Shale reservoir geological engineering dessert integrated comprehensive evaluation method

Abstract

The invention relates to the technical field of petroleum and natural gas engineering, in particular to a shale reservoir geological engineering dessert integrated comprehensive evaluation method, which comprises the following steps of: carrying out underground coring work on an evaluation section of a shale target layer, dividing the evaluation section into a plurality of units, and testing physical property parameters and mineral components of a rock sample; calculating a rock pore flow capacity factor according to the physical property parameters of the measured rock sample, and quantitatively evaluating the geological dessert of the shale reservoir; the method comprises the following steps: calculating rock microfracture complex factors according to rock mineral components, quantitatively evaluating the development degree of original reservoir microfractures, calculating brittleness factors in each rock sample bedding according to rock mechanical parameters, and further quantitatively evaluating the engineering dessert of a shale reservoir; and defining a shale reservoir sweetness factor, and carrying out geological engineering sweet spot integrated comprehensive evaluation on the shale reservoir. The method is simple and feasible, does not need a large number of mine field tests, greatly reduces the economic cost, can rapidly evaluate similar unconventional shale oil and gas reservoirs, and has a good application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas engineering, and is an integrated comprehensive evaluation method for geological engineering sweet spots in shale reservoirs. Background Art

[0002] China is extremely rich in shale oil and gas resources, which are mainly distributed in large basins such as Ordos, Junggar, Songliao, Bohai Bay, and Sichuan Basin. With huge development potential, they have become the most strategic oil replacement resources and the main force for increasing crude oil reserves and production. However, with the transformation towards exploration and development of unconventional resources, the geological characteristics are significantly different, with strong heterogeneity, obvious bedding development, extremely complex fracture propagation, and many challenges in the matching of volume fracturing technology with the reservoir. Technical optimization and improvement are the key to cost reduction and efficiency increase. Among them, finding favorable "sweet spots" is the core goal of exploration and development, the key to achieving breakthroughs in single-well production, and the basis for optimizing horizontal well volume fracturing. However, there are many factors affecting the identification of sweet spots in unconventional shale oil and gas, and their relationships are complex. Scholars at home and abroad have conducted a large number of studies on the evaluation and division of sweet spots. Currently, the main evaluation methods for sweet spots are as follows:

[0003] (1) Liao Dongliang et al. (Liao Dongliang, Lu Baoping, Wang Wei, etc. A quantitative evaluation method for engineering sweet spots based on parameter optimization, Patent No.: CN201710208633.5). This method uses the independent weight coefficient method to determine the weights of the main engineering sweet spot parameters; calculates the engineering sweet spot coefficient represented by weights for the shale formation based on the weights of the main engineering sweet spot parameters; quantitatively evaluates the engineering sweet spots 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, the integration of geological and engineering sweet spots is pursued, and geological sweet spots have not been considered.

[0004] (2) Wei Zhipeng et al. (Wei Zhipeng, Shi Ruisheng, Wang Hui. Prediction and evaluation of geological-engineering sweet spots in the fourth member of the Shihezi Formation in Block L of the 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 co-Kriging algorithm to predict the porosity distribution of the reservoir, uses the seismic high-frequency attenuation algorithm to predict the gas-bearing distribution, and obtains the favorable area of geological sweet spots through multi-parameter fusion. Uses the seismic ant body algorithm to predict the fracture development status of the reservoir, then calculates the brittleness index, and then judges 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 comprehensive evaluation of geological and engineering sweet spots at the same time, and finally guides the optimization design of fracturing and evaluates the risk of engineering construction.

[0005] (3) Xu Jingling et al. (Xu Jingling, Huo Jiaqing, Liu Shuanglian, etc. Method and system for predicting lithofacies sweet spots in shale reservoirs, Patent No.: CN202110232294.0). According to the cross-plot relationship between the attribute parameters of the lithofacies in the shale reservoir and the daily oil production per meter, this method obtains the attribute parameters related to the oil production per meter, and based on the attribute parameters, establishes a comprehensive evaluation model for sweet spots to visually evaluate the quality of the reservoir. It only considers the influencing factors of geological sweet spots and ignores engineering factors.

[0006] The above three representative methods do not simultaneously consider the integrated evaluation of geological and engineering sweet spots, and do not consider the special geological characteristics of shale-developed bedding, which is the main engineering factor controlling the complexity of the fracture network. Therefore, it is necessary to establish a new comprehensive evaluation method for the integration of geological and engineering sweet spots in unconventional shale reservoirs to provide important guidance for the efficient exploration of shale oil. Summary of the Invention

[0007] The present invention provides a comprehensive evaluation method for the integration of geological and engineering sweet spots in shale reservoirs, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problems that the prior art does not simultaneously consider the integrated evaluation of geological and engineering sweet spots and does not consider the special geological characteristics of shale-developed bedding.

[0008] The technical solution of the present invention is achieved by the following measures: A comprehensive evaluation method for the integration of geological and engineering sweet spots in shale reservoirs includes the following steps: Conduct downhole coring work on the evaluation section of the shale target layer, divide it into several units, and test the physical properties parameters and mineral compositions of the rock samples; Calculate the rock pore flow ability factor according to the physical properties parameters of the measured rock samples to quantitatively evaluate the geological sweet spots of the shale reservoir; Calculate the rock microfracture complexity factor according to the rock mineral composition to quantitatively evaluate the development degree of the original reservoir microfractures, and at the same time calculate the brittleness factor in the bedding of each rock sample according to the rock mechanics parameters, and then quantitatively evaluate the engineering sweet spots of the shale reservoir; Define the sweetness factor of the shale reservoir to conduct a comprehensive evaluation of the integration of geological and engineering sweet spots in the shale reservoir.

[0009] The following is a further optimization or / and improvement of the above-mentioned technical solution of the invention:

[0010] Specifically, it may include the following steps:

[0011] Step S1, Rock sample preparation: Divide the target evaluation section of the shale reservoir into several units, and conduct continuous coring work on each evaluation unit;

[0012] Step S2, Calculation of shale flow ability index: Test the porosity and permeability k of the rock samples in Step S1 respectively, define the flow ability factor RI to quantitatively evaluate the geological sweet spots; where the calculation formula of RI is:

[0013]

[0014] Where: RI is the rock sample flow ability index, μm2; k is the permeability of the rock sample, μm2; is the porosity of the rock sample, %.

[0015] Step S3: Shale mineral composition content test: Test the mineral composition of the rock sample in Step S1;

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

[0017] Step S5: Reservoir sweetness factor calculation: Comprehensively consider the flow ability of the reservoir, the development degree of the initial microfractures, and the brittle layer number of the reservoir bedding to determine the sweetness factor.

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

[0019] S4.1: Calculate the mass percentage content of different minerals in the shale according to the test quality results of different mineral compositions of the shale sample in the evaluation section of the shale target layer. The calculation formula is as follows:

[0020]

[0021] Where: Mi is the mass percentage content of the i-th type of mineral in the shale sample, %, where i is the number of different minerals, dimensionless; n is the type of minerals, species; m is the mass of different minerals in the shale sample, g;

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

[0023]

[0024] Where: EI is the uniformity coefficient between different minerals in the shale sample, %.

[0025] S4.3: Calculate the difference coefficient DI between different minerals according to the mass percentage content of different minerals in the shale sample and the uniformity coefficient. The calculation formula is as follows:

[0026]

[0027] Where: DI is the difference coefficient between different minerals, dimensionless;

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

[0029]

[0030] Where: FI is the microfracture complexity factor, dimensionless.

[0031] The above-mentioned step S5 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 the bedding: Use probability to determine the number of brittle layers in the core area. When a bedding number is taken and it meets the two conditions of Equation (7) and Equation (8), then this value is equal to the number of brittle layers in the calculated area; among them, Equation (7) and Equation (8) are:

[0034]

[0035]

[0036] Where: P M is the probability of having M brittle layers in the shale with n bedding layers, %; P M+1 is the probability of having (M + 1) brittle layers in the shale with n bedding layers, %.

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

[0038]

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

[0040] S5.3. Calculation of the sweetness factor, and the calculation formula is as follows:

[0041] RE = RI·FI·PI (10)

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

[0043] In the above step S5.1, the bedding can be divided into three categories. Among them, the bedding with a thickness less than 0.05 mm is called thin-bedded bedding; the bedding with a thickness in the range of 0.05 mm to 0.1 mm is called medium-bedded bedding; the bedding with a thickness greater than 0.1 mm is called thick-bedded bedding.

[0044] The above may further include the following steps: Combining the actual data of oil and gas exploration to determine the favorable zone of the lithology of laminated fine-grained sedimentary rocks.

[0045] The present invention simultaneously considers the geological and engineering factors affecting shale sweet spots, and defines a new sweetness factor for the integrated quantitative characterization of geological and engineering sweet spots. The present invention redefines the traditional shale sweet spot evaluation method, for the first time taking bedding properties into account in engineering evaluation, while pursuing enrichment optimization and maximum productivity, making up for the previous steps of simply considering reservoir parameters or engineering parameters. Its calculation method is simple and feasible, without the need for a large number of field tests, greatly reducing the economic cost, enabling rapid evaluation of similar unconventional shale oil and gas reservoirs, having good application prospects, and providing important guidance for the exploration and efficient development of unconventional resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] FIG. Figure 1 is a comparison chart of the flow capacity index of shale samples in an embodiment of the present invention.

[0047] FIG. Figure 2 is a test chart of the mineral composition content of shale samples in an embodiment of the present invention.

[0048] FIG. Figure 3 is a comparison chart of the microfracture complexity factor of shale samples in an embodiment of the present invention.

[0049] FIG. Figure 4 is a comparison chart of the sweetness factor of shale samples in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0051] The present invention will be further described below in conjunction with the embodiments and the drawings:

[0052] Embodiment 1: The integrated comprehensive evaluation method for geological and engineering sweet spots of the shale reservoir includes the following steps: First, carry out downhole coring work on the evaluation section of the shale target layer, divide it into several units, and test the physical properties and mineral composition of the rock samples; Secondly, calculate the rock pore flow capacity factor according to the physical properties of the measured rock samples to quantitatively evaluate the geological sweet spots of the shale reservoir; On this basis, calculate the rock microfracture complexity factor according to the rock mineral composition to quantitatively evaluate the development degree of the original reservoir microfractures. At the same time, calculate the brittleness factor in the bedding of each rock sample according to the rock mechanical parameters. The larger its value, the higher the complexity of the fracture network formed by volume fracturing, and then quantitatively evaluate the engineering sweet spots of the shale reservoir; Finally, define the sweetness factor of the shale reservoir. The larger its value, the better the geological and engineering sweet spots of the reservoir, and the greater the potential for obtaining high productivity after volume fracturing, so as to conduct an integrated comprehensive evaluation of the geological and engineering sweet spots of the shale reservoir.

[0053] An embodiment of the present invention provides a comprehensive evaluation method for integrating geological engineering sweet spots in shale reservoirs. This method simultaneously considers geological and engineering factors affecting shale sweet spots and defines a new sweetness factor for quantitative characterization of the integration of geological engineering sweet spots. This method redefines the traditional shale sweet spot evaluation method, for the first time considering bedding properties in engineering evaluation, while pursuing enrichment optimization and maximum productivity, making up for the previous steps of simply considering reservoir parameters or engineering parameters. Its calculation method is simple and feasible, without the need for a large number of field tests, greatly reducing economic costs, and can quickly evaluate similar unconventional shale oil and gas reservoirs, having good application prospects and providing important guidance for the exploration and efficient development of unconventional resources.

[0054] Example 2: The comprehensive evaluation method for integrating 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 conduct continuous coring work on each evaluation unit; make the rock in the shale reservoir section into standard rock samples with a diameter of 2.5 cm and a length of 5 cm, and place the standard rock samples in an oven at 100 °C and dry them to constant weight.

[0056] Step S2, calculation of shale flowability index: Use a helium porosity automatic measuring instrument and an ultra-low permeability measuring instrument to respectively measure the porosity and permeability k of the dried rock samples in Step S1, define the flowability factor RI, and quantitatively evaluate geological sweet spots. The larger its value, the better the physical properties of the shale reservoir and the stronger the flowability; among them, the calculation formula of RI is:

[0057]

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

[0059] Step S3, measurement of shale mineral composition content: Use an X-ray diffractometer to measure the mineral composition of the rock samples in Step S1;

[0060] Step S4, calculate the microfracture complexity factor using the measurement results of the mineral composition content of shale samples to evaluate the development degree of original microfractures in the reservoir;

[0061] Among them, in the embodiment of the present invention, Step S4 specifically includes the following steps:

[0062] S4.1, according to the test mass results of different mineral compositions of shale samples in the target layer evaluation section of shale, calculate the mass percentage content of different minerals in shale, and the calculation formula is as follows:

[0063]

[0064] Where: Mi is the mass percentage of the i-th type of mineral in the shale sample, %, where i is the number of different minerals, dimensionless; n is the type of minerals, types; m is the mass of different minerals in the shale sample, g;

[0065] S4.2. Calculate the uniformity coefficient according to the mass percentage of different minerals and the number of types of minerals in the shale sample. The calculation formula is as follows:

[0066]

[0067] Where: EI is the uniformity coefficient between different minerals in the shale sample, %;

[0068] S4.3. Calculate the difference coefficient DI between different minerals according to the mass percentage of different minerals and the uniformity coefficient in the shale sample. The calculation formula is as follows:

[0069]

[0070] Where: DI is the difference coefficient between different minerals, dimensionless;

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

[0072]

[0073] Where: FI is the microfracture complexity factor, dimensionless.

[0074] Step S5. Calculate the reservoir sweetness factor: Considering the flow capacity of the reservoir, the development degree of initial microfractures, and the brittle layer number of the reservoir bedding, further determine the sweetness factor. The larger its value, the better the reservoir geology and engineering sweet spots, and the greater the potential for obtaining high production capacity after volume fracturing.

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

[0076] S5.1. Classification of shale bedding thickness: In step S5.1, the bedding is divided into three categories. Among them, the bedding with a thickness less than 0.05 mm is called thin-layer bedding; the bedding with a thickness in the range of 0.05 mm to 0.1 mm is called medium-layer bedding; the bedding with a thickness greater than 0.1 mm is called thick-layer bedding. Each rock sample in step S1 is evenly divided into 5 regions with a length of 1 cm. Starting from the position of 0.5 cm for each region, a length of 0.5 mm is taken, and the total number of thin-layer bedding, medium-layer bedding, and thick-layer bedding in this length is recorded, as shown in expression (6):

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

[0078] Where: B Z is the total number of bedding planes in the rock sample; B T is the number of thin-bedded bedding planes in the rock sample; B M is the number of laminated bedding planes in the rock sample; B K is the number of thick-bedded bedding planes in the rock sample.

[0079] S5.2. Calculation of the number of brittle layers in bedding: When the Young's modulus value of a bedding is between the Young's modulus values of the upper and lower beddings, this bedding is called a brittle layer and is more likely to be damaged. To reduce the workload of the experiment, the number of brittle layers in the core area is determined by probability here. When a bedding number is taken and it satisfies the two conditions of Equation (7) and Equation (8), then this value is equal to the number of brittle layers in the calculated area; among them, Equation (7) and Equation (8) are:

[0080]

[0081]

[0082] Where: P M is the probability of having M brittle layers in an n-layer bedding shale, %; P M+1 is the probability of having (M + 1) brittle layers in an n-layer bedding shale, %.

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

[0084]

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

[0086] S5.3. Calculation of the sweetness factor: According to the rock sample flow factor in step S2, the microfracture complexity factor in step S4, and the calculation result of the number of brittle layers in bedding in step S5, the sweetness factor is further defined. The larger its value, the better the reservoir geology and engineering sweet spots. The calculation formula is as follows:

[0087] RE = RI·FI·PI (10)

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

[0089] According to needs, the embodiments of the present invention may further include the following steps: combining the actual data of oil and gas exploration to determine the favorable zone of the lithology of laminated fine-grained sedimentary rocks.

[0090] The embodiments of the present invention disclose the specific steps of the integrated comprehensive evaluation method for geological engineering sweet spots in shale reservoirs, taking into account both geological and engineering factors affecting shale sweet spots, and defining a new sweetness factor for the integrated quantitative characterization of geological engineering sweet spots. This method redefines the traditional shale sweet spot evaluation method, for the first time considering bedding properties in engineering evaluation, while pursuing enrichment optimization and maximum productivity, making up for the previous steps that only considered reservoir parameters or engineering parameters. Its calculation method is simple and feasible, without the need for a large number of field tests, greatly reducing economic costs, enabling rapid evaluation of similar unconventional shale oil and gas reservoirs, having good application prospects, and providing important guidance for the exploration and efficient development of unconventional resources.

[0091] Example 3: As shown in Attachments Figure 1 、 2 、3, and 4, this embodiment details the specific implementation manner of the present invention based on the attached drawings and downhole cores in a certain block. The integrated comprehensive evaluation method for geological engineering sweet spots in shale reservoirs specifically includes the following steps:

[0092] (1) Rock sample preparation: Take actual downhole cores from a shale gas well, with the coring depth of 2540 - 2500 m, and make 5 standard rock samples with a diameter of 2.5 cm and a length of 5 cm. The rock sample numbers are S1 - S3, and place them in an oven at 100 °C to dry to constant weight, as shown in Table 1;

[0093] Table 1 Basic parameter table of shale samples

[0094]

[0095] (2) Calculation of shale flowability index: Use a helium porosity automatic measuring instrument and an ultra-low permeability measuring instrument to respectively measure the porosity and permeability k of the dried rock samples in step S1, as shown in Table 1, and use formula (1) to calculate the reservoir capacity index RI of the rock samples to quantitatively evaluate geological sweet spots in the reservoir. The calculation results are shown in Table 1 and Attachments Figure 1 .

[0096] (3) Measurement of shale mineral composition content: Use an X-ray diffractometer to measure the mineral composition of the rock samples in step S2, and the measurement results are shown in Table 2.

[0097] Table 2 Mineral composition table 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: Use the measurement results of the mineral composition content of shale samples to calculate the microfracture complexity factor to evaluate the development degree of original microfractures in the reservoir, specifically including the following content:

[0100] ① According to the test results of different mineral components of shale samples in the evaluation section of the shale target layer, calculate the percentage of different mineral masses in shale, and use the formula Calculate and record the results in percentage form 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 of different mineral masses and the number of mineral types in the shale sample, using the formula Here n is equal to 6, so EI = 16.67%.

[0104] ③ According to the percentage content and uniformity coefficient of different minerals in shale samples, calculate the difference coefficient DI between different minerals, using the formula The calculation results are shown in Table 4.

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

[0106]

[0107] ④Use the formula The microcrack complexity factor FI is calculated, and the calculation results are shown in Table 5.

[0108] Table 5 Calculation results of microcrack complexity factors of shale samples

[0109] Rock sample number Coefficient of difference (%) Microfracture 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 is divided into five parts in length direction, and the thin-layered bedding, medium-layered bedding and thick-layered bedding are measured in the 0.5 mm area in the middle of each area. S11 is the bedding number of the first 0.5 mm area from the top to the bottom of the first core. The bedding number representation method of other cores is similar. The results are shown in Table 6.

[0112] Table 6 Bedding classification of different regions of shale core

[0113] Bedding type S11 S12 S13 S14 S15 Thin-bedded bedding 8 4 8 12 4 Medium-bedded bedding 5 4 3 2 6 Thick-bedded bedding 0 1 0 0 0 Total number of bedding 13 9 11 14 10 Bedding type S21 S22 S23 S24 S25 Thin-bedded bedding 3 3 8 3 2 Medium-bedded bedding 4 5 4 5 4 Thick-bedded bedding 1 0 0 1 1 Total number of bedding 8 8 12 9 7 Bedding type S31 S32 S33 S34 S35 Thin-bedded bedding 14 14 14 10 14 Medium-bedded bedding 2 1 1 3 0 Thick-bedded bedding 0 0 0 0 0 Total number of bedding 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 area 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 table of shale core brittleness factor

[0116]

[0117] ③ Sweetness factor calculation: Using for calculation, and the calculation results are shown in Table 8.

[0118] Table 8 Fracture network complexity factor table

[0119]

[0120] In the embodiment of the present invention, taking the downhole core of a certain block as an example, the specific application of the integrated comprehensive evaluation method for geological engineering sweet spots in shale reservoirs is described in detail. It can be seen from Table 8 that the sweetness factors of shale samples from large to small are S1 > S2 > S3. Therefore, the sweet spot ranking is S1 > S2 > S3.

[0121] The above technical features constitute the embodiment of the present invention, which has strong adaptability and implementation effects. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A comprehensive evaluation method for integrating geological and engineering sweet spots in shale reservoirs, characterized in that it includes the following steps: Carry out downhole coring work on the evaluation section of the shale target layer, divide it into several units, and test the physical properties and mineral compositions of the rock samples; calculate the rock pore flow ability factor based on the measured physical properties of the rock samples to quantitatively evaluate the geological sweet spots of the shale reservoir; calculate the rock microfracture complexity factor based on the rock mineral composition to quantitatively evaluate the development degree of the original reservoir microfractures, and at the same time calculate the brittleness factor in the bedding of each rock sample according to the rock mechanics parameters, and then quantitatively evaluate the engineering sweet spots of the shale reservoir; define the sweetness factor of the shale reservoir to conduct a comprehensive evaluation of the integration of geological and engineering sweet spots in the shale reservoir.

2. The comprehensive evaluation method for integrating geological and engineering sweet spots in shale reservoirs according to claim 1, characterized in that it specifically includes the following steps: Step S1, rock sample preparation: Divide the target evaluation section of the shale reservoir into several units, and carry out continuous coring work on each evaluation unit; Step S2, calculation of shale flow capacity index: The porosity of the rock sample described in step S1 is tested respectively and permeability k, a flow capacity factor RI is defined to quantitatively evaluate geological sweet spots; wherein, the calculation formula of RI is: Where: RI is the rock sample flow capacity index, μm2; k is the permeability of the rock sample, μm2; is the porosity of the rock sample, %. Step S3, test of shale mineral composition content: Test the mineral composition of the rock samples in Step S1; Step S4, calculate the microfracture complexity factor using the test results of the mineral composition content of the shale samples to evaluate the development degree of the original microfractures in the reservoir; Step S5, calculation of the sweetness factor of the reservoir: Comprehensively consider the flow ability of the reservoir, the development degree of the initial microfractures, and the brittle layer number of the reservoir bedding to determine the sweetness factor.

3. The comprehensive evaluation method for integrating geological and engineering sweet spots in shale reservoirs according to claim 2, characterized in that Step S4 specifically includes the following steps: S4.1, According to the test quality results of different mineral compositions of the shale samples in the evaluation section of the shale target layer, calculate the mass percentage content of different minerals in the shale, and the calculation formula is as follows: In the formula: Mi is the mass percentage content of the i-th type of mineral in the shale sample, %, where i is the number of different minerals, dimensionless; n is the type of minerals, species; m is the mass of different minerals in the shale sample, g; S4.2, Calculate the uniformity coefficient according to the mass percentage content of different minerals in the shale sample and the number of types of minerals, and the calculation formula is as follows: In the formula: EI is the uniformity coefficient between different minerals in the shale sample, %. S4.3, Calculate the difference coefficient DI between different minerals according to the mass percentage content of different minerals in the shale sample and the uniformity coefficient, and the calculation formula is as follows: In the formula: DI is the difference coefficient between different minerals, dimensionless; S4.4, Calculate the microfracture complexity factor FI, and the calculation formula is as follows: In the formula: FI is the microfracture complexity factor, dimensionless.

4. The comprehensive evaluation method for integrating geological and engineering sweet spots in shale reservoirs according to claim 2, characterized in that Step S5 specifically includes the following steps: S5.1, Classification of shale bedding thickness; S5.2, Calculation of the brittle layer number in the bedding: Use the method of probability to determine the brittle layer number of the core area. When taking a value of a layer number, when it meets the two conditions of formula (7) and formula (8), then this value is equal to the brittle layer number of the calculated area; among them, formula (7) and formula (8) are: Where: P M is the probability of calculating the number of brittle layers as M in the n-layer laminated shale, %; P M+1 is the probability of calculating the number of brittle layers as (M + 1) in the n-layer laminated shale, %; The calculation formula for the brittleness factor of each core is as follows: Where: PI is the brittleness factor, dimensionless; m is the total number of brittle layers of the core, in layers; E is the total number of bedding planes of the core, in layers; S5.

3. Calculation of the sweetness factor. The calculation formula is as follows: RE = RI·FI·PI (10) Where: RE is the sweetness factor, in μm2.

5. The integrated comprehensive evaluation method for the geological engineering sweet spots of shale reservoirs according to claim 3, characterized in that Step S5 specifically includes the following steps: S5.

1. Classification of shale bedding thicknesses; S5.

2. Calculation of the number of brittle layers in the bedding: Use probability to determine the number of brittle layers in the core area. When taking a bedding number value, if it satisfies both conditions of Equation (7) and Equation (8), then this value is equal to the number of brittle layers in the calculated area; among them, Equation (7) and Equation (8) are: Where: P M is the probability of calculating the number of brittle layers M in the n-layer laminated shale, %; P M+1 is the probability of calculating the number of brittle layers (M + 1) in the n-layer laminated shale, %; The calculation formula for the brittleness factor of each core is as follows: Where: PI is the brittleness factor, dimensionless; m is the total number of brittle layers of the core, in layers; E is the total number of bedding planes of the core, in layers; S5.

3. Calculation of the sweetness factor. The calculation formula is as follows: RE = RI·FI·PI (10) Where: RE is the sweetness factor, in μm2.

6. The integrated comprehensive evaluation method for the geological engineering sweet spots of shale reservoirs according to claim 4 or 5, characterized in that In step S5.1, the bedding is divided into three categories. Among them, the bedding with a thickness less than 0.05 mm is called thin-bedded bedding; the bedding with a thickness in the range of 0.05 mm to 0.1 mm is called medium-bedded bedding; and the bedding with a thickness greater than 0.1 mm is called thick-bedded bedding.

7. The integrated comprehensive evaluation method for the geological engineering sweet spots of shale reservoirs according to claim 1 or 2 or 3 or 4 or 5, characterized in that It further includes the following step: Combining the actual data of oil and gas exploration to determine the favorable lithologic zones of laminated fine-grained sedimentary rocks.

8. The integrated comprehensive evaluation method for the geological engineering sweet spots of shale reservoirs according to claim 6, characterized in that It further includes the following step: Combining the actual data of oil and gas exploration to determine the favorable lithologic zones of laminated fine-grained sedimentary rocks.

Images