A sweet spot evaluation and layered fracturing decision-making method for multi-layered shale gas reservoirs

Abstract

The present invention relates to a method for evaluating sweet spots in multi-layered shale gas reservoirs and making decisions on layered fracturing, belonging to the technical field of oil and gas field development. The method comprises the following steps: calculating geological parameters such as porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance; calculating engineering parameters such as brittleness index and fracture toughness; employing the CRITIC weight method for normalization and weight distribution, constructing a comprehensive geological-engineering evaluation model, and dividing sweet spots into four categories; employing a hybrid method of "main parameter priority + outlier verification" to prioritize the analysis of interlayer gradients of high-weight parameters, and performing local fine verification of abnormal mutations of low-weight parameters based on the 3σ principle; and combining a rigid-flexible layered model to dynamically determine the fracturing layering strategy and optimize the complexity of the fracture network. The present invention improves reservoir identification accuracy through multi-parameter fusion evaluation, enhances fracturing efficiency through dynamic layered decision-making, and ultimately reduces development costs, providing technical support for the efficient development of shale gas.

Description

Technical Field

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

[0002] Shale gas reservoirs are characterized by low porosity and ultra-low permeability, and can only be effectively developed using volume fracturing technology. Due to the strong lateral heterogeneity of the reservoir and the significant differences in reservoir properties along the wellbore trajectory, it is particularly important to select the sweet spot in the well section for volume fracturing. Sweet spots are areas that are easy to fracture and have relatively high production in shale gas exploration and development. Their optimization needs to be carried out from two aspects: first, from a geological perspective, select high-quality reservoirs with high gas content, high TOC (total organic carbon) and high porosity; second, from an engineering transformation perspective, select layers with small horizontal stress differences and high brittleness index to form a complex fracture network and maximize the stimulation volume (SRV).

[0003] Reservoir heterogeneity is a major factor hindering shale gas development. Therefore, optimizing the sweet spot for volumetric fracturing is crucial. Currently, evaluating shale gas sweet spots faces numerous challenges. While numerous compressibility evaluation methods exist, most rely on single-factor indices, and research on the integration of geological and engineering parameters is insufficient. Therefore, it is necessary to develop high-precision, user-friendly methods for calculating fracturing potential that account for multiple factors. Furthermore, shale gas productivity is influenced not only by geological and engineering parameters but also by fracturing operation parameters, leading to errors in developing sweet spot evaluation methods. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a method for evaluating sweet spots in multi-layered shale gas reservoirs and making decisions on layered fracturing.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs has the following steps:

[0007] S1. Through core description and well logging curves, four geological parameters, porosity, adsorbed gas content, organic carbon content and vitrinite reflectance, are selected to evaluate the reservoir development potential;

[0008] S11. The volume density obtained from well 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:

[0009]

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

[0011]

[0012] Where, φ D and φ N are density porosity and neutron porosity, %; ρ ma , ρ mf and ρ vl are the 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, %.

[0013] 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 theory model is used to calculate the adsorbed gas content of deep shale. The formula is as follows:

[0014] 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:

[0015]

[0016] 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, 8.314 J / (mol·K); T is the temperature, K.

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

[0018]

[0019] Where C is a dimensionless constant related to the heat of adsorption in the BET model; 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.

[0020] Referring to the classic BET theoretical method, an improved BET multilayer adsorption model is obtained:

[0021]

[0022] Where V aMBETis the improved BET multilayer adsorbed gas content, cm 3 / g.

[0023] S13. Total organic carbon content (TOC) is a widely used indicator of organic matter abundance both domestically and internationally. 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. 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 time difference) curve scale when the acoustic time difference and resistivity curves are superimposed, and are dimensionless.

[0027] S14, by the vitrinite reflectance R o A single factor correlation analysis was conducted between the results and the conventional logging responses. The logging responses with good correlation were selected for multivariate regression to establish the following model:

[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 reconstruction 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.

[0031] S21. The brittleness index describes the ability of a reservoir to produce brittle deformation under the action of fracturing. The brittleness index is an essential evaluation indicator in the evaluation of reservoir engineering sweet spots and fracturability. Dynamic parameters are calculated based on well 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 for dynamic Young's modulus and Poisson's ratio are as follows:

[0032]

[0033] Where 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 .

[0034] Well logging curves yield dynamic parameters, while the static parameters in the formula are derived from measured stress and strain data. Acoustic logging provides P- and S-wave time difference data. Combined with density logging data, the dynamic Young's modulus and dynamic Poisson's ratio of tight reservoirs at any measured depth can be calculated. Dynamic and static parameter conversion can then yield static Young's modulus and static Poisson's ratio. The dynamic and static parameter conversion formula is as follows:

[0035] σ s =0.1552σ d +0.2209 (12)

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

[0037] Where: 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] 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.

[0041] S22. The pressure of the fracturing fluid is concentrated at the crack tip. The description of the stress at the hydraulic crack tip indirectly indicates the difficulty of crack expansion. The stress intensity factor (SIF) can be used to predict the stress state near the crack tip. When the SIF reaches a critical value, the rock is fractured. The stress intensity at this time is called fracture toughness, and when elastic deformation occurs, it is also called the strain energy release rate. Fracture toughness describes the ability of rock to prevent crack expansion. It is a property of the rock itself. Its size is related to the difficulty of crack extension. The smaller its value, the easier the crack extends and the more conducive it is to hydraulic fracturing. Its 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] Where 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, 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 are normalized, and then the CRITIC weight method is used to calculate the indicator weight values. A single-well multi-scale geological sweet spot model and an engineering sweet spot prediction model are established respectively, and finally a comprehensive sweet spot evaluation index is obtained.

[0047] S31. In order to eliminate the influence of different dimensions on the evaluation results, it is necessary to perform dimensionless processing on each indicator:

[0048] If the value of the indicator used is as large as possible (positive indicator):

[0049]

[0050] If the value of the indicator used is as small as possible (contrary indicator):

[0051]

[0052] Where x ij正 is the normalized value of the jth index of the i-th sample, dimensionless; xij 、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.

[0053] S32. In the CRITIC method, the standard deviation is used to represent the internal value fluctuation of each indicator. The larger the standard deviation, the greater the difference in the value of the indicator. The more information can be reflected, the stronger the evaluation strength of the indicator itself is. More weight should be assigned to the indicator. j Indicates the standard deviation of the j-th indicator:

[0054]

[0055] Where S j is the standard deviation of the jth indicator, and the unit is determined by the indicator type; n is the number of samples, dimensionless.

[0056] The conflict of indicators is expressed in the form of correlation coefficient, r ij Represents the correlation coefficient between evaluation indicators i and j.

[0057]

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

[0059]

[0060] The objective weight w of the jth indicator j for:

[0061]

[0062] Where C j is the information amount 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.

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

[0064] F G =w1φ n +w2V aMBETn +w3TOC n +w4R on (twenty four)

[0065] Where FG 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;

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

[0067] F E =w1B In +w2K ICn (25)

[0068] 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, dimensionless.

[0069] S35. Based on the above analysis, the weights of the engineering factors affecting fracture network formation and the geological factors affecting reservoir development conditions on the production capacity of production wells were determined. Furthermore, based on the long-term development practices in the study area, the weights of the geological and engineering parameters currently determined by the production unit on the production capacity of producing wells were α and β, respectively. Based on this, a comprehensive evaluation weight calculation model for sweet spots in volumetric fracturing well sections was established:

[0070]

[0071] Where 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 factors, dimensionless; F E ' is the normalized engineering sweet spot index, dimensionless; α and β are the weights of the impact of the geological and engineering parameters currently determined by the production unit on the production capacity of the put into production wells, dimensionless.

[0072] 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. The standard relationship curve between the comprehensive average value of sweetness and the initial production is drawn, and 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, the sweetness value range corresponding to each sweet spot area is calculated. Using the established sweet spot type classification standard, based on the calculation results of the sweetness value distribution along the well section, the sweet spot type distribution map of the corresponding well section can be finally obtained, which will provide an important theoretical basis for the design of the subsequent stratified fracturing construction plan.

[0073] S4 adopts a hybrid method of "main parameter priority + outlier verification" to perform a complete inter-layer gradient calculation on the high-weight parameters (weight > 0.2) determined by the CRITIC weight method in S3, which serves as the main basis for stratification decision-making; a higher gradient threshold is set for low-weight parameters, and verification is triggered only when the gradient of the low-weight parameter exceeds 3 times the standard deviation, achieving "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 (jth layer and j+1th layer) j Calculated as:

[0075]

[0076] Where: P j 、P j+1 is the parameter value of the jth layer and the j+1th layer (must be normalized), dimensionless; h is the vertical distance between adjacent small layers, m.

[0077] S42. For the low-weight parameters determined by the CRITIC weighting method (such as adsorbed gas content and vitrinite reflectance), the global standard deviation (σ) of their gradient distribution is first calculated, and the 3σ principle is used to set the anomaly threshold; the gradient value is scanned longitudinally through the sliding window algorithm (window length 3-5 small layers, step length 1 small layer). When the gradient of a certain layer exceeds 3σ, a local fine analysis including core review, adjacent well comparison and microseismic data verification is triggered; if it is confirmed that the anomaly reflects a sudden change in real reservoir heterogeneity, the layer segment is added as an independent fracturing segment, and the fracturing parameters are optimized accordingly.

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

[0079] S51. As the vertical stress difference increases, the stress isolation effect between strata increases, resulting in an increase in the vertical stress constraint on the fracture initiation and propagation process, a weakening of the fracture's vertical extension ability, and a decrease in the complexity of the resulting fracture network. The horizontal minimum principal stress can be obtained from differential strain experiments, and the calculation formula is as follows:

[0080]

[0081] Where: △σ v is the vertical stress difference between layers, MPa; σ h is the minimum horizontal ground 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, with a default value of 1.

[0082] 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 TOC longitudinal gradient ≥1.5% / m or adjacent layer porosity difference ≥15%) or engineering parameters (including brittleness index longitudinal gradient ≥0.2 / m or fracture toughness difference ≥2MPa·m) are 0.5 ) If any indicator reaches the threshold, additional stratification is required; when △σ v Single layer fracturing is performed when the pressure is less than 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 needs to be established.

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

[0084] The present invention provides a method for evaluating sweet spots in multi-layered shale gas reservoirs and making decisions on stratified fracturing. By integrating geological parameters such as porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance with brittleness index and fracture toughness engineering parameters, the CRITIC weighting method is used for multi-parameter normalization and weight distribution to construct a comprehensive evaluation model for coupling geological sweet spots with engineering sweet spots, significantly improving the comprehensiveness and accuracy of reservoir potential identification. Based on the rigid stratification judgment rule of interlayer vertical stress difference, a flexible optimization strategy combining geological parameters (TOC gradient, porosity difference) and engineering parameters (brittleness index gradient, fracture toughness difference) is used to achieve fracturing separation. The dynamic decision-making of the fracture layer can effectively enhance the complexity and conductivity of the fracture network; through the hybrid method of "main parameter priority + outlier verification", high-weight parameters are prioritized 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 wells that have been put into production, a standard relationship curve between sweetness value and initial production is established, and four types of sweet spots are divided to provide intuitive guidance for fracturing construction; finally, by accurately identifying high sweet spots and optimizing the layered fracturing scheme, the ineffective fracturing sections are reduced, which is of great significance for guiding the selection of fracturing layers, improving fracturing effects and increasing the productivity of shale reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0086] Figure 1 This is a flow chart of a method for evaluating sweet spots and making decisions on stratified fracturing in multi-layered shale gas reservoirs according to the present invention;

[0087] Figure 2 This is the logging interpretation result diagram of Well X;

[0088] Figure 3 This is a standard diagram for classifying sweet spots in shale gas production wells in the present invention;

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

[0090] Figure 5 This is a schematic diagram of the three-layer three-dimensional development of the present invention;

[0091] Figure 6 It is the expanded form of the upper and lower layer seam network in the present invention;

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

Claims

1. A method for evaluating sweet spots in multi-layered shale gas reservoirs and making decisions on stratified 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. Use logging data to calculate engineering parameters such as brittleness index, fracture toughness, and vertical stress difference between layers; S3. First, the geological parameters obtained in S1 and S2, namely porosity, adsorbed gas content, organic carbon content, and vitrinite reflectance, as well as the engineering parameters, namely brittleness index and fracture toughness, are normalized. Then, the CRITIC weighting method is used to calculate the index weight values. A single-well multi-scale geological sweet spot model and an engineering sweet spot prediction model are established respectively, and finally a comprehensive sweet spot evaluation index is obtained; S4 uses a hybrid approach of "main parameter priority + outlier verification" to perform complete inter-layer gradient calculations on high-weight parameters with weights > 0.2, as determined by the CRITIC weighting method in S3, and use these as the primary basis for stratification decisions. A higher gradient threshold is set for low-weight parameters, triggering verification only when the gradient of a low-weight parameter exceeds three standard deviations, achieving "exception detection" through a fast algorithm. S5. Calculate the vertical stress difference between layers, i.e., the minimum horizontal principal stress difference between adjacent strata, and divide the stress difference into three intervals according to the stress difference threshold: When the stress difference is greater than or equal to 5 MPa, three-layer fracturing is performed. When the stress difference is 3-5 MPa, the stratification is optimized based on geological-engineering parameters. When the stress difference is less than or equal to 3 MPa, single-layer fracturing is performed, but independent stratification is required in special cases.

2. 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 S1 also includes: S11. The volume density obtained from well 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) Where, φ D and φ N are density porosity and neutron porosity, %; ρ ma , ρ mf and ρ vl are the 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 theory 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-1th 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, 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 dimensionless constant related to the heat of adsorption in the BET model; 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 adsorption gas content, cm 3 / g; S13. Total organic carbon (TOC) is a widely used indicator of organic matter abundance both domestically and internationally. It refers to the carbon content in the organic matter remaining in the rock after oil and gas escape from the source rock. The ΔlogR 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 ΔlogR model is used to evaluate the total organic carbon content. The calculation formula is as follows: ΔlogR=logR+log(R max / R min ) / (Δt max -Δt min )×(Δt-Δt max )-logR min (7) TOC=ΔlgR×10 (2.297-0.1688LOM) +ΔTOC (8) Among them, TOC is the total organic carbon content, %; R min and R max are the minimum and maximum values ​​of the resistivity curve scale when the acoustic time difference and the resistivity curve are superimposed, dimensionless; Δt min and Δt max The minimum and maximum values ​​of the acoustic transit time curve scale when the acoustic transit time curve is superimposed on the resistivity curve, respectively, dimensionless; S14, by the vitrinite reflectance R o A single factor correlation analysis was conducted between the results and the conventional logging responses. The logging responses with good correlation were 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 layered 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 a reservoir to produce brittle deformation under the action of fracturing. The brittleness index is an essential evaluation indicator in the evaluation of reservoir engineering sweet spots and fracturability. Dynamic parameters are calculated based on well 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 for dynamic Young's modulus and Poisson's ratio are as follows: Where σ d is the dynamic Poisson's ratio, dimensionless; E d is the dynamic Young's modulus, GPa; Δt s is the shear wave time difference, μs·m -1 ;Δt p is the longitudinal wave time difference, μs·m -1 ρ b is the rock density, g·cm -3 ; The dynamic parameters are obtained from the logging curve, while the static parameters in the formula are obtained based on the measured stress and strain data. Acoustic logging can provide the time difference data of the longitudinal and shear waves. Combined with the 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, by converting the 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 and E smin are the maximum and minimum static Young's modulus, GPa; σ smax and σ smin are the maximum and minimum static Poisson's ratio, dimensionless; S22. The pressure of the fracturing fluid is concentrated at the crack tip. The description of the stress at the hydraulic crack tip indirectly indicates the difficulty of crack expansion. The stress intensity factor can be used to predict the stress state near the crack tip. When the stress intensity factor reaches a critical value, the rock is fractured. The stress intensity at this time is called fracture toughness, which is also called the strain energy release rate when elastic deformation occurs. Fracture toughness describes the ability of rock to resist crack expansion and is a property of the rock itself. Its value is related to the difficulty of crack extension. The smaller its value, the easier the crack extends and the more favorable it is for 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) Where 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. The method for evaluating sweet spots and making decisions on layered 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. The calculation formula for positive indicators with larger values ​​is as follows: The calculation formula for negative indicators with smaller values ​​is as follows: Where x ij正 is the normalized value of the jth index of the i-th 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, the standard deviation is used to represent the internal value fluctuation of each indicator. The larger the standard deviation, the greater the difference in the value of the indicator. The more information can be reflected, the stronger the evaluation strength of the indicator itself is. More weight should be assigned to the indicator. j Indicates the standard deviation of the j-th indicator: Where S j is the standard deviation of the jth indicator, and the unit is determined by the indicator type; n is the number of samples, 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 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: Where C j is the information amount 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 jth indicator, dimensionless; S33. Substitute the weight values ​​of each indicator to calculate the single-well geological sweet spot model that couples 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 the fusion of multi-scale parameters, coupling 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, dimensionless; S35. Based on the above analysis, the weights of the engineering factors that affect fracture network formation and the geological factors that affect reservoir development conditions on the production capacity of production wells were determined respectively. Based on 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 producing wells were α and β, respectively. Based on this, a comprehensive evaluation weight calculation model for the sweet spot of the volume fracturing well section was established: Where 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 factors, dimensionless; F E ' is the normalized engineering sweet spot index, dimensionless; α and β are the weights of the impact of the geological and engineering parameters currently determined by the production unit on the production capacity of the put into production well, 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. The standard relationship curve between the comprehensive average value of sweetness and the initial production is drawn, and 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, the sweetness value range corresponding to each sweet spot area is calculated. Using the established sweet spot type classification standard, based on the calculation results of the sweetness value distribution along the well section, the sweet spot type distribution map of the corresponding well section can be finally obtained, 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 layered 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 between adjacent layers, i.e., layer j and layer j+1, is j The calculation formula is: Where: P j 、P j+1 is the normalized parameter value of the jth layer and the j+1th layer, dimensionless; h is the vertical distance between adjacent small layers, m; S42. For low-weight parameters determined by the CRITIC weighting method, such as adsorbed gas content and vitrinite reflectance, the global standard deviation σ of their gradient distribution is first calculated, and the 3σ principle is used to set the anomaly threshold; through the sliding window algorithm, the gradient value is scanned longitudinally with a window length of 3-5 small layers and a step length of 1 small layer. When the gradient of a layer segment exceeds 3σ, a local fine analysis including core review, adjacent well comparison and microseismic data verification is triggered; if it is confirmed that the anomaly reflects a sudden change in real reservoir heterogeneity, the layer segment is added as an independent fracturing segment, and the fracturing parameters are optimized accordingly.

6. The method for evaluating sweet spots and making decisions on layered 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 is enhanced, resulting in an increase in the vertical stress constraint on the fracture initiation and propagation process, a weakening of the fracture's vertical extension ability, and a decrease in the complexity of the resulting fracture network. The horizontal minimum principal stress can be obtained from differential strain experiments, and the calculation formula is as follows: Where: Δσ v is the vertical stress difference between layers, MPa; σ h is the minimum horizontal ground 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; P p is the formation pressure, MPa; α is the Biot coefficient, the default value is 1; S52. First, a rigid stratification judgment is made based on the vertical stress difference. When the vertical stress difference is ≥5MPa, the fracture is divided into three independent fracture layers regardless of other parameters. When the vertical stress difference is 3MPa < 5MPa, the flexible optimization stage is entered, which is usually divided into two layers. If the geological parameter, i.e., the vertical gradient of the total organic carbon content (TOC), is ≥1.5% / m or the porosity difference between adjacent layers is ≥15%, or the engineering parameter, i.e., the vertical gradient of the brittleness index is ≥0.2 / m or the fracture toughness difference is ≥2MPa·m, the fracture is divided into three independent fracture layers. 0.5 If any indicator reaches the threshold, additional stratification is required; when the vertical stress difference is ≤3MPa, single-layer fracturing is performed, but when there are significant abnormalities in geological parameters, such as total organic carbon content TOC gradient ≥3% / m or porosity difference ≥20%, or when there is a serious mismatch in engineering parameters, such as fracture toughness difference ≥3MPa·m 0.5 When the fracture is generated, an independent fracturing layer section needs to be established.

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