-layer adsorption gas content, cm 3 / g.

[0023] S13. The total organic carbon content (TOC) is an organic matter abundance index commonly used at home and abroad, which refers to the carbon content in the organic matter remaining in the rock after the oil and gas have escaped from the source rock. As a traditional method for evaluating the total organic carbon content of shale reservoirs, the △logR method is also the most widely used method for evaluating the total organic carbon content. Using the improved △logR model to evaluate the organic carbon content TOC, the calculation formula is as follows:

[0024] △logR = logR + log(R max / R min ) / (△t max -△t min ) × (△t - △t max ) - logR min (7)

[0025] TOC = △lgR × 10 (2.297-0.1688LOM) +△TOC (8)

[0026] Among them, TOC is the total organic carbon content, %; R min (△t min ) and R max (△t max ) are the minimum and maximum values of the resistivity (acoustic travel time) curve scale when the acoustic travel time and resistivity curves are superimposed, dimensionless.

[0027] S14. By performing a single-factor correlation analysis between the vitrinite reflectance R o and the conventional logging response, and selecting the logging response with good correlation for multiple regression, the following model is established:

[0028] R o = 3.264 - 1.437GR - 1.776SP R 2 = 0.68 (9)

[0029] Among them, R o is the vitrinite reflectance, %; GR is the relative value of natural gamma, dimensionless; SP is the relative value of spontaneous potential, dimensionless.

[0030] S2. The engineering parameters of the reservoir determine the quality of the reservoir stimulation effect under the same fracturing conditions in the later stage; therefore, logging data is used to calculate the engineering parameters (brittleness index, fracture toughness, interlayer vertical stress difference).

[0031] S21. The brittleness index describes the ability of a reservoir to produce brittle deformation under the action of a fracturing operation. The brittleness index is an essential evaluation index in reservoir engineering sweet spot evaluation and fracability evaluation. Based on logging data, dynamic parameters are calculated, and the dynamic Young's modulus and Poisson's ratio are converted into static Young's modulus and Poisson's ratio. The calculation formulas for the dynamic Young's modulus and Poisson's ratio are as follows:

[0032]

[0033] In the formula, σ d is the dynamic Poisson's ratio, dimensionless; E d is the dynamic Young's modulus, GPa; △t s is the shear wave slowness, μs·m -1 ; △t p is the compressional wave slowness, μs·m -1 ; ρ b is the rock density, g·cm -3 .

[0034] The logging curves yield dynamic parameters, while the static parameters in the formula are obtained from measured stress and strain data. Sonic logging can provide compressional wave and shear wave slowness data. Combining with density logging data, the dynamic Young's modulus and dynamic Poisson's ratio of tight reservoirs at any measured depth can be calculated. Then, through the conversion between dynamic and static parameters, the static Young's modulus and static Poisson's ratio can be obtained. The conversion formulas for dynamic and static parameters are as follows:

[0035] σ s = 0.1552σ d + 0.2209 (12)

[0036] E s = 0.7095E d + 2.2526 (13)

[0037] Where: σ s is the static Poisson's ratio, dimensionless; E s is the static Young's modulus, GPa.

[0038] Calculate the required static parameters and calculate the brittleness index:

[0039]

[0040] In the formula, BI is the brittleness index, dimensionless; E smax (E smin ) is the maximum (minimum) static Young's modulus, GPa; σ smax (σ smin ) is the maximum (minimum) static Poisson's ratio, dimensionless.

[0041] S22. The pressure of the fracturing fluid concentrates at the crack tip. The description of the stress at the tip of the hydraulic fracture indirectly indicates the ease of crack propagation. The stress intensity factor (SIF) can be used to predict the stress state near the crack tip. When the SIF reaches the critical value, the rock is fractured, and the stress intensity at this time is called the fracture toughness, which is also called the strain energy release rate during elastic deformation. The fracture toughness describes the ability of the rock to prevent crack propagation and belongs to the nature of the rock itself. Its magnitude is related to the ease of crack extension. The smaller its value, the easier the crack extends, which is more conducive to hydraulic fracturing. The calculation method is as follows:

[0042] K IC = 0.271 + 0.107S t (15)

[0043] σ c = (0.0045 + 0.0035V cl )E d (16)

[0044] S t = σ c / K (17)

[0045] In the formula, K IC is the fracture toughness, MPa·m 0.5 ; S t is the tensile strength, MPa; σ c is the compressive strength, MPa; K is a constant, K = 12.26.

[0046] S3. First, normalize the geological parameters (porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance) and engineering parameters (brittleness index, fracture toughness) obtained in S1 and S2, then calculate the index weight value using the CRITIC weight method, establish a single-well multi-scale geological sweet spot model and an engineering sweet spot prediction model respectively, and finally obtain the comprehensive sweet spot evaluation index.

[0047] S31. To eliminate the influence of different dimensions on the evaluation results, it is necessary to dimensionless the various indicators:

[0048] If the value of the indicator used is better the larger it is (positive indicator):

[0049]

[0050] If the value of the indicator used is better the smaller it is (negative indicator):

[0051]

[0052] In the formula, x ij正 is the value of the jth indicator of the ith sample after normalization, dimensionless; xij , x j,max , x j,min are the original index value, the maximum value of the j-th index, and the minimum value of the j-th index respectively. The unit is determined according to the index type.

[0053] S32. In the CRITIC method, the standard deviation is used to represent the difference and fluctuation of the values within each index. The larger the standard deviation, the greater the numerical difference of the index, the more information it can reflect, and the stronger the evaluation intensity of the index itself. More weight should be assigned to this index, S j represents the standard deviation of the j-th index:

[0054]

[0055] In the formula, S j is the standard deviation of the j-th index, and the unit is determined according to the index type; n is the sample size, dimensionless.

[0056] The index conflict is expressed in the form of a correlation coefficient, r ij represents the correlation coefficient between evaluation indices i and j

[0057]

[0058] C j The larger it is, the greater the role of the j-th evaluation index in the entire evaluation index system, and more weight should be assigned to it

[0059]

[0060] The objective weight w of the j-th index j is:

[0061]

[0062] In the formula, C j is the information content of the j-th index, dimensionless; S j is the standard deviation of the j-th index, and the unit is determined according to the index type; w j is the objective weight of the j-th index, dimensionless.

[0063] S33. Substitute the weight values of each index to calculate the single-well geological sweet spot model of the coupled reservoir development potential and the reservoir seepage capacity:

[0064] F G = w 1 φ n + w 2 V aMBETn + w 3 TOC n + w 4 Ron (24)

[0065] In the formula, F G represents the geological sweet spot, dimensionless; w 1 -w 4 respectively represent the weights of various factors of the geological sweet spot, dimensionless; φ n represents the normalized reservoir porosity, dimensionless; V aMBETn represents the normalized adsorbed gas content, dimensionless; TOC n represents the normalized organic carbon content, dimensionless; R on represents the normalized vitrinite reflectance, dimensionless;

[0066] S34. Substitute the weight values of each index to construct a single-well multi-scale engineering sweet spot prediction model for coupling reservoir compressibility and fracture conductivity based on multi-scale parameter fusion:

[0067] F E = w 1 B In + w 2 K ICn (25)

[0068] In the formula, F E represents the engineering sweet spot, dimensionless; w 1 -w 2 respectively represent the weights of various factors of the engineering sweet spot, dimensionless; B In represents the normalized brittleness index, dimensionless; K ICn represents the normalized fracture toughness, dimensionless.

[0069] S35. Based on the above analysis, the weights of the engineering factors affecting the formation of fracture networks and the geological factors affecting the development reservoir conditions on the production well development productivity are determined respectively. According to the long-term development practice of the study area, the weights of the current geological and engineering parameters determined by the production unit on the productivity of the production wells are α and β respectively. Based on this, a comprehensive evaluation weight calculation model for the sweet spot of the volume fracturing well section is established:

[0070]

[0071] In the formula, w 地i is the weight of the geological influence factor, dimensionless; F G ' is the normalized geological sweet spot index, dimensionless; w 工i is the weight of the engineering influence factor, dimensionless; F E ' is the normalized engineering sweet spot index, dimensionless; α and β are the weights of the current geological and engineering parameters determined by the production unit on the productivity of the production wells, dimensionless.

[0072] S36. Using the sample data of the wells that have been put into production, calculate the comprehensive average value of the sweet spots in the fracturing area of their well sections. Taking the average daily gas production in the initial stage of the production wells as the evaluation index, draw the standard relationship curve between the comprehensive average value of the sweetness and the initial production. Divide the sweet spot area into four types: type I sweet spot, type II sweet spot, type III sweet spot, and non-sweet spot. At the same time, corresponding to the sweetness value ranges of each type of sweet spot area, using the established sweet spot type classification standard, based on the calculation results of the sweetness value distribution along the well section, finally, the sweet spot type distribution map of the corresponding well section can be obtained, which will provide an important theoretical basis for the design of the later stage of the layered fracturing construction plan.

[0073] S4. Adopt a hybrid method of "prioritizing main parameters + outlier verification" to perform a complete interlayer gradient calculation on the high-weight parameters (weight > 0.2) determined by the CRITIC weight method in S3 as the main basis for layering decisions; set a higher gradient threshold for low-weight parameters, and trigger verification only when the gradient of low-weight parameters exceeds 3 times the standard deviation, and achieve "exception detection" through a fast algorithm.

[0074] S41. For any parameter (such as porosity, organic carbon content, brittleness index, fracture toughness), the gradient G of adjacent small layers (the jth layer and the j + 1th layer) j is calculated as:

[0075]

[0076] where: P j and P j+1 are the parameter values of the jth layer and the j + 1th layer (which need to be normalized), dimensionless; h is the vertical distance between adjacent small layers, m.

[0077] S42. For the low-weight parameters (such as adsorbed gas content and vitrinite reflectance) determined by the CRITIC weight method, first calculate the global standard deviation (σ) of their gradient distribution, and set the anomaly threshold using the 3σ principle; longitudinally scan the gradient values through the sliding window algorithm (window length 3 - 5 small layers, step size 1 small layer). When the gradient of a certain layer section exceeds 3σ, trigger a local detailed analysis including core review, adjacent well comparison, and microseismic data verification; if it is confirmed that the anomaly reflects a real reservoir heterogeneity mutation, supplement this layer section as an independent fracturing section and optimize the fracturing parameters accordingly.

[0078] S5. Calculate the interlayer vertical stress difference (the minimum horizontal principal stress difference between adjacent formations), and divide it into three intervals according to the stress difference threshold: when it is greater than or equal to 5 MPa, perform 3-layer fracturing; when it is greater than 3 MPa and less than 5 MPa, determine the layering situation by combining geological-engineering parameters; when it is less than or equal to 3 MPa, perform single-layer fracturing, but in special cases, independent layering is required.

[0079] S51. As the vertical stress difference increases, the stress barrier effect between formations is enhanced, resulting in increased vertical stress constraint during the fracture initiation and propagation process, weakened vertical extension ability of fractures, and reduced complexity of the formed fracture network. Among them, the horizontal minimum principal stress can be obtained from differential strain experiments, and the calculation formula is as follows:

[0080]

[0081] In the formula: △σ v is the vertical stress difference between layers, MPa; σ h is the horizontal minimum in-situ stress, MPa; μ is the Poisson's ratio of the formation, dimensionless; σ v is the vertical stress, MPa; β h is the minimum tectonic stress field coefficient, dimensionless; Pp is the formation pressure, MPa; α is the Biot coefficient, and the default value is 1.

[0082] S52. First, rigid layer division is determined based on the vertical stress difference. When △σ v ≥5 MPa, it is forced to be divided into 3 independent fracturing layers regardless of other parameters; when 3 MPa < △σ v <5 MPa, it enters the flexible optimization stage and is usually divided into 2 layers. If at this time the geological parameters (including TOC longitudinal gradient ≥ 1.5% / m or adjacent layer porosity difference ≥ 15%) or engineering parameters (including brittle index longitudinal gradient ≥ 0.2 / m or fracture toughness difference ≥ 2 MPa·m 0.5 ) reach the threshold of any index, additional layer division is required; when △σ v ≤3 MPa, single-layer fracturing is carried out, but when significant anomalies appear in geological parameters (such as TOC gradient ≥ 3% / m or porosity difference ≥ 20%) or serious mismatches in engineering parameters (such as fracture toughness difference ≥ 3 MPa·m 0.5 ), independent fracturing layers need to be set up.

[0083] The beneficial effects of the present invention are as follows:

[0084] The present invention provides a method for evaluating sweet spots and making decisions on layered fracturing in a multi-layer stacked shale gas reservoir. By integrating geological parameters such as porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance with engineering parameters such as brittleness index and fracture toughness, the CRITIC weight method is used for multi-parameter normalization and weight allocation to construct a comprehensive evaluation model that couples geological sweet spots and engineering sweet spots, significantly improving the comprehensiveness and accuracy of reservoir potential identification; based on the rigid layering determination rule of the vertical stress difference between layers, combined with the flexible optimization strategy of geological parameters (TOC gradient, porosity difference) and engineering parameters (brittleness index gradient, fracture toughness difference), dynamic decision-making for fracturing layering is realized, effectively enhancing the complexity and conductivity of the fracture network; through the "main parameter priority + outlier verification" hybrid method, high-weight parameters are processed first and the 3σ principle is used to trigger local fine analysis, taking into account the dominance of core parameters and the detection of heterogeneity mutations; combined with normalization processing and sample data of the produced wells, a standard relationship curve between the sweetness value and the initial production is established, and four types of sweet spot areas are divided, providing intuitive guidance for fracturing construction; finally, by accurately identifying high sweet spot areas and optimizing the layered fracturing plan, reducing ineffective fracturing sections, it is of great significance for guiding the selection of fracturing intervals, improving the fracturing effect, and increasing the productivity of shale reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0086] Figure 1 It is a flow chart of a method for evaluating sweet spots and making decisions on layered fracturing in a multi-layer stacked shale gas reservoir in the present invention

[0087] Figure 2 It is a well logging interpretation result diagram of Well X

[0088] Figure 3 It is a classification standard diagram of sweet spot areas of shale gas produced wells in the present invention

[0089] Figure 4 It is a schematic diagram of 2-layer three-dimensional development in the present invention

[0090] Figure 5 It is a schematic diagram of 3-layer three-dimensional development in the present invention

[0091] Figure 6 It is the fracture network expansion form of the upper and lower layers in the present invention

[0092] Figure 7 It is the overall fracture network expansion form of two-layer fracturing in the present invention.

Claims

1. A method for evaluating sweet spots in multi-layered shale gas reservoirs and making decisions on layered fracturing, which mainly includes the following steps: S1. Through core description and well logging curves, four geological parameters, namely porosity, adsorbed gas content, organic carbon content and vitrinite reflectance, are selected to evaluate the reservoir development potential; S2. The engineering parameters of the reservoir determine the quality of the reservoir transformation effect under the same fracturing conditions in the later stage; Therefore, the engineering parameters (brittleness index, fracture toughness, and vertical stress difference between layers) are calculated using logging data; S3, first normalize the geological parameters (porosity, adsorbed gas content, organic carbon content and vitrinite reflectance) and engineering parameters (brittleness index, fracture toughness) obtained in S1 and S2, and then use the CRITIC weight method to calculate the index weight value, respectively establish a single well multi-scale geological sweet spot model and an engineering sweet spot prediction model, and finally obtain a comprehensive sweet spot evaluation index; S4, adopts the hybrid method of "main parameter priority + outlier check": perform complete inter-layer gradient calculation on the high-weight parameters (weight>0.2) determined by the CRITIC weight method in S3 as the main basis for stratification decision-making; set a higher gradient threshold for low-weight parameters, and trigger the check only when the gradient of the low-weight parameter exceeds 3 times the standard deviation, and realize "exception detection" through a fast algorithm; S5. Calculate the vertical stress difference between layers (the minimum horizontal principal stress difference between adjacent strata) and divide it into three intervals according to the stress difference threshold: when it is greater than or equal to 5MPa, perform three-layer fracturing; when it is 3-5MPa, optimize the stratification based on geological-engineering parameters; when it is less than or equal to 3MPa, perform single-layer fracturing, but special cases require independent stratification.

2. A method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to claim 1, characterized in that: Step S1 also includes: S11. The volume density obtained by logging is a comprehensive response of the rock skeleton and pore fluid density. The porosity parameter model uses the rock volume model to obtain the porosity expression: φ N =0.01×(CN-LOCR-0.5×V cl ×N cl ) (2) In the formula, φ D and φ N are density porosity and neutron porosity, %; ρ ma , mf and ρ vl are rock skeleton density, formation fluid density and mudstone density, g·cm -3 ; CN is the target layer density logging value, g·cm -3 ; LOCR is the rock skeleton neutron value, %; N cl is the neutron value of mudstone, %. S12, adsorbed gas accounts for 20% to 80% of the total gas content of shale, and is often adsorbed on the surface of shale organic matter. The BET multilayer adsorption theoretical model is used to calculate the adsorbed gas content of deep shale. The formula is as follows: According to the thermodynamic relationship between adsorption and desorption of the (n–1)th layer and the nth layer in the classic BET model, the adsorption-desorption coefficient k is defined as: Where, k is the adsorption-desorption coefficient, dimensionless; a n , b n are the adsorption coefficient and desorption coefficient of the nth layer, s -1 ; E L is the liquid adsorption energy, 4.187 J / mol; R is the universal gas constant, with a value of 8.314 J / (mol·K); T is the temperature, K. The adsorption heat correlation coefficient C in the BET model is: Where C is a constant related to the adsorption heat in the BET model, dimensionless; a1 and b1 are the adsorption coefficient and desorption coefficient of the first layer, respectively; s -1 ; E1 is the adsorption energy of the first layer, 4.187 J / mol. Referring to the classic BET theoretical method, an improved BET multilayer adsorption model is obtained: Where V aMBET is the improved BET multilayer adsorbed gas content, cm 3 / g. S13. Total organic carbon content (TOC) is an organic matter abundance index commonly used at home and abroad. It refers to the carbon content in the organic matter remaining in the rock after the oil and gas escape from the source rock. The △logR method is a traditional method for evaluating the total organic carbon content of shale reservoirs and is also the most widely used method for evaluating the total organic carbon content. The improved △logR model is used to evaluate the organic carbon content TOC. The calculation formula is as follows: △logR=logR+log(R max / R min ) / (△t max -△t min )×(△t-△t max )-logR min (7) TOC=△lgR×10 (2.297-0.1688LOM) +△TOC (8) Wherein, TOC is the total organic carbon content, %; R min (△t min ) and R max (△t max ) are the minimum and maximum values ​​of the resistivity (acoustic time difference) curve scale when the acoustic time difference and resistivity curves are superimposed, and are dimensionless. S14, by the vitrinite reflectance R o A single factor correlation analysis was performed between the conventional logging response and the logging response with good correlation was selected for multivariate regression to establish the following model: <h2 style=";text-align:left;direction:ltr">R<h2 style=";text-align:left;direction:ltr"> o <h2 style=";text-align:left;direction:ltr"> =3.264-1.437GR-1.776SP R<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> <0.68 (9) Among them, R o is the vitrinite reflectance, %; GR is the relative value of natural gamma, dimensionless; SP is the relative value of spontaneous potential, dimensionless.

3. The method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to claim 1, characterized in that: Step S2 also includes: S21. The brittleness index describes the ability of the reservoir to produce brittle deformation under the action of fracturing construction. The brittleness index is an indispensable evaluation index in the evaluation of reservoir engineering sweet spots and fracturability. The dynamic parameters are calculated based on the logging data, and the dynamic Young's modulus and Poisson's ratio are converted into static Young's modulus and Poisson's ratio. The calculation formulas of dynamic Young's modulus and Poisson's ratio are as follows: In the formula, σ d is the dynamic Poisson's ratio, dimensionless; E d is the dynamic Young's modulus, GPa; △t s is the shear wave time difference, μs·m -1 ; △t p is the longitudinal wave time difference, μs·m -1 ρ b is the rock density, g·cm -3 . The logging curves obtain dynamic parameters, while the static parameters in the formula are obtained based on the measured stress and strain data. Acoustic logging can provide P-wave and S-wave time difference data. Combined with density logging data, the dynamic Young's modulus and dynamic Poisson's ratio of the tight reservoir at any measurement depth can be calculated. Then, through the conversion of dynamic and static parameters, the static Young's modulus and static Poisson's ratio can be obtained. The dynamic and static parameter conversion formula is as follows: s s =0.1552σ d +0.2209 (12) E s =0.7095E d +2.2526 (13) Where: s is the static Poisson's ratio, dimensionless; E s is the static Young's modulus, GPa. Calculate the required static parameters and calculate the brittleness index: Where BI is the brittleness index, dimensionless; E smax (E smin ) is the maximum (minimum) static Young's modulus, GPa; σ smax (σ smin ) is the maximum (minimum) static Poisson’s ratio, dimensionless. S22. The pressure of the fracturing fluid is concentrated at the tip of the crack. The description of the stress at the tip of the hydraulic crack indirectly indicates the difficulty of the crack to expand. The stress intensity factor (SIF) can be used to predict the stress state near the crack tip. When the SIF reaches the critical value, the rock is fractured. The stress intensity at this time is called fracture toughness, and it is also called the strain energy release rate when elastic deformation occurs. Fracture toughness describes the ability of rock to prevent crack expansion. It belongs to the property of the rock itself. Its size is related to the difficulty of crack extension. The smaller its value, the easier the crack is to extend, and the more conducive it is to hydraulic fracturing. Its calculation method is as follows: TO IC =0.271+0.107S t (15) s c =(0.0045+0.0035V cl )E d (16) S t =s c / K (17) In the formula, K IC is the fracture toughness, MPa·m 0.5 ; S t is the tensile strength, MPa; σ c is the compressive strength, MPa; K is a constant, K=12.

26.

4. A method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to claim 1, characterized in that: Step S3 also includes: S31. In order to eliminate the influence of different dimensions on the evaluation results, it is necessary to perform dimensionless processing on each indicator: If the value of the indicator used is as large as possible (positive indicator): If the value of the indicator used is as small as possible (reverse indicator): In the formula, x ij正 is the normalized value of the jth index of the ith sample, dimensionless; x ij 、x j,max 、x j,min They are the original indicator value, the maximum value of the j-th indicator, and the minimum value of the j-th indicator, respectively. The unit is determined by the indicator type. S32. In the CRITIC method, standard deviation is used to indicate the fluctuation of internal values ​​of each indicator. The larger the standard deviation, the greater the difference in the value of the indicator. The more information can be displayed, the stronger the evaluation strength of the indicator itself is, and more weight should be assigned to the indicator. j Represents the standard deviation of the jth indicator: In the formula, S j is the standard deviation of the jth indicator, and the unit is determined by the indicator type; n is the sample size, dimensionless. The conflict of indicators is expressed in the form of correlation coefficient, r ij Represents the correlation coefficient between evaluation indicators i and j C j The larger the value is, the greater the role of the jth evaluation index in the entire evaluation index system, and more weight should be assigned to it. The objective weight w of the jth indicator j for: In the formula, C j is the information content of the jth indicator, dimensionless; S j is the standard deviation of the jth indicator, and the unit is determined by the indicator type; w j is the objective weight of the j-th indicator, dimensionless. S33. Substitute the weight values ​​of each indicator to calculate the single well geological sweet spot model of coupled reservoir development potential and reservoir seepage capacity: F G =w1φ n +w2V aMBETn +w3TOC n +w4R on (24) Where F G represents the geological sweet spot, dimensionless; w1-w4 represent the weights of each factor of the geological sweet spot, dimensionless; φ n represents the normalized reservoir porosity, dimensionless; V aMBETn Represents the normalized adsorbed gas content, dimensionless; TOC n Represents the normalized organic carbon content, dimensionless; R on represents the normalized vitrinite reflectance, dimensionless; S34. Substitute the weight values ​​of each indicator to construct a single-well multi-scale engineering sweet spot prediction model based on multi-scale parameter fusion for coupled reservoir compressibility and fracture conductivity: F E =w1B In +w2K ICn (25) Where F E represents the engineering sweet spot, dimensionless; w1-w2 represent the weights of each factor of the engineering sweet spot, dimensionless; B In represents the normalized brittleness index, dimensionless; K ICn Represents the normalized fracture toughness and is dimensionless. S35. Based on the above analysis, the weights of the engineering factors that affect the formation of fracture networks and the geological factors that affect the development reservoir conditions on the production capacity of production wells are determined respectively. And according to the long-term development practice in the study area, the weights of the geological and engineering parameters currently determined by the production unit on the production capacity of the put into production wells are α and β respectively. Based on this, a weight calculation model for the comprehensive evaluation of the sweet spot of the volume fracturing well section is established: In the formula, w 地i is the weight of geological influencing factors, dimensionless; F G ' is the normalized geological sweet spot index, dimensionless; w 工i is the weight of the engineering influencing factor, dimensionless; F E ' is the normalized engineering sweet spot index, dimensionless; α and β are the impact weights of the geological and engineering parameters currently determined by the production unit on the production capacity of the put into production wells, dimensionless. S36. Using the sample data of the wells that have been put into production, the comprehensive average value of the sweet spots in the fracturing area of ​​the well section is calculated, and the average daily gas production of the wells in the initial development stage is used as the evaluation index to draw a standard relationship curve between the comprehensive average value of sweetness and the initial production. The sweet spot area is divided into four categories: Class I sweet spot, Class II sweet spot, Class III sweet spot, and non-sweet spot. At the same time, corresponding to the sweetness value range of each sweet spot area, using the established sweet spot type classification standard, based on the calculation results of the sweetness value distribution along the well section, it is finally possible to obtain the sweet spot type distribution map of the corresponding well section, which will provide an important theoretical basis for the design of the subsequent stratified fracturing construction plan.

5. The method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to claim 1, characterized in that: Step S4 also includes: S41. For any parameter (such as porosity, organic carbon content, brittleness index, fracture toughness), the gradient G of adjacent small layers (jth layer and j+1th layer) j Calculated as: Where: P j , P j+1 is the parameter value of the jth layer and the j+1th layer (needs to be normalized), dimensionless; h is the vertical distance between adjacent small layers, m. S42. For the low-weight parameters determined by the CRITIC weight method (such as adsorbed gas content and vitrinite reflectance), first calculate the global standard deviation (σ) of its gradient distribution, and use the 3σ principle to set the abnormal threshold; use the sliding window algorithm (window length 3-5 small layers, step length 1 small layer) to scan the gradient value vertically. When the gradient of a layer segment exceeds 3σ, trigger a local fine analysis including core review, adjacent well comparison and microseismic data verification; if it is confirmed that the anomaly reflects a sudden change in real reservoir heterogeneity, then add the layer segment as an independent fracturing segment and optimize the fracturing parameters accordingly.

6. The method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to claim 1, characterized in that: Step S5 also includes: S51. As the vertical stress difference increases, the stress isolation effect between strata increases, resulting in the vertical stress constraint during the crack initiation and expansion process becoming more severe, the vertical extension ability of the crack weakens, and the complexity of the formed crack network decreases. Among them, the horizontal minimum principal stress can be obtained by the differential strain experiment, and the calculation formula is as follows: Where: △σ v is the vertical stress difference between layers, MPa; σ h is the minimum horizontal ground stress, MPa; μ is the formation Poisson’s ratio, dimensionless; σ v is the vertical stress, MPa; β h is the minimum tectonic stress field coefficient, dimensionless; Pp is the formation pressure, MPa; α is the Biot coefficient, and the default value is 1. S52, firstly, the rigid stratification is determined based on the vertical stress difference. v ≥5MPa, regardless of other parameters, it is forced to be divided into 3 independent fracturing layers; when 3MPa<△σ v <5MPa, it enters the flexible optimization stage, which is usually divided into two layers. If the geological parameters (including the longitudinal gradient of TOC ≥1.5% / m or the porosity difference between adjacent layers ≥15%) or the engineering parameters (including the longitudinal gradient of brittleness index ≥0.2 / m or the fracture toughness difference ≥2MPa·m 0.5 ) If any of the indicators reaches the threshold, additional stratification is required; when △σ v Single-layer fracturing is performed when the pressure is ≤3MPa. However, when the geological parameters are significantly abnormal (such as TOC gradient ≥3% / m or porosity difference ≥20%) or the engineering parameters are seriously mismatched (such as fracture toughness difference ≥3MPa·m 0.5 ), an independent fracturing layer section needs to be established.

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