Integrated evaluation method for shale reservoir geology engineering dessert

Abstract

This invention relates to the field of oil and gas engineering technology, and is an integrated evaluation method for geological and engineering sweet spots in shale reservoirs. It involves downhole coring of the target shale layer evaluation section, dividing it into several units, and testing the physical properties and mineral composition of the rock samples. Based on the measured physical properties of the rock samples, a rock porosity flow capacity factor is calculated to quantitatively evaluate the geological sweet spot of the shale reservoir. Based on the rock mineral composition, a rock microfracture complexity factor is calculated to quantitatively evaluate the degree of microfracture development in the original reservoir. Simultaneously, based on rock mechanical parameters, a brittleness factor in the bedding of each rock sample is calculated, thereby quantitatively evaluating the engineering sweet spot of the shale reservoir. A shale reservoir sweetness factor is defined to perform an integrated evaluation of the geological and engineering sweet spot of the shale reservoir. This invention is simple and feasible, requires no extensive field testing, greatly reduces economic costs, and can rapidly evaluate similar unconventional shale oil and gas reservoirs, showing promising application prospects.

Description

Technical Field

[0001] This invention relates to the field of oil and gas engineering technology, and is an integrated evaluation method for the geological engineering sweet spots of shale reservoirs. Background Technology

[0002] China possesses extremely rich shale oil and gas resources, mainly distributed in large basins such as the Ordos, Junggar, Songliao, Bohai Bay, and Sichuan Basin. These resources have enormous development potential, making them the most strategic oil replacement resources and the main force driving crude oil reserve and production growth. However, with the shift towards unconventional resource exploration and development, geological characteristics vary significantly, exhibiting strong heterogeneity, well-developed bedding, and extremely complex fracture propagation. The compatibility of volumetric fracturing technology with reservoirs faces numerous challenges, and technological optimization is crucial for cost reduction and efficiency improvement. Identifying favorable "sweet spots" is the core objective of exploration and development, key to achieving breakthroughs in single-well production, and the foundation for optimizing horizontal well volumetric fracturing. However, numerous factors influence the identification of sweet spots in unconventional shale oil and gas, and their relationships are complex. Domestic and international scholars have conducted extensive research on the evaluation and classification of sweet spots. Currently, the main methods for evaluating sweet spots include the following:

[0003] (1) Liao Dongliang et al. (Liao Dongliang, Lu Baoping, Wang Wei et al. A quantitative evaluation method for engineering sweet spots based on parameter optimization, Patent No.: CN201710208633.5). This method uses the independence weight coefficient method to determine the weights of the main engineering sweet spot parameters; calculates the engineering sweet spot coefficient of shale formation expressed in weights based on the weights of the main engineering sweet spot parameters; and quantitatively evaluates the engineering sweet spot of the shale formation according to the engineering sweet spot coefficient. This method only considers the engineering parameters of the rock and uses the weight coefficient to obtain the correlation with the engineering sweet spot. This method is a feasible method for obtaining engineering parameters, but it considers fewer factors. At the same time, for unconventional reservoirs, it is necessary to pursue the integration of geology and engineering sweet spots, and the geological sweet spot has not yet been considered.

[0004] (2) Wei Zhipeng et al. (Wei Zhipeng, Shi Ruisheng, Wang Hui. Prediction and evaluation of geological and engineering sweet spots in tight sandstone gas reservoirs of the Shihezi Formation 4 in Block L of Ordos Basin [J]. Natural Gas Exploration and Development, 2021, 44(4):107-114). This method uses geostatistical inversion to predict reservoir sand bodies, uses the cokriging algorithm to predict reservoir porosity distribution, and uses the seismic high-frequency attenuation algorithm to predict gas content distribution. The favorable area of ​​geological sweet spots is obtained through multi-parameter fusion. The development status of reservoir fractures is predicted by the seismic ant body algorithm, and then the brittleness index is calculated to determine the compressibility of the reservoir, thereby obtaining the favorable area of ​​engineering sweet spots. This method calculates geological and engineering sweet spots separately, but does not consider the integrated evaluation of geological and engineering sweet spots at the same time, so as to guide the fracturing optimization design and evaluate the risk of engineering construction.

[0005] (3) Xu Jingling et al. (Xu Jingling, Huo Jiaqing, Liu Shuanglian et al. Method and system for predicting sweet spots in shale and mudstone reservoirs, Patent No.: CN202110232294.0). This method obtains attribute parameters related to oil production per meter based on the intersection of attribute parameters of shale and mudstone reservoir facies and daily oil production per meter. Based on these attribute parameters, a comprehensive evaluation model for sweet spots is established to intuitively evaluate the quality of the reservoir. However, it only considers the influencing factors of geological sweet spots and ignores engineering factors.

[0006] None of the three representative methods mentioned above simultaneously considers the integrated evaluation of geological and engineering sweet spots, nor do they take into account the unique geological characteristics of shale bedding, which is a key engineering factor controlling the complexity of fracture networks. Therefore, a new comprehensive evaluation method for the integrated evaluation of geological and engineering sweet spots in unconventional shale reservoirs is needed to provide important guidance for efficient shale oil exploration. Summary of the Invention

[0007] This invention provides an integrated evaluation method for geological engineering sweet spots in shale reservoirs, which overcomes the shortcomings of the existing technology. It can effectively solve the problems that the existing technology does not consider the integrated evaluation of geological engineering sweet spots at the same time, nor does it consider the special geological characteristics of shale bedding.

[0008] The technical solution of this invention is achieved through the following measures: a comprehensive evaluation method for geological and engineering sweet spots in shale reservoirs, comprising the following steps: conducting downhole coring of the target shale layer evaluation section, dividing it into several units, and testing the physical properties and mineral composition of the rock samples; calculating the rock porosity flow capacity factor based on the measured rock sample physical properties to quantitatively evaluate the geological sweet spots of the shale reservoir; calculating the rock microfracture complexity factor based on the rock mineral composition to quantitatively evaluate the degree of microfracture development in the original reservoir, and simultaneously calculating the brittleness factor in the bedding of each rock sample based on the rock mechanical parameters to quantitatively evaluate the engineering sweet spots of the shale reservoir; defining the shale reservoir sweetness factor to conduct a comprehensive evaluation of the geological and engineering sweet spots of the shale reservoir.

[0009] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0010] The above may specifically include the following steps:

[0011] Step S1, Rock Sample Preparation: Divide the target evaluation section of the shale reservoir into several units, and carry out continuous core sampling for each evaluation unit;

[0012] Step S2, Shale Flowability Index Calculation: Test the porosity of the rock samples described in Step S1. And permeability k, define the flow capacity factor RI, and quantitatively evaluate geological sweet spots; where, the formula for calculating RI is:

[0013]

[0014] In the formula: RI is the rock sample flowability index, μm2; k is the rock sample permeability, μm2; The porosity of the rock sample is expressed as %.

[0015] Step S3, Shale mineral composition test: Test the mineral composition of the rock sample from step S1;

[0016] Step S4: Calculate the microfracture complexity factor using the mineral composition test results of the shale sample to evaluate the degree of development of the original microfractures in the reservoir;

[0017] Step S5: Calculation of reservoir sweetness factor: Taking into account the reservoir's flow capacity, the degree of initial microfracture development, and the number of brittle layers in the reservoir bedding, the sweetness factor is determined.

[0018] Step S4 above may specifically include the following steps:

[0019] S4.1 Based on the test quality results of different mineral components of shale samples in the target layer evaluation section, calculate the percentage content of different minerals in the shale. The calculation formula is as follows:

[0020]

[0021] In the formula: Mi is the mass percentage of the i-th type of mineral in the shale sample, %, where i is the different mineral number and has no dimension; n is the mineral type, species; m is the mass of different minerals in the shale sample, g;

[0022] S4.2 Calculate the uniformity coefficient based on the percentage content of different minerals and the quantity of mineral types in the shale sample. The calculation formula is as follows:

[0023]

[0024] In the formula: EI is the uniformity coefficient among different minerals in the shale sample, %;

[0025] S4.3. Based on the percentage content and uniformity coefficient of different minerals in the shale sample, calculate the difference coefficient DI between different minerals. The calculation formula is as follows:

[0026]

[0027] In the formula: DI is the difference coefficient between different minerals, which is dimensionless;

[0028] S4.4 Calculate the microcrack complexity factor FI. The calculation formula is as follows:

[0029]

[0030] In the formula: FI is the microcrack complexity factor, which is dimensionless.

[0031] Step S5 above may specifically include the following steps:

[0032] S5.1 Classification of Shale Bedding Thickness;

[0033] S5.2 Calculation of the number of brittle layers in bedding: The number of brittle layers in the core area is determined using a probabilistic method. When a bedding number is selected, if it satisfies the two conditions of equation (7) and equation (8), then the value is equal to the number of brittle layers in the calculated area; where equation (7) and equation (8) are:

[0034]

[0035]

[0036] In the formula: P M To calculate the probability, %; P, that there are M brittle layers in an n-layered bedding shale. M+1 To calculate the probability, %, that an n-layered shale contains (M+1) brittle layers;

[0037] The formula for calculating the brittleness factor of each core sample is as follows:

[0038]

[0039] In the formula: PI is the brittleness factor, which is dimensionless; m is the total number of brittle layers in the core, in layers; E is the total number of bedding planes in the core, in layers;

[0040] S5.3 Sweetness factor calculation, the calculation formula is as follows:

[0041] RE = RI·FI·PI (10)

[0042] In the formula: RE is the sweetness factor, μm2.

[0043] In step S5.1 above, bedding can be divided into three categories: bedding with a thickness of less than 0.05 mm is called thin bedding; bedding with a thickness between 0.05 mm and 0.1 mm is called medium bedding; and bedding with a thickness greater than 0.1 mm is called thick bedding.

[0044] The above may also include the following steps: combining actual oil and gas exploration data to determine favorable zones for the lithology of lamellar fine-grained sedimentary rocks.

[0045] This invention considers both geological and engineering factors influencing shale sweetness and defines a new sweetness factor for a comprehensive quantitative characterization of geological and engineering sweetness. This invention redefines traditional shale sweetness evaluation methods, for the first time incorporating bedding properties into engineering evaluation, while simultaneously pursuing enrichment optimization and maximizing production capacity. It overcomes the previous approach of solely considering reservoir or engineering parameters. Its calculation method is simple and feasible, requiring no extensive field testing, significantly reducing economic costs. It can rapidly evaluate similar unconventional shale oil and gas reservoirs, showing promising application prospects and providing important guidance for the exploration and efficient development of unconventional resources. Attached Figure Description

[0046] Appendix Figure 1 This is a comparison chart of the flowability index of shale samples according to an embodiment of the present invention.

[0047] Appendix Figure 2 This is a graph showing the mineral composition content of a shale sample according to an embodiment of the present invention.

[0048] Appendix Figure 3 This is a comparison chart of the complexity factors of microcracks in shale samples according to an embodiment of the present invention.

[0049] Appendix Figure 4 This is a comparison chart of sweetness factors of shale samples from embodiments of the present invention. Detailed Implementation

[0050] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0051] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0052] Example 1: The integrated evaluation method for geological and engineering sweet spots in shale reservoirs includes the following steps: First, core sampling is carried out in the target shale layer evaluation section, dividing it into several units, and testing the physical properties and mineral composition of the rock samples; second, the rock porosity flow capacity factor is calculated based on the measured rock sample physical properties to quantitatively evaluate the geological sweet spot of the shale reservoir; on this basis, the rock microfracture complexity factor is calculated based on the rock mineral composition to quantitatively evaluate the degree of microfracture development in the original reservoir, and the brittleness factor in the bedding of each rock sample is calculated based on the rock mechanical parameters. The larger the value, the higher the complexity of the fracture network formed by volumetric fracturing, thus quantitatively evaluating the engineering sweet spot of the shale reservoir; finally, a shale reservoir sweetness factor is defined. The larger the value, the better the reservoir geology and engineering sweet spot, and the greater the potential for high production capacity after volumetric fracturing, thus conducting an integrated evaluation of the geological and engineering sweet spot of the shale reservoir.

[0053] This invention proposes an integrated evaluation method for shale reservoir geological and engineering sweet spots. This method considers both geological and engineering factors influencing shale sweet spots and defines a new sweetness factor for a comprehensive quantitative characterization of geological and engineering sweet spots. This method redefines traditional shale sweet spot evaluation methods, for the first time incorporating bedding properties into engineering evaluation, while simultaneously pursuing enrichment optimization and maximizing production capacity. It overcomes the previous approach of solely considering reservoir or engineering parameters. Its calculation method is simple and feasible, requiring no extensive field testing, significantly reducing economic costs. It can rapidly evaluate similar unconventional shale oil and gas reservoirs, showing promising application prospects and providing important guidance for the exploration and efficient development of unconventional resources.

[0054] Example 2: The integrated evaluation method for geological engineering sweet spots in shale reservoirs specifically includes the following steps:

[0055] Step S1, Rock Sample Preparation: Divide the target evaluation section of the shale reservoir into several units and carry out continuous core sampling for each evaluation unit; prepare standard rock samples with a diameter of 2.5 cm and a length of 5 cm from the rocks of the shale reservoir section, and dry the standard rock samples in a 100℃ oven until constant weight.

[0056] Step S2, Shale Flowability Index Calculation: The porosity of the dried rock sample described in Step S1 was tested using an automatic helium porosity analyzer and an ultra-low permeability analyzer. Together with permeability k, the flowability factor RI is defined to quantitatively evaluate geological sweet spots. The larger the RI value, the better the physical properties of the shale reservoir and the stronger its flowability. The formula for calculating RI is:

[0057]

[0058] In the formula: RI is the rock sample flowability index, μm2; k is the rock sample permeability, μm2; The porosity of the rock sample is expressed as %.

[0059] Step S3, Shale mineral composition test: The mineral composition of the rock sample in step S1 is tested using an X-ray diffractometer;

[0060] Step S4: Calculate the microfracture complexity factor using the mineral composition test results of the shale sample to evaluate the degree of development of the original microfractures in the reservoir;

[0061] In this embodiment of the invention, step S4 specifically includes the following steps:

[0062] S4.1 Based on the test quality results of different mineral components of shale samples in the target layer evaluation section, calculate the percentage content of different minerals in the shale. The calculation formula is as follows:

[0063]

[0064] In the formula: Mi is the mass percentage of the i-th type of mineral in the shale sample, %, where i is the different mineral number and has no dimension; n is the mineral type, species; m is the mass of different minerals in the shale sample, g;

[0065] S4.2 Calculate the uniformity coefficient based on the percentage content of different minerals and the quantity of mineral types in the shale sample. The calculation formula is as follows:

[0066]

[0067] In the formula: EI is the uniformity coefficient among different minerals in the shale sample, %;

[0068] S4.3. Based on the percentage content and uniformity coefficient of different minerals in the shale sample, calculate the difference coefficient DI between different minerals. The calculation formula is as follows:

[0069]

[0070] In the formula: DI is the difference coefficient between different minerals, which is dimensionless;

[0071] S4.4 Calculate the microfracture complexity factor FI. The larger the value, the more developed the original microfractures in the shale sample. The calculation formula is as follows:

[0072]

[0073] In the formula: FI is the microcrack complexity factor, which is dimensionless.

[0074] Step S5: Calculation of reservoir sweetness factor: Taking into account the reservoir's flow capacity, the degree of initial microfracture development, and the number of brittle layers in the reservoir bedding, the sweetness factor is further determined. The larger the value, the better the reservoir's geological and engineering sweetness, and the greater the potential to obtain high production capacity after volumetric fracturing.

[0075] In this embodiment of the invention, step S5 specifically includes the following steps:

[0076] S5.1 Classification of Shale Bedding Thickness: In step S5.1, bedding is divided into three categories. Bedding with a thickness less than 0.05 mm is called thin bedding; bedding with a thickness between 0.05 mm and 0.1 mm is called medium bedding; and bedding with a thickness greater than 0.1 mm is called thick bedding. Each rock sample in step S1 is divided into 5 regions with a length of 1 cm. For each region, a length of 0.5 mm is taken from the 0.5 cm position, and the total number of thin bedding, medium bedding, and thick bedding in this length is recorded, as shown in expression (6):

[0077] B Z =BT +B M +B K (6)

[0078] In the formula: B Z B represents the total bedding number of the rock sample. T B represents the thin-layered bedding number of the rock sample. M B represents the bedding number of the rock sample. K The number represents the thick-layered bedding of the rock sample.

[0079] S5.2 Calculation of the number of brittle layers in bedding: When the Young's modulus of a bedding layer is between the Young's modulus values ​​of the two bedding layers above and below it, this bedding layer is called a brittle layer and is more prone to failure. To reduce the workload of experiments, the number of brittle layers in the core area is determined by probability. When a bedding number is selected, if it satisfies the two conditions of equation (7) and equation (8), then the value is equal to the number of brittle layers in the calculated area; where equation (7) and equation (8) are:

[0080]

[0081]

[0082] In the formula: P M To calculate the probability, %; P, that there are M brittle layers in an n-layered bedding shale. M+1 To calculate the probability, %, that an n-layered shale contains (M+1) brittle layers;

[0083] The formula for calculating the brittleness factor of each core sample is as follows:

[0084]

[0085] In the formula: PI is the brittleness factor, which is dimensionless; m is the total number of brittle layers in the core, in layers; E is the total number of bedding planes in the core, in layers;

[0086] S5.3 Sweetness Factor Calculation: Based on the calculation results of the rock sample flow factor in step S2, the microfracture complexity factor in step S4, and the number of brittle layers in the bedding in step S5, the sweetness factor is further defined. The larger the value, the better the reservoir geological and engineering sweetness. The calculation formula is as follows:

[0087] RE = RI·FI·PI (10)

[0088] In the formula: RE is the sweetness factor, μm2.

[0089] As needed, embodiments of the present invention may further include the following steps: determining favorable zones for the lithology of lamellar fine-grained sedimentary rocks by combining actual oil and gas exploration data.

[0090] This invention discloses the specific steps of an integrated evaluation method for shale reservoir geological and engineering sweet spots. It considers both geological and engineering factors influencing shale sweet spots and defines a new sweetness factor for a comprehensive quantitative characterization of geological and engineering sweet spots. This method redefines traditional shale sweet spot evaluation methods, incorporating bedding properties into engineering evaluation for the first time. It simultaneously pursues enrichment optimization and production maximization, overcoming the previous approach of solely considering reservoir or engineering parameters. Its calculation method is simple and feasible, requiring no extensive field testing, significantly reducing economic costs. It can rapidly evaluate similar unconventional shale oil and gas reservoirs, showing promising application prospects and providing important guidance for the exploration and efficient development of unconventional resources.

[0091] Example 3: As shown in the attached document Figure 1 , 2 As shown in Figures 3 and 4, this embodiment describes the specific implementation of the present invention in detail with reference to the accompanying drawings and a downhole core sample from a certain block. The integrated evaluation method for shale reservoir geological engineering sweet spots specifically includes the following steps:

[0092] (1) Rock sample preparation: The actual downhole cores of the shale gas well were taken from a depth of 2540-2500m and prepared into 5 standard rock samples with a diameter of 2.5cm and a length of 5cm. The rock samples were numbered S1-S3 and placed in an oven at 100℃ to dry to constant weight. See Table 1.

[0093] Table 1 Basic Parameters of Shale Samples

[0094]

[0095] (2) Calculation of shale flowability index: The porosity of the dried rock sample described in step S1 was tested using an automatic helium porosity analyzer and an ultra-low permeability analyzer. The permeability k is shown in Table 1, and the reservoir capacity index RI of the rock samples is calculated using formula (1) to quantitatively evaluate the reservoir geological sweet spot. The calculation results are shown in Table 1 and Appendix. Figure 1 .

[0096] (3) Shale mineral composition test: The mineral composition of the rock sample in step S2 was tested using an X-ray diffractometer. The test results are shown in Table 2.

[0097] Table 2 Mineral composition of shale samples

[0098] Rock sample number Dolomite (g) Quartz (g) Calcite (g) Feldspar (g) Pyrite (g) Clay(g) S1 19.14 41.86 25.15 27.85 2.46 83.54 S2 35.35 32.65 32.74 13.26 2.33 83.67 S3 25.64 26.36 27.71 17.29 0.96 102.04

[0099] (4) Calculation of microfracture complexity factor: The microfracture complexity factor is calculated using the mineral composition test results of shale samples to evaluate the degree of development of original microfractures in the reservoir. Specifically, it includes the following:

[0100] ① Based on the test quality results of different mineral components of shale samples from the target shale layer evaluation section, calculate the percentage content of different minerals in the shale, and use the formula... Perform the calculations and record the results as percentages, as shown in Table 3.

[0101] Table 3 Mineral composition of shale samples

[0102] Rock sample number dolomite(%) quartz(%) Calcite (%) Feldspar (%) Pyrite (%) clay(%) S1 9.57 20.93 12.58 13.92 1.23 41.77 S2 17.68 16.32 16.37 6.63 1.17 41.83 S3 12.82 13.18 13.86 8.64 0.48 51.02

[0103] ② Calculate the uniformity coefficient based on the percentage content of different minerals and the quantity of mineral types in the shale sample, using the formula... Here, n equals 6, so EI = 16.67%.

[0104] ③ Based on the percentage content and uniformity coefficient of different minerals in the shale sample, calculate the difference coefficient DI between different minerals, and use the formula... The calculations were performed, and the results are shown in Table 4.

[0105] Table 4. Calculation results of the coefficient of difference for shale samples.

[0106]

[0107] ④ Using formulas The complexity factor FI of the microcracks was calculated, and the results are shown in Table 5.

[0108] Table 5. Calculation results of microfracture complexity factor in shale samples.

[0109] Rock sample number Coefficient of difference (%) Microcrack complexity factor (dimensionless) S1 58.74 0.81 S2 52.36 0.85 S3 68.72 0.75

[0110] (5) Calculation of reservoir sweetness factor:

[0111] ① Classification of shale bedding thickness: Each rock sample was divided into five equal parts along its length. A 0.5 mm section was taken from the middle of each section to measure its thin-layered, medium-layered, and thick-layered bedding. S11 is the bedding number of the first 0.5 mm section from top to bottom of the first core sample. The bedding numbers of other core samples are represented in the same way. The results are shown in Table 6.

[0112] Table 6. Classification of bedding in different regions of shale cores.

[0113] Stratification type S11 S12 S13 S14 S15 Thin layered stratification 8 4 8 12 4 medium-layered bedding 5 4 3 2 6 Thick layered bedding 0 1 0 0 0 Total number of layers 13 9 11 14 10 Stratification type S21 S22 S23 S24 S25 Thin layered stratification 3 3 8 3 2 medium-layered bedding 4 5 4 5 4 Thick layered bedding 1 0 0 1 1 Total number of layers 8 8 12 9 7 Stratification type S31 S32 S33 S34 S35 Thin layered stratification 14 14 14 10 14 medium-layered bedding 2 1 1 3 0 Thick layered bedding 0 0 0 0 0 Total number of layers 16 15 15 13 14

[0114] ② Calculation of the number of brittle layers in the bedding: The number of brittle layers in a specific region is calculated using the data in Table 6 and formulas (7) and (8), and the brittle factor is calculated using formula (9). The results are shown in Table 7.

[0115] Table 7 Calculation of Brittleness Factor in Shale Core

[0116]

[0117] ③ Sweetness factor calculation: using The calculations are shown in Table 8.

[0118] Table 8. Complexity Factors of Sewn Mesh

[0119]

[0120] This invention describes in detail the application of the integrated evaluation method for the sweetness of shale reservoirs using a downhole core sample from a certain block as an example. As can be seen from Table 8, the sweetness factors of the shale samples from largest to smallest are S1 > S2 > S3. Therefore, the sweetness ranking is S1 > S2 > S3.

[0121] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A shale reservoir geology engineering dessert integrated comprehensive evaluation method, characterized in that The method comprises the following steps: The shale target layer evaluation section is cored downhole, the rock sample is divided into several units, and the physical property parameters and mineral composition of the rock sample are tested; the rock pore flow capacity factor is calculated according to the measured physical property parameters of the rock sample, the geological sweet spot of the shale reservoir is quantitatively evaluated, the rock microfracture complexity factor is calculated according to the rock mineral composition, the development degree of the original reservoir microfracture is quantitatively evaluated, and the engineering sweet spot of the shale reservoir is quantitatively evaluated according to the rock mechanical parameters, and then the shale reservoir sweetness factor is defined to comprehensively evaluate the geological and engineering sweet spots of the shale reservoir; The method comprises the following steps: Step S1, rock sample preparation: the shale reservoir target evaluation section is divided into several units, and continuous coring is performed on each evaluation unit; Step S2, Shale Flowability Index Calculation: Test the porosity of the rock samples described in Step S1. And permeability k, define the flow capacity factor RI, and quantitatively evaluate geological sweet spots; where, the formula for calculating RI is: where: Rl is the rock sample flow capacity index, μm 2 ; k is the permeability of the rock sample, μm 2 ; porosity of the rock sample, %; Step S3, shale mineral composition content test: the mineral composition of the rock sample in step S1 is tested; Step S4, calculating the microfracture complexity factor FI using the test results of the mineral composition content of the shale sample to evaluate the development degree of the original microfracture of the reservoir; Step S5, reservoir sweetness factor calculation: comprehensively considering the flow capacity, initial microfracture development degree and brittle layer number of the reservoir, the sweetness factor is determined; Step S5 specifically comprises the following steps: S5.1, shale bedding thickness classification; S5.2, brittle layer number calculation in the bedding: the brittle layer number of the core area is determined by probability, when the value of a bedding number satisfies the two conditions of formula (7) and formula (8), the value is equal to the brittle layer number of the calculated area; wherein, formula (7) and formula (8) are: where: P M is the probability, in %, that n layers of bedded shale have M numbers of brittle layers; P M+1 is the probability, in %, that n layers of bedded shale have (M+1) numbers of brittle layers; m is the total number of brittle layers in the core, layers. The formula for calculating the brittleness factor of each core is as follows: In the formula, PI is the brittleness factor, which is dimensionless; m is the total brittle layer number of the core, layer; E is the total bedding number of the core, layer; S5.3, sweetness factor calculation, the calculation formula is as follows: RE=RI·FI·PI (10) where: RE is a sweetness factor, μm 2 ; FI is a microfracture complexity factor, dimensionless.

2. The shale reservoir geologic engineering dessert integration comprehensive evaluation method according to claim 1, characterized in that Step S4 specifically comprises the following steps: S4.1, according to the test quality results of the shale sample of the shale target layer evaluation section, the mass percentage content of different minerals of the shale is calculated, and the calculation formula is as follows: wherein: M i is the mass percentage of the i-th mineral of the shale sample, %, where i is the number of the different mineral, dimensionless; n is the number of mineral species, species; m is the mass of the different mineral of the shale sample, g; S4.2, according to the mass percentage content of different minerals of the shale sample and the number of mineral types, the uniformity coefficient is calculated, and the calculation formula is as follows: In the formula, EI is the uniformity coefficient between different minerals of the shale sample, %; S4.3, according to the mass percentage content of different minerals of the shale sample and the uniformity coefficient, the difference coefficient DI between different minerals is calculated, and the calculation formula is as follows: In the formula, DI is the difference coefficient between different minerals, which is dimensionless; S4.4, calculating the microfracture complexity factor FI, the calculation formula is as follows: In the formula, FI is the microfracture complexity factor, which is dimensionless.

3. The shale reservoir geologic engineering dessert integration comprehensive evaluation method according to claim 1 or 2, characterized in that In step S5.1, the bedding is divided into three categories, the bedding with a thickness less than 0.05mm is called thin-bedded bedding, the bedding with a thickness between 0.05mm and 0.1mm is called medium-bedded bedding, and the bedding with a thickness greater than 0.1mm is called thick-bedded bedding.

4. The shale reservoir geologic engineering dessert integration comprehensive evaluation method according to claim 1 or 2, characterized in that Further comprising the following steps: combined with the actual data of oil and gas exploration, the favorable zone of laminated fine-grained sedimentary rock lithology is determined.

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