Quantitative evaluation method for shale gas reservoir development dessert

Abstract

The invention discloses a quantitative evaluation method for shale gas reservoir development desserts. The quantitative evaluation method comprises the following steps: collecting and establishing a geological engineering parameter database of an implemented well in a finishing work area; geological factor analysis influencing gas well productivity is carried out, and geological main control factors are determined; engineering factor analysis influencing gas well productivity is carried out, and engineering main control factors are determined; analyzing geological main control factors, and comprehensively establishing a geological parameter group which can reflect the advantages and disadvantages of geological conditions and has physical significance; analyzing engineering main control factors, and comprehensively establishing an engineering parameter group which can reflect the advantages and disadvantages of engineering conditions and has physical significance; according to the parameter group, combining with a block parameter plane distribution diagram to calculate and obtain work area geological dessert and engineering dessert distribution; according to the geology and the engineering dessert distribution, working area development dessert distribution is obtained in combination with a development dessert judgment criterion; the method can accurately reflect the influence of the reservoir quality and the engineering quality on the development effect, and can be applied to the optimization evaluation of the favorable development area.

Description

Technical Field

[0001] The present invention relates to the technical field of shale gas development, and in particular to a method for quantitatively evaluating sweet spots in shale gas reservoir development. Background Art

[0002] my country has rich shale gas resources and great development potential, especially the large-scale, continuous and stable distribution of marine shale in the Sichuan Basin, which is currently the most realistic area for increasing production.

[0003] As a typical unconventional resource, shale gas generally adopts the "horizontal well + volume fracturing" technology to achieve economical and efficient development. Therefore, the evaluation of shale gas development sweet spots is significantly different from that of conventional gas reservoirs. On the one hand, it is necessary to clarify the distribution characteristics of the highest quality shale reservoirs, and on the other hand, it is also necessary to master the distribution of shale reservoirs with favorable engineering conditions.

[0004] At present, there are still the following problems in the shale gas selection evaluation index system:

[0005] (1) There is a phenomenon of "focusing on geology and neglecting engineering" in the evaluation of development sweet spots. In theory, it is still believed that the difference in shale gas development effects mainly depends on the physical properties of the reservoir;

[0006] (2) Simply adding a number of engineering parameters to the geological sweet spot evaluation method and then ranking the parameters to achieve the development sweet spot evaluation, lacks the constraints of the physical meaning of the engineering sweet spot, resulting in deviations in the evaluation results;

[0007] (3) Using qualitative or semi-quantitative methods to evaluate and optimize the “double sweet spots” is difficult to support actual development and application. Summary of the invention

[0008] The purpose of the present invention is to provide a shale gas reservoir development sweet spot quantitative evaluation method to address the above-mentioned deficiencies. This method can accurately reflect the impact of reservoir quality and engineering quality on development results and can be applied to the optimal evaluation of favorable development areas.

[0009] First of all, explain the relevant keywords:

[0010] Geological sweet spots: areas where reservoir conditions (porosity, permeability, saturation, gas content, reservoir thickness, etc.) are considered relatively favorable during shale gas development;

[0011] Engineering sweet spots: areas where engineering conditions (minimum horizontal principal stress, stress difference, brittleness, etc.) are considered relatively favorable during shale gas development;

[0012] Development sweet spots: areas with relatively good geological and engineering conditions, taking into account the distribution of geological and engineering sweet spots, are the most favorable target areas for shale gas development;

[0013] EUR (The Estimated Ultimate Recovery): A shale gas well production effect evaluation indicator. In China, it generally refers to the cumulative shale gas production within 20 years, in units of 100 million cubic meters.

[0014] First-year daily gas production: a shale gas well production performance evaluation indicator, generally refers to the average daily gas production of a shale gas well in the first year of production, in units of 10,000 cubic meters per day;

[0015] IP60 (The Initial Production of 60days): An indicator for evaluating the production effect of a shale gas well, generally referring to the cumulative gas production within the first 60 days of the shale gas well being put into production.

[0016] The present invention is achieved through the following scheme:

[0017] A method for quantitatively evaluating sweet spots in shale gas reservoir development comprises the following steps:

[0018] It includes the following steps:

[0019] Step 1: Collect and establish a database of geological engineering parameters of wells that have been implemented in the work area;

[0020] Step 2: Analyze the geological factors that affect gas well productivity and identify the main geological controlling factors;

[0021] Step 3: Conduct an analysis of engineering factors that affect gas well productivity and identify the main engineering control factors;

[0022] Step 4: Analyze the main geological controlling factors and comprehensively establish geological parameter groups that can reflect the advantages and disadvantages of geological conditions and have physical significance;

[0023] Step 5: Analyze the main controlling factors of the project and comprehensively establish a group of engineering parameters that can reflect the quality of the project conditions and have physical significance;

[0024] Step 6: Based on the above parameter groups and the block parameter plane distribution map, calculate the distribution of geological sweet spots and engineering sweet spots in the work area;

[0025] Step 7: Based on the above geological and engineering sweet spot distribution and the development sweet spot determination criteria, obtain the development sweet spot distribution of the work area.

[0026] In step 2 and step 3, the influencing factor analysis uses the MATLAB neural network module to obtain the normalized ranking of the influence of different parameters on gas well productivity.

[0027] In step one, the specific steps are:

[0028] Establish a database of geological condition parameters, including porosity, permeability, gas content, water saturation, TOC, and continuous thickness of Class I reservoirs;

[0029] Establish a database of engineering condition parameters, including minimum horizontal principal stress, formation temperature, stress difference, and brittleness;

[0030] Establish a database of engineering implementation parameters, including high-quality reservoir drilling rate, wellbore smoothness, fracturing main displacement, sand addition intensity, and fluid intensity;

[0031] And form a database of geological engineering parameters of the wells implemented in the work area.

[0032] In step 2, the specific steps are:

[0033] Based on the establishment of a basic database containing geological and engineering parameters in step 1, it is necessary to carry out an analysis of the main controlling factors affecting the production capacity of the block; use matlab to establish a correlation analysis method to evaluate the correlation between different geological parameters and single well EUR;

[0034] The method steps are as follows:

[0035] S1: Determine the reference sequence reflecting the system behavior characteristics and the comparison sequence affecting the system behavior; among them, the test output is selected as the reference sequence and the influencing factors are selected as the comparison sequence;

[0036] S2: Dimensionless processing of reference series and comparison series:

[0037] More specifically, the mean value method is used as a dimensionless data processing method in multi-factor analysis;

[0038] S3: Calculate the correlation coefficient

[0039]

[0040] Among them, y(k) is the dimensionless value of the reference series, x i (k) is the value of the comparison series after dimensionless conversion, and ρ is the resolution coefficient;

[0041] S4: Calculate the correlation

[0042]

[0043] S5: Relevance ranking

[0044] Sort the correlation degrees. The larger the correlation degree, the greater the impact, and the smaller the correlation degree, the smaller the impact. Find out the main controlling geological factors based on the sorting results.

[0045] In step three, the specific steps are:

[0046] The same method as in step 2 is used to evaluate the correlation between different engineering parameters and the EUR of a single well; the main controlling factors of the engineering are found based on the sorting results.

[0047] In step 4, the specific steps are:

[0048] According to the results of the geological main controlling factors in step 2, a parameter group is established as a comprehensive characterization indicator of the geological sweet spot.

[0049] Reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g It is the most important parameter to determine the geological sweet spot. g R g ) as a comprehensive characterization indicator of geological sweet spots.

[0050] In step five, the specific steps are:

[0051] Considering that the fracturing effect is the core factor affecting the productivity of shale gas wells, the engineering sweet spot should be analyzed in step 3 to obtain the main controlling factor as the minimum horizontal principal stress σ h , horizontal stress difference Δσ and natural fracture density I f On this basis, a comprehensive parameter group FI that can reflect compressibility is established;

[0052]

[0053] Where FI is the fracturing coefficient, dimensionless; S i (i=1, 2, ..., n) is the standardized value of shale reservoir parameters, dimensionless; W i (i=1, 2, ..., n) are different weight coefficients, dimensionless; n is the number of main control parameters.

[0054] W i Mainly through step three, the correlation between the main control parameters is determined by normalization;

[0055] Natural fractures are positively correlated with fracturability, while minimum horizontal principal stress and horizontal stress difference are inversely correlated with fracturability. For these indicators, range transformation is used for standardization. In range transformation, parameters are divided into positive indicators and negative indicators. The positive indicator, i.e., the larger the indicator value, the better, while the negative indicator, i.e., the smaller the indicator value, the better.

[0056] For positive indicators, take

[0057]

[0058] For the reverse indicator, take

[0059]

[0060] In the formula, S is the standardized value of the parameter, X is the parameter value, maxX is the maximum value of the parameter, and minX is the minimum value of the parameter; after the range transformation, the index values ​​are all 0-1, the positive and reverse indicators are averaged into positive indicators, the best value is 1, and the worst value is 0;

[0061] Finally, the distribution characteristics of the compressibility comprehensive parameter FI can be obtained.

[0062] In step six, the specific steps are:

[0063] According to geological parameters, reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g The plane distribution characteristics of the geological parameter cluster are obtained by equal plane distribution; according to the plane distribution of the minimum horizontal principal stress σh, the horizontal stress difference Δσ and the natural fracture density If, the distribution characteristics of the compressibility comprehensive parameter FI can also be obtained through step four.

[0064] In step seven, the specific steps are:

[0065] On the basis of obtaining comprehensive geological and engineering evaluation, the geological and engineering sweet spots were divided into three categories according to the principle of 1:1:1;

[0066] The geological sweet spots and engineering sweet spots are graded and evaluated, and the development sweet spots are finally divided according to the different combinations of geological and engineering sweet spots.

[0067] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0068] 1. This method can accurately reflect the impact of reservoir quality and engineering quality on development results, and can be applied to the optimization and evaluation of favorable development areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is the analysis result diagram of the main controlling geological factors of high-yield gas wells;

[0070] Figure 2 This is the result of the analysis of the main control engineering factors of high-yield gas wells;

[0071] Figure 3 This is the distribution map of geological parameters of XX shale gas reservoir;

[0072] Figure 4 It is the comprehensive scoring diagram of the geological sweet spot of the XX shale gas reservoir;

[0073] Figure 5 It is the XX shale gas reservoir geological and engineering sweet spot classification evaluation map;

[0074] Figure 6 To develop a dessert grading evaluation result chart;

[0075] Figure 7 Flowchart of this method. DETAILED DESCRIPTION

[0076] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0077] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other equivalent or alternative features having similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0078] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by the terms "up", "down", "left", "right", etc. are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0079] In addition, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features.

[0080] Example 1

[0081] like Figure 7 As shown, the present invention provides a technical solution:

[0082] A method for quantitatively evaluating sweet spots in shale gas reservoir development, the relevant steps are as follows:

[0083] Step 1: Collect and establish a database of geological engineering parameters of wells that have been implemented in the work area;

[0084] Step 2: Analyze the geological factors that affect gas well productivity and identify the main geological controlling factors;

[0085] Step 3: Conduct an analysis of engineering factors that affect gas well productivity and identify the main engineering control factors;

[0086] Step 4: Analyze the main geological controlling factors and comprehensively establish geological parameter groups that can reflect the advantages and disadvantages of geological conditions and have physical significance;

[0087] Step 5: Analyze the main controlling factors of the project and comprehensively establish a group of engineering parameters that can reflect the quality of the project conditions and have physical significance;

[0088] Step 6: Based on the above parameter groups and the block parameter plane distribution map, calculate the distribution of geological sweet spots and engineering sweet spots in the work area.

[0089] Step 7: Based on the above geological and engineering sweet spot distribution and the development sweet spot determination criteria, obtain the development sweet spot distribution of the work area.

[0090] Furthermore, the analysis of influencing factors in steps 2 and 3 recommends using the MATLAB neural network module to obtain the normalized ranking of the impact of different parameters on gas well productivity.

[0091] In step one, the specific steps are:

[0092] According to the existing shale gas reservoir development practice, the gas well effect is affected by multiple factors, which can be roughly divided into three types. The first is geological conditions, mainly including porosity, permeability, gas content, water saturation, TOC, continuous thickness of Class I reservoirs and other parameters. They are all native parameters of shale gas reservoirs, which mainly affect and determine the material basis of the reservoir. The second is engineering conditions, mainly including minimum horizontal principal stress, formation temperature, stress difference, brittleness, etc., which are also important native parameters of shale gas reservoirs, which determine the difficulty of reservoir engineering implementation and affect the effect of engineering transformation. The third is engineering implementation parameters, including high-quality reservoir drilling rate, wellbore smoothness, main fracturing displacement, sand addition intensity, liquid intensity, etc. They are parameters when artificially transforming shale gas reservoirs. Their size is an important manifestation of the effect of engineering transformation. This type of parameters is not only related to geological and engineering conditions, but also affected by subjective engineering design. The first two types of parameters have been determined in the native state of shale gas reservoirs, so they are the main parameters considered in the development of sweet spot optimization.

[0093] On the other hand, for gas well effect evaluation indicators, the main parameters at home and abroad currently include single well EUR, first-year daily gas production, test production and IP90. In order to unify the effect evaluation, this method recommends the use of single well EUR as the effect evaluation indicator, because its calculation requires the gas well to have relatively long production dynamic data, which is less affected by early backflow and production system, and the result is more reliable and stable.

[0094] Based on the above analysis, a database of geological engineering parameters of the implemented wells in the work area was collected and established, as shown in Tables 1 and 2.

[0095] Table 1. Geological parameters of shale gas wells in typical blocks

[0096]

[0097] Table 2 Typical block shale gas well engineering parameters

[0098]

[0099] In step 2, the specific steps are:

[0100] Based on the establishment of a basic database containing geological and engineering parameters in step 1, it is necessary to carry out an analysis of the main controlling factors affecting the production capacity of the block; use matlab to establish a correlation analysis method to evaluate the correlation between different geological parameters and single well EUR;

[0101] The method steps are as follows:

[0102] S1: Determine the reference sequence reflecting the system behavior characteristics and the comparison sequence affecting the system behavior; among them, the test output is selected as the reference sequence and the influencing factors are selected as the comparison sequence;

[0103] S2: Dimensionless processing of reference series and comparison series:

[0104] There are many dimensionless methods, and the commonly used methods in practice are range transformation method, standardization method and averaging method. Assume that the evaluation index x corresponds to n units (i = 1, 2, ..., n), y i represents the value of the i-th unit after dimensionless processing, For x i The mean value, σ i is the standard deviation of the indicator;

[0105] The range transformation method is:

[0106]

[0107] When x i When the maximum and minimum values ​​of are very different, y will be reduced. i The weight of i When the difference between the maximum and minimum values ​​is very small, y will be increased. i The maximum and minimum values ​​of the indicators have a great influence on the weight of the indicators and are not suitable for multi-factor analysis.

[0108] The standardization method is:

[0109]

[0110] After standardization, not only the influence of dimension and order of magnitude is eliminated, but also the differences in the degree of variation of each indicator are eliminated. The variance of the indicators is 1, which is also not suitable for multivariate analysis.

[0111] The averaging method is:

[0112]

[0113] After averaging, not only the influence of dimension and order of magnitude is eliminated, but also the variation degree information of each indicator is retained, which is suitable for multi-factor analysis.

[0114] After comparing the above methods, the mean value method was selected as the dimensionless data processing method in multi-factor analysis;

[0115] S3: Calculate the correlation coefficient;

[0116]

[0117] Among them, y(k) is the dimensionless value of the reference series, x i (k) is the dimensionless value of the comparison series, and ρ is the resolution coefficient, which is usually 0.5.

[0118] S4: Calculate the correlation;

[0119]

[0120] S35: relevance ranking;

[0121] The correlation degrees are sorted. The larger the correlation degree, the greater the impact, and the smaller the correlation degree, the smaller the impact.

[0122] The analysis results are as follows Figure 1 As shown in the figure, the continuous thickness, porosity, pressure coefficient and water saturation of Type I reservoirs are the main controlling geological factors for high-yield gas wells in this block.

[0123] In step three, the specific steps are:

[0124] The same method as in step 2 was used to evaluate the correlation between different engineering parameters and single well EUR. The analysis results are shown in Figure 2 As shown in the figure, the minimum horizontal principal stress, horizontal stress difference and natural fracture strength are the main controlling engineering factors of gas wells in the block.

[0125] In step 4, the specific steps are:

[0126] According to step 2, on the basis of clarifying the important geological and engineering parameters that affect the production capacity of the block, a control parameter group with physical significance is further established to achieve quantitative evaluation of geological and engineering sweet spots;

[0127] According to the above ideas, the analysis in step 2 shows that the reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g It is the most important parameter to determine the geological sweet spot, and can establish a parameter group (hΦS g R g ) is a comprehensive characterization indicator of geological sweet spots, and its physical meaning reflects the amount of gas in the rock per unit reservoir volume.

[0128] In step five, the specific steps are:

[0129] Considering that the fracturing effect is the core factor affecting the productivity of shale gas wells, the engineering sweet spot should be analyzed in step 3 to obtain the main controlling factor as the minimum horizontal principal stress σ h , horizontal stress difference Δσ and natural fracture density I f On this basis, a comprehensive parameter group FI that can reflect the compressibility is established.

[0130]

[0131] Where FI is the fracturing coefficient, dimensionless; S i (i=1, 2, ..., n) is the standardized value of shale reservoir parameters, dimensionless; W i (i=1, 2, ..., n) are different weight coefficients, dimensionless; n is the number of main control parameters.

[0132] W i Mainly through step three, the correlation between the main control parameters is determined by normalization;

[0133] Since the influencing factors have different units and dimensions, and the numerical values ​​and effective ranges of each factor are different, in order to calculate the fracturing coefficient, it is necessary to obtain the standardized value Si of each main control parameter; natural cracks are positively correlated with fracturing, and the minimum horizontal principal stress and horizontal stress difference are inversely correlated with fracturing; for such indicators, range transformation is used for standardization; in the range transformation, the parameters are divided into positive indicators and reverse indicators. The positive indicator, that is, the larger the indicator value, the better, and the reverse indicator, that is, the smaller the indicator value, the better.

[0134] For positive indicators, take

[0135]

[0136] For the reverse indicator, take

[0137]

[0138] In the formula, S is the standardized value of the parameter, X is the parameter value, maxX is the maximum value of the parameter, and minX is the minimum value of the parameter; after the range transformation, the index values ​​are all 0-1, the positive and reverse indicators are averaged into positive indicators, the optimal value is 1, and the worst value is 0.

[0139] Through the above method, the distribution characteristics of the compressibility comprehensive parameter FI can be finally obtained.

[0140] In step six, the specific steps are:

[0141] According to geological parameters, reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g Isoplanar distribution diagram ( Figure 3), the geological parameter group (hΦS g R g ) Plane distribution characteristics( Figure 4 ). Similarly, according to the plane distribution of the minimum horizontal principal stress σh, the horizontal stress difference Δσ and the natural fracture density If, the distribution characteristics of the compressibility comprehensive parameter FI can also be obtained through step 4.

[0142] In step seven, the specific steps are:

[0143] Based on the comprehensive evaluation of geology and engineering, the geology and engineering sweet spots are divided into three categories according to the principle of 1:1:1: Figure 5 ).

[0144] The classification and evaluation of superimposed geological sweet spots and engineering sweet spots are carried out. According to the different combinations of geological and engineering sweet spots, the development sweet spots are finally divided. The relevant standards are as follows: Figure 6 .

[0145] Table 3 Development sweet spot division standard table

[0146]

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantitatively evaluating sweet spots in shale gas reservoir development, characterized in that: It includes the following steps: Step 1: Collect and establish a database of geological engineering parameters of wells that have been implemented in the work area; Step 2: Analyze the geological factors that affect gas well productivity and identify the main geological controlling factors; Step 3: Conduct an analysis of engineering factors that affect gas well productivity and identify the main engineering control factors; Step 4: Analyze the main geological controlling factors and comprehensively establish geological parameter groups that can reflect the advantages and disadvantages of geological conditions and have physical significance; Step 5: Analyze the main controlling factors of the project and comprehensively establish a group of engineering parameters that can reflect the quality of the project conditions and have physical significance; Step 6: Based on the above parameter groups and the block parameter plane distribution map, calculate the distribution of geological sweet spots and engineering sweet spots in the work area; Step 7: Based on the above geological and engineering sweet spot distribution and the development sweet spot determination criteria, obtain the development sweet spot distribution of the work area.

2. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 1, characterized in that: In step 2 and step 3, the influencing factor analysis uses the MATLAB neural network module to obtain the normalized ranking of the influence of different parameters on gas well productivity.

3. A method for quantitatively evaluating sweet spots in shale gas reservoir development according to claim 1 or 2, characterized in that: In step one, the specific steps are: Establish a database of geological condition parameters, including porosity, permeability, gas content, water saturation, TOC, and continuous thickness of Class I reservoirs; Establish a database of engineering condition parameters, including minimum horizontal principal stress, formation temperature, stress difference, and brittleness; Establish a database of engineering implementation parameters, including high-quality reservoir drilling rate, wellbore smoothness, fracturing main displacement, sand addition intensity, and fluid intensity; And form a database of geological engineering parameters of the wells implemented in the work area.

4. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 3, characterized in that: In step 2, the specific steps are: Based on the establishment of a basic database containing geological and engineering parameters in step 1, it is necessary to carry out an analysis of the main controlling factors affecting the production capacity of the block; use matlab to establish a correlation analysis method to evaluate the correlation between different geological parameters and single well EUR; The method steps are as follows: S1: Determine the reference sequence reflecting the system behavior characteristics and the comparison sequence affecting the system behavior; among them, the test output is selected as the reference sequence and the influencing factors are selected as the comparison sequence; S2: Dimensionless processing of reference series and comparison series: More specifically, the mean value method is used as a dimensionless data processing method in multi-factor analysis; S3: Calculate the correlation coefficient Among them, y(k) is the dimensionless value of the reference series, x i (k) is the value of the comparison series after dimensionless conversion, and ρ is the resolution coefficient; S4: Calculate the correlation S5: Relevance ranking Sort the correlation degrees. The larger the correlation degree, the greater the impact, and the smaller the correlation degree, the smaller the impact. Find out the main controlling geological factors based on the sorting results.

5. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 4, characterized in that: In step three, the specific steps are: The same method as in step 2 is used to evaluate the correlation between different engineering parameters and the EUR of a single well; the main controlling factors of the engineering are found based on the sorting results.

6. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 5, characterized in that: In step 4, the specific steps are: According to the results of the geological main controlling factors in step 2, a parameter group is established as a comprehensive characterization indicator of the geological sweet spot.

7. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 6, characterized in that: Reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g It is the most important parameter to determine the geological sweet spot. g R g ) as a comprehensive characterization indicator of geological sweet spots.

8. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 7, characterized in that: In step five, the specific steps are: Considering that the fracturing effect is the core factor affecting the productivity of shale gas wells, the engineering sweet spot should be analyzed in step 3 to obtain the main controlling factor as the minimum horizontal principal stress σ h , horizontal stress difference Δσ and natural fracture density I f On this basis, a comprehensive parameter group FI that can reflect compressibility is established; Where FI is the fracturing coefficient, dimensionless; S i (i=1, 2, ..., n) is the standardized value of shale reservoir parameters, dimensionless; W i (i=1, 2, ..., n) are different weight coefficients, dimensionless; n is the number of main control parameters; W i Mainly through step three, the correlation between the main control parameters is determined by normalization; Natural fractures are positively correlated with fracturability, while minimum horizontal principal stress and horizontal stress difference are inversely correlated with fracturability. For these indicators, range transformation is used for standardization. In range transformation, parameters are divided into positive indicators and negative indicators. The positive indicator, i.e., the larger the indicator value, the better, while the negative indicator, i.e., the smaller the indicator value, the better. For positive indicators, take For the reverse indicator, take In the formula, S is the standardized value of the parameter, X is the parameter value, maxX is the maximum value of the parameter, and minX is the minimum value of the parameter; after the range transformation, the index values ​​are all 0-1, the positive and reverse indicators are averaged into positive indicators, the best value is 1, and the worst value is 0; Finally, the distribution characteristics of the compressibility comprehensive parameter FI can be obtained.

9. A method for quantitatively evaluating sweet spots in shale gas reservoir development as claimed in claim 8, characterized in that: In step six, the specific steps are: According to geological parameters, reservoir thickness h, porosity Φ, gas saturation S g , pressure coefficient R g的 Planar distribution, obtain the plane distribution characteristics of the geological parameter group; according to the plane distribution of the minimum horizontal principal stress σh, the horizontal stress difference Δσ and the natural fracture density If, the distribution characteristics of the comprehensive compressibility parameter FI can also be obtained through step four.

10. A method for quantitatively evaluating sweet spots in shale gas reservoir development according to claim 9, characterized in that: In step seven, the specific steps are: On the basis of obtaining comprehensive geological and engineering evaluation, the geological and engineering sweet spots were divided into three categories according to the principle of 1:1:1; The geological sweet spots and engineering sweet spots are graded and evaluated, and the development sweet spots are finally divided according to the different combinations of geological and engineering sweet spots.

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