Dynamic and static parameter combined low-permeability reservoir sweet spot identification method and device

Abstract

The invention relates to the technical field of sweet spot identification, in particular to a low-permeability reservoir sweet spot identification method and device combining dynamic and static parameters, and the method comprises the steps: evaluating the development degree of each cluster of cracks according to the change quantity of the anisotropy of a stratum before and after fracturing; calculating the yield contribution rate of each cluster according to the yield of each cluster; and determining the reservoir quality score of each cluster by using the evaluation result of the fracture development degree of each cluster and the yield contribution rate of each cluster, and screening out all clusters with the reservoir quality scores greater than or equal to a threshold score, and regarding the clusters as optimal sweet spot reservoir sections. According to the method, the yield contribution rate of each cluster after fracturing and the fracture development degree after fracturing are combined, the optimal sweet spot reservoir with high yield and fracture development after fracturing is screened out, the sweet spot recognition precision of the low-permeability reservoir is improved, the main control factors of the optimal sweet spot reservoir in data such as logging and logging can be reversely analyzed in combination with a grey correlation method, and the method is suitable for mass production. Later drilling geosteering is guided, the drilling encounter rate of the optimal dessert reservoir is improved, fracturing staged clustering is guided, and the production rate of the optimal dessert reservoir is improved.

Description

Technical Field

[0001] The invention relates to the technical field of sweet spot identification, in particular to a method and a device for identifying a low-permeability reservoir sweet spot by combining dynamic and static parameters. Background Art

[0002] With the continuous deepening of domestic oil and gas exploration and development, the oil and gas reserves in low-permeability reservoirs account for more than two-thirds of the proven reserves, and the exploration and development potential is huge. Low-permeability reservoirs generally have the characteristics of complex lithology and strong heterogeneity. Compared with conventional oil and gas resources, they are difficult to exploit. In recent years, the term "sweet spot" has been widely used in low-permeability reservoirs. It refers to reservoirs with good physical properties and oil content, developed thickness, and easy to be fractured, with high production capacity after fracturing and strong stable production capacity. Sweet spots are very important for the exploration and development of low-permeability reservoirs. Accurately identifying sweet spots and increasing the sweet spot drilling rate can help reduce exploration and development costs and increase single well production capacity.

[0003] Due to the complex conditions of low-permeability reservoirs, some wells have developed reservoir sweet spots, but the production capacity is low after fracturing. Some wells have good reservoir compressibility, but there is still no production after fracturing. Therefore, there is a mismatch between the degree of sweet spot development and the compressibility of the reservoir and the production capacity after fracturing in low-permeability reservoirs. Therefore, it is still necessary to improve the accuracy of identifying sweet spots in low-permeability reservoirs. Summary of the invention

[0004] The present invention provides a method and device for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem of low accuracy in the existing low-permeability reservoir sweet spot identification method.

[0005] One of the technical solutions of the present invention is achieved by the following measures: a method for identifying sweet spots in low-permeability reservoirs combining dynamic and static parameters, comprising:

[0006] According to the change of formation anisotropy before and after fracturing, the development degree of each cluster of fractures is evaluated;

[0007] According to the yield of each cluster, calculate the yield contribution rate of each cluster;

[0008] The reservoir quality score of each cluster is determined by using the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster. All clusters with reservoir quality scores greater than or equal to the threshold score are screened out and regarded as the best sweet spot reservoir sections.

[0009] Q n =C n x ×X n

[0010] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; Cn x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

[0011] The following are further optimizations and / or improvements to the above technical solutions:

[0012] The above also includes combining the grey correlation method to screen out the main controlling factors affecting the best sweet spot reservoir, including:

[0013] For a certain optimal sweet spot reservoir, multiple wells were selected in the same development layer for liquid production profile and dipole acoustic logging, and multiple optimal sweet spot reservoir samples were screened out. The geological parameters of each optimal sweet spot reservoir sample were used as independent variables, and the optimal sweet spot reservoir was used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable was selected as the main controlling factor affecting the optimal sweet spot reservoir by combining the grey correlation method.

[0014] The lower limit value of the main controlling factor is taken as the lower limit standard of the optimal sweet spot reservoir, where the lower limit value of the main controlling factor is the minimum value of the main controlling factor.

[0015] The above evaluation of the development degree of each cluster of fractures based on the change in formation anisotropy before and after fracturing includes:

[0016] Dipole acoustic logging is performed before fracturing to obtain the anisotropy G of the formation before fracturing. n ;

[0017]

[0018] Where S1 is the fast shear wave velocity, S2 is the slow shear wave velocity, ΔS = S1-S2; G n is the formation anisotropy, n is the cluster number, i.e. cluster 1, cluster 2, ... cluster n;

[0019] Fracturing the well, draining and testing production after fracturing, cleaning the wellbore, and performing dipole acoustic logging after fracturing to obtain the anisotropic G of the formation after fracturing. n ′;

[0020] Determine the change in formation anisotropy E before and after fracturing n ;

[0021] E n =G n ′-G n

[0022] According to the change of formation anisotropy before and after fracturing, the development degree of each cluster of fractures is evaluated;

[0023]

[0024] Among them, Emin is the minimum value of anisotropy change; E max is the maximum value of the anisotropy variation.

[0025] The above is based on the output of each cluster and calculates the output contribution rate of each cluster, including:

[0026] Measure the fluid production profile of each cluster after fracturing to obtain the production D of each cluster n ;

[0027] Using the yield D of each cluster n Calculate the yield contribution rate C of each cluster based on the total yield D of the day n x ;

[0028]

[0029] The threshold score Q of the optimal sweet spot reservoir 门槛 As shown below:

[0030] Q 门槛 =C 平均 ×X 平均

[0031] Among them, C 平均 is the average contribution rate of cluster production; X 平均 is the average score of fracture development of the cluster,

[0032] The second technical solution of the present invention is achieved by the following measures: a low permeability reservoir sweet spot identification device combining dynamic and static parameters, comprising:

[0033] The first processing unit evaluates the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0034] The second processing unit calculates the yield contribution rate of each cluster according to the yield of each cluster;

[0035] Identify the unit, use the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster to determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score as the best sweet spot reservoir segment;

[0036] Q n =C n x ×X n

[0037] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n xis the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

[0038] The following are further optimizations and / or improvements to the above technical solutions:

[0039] The above also includes a main control factor search unit, which combines the grey correlation method to screen out the main control factors that affect the best sweet spot reservoir, including:

[0040] The first search module selects multiple wells for production profile and dipole acoustic logging in the same development layer for a certain optimal sweet spot reservoir, and selects multiple optimal sweet spot reservoir samples. The geological parameters of each optimal sweet spot reservoir sample are used as independent variables, and the optimal sweet spot reservoir is used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable is selected as the main controlling factor affecting the optimal sweet spot reservoir by combining the grey correlation method.

[0041] The second search module uses the lower limit value of the main control factor as the lower limit standard of the optimal sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor.

[0042] The present invention combines the production contribution rate of each cluster after fracturing and the degree of crack development after fracturing to screen out the best sweet spot reservoir with high production and developed fracturing cracks, thereby improving the identification accuracy of the sweet spot of the low-permeability reservoir. The present invention can also combine the grey correlation method to reversely analyze the main controlling factors of the best sweet spot reservoir in the mud logging and well logging data, guide the geological guidance of the later drilling, improve the drilling rate of the best sweet spot reservoir, guide the fracturing segmentation and clustering, and improve the utilization rate of the best sweet spot reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Attached Figure 1 The present invention is a schematic flow chart of a method.

[0044] Attached Figure 2 The present invention is another method flow chart.

[0045] Attached Figure 3 It is a schematic flow chart of the method for evaluating the development degree of each cluster of cracks in the present invention.

[0046] Attached Figure 4 The figure is a flow chart of the method for calculating the yield contribution rate of each cluster in the present invention.

[0047] Attached Figure 5 The figure is a schematic diagram of the structure of a device of the present invention.

[0048] Attached Figure 6 This is a schematic diagram of another device structure of the present invention. DETAILED DESCRIPTION

[0049] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0050] The present invention will be further described below in conjunction with embodiments and drawings:

[0051] Embodiment 1: As attached Figure 1 As shown, the embodiment of the present invention discloses a method for identifying a low-permeability reservoir sweet spot by combining dynamic and static parameters, comprising:

[0052] Step S110, evaluating the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0053] Step S120, calculating the yield contribution rate of each cluster according to the yield of each cluster;

[0054] Step S130, using the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster, determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score, as the best sweet spot reservoir segment;

[0055] Q n =C n x ×X n

[0056] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

[0057] The present invention discloses a method for identifying sweet spots in low-permeability reservoirs by combining dynamic and static parameters. The method combines the production contribution rate of each cluster (segment) after fracturing and the degree of crack development after fracturing to screen out the best sweet spot reservoir with high production and developed fracturing cracks, thereby improving the identification accuracy of sweet spots in low-permeability reservoirs, making the degree of sweet spot development of the screened sweet spot reservoir and the compressibility of the reservoir match the production capacity after fracturing, effectively improving the drilling rate of the sweet spot reservoir and improving the production capacity of a single well.

[0058] Embodiment 2: As attached Figure 2 As shown, the embodiment of the present invention discloses a method for identifying a low-permeability reservoir sweet spot by combining dynamic and static parameters, comprising:

[0059] Step S210, evaluating the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0060] As attached Figure 3 As shown, the above step S210 includes:

[0061] Step S211, performing dipole acoustic logging before fracturing to obtain the anisotropy G of the formation before fracturing n ;

[0062]

[0063] Where S1 is the fast shear wave velocity, S2 is the slow shear wave velocity, ΔS = S1-S2; G n is the formation anisotropy, n is the cluster number, i.e. cluster 1, cluster 2, ... cluster n;

[0064] Step S212, fracturing the well, draining and testing production after fracturing, cleaning the wellbore, and performing post-fracturing dipole acoustic logging to obtain the post-fracturing formation anisotropy G n ′;

[0065] Step S213, determining the change in formation anisotropy E before and after fracturing n ;

[0066] E n =G n ′-G n

[0067] Step S214, evaluating the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0068]

[0069] Among them, E min is the minimum value of anisotropy change; E max is the maximum value of the anisotropy variation.

[0070] The maximum value of the anisotropic variation E in each cluster is max , then X is assigned 100 points, and the minimum value of the anisotropic change is E min , X is assigned a value of 0, and the scoring results of the fracture development degree in each cluster are shown in the above formula.

[0071] Step S220, calculating the yield contribution rate of each cluster according to the yield of each cluster;

[0072] As attached Figure 4 As shown, the above step S220 includes:

[0073] Step S221, measuring the fluid production profile of each cluster after fracturing to obtain the production D of each cluster n ;

[0074] Step S222, using the yield D of each cluster n Calculate the yield contribution rate C of each cluster based on the total yield D of the day n x ;

[0075]

[0076] Step S230, using the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster, determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score, as the best sweet spot reservoir segment;

[0077] Q n =C n x ×X n

[0078] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2... cluster n, and x is the coefficient;

[0079] In this step, the threshold score Q of the optimal sweet spot reservoir 门槛 As shown below:

[0080] Q 门槛 =C 平均 ×X 平均

[0081] Among them, C 平均 is the average contribution rate of cluster production; X 平均 is the average score of fracture development of the cluster,

[0082] Step S240, combining the grey correlation method, screen out the main controlling factors affecting the best sweet spot reservoir, including:

[0083] (1) For a certain sweet spot reservoir, multiple wells were selected in the same development layer for liquid production profile and dipole acoustic logging, and multiple sweet spot reservoir samples were screened out. The geological parameters of each sweet spot reservoir sample were used as independent variables, and the best sweet spot reservoir was used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable was selected as the main controlling factor affecting the best sweet spot reservoir by combining the grey correlation method;

[0084] (2) The lower limit value of the main controlling factor is used as the lower limit standard of the optimal sweet spot reservoir, where the lower limit value of the main controlling factor is the minimum value of the main controlling factor.

[0085] The present invention combines the production contribution rate of each cluster after fracturing and the degree of crack development after fracturing to screen out the best sweet spot reservoir with high production and developed fracturing cracks, thereby improving the identification accuracy of the sweet spot of the low-permeability reservoir. The present invention can also combine the grey correlation method to reversely analyze the main controlling factors of the best sweet spot reservoir in the mud logging and well logging data, guide the geological guidance of the later drilling, improve the drilling rate of the best sweet spot reservoir, guide the fracturing segmentation and clustering, and improve the utilization rate of the best sweet spot reservoir.

[0086] Example 4: Taking the horizontal well XJHW061 as an example, the method of the present invention is used to identify the sweet spots of low-permeability reservoirs. The well has a vertical depth of 4,100 meters and a horizontal section length of 800 meters. Specifically, the method includes:

[0087] The first step is to conduct dipole acoustic logging before fracturing in the horizontal well XJHW061, which has a vertical depth of 4,100 meters and a horizontal section length of 800 meters, to obtain the anisotropic G of the formation before fracturing. n , see Table 1.

[0088] In the second step, the XJHW061 well was divided into 10 sections for perforation-bridge plug staged fracturing. The fracturing process and construction parameters of each cluster were basically the same. After fracturing, the wellbore was cleaned by drilling and grinding bridge plugs. After fracturing, the XJHW061 well was subjected to dipole acoustic logging to obtain the anisotropic G of the formation after fracturing. n ′, calculate the change of formation anisotropy E before and after fracturing n , E n =G n ′-G n . Using the change in anisotropy E before and after fracturing n Quantitative evaluation of the degree of development of each cluster of fractures X n , see Table 1.

[0089] Table 1 Formation anisotropy data and fracture development scores of Well XJHW061 before and after fracturing

[0090]

[0091]

[0092] The third step is to measure the production profile of each section after fracturing by using the array production profiler to obtain the production contribution rate C of each cluster in the XJHW061 well. n , see Table 2.

[0093] The fourth step is to select the best sweet spot reservoir. Considering that the area is in the early stage of exploration, x is taken as 2, and the reservoir quality score Q of the single cluster of well XJHW061 is calculated. n , the average contribution rate of production is calculated to be 10%, and the average score of fracture development of the cluster is 56, as shown in Table 2; the threshold score of the best sweet spot reservoir is 0.560, then Q nThe clusters with values ​​≥0.560 correspond to the best sweet spot reservoirs, which are clusters 1, 3, 4, 7, 8, and 10, respectively, as shown in Table 2.

[0094] Table 2 Production contribution rate of each cluster and optimal sweet spot reservoir calculation table of XJHW061 well

[0095]

[0096] The fifth step is to conduct fluid production profiles and dipole acoustic logging in 5 horizontal wells in the same development layer to screen out 30 best sweet spot reservoir sections. The best sweet spot reservoir samples screened out are analyzed for their attribute characteristics based on recording and measurement data, among which the thickness, nuclear magnetic effective porosity, dolomite content, oil saturation, brittleness index and organic matter content are closely related to the best sweet spot reservoir section Q. n Taking these geological parameters as independent variables, the optimal sweet spot reservoir Q n As the dependent variable, the grey correlation method is used to calculate the grey correlation degree and weight coefficient, and the weight coefficients obtained are: 0.31, 0.24, 0.19, 0.11, 0.08, 0.07, and the corresponding geological parameters are: thickness, nuclear magnetic effective porosity, brittleness index, oil saturation, organic matter content, dolomite content. The average value of the calculated weight coefficient is 0.17, and the geological parameters greater than the average value of the weight coefficient are: thickness, nuclear magnetic effective porosity, brittleness index, which are the main controlling factors affecting the best sweet spot reservoir.

[0097] Step 6. Among the 30 best sweet spot reservoir sections, the minimum values ​​of the main controlling factors thickness, NMR effective porosity, and brittleness index are 3 meters, 8%, and 12, respectively. The lower limit standard of the best sweet spot reservoir is: thickness 3 meters, NMR effective porosity 8%, and brittleness index 12.

[0098] Embodiment 5: As attached Figure 5 As shown, the embodiment of the present invention discloses a low-permeability reservoir sweet spot identification device combining dynamic and static parameters, including:

[0099] The first processing unit evaluates the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0100] The second processing unit calculates the yield contribution rate of each cluster according to the yield of each cluster;

[0101] Identify the unit, use the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster to determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score as the best sweet spot reservoir segment;

[0102] Q n =C n x ×Xn

[0103] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

[0104] Embodiment 6: As attached Figure 6 As shown, the embodiment of the present invention discloses a low-permeability reservoir sweet spot identification device combining dynamic and static parameters, including:

[0105] The first processing unit evaluates the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing;

[0106] The second processing unit calculates the yield contribution rate of each cluster according to the yield of each cluster;

[0107] Identify the unit, use the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster to determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score as the best sweet spot reservoir segment;

[0108] Q n =C n x ×X n

[0109] Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2... cluster n, and x is the coefficient;

[0110] The main control factor search unit, combined with the grey correlation method, screens out the main control factors that affect the best sweet spot reservoir, including:

[0111] The first search module selects multiple wells for production profile and dipole acoustic logging in the same development layer for a certain optimal sweet spot reservoir, and selects multiple optimal sweet spot reservoir samples. The geological parameters of each optimal sweet spot reservoir sample are used as independent variables, and the optimal sweet spot reservoir is used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable is selected as the main controlling factor affecting the optimal sweet spot reservoir by combining the grey correlation method.

[0112] The second search module uses the lower limit value of the main control factor as the lower limit standard of the optimal sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor.

[0113] Embodiment 7: The embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is configured to execute a method for identifying low-permeability reservoir sweet spots combining dynamic and static parameters when running.

[0114] The above storage medium may include, but is not limited to: a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0115] Embodiment 8: The embodiment of the present invention discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement a method for identifying a low-permeability reservoir sweet spot by combining dynamic and static parameters.

[0116] The above processor may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. It may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The memory may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk.

[0117] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A method for identifying sweet spots in low-permeability reservoirs combining dynamic and static parameters, characterized in that: include: According to the change of formation anisotropy before and after fracturing, the development degree of each cluster of fractures is evaluated; According to the yield of each cluster, calculate the yield contribution rate of each cluster; The reservoir quality score of each cluster is determined by using the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster. All clusters with reservoir quality scores greater than or equal to the threshold score are screened out and regarded as the best sweet spot reservoir sections. Q n =C n x ×X n Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

2. The method for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters according to claim 1, characterized in that: It also includes combining the grey correlation method to screen out the main controlling factors that affect the best sweet spot reservoir, including: For a certain optimal sweet spot reservoir, multiple wells were selected in the same development layer for liquid production profile and dipole acoustic logging, and multiple optimal sweet spot reservoir samples were screened out. The geological parameters of each optimal sweet spot reservoir sample were used as independent variables, and the optimal sweet spot reservoir was used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable was selected as the main controlling factor affecting the optimal sweet spot reservoir by combining the grey correlation method. The lower limit value of the main controlling factor is taken as the lower limit standard of the optimal sweet spot reservoir, where the lower limit value of the main controlling factor is the minimum value of the main controlling factor.

3. The method for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters according to claim 1 or 2, characterized in that: The evaluation of the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing includes: Dipole acoustic logging is performed before fracturing to obtain the anisotropy G of the formation before fracturing. n ; Where S1 is the fast shear wave velocity, S2 is the slow shear wave velocity, ΔS = S1-S2; G n is the formation anisotropy, n is the cluster number, i.e. cluster 1, cluster 2, ... cluster n; Fracturing the well, draining and testing production after fracturing, cleaning the wellbore, and performing dipole acoustic logging after fracturing to obtain the anisotropic G of the formation after fracturing. n ′; Determine the change in formation anisotropy E before and after fracturing n ; E n =G n ′-G n According to the change of formation anisotropy before and after fracturing, the development degree of each cluster of fractures is evaluated; Among them, E min is the minimum value of anisotropy change; E max is the maximum value of the anisotropy variation.

4. The method for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters according to claim 1 or 2, characterized in that: The method of calculating the yield contribution rate of each cluster according to the yield of each cluster includes: Measure the fluid production profile of each cluster after fracturing to obtain the production D of each cluster n ; Using the yield D of each cluster n Calculate the yield contribution rate C of each cluster based on the total yield D of the day n x ; 5. The method for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters according to claim 3, characterized in that: The method of calculating the yield contribution rate of each cluster according to the yield of each cluster includes: Measure the fluid production profile of each cluster after fracturing to obtain the production D of each cluster n ; Using the yield D of each cluster n Calculate the yield contribution rate C of each cluster based on the total yield D of the day n x ; 6. The method for identifying sweet spots in low-permeability reservoirs by combining dynamic and static parameters according to any one of claims 1 to 5, characterized in that: The threshold score Q of the optimal sweet spot reservoir 门槛 As shown below: Q 门槛 =C 平均 ×X 平均 Among them, C 平均 is the average contribution rate of cluster production; X 平均 is the average score of fracture development of the cluster, 7. A low permeability reservoir sweet spot identification device combining dynamic and static parameters using the method as described in any one of claims 1 to 6, characterized in that: include: The first processing unit evaluates the development degree of each cluster of fractures according to the change in formation anisotropy before and after fracturing; The second processing unit calculates the yield contribution rate of each cluster according to the yield of each cluster; Identify the unit, use the evaluation results of the fracture development degree of each cluster and the production contribution rate of each cluster to determine the reservoir quality score of each cluster, and screen out all clusters with reservoir quality scores greater than or equal to the threshold score as the best sweet spot reservoir segment; Q n =C n x ×X n Among them, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the development degree of a single cluster of fractures; C n x is the production contribution rate of each cluster, n is the cluster number, i.e. cluster 1, cluster 2...cluster n, and x is the coefficient.

8. The low permeability reservoir sweet spot identification device combining dynamic and static parameters according to claim 7, characterized in that: It also includes a main control factor search unit, which combines the grey correlation method to screen out the main control factors that affect the best sweet spot reservoir, including: The first search module selects multiple wells for production profile and dipole acoustic logging in the same development layer for a certain optimal sweet spot reservoir, and selects multiple optimal sweet spot reservoir samples. The geological parameters of each optimal sweet spot reservoir sample are used as independent variables, and the optimal sweet spot reservoir is used as the dependent variable. The independent variable with a weight coefficient greater than the average weight coefficient of the independent variable is selected as the main controlling factor affecting the optimal sweet spot reservoir by combining the grey correlation method. The second search module uses the lower limit value of the main control factor as the lower limit standard of the optimal sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor.

9. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the method for identifying low-permeability reservoir sweet spots combining dynamic and static parameters as claimed in any one of claims 1 to 6 when running.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the method for identifying low-permeability reservoir sweet spots by combining dynamic and static parameters as claimed in any one of claims 1 to 6.

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