Sweet spot identification method and device for low-permeability reservoirs combining dynamic and static parameters

Abstract

The present application relates to a dessert identification technology field, and is a low-permeability reservoir dessert identification method and device combining dynamic and static parameters, which comprises the following steps: evaluating the development degree of each cluster of fractures according to the change amount of anisotropy of the formation before and after fracturing; calculating the yield contribution rate of each cluster according to the yield of each cluster; determining the reservoir quality score of each cluster by using the evaluation result of the development degree of each cluster of fractures and the yield contribution rate of each cluster; and screening out the cluster with a reservoir quality score greater than or equal to a threshold score, which is regarded as the best dessert reservoir section. The present application screens out the best dessert reservoir with high yield and developed fracturing fractures by combining the yield contribution rate of each cluster after fracturing and the development degree of the fractures after fracturing, thereby improving the identification precision of the dessert reservoir in the low-permeability reservoir. In addition, the present application can also combine the grey correlation method to reversely analyze the main control factors of the best dessert reservoir on the logging and well logging data, thereby guiding the geological steering in the later drilling, improving the drilling rate of the best dessert reservoir, guiding the fracturing segmentation and clustering, and improving the production rate of the best dessert reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to a sweet spot identification technology field, and is a low-permeability reservoir sweet spot identification method and device combining dynamic and static parameters. BACKGROUND

[0002] With the continuous deepening of domestic oil and gas exploration and development, the oil and gas reserves of 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 complex lithology, strong heterogeneity and other characteristics, and the exploitation difficulty is large compared with conventional oil and gas resources. In recent years, the term "sweet spot" has been widely used in low-permeability reservoirs. It refers to a reservoir with good physical properties, oil-bearing properties and thickness development, and is easy to be fractured and transformed, and has high productivity and strong stable production capacity after fracturing. The sweet spot is very important for the exploration and development of low-permeability reservoirs. Precise identification of sweet spots and improvement of sweet spot drilling rate can help reduce exploration and development costs and improve single-well productivity.

[0003] Due to the complex conditions of low-permeability reservoirs, the reservoir sweet spots of some wells are developed, but the productivity is low after fracturing transformation. Some wells have good reservoir fracturing properties, but still have no production after fracturing. Therefore, the development degree of sweet spots in low-permeability reservoirs, the fracturing properties of reservoirs and the productivity after fracturing do not match, so it is still necessary to improve the accuracy of sweet spot identification in low-permeability reservoirs. SUMMARY

[0004] The present application provides a low-permeability reservoir sweet spot identification method and device combining dynamic and static parameters, which overcomes the shortcomings of the prior art and effectively solves the problem of low accuracy of existing low-permeability reservoir sweet spot identification methods.

[0005] One of the technical solutions of the present application is realized by the following measures: a low-permeability reservoir sweet spot identification method combining dynamic and static parameters, comprising:

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

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

[0008] Using the evaluation results of the development degree of each cluster of fractures and the yield contribution rate of each cluster, the reservoir quality score of each cluster is determined, and all clusters with a reservoir quality score greater than or equal to a threshold score are screened out as the best sweet spot reservoir section;

[0009] Q n = C n x X n

[0010] Wherein, 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 The contribution rate of each cluster yield, n is the cluster number, i.e. the first cluster, the second cluster,..., the nth cluster, and x is a coefficient.

[0011] The following is a further optimization or / and improvement of the above technical solutions:

[0012] The above also includes screening the main control factors affecting the best dessert reservoir by combining the grey correlation method, including:

[0013] For a certain best dessert reservoir, in the same development layer, select multiple wells for fluid production profile and dipole acoustic logging, screen multiple best dessert reservoir samples, take the geological parameters of each best dessert reservoir sample as the independent variable, and the best dessert reservoir as the dependent variable, and combine the grey correlation method to select the independent variable with a weight coefficient greater than the average value of the independent variable weight coefficient as the main control factor affecting the best dessert reservoir.

[0014] The lower limit value of the main control factor is taken as the lower limit standard of the best dessert reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor.

[0015] The above evaluates the development degree of each cluster fracture according to the change amount of the formation anisotropy before and after fracturing, including:

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

[0017]

[0018] Wherein, S1 is the fast shear wave velocity, S2 is the slow shear wave velocity, ΔS = S1-S2; G n is the formation anisotropy, and n is the cluster number, i.e. the first cluster, the second cluster,..., the nth cluster;

[0019] The well is fractured, and after fracturing, the production test is carried out, the wellbore is cleaned, and the dipole acoustic logging is performed after fracturing to obtain the formation anisotropy G n ′ after fracturing;

[0020] The change amount E n of the formation anisotropy before and after fracturing is determined;

[0021] E n = G n ′-G n

[0022] The development degree of each cluster fracture is evaluated according to the change amount of the formation anisotropy before and after fracturing;

[0023]

[0024] Wherein, Emin is the minimum value of the anisotropy change amount; E max is the maximum value of the anisotropy change amount.

[0025] The yield of each cluster is calculated, and the yield contribution rate of each cluster is calculated, including:

[0026] The cluster liquid production profile after fracturing is measured to obtain the yield D n of each cluster.

[0027] The yield D n of each cluster and the total yield D of the day are used to calculate the yield contribution rate C n of each cluster. x ;

[0028]

[0029] The threshold score Q of the best sweet spot reservoir is calculated as follows: 门槛 Q 门槛 = C 平均 × X 平均

[0030] Q 门槛 = C 平均 × X 平均

[0031] Wherein, C 平均 is the average yield contribution rate of the cluster; X 平均 is the average score of the cluster fracture development,

[0032] The second technical solution of the application is realized by the following measures: a low-permeability reservoir sweet spot identification device combined with dynamic and static parameters, comprising:

[0033] The first processing unit evaluates the fracture development degree of each cluster according to the anisotropy change amount of the formation 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] The identification unit determines 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 screens out clusters with a reservoir quality score greater than or equal to a threshold score as the best sweet spot reservoir section.

[0036] Q n = C n x × X n

[0037] Wherein, Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the fracture development degree of a single cluster; C n xFor each cluster yield contribution rate, n is the cluster number, i.e. the first cluster, the second cluster... the nth cluster, x is the coefficient.

[0038] The following is a further optimization or / and improvement of the above technical solutions:

[0039] The above also includes a master factor finding unit, which screens out the master control factors affecting the best dessert reservoir in combination with the grey correlation method, including:

[0040] The first finding module selects multiple wells for fluid production profile and dipole acoustic logging in the same development layer for a certain best dessert reservoir, screens out multiple best dessert reservoir samples, takes the geological parameters of each best dessert reservoir sample as the independent variable, and takes the best dessert reservoir as the dependent variable, and selects the independent variable with a weight coefficient greater than the average value of the independent variable weight coefficient as the master control factor affecting the best dessert reservoir in combination with the grey correlation method.

[0041] The second finding module takes the lower limit value of the master control factor as the lower limit standard of the best dessert reservoir, wherein the lower limit value of the master control factor is the minimum value of the master control factor.

[0042] The present application combines the yield contribution rate of each cluster after fracturing and the fracture development degree after fracturing to screen out the best dessert reservoir with high yield and developed fracturing fractures, improves the precision of dessert identification in low permeability reservoirs, and can also combine the grey correlation method to reversely analyze the master control factors of the best dessert reservoir on the logging and logging data, guide the later drilling geosteering, improve the drilling rate of the best dessert reservoir, guide the fracturing segmentation and clustering, and improve the production rate of the best dessert reservoir. BRIEF DESCRIPTION OF DRAWINGS

[0043] The Figure 1 It is a method flowchart of the present application.

[0044] The Figure 2 It is another method flowchart of the present application.

[0045] The Figure 3 It is a method flowchart for evaluating the fracture development degree of each cluster in the present application.

[0046] The Figure 4 It is a method flowchart for calculating the yield contribution rate of each cluster in the present application.

[0047] The Figure 5 It is a device structure diagram of the present application.

[0048] The Figure 6 It is another device structure diagram of the present application. DETAILED DESCRIPTION

[0049] The application is not limited by the following examples, and the specific implementation can be determined according to the technical scheme of the application and the actual situation.

[0050] The application is further described below in combination with examples and drawings:

[0051] Example 1: as shown in the accompanying Figure 1 The application embodiment discloses a low-permeability reservoir sweet spot identification method combining dynamic and static parameters, comprising:

[0052] Step S110, evaluating the development degree of each cluster of fractures according to the change amount of anisotropy of the formation 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, determining the reservoir quality score of each cluster by using the evaluation result of the development degree of each cluster of fractures and the yield contribution rate of each cluster, and screening out the cluster with a reservoir quality score greater than or equal to a threshold score as the best sweet spot reservoir section;

[0055] Q n =C n x X n

[0056] wherein 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 yield contribution rate of each cluster, n is the cluster number, i.e., the first cluster, the second cluster,..., the nth cluster, and x is a coefficient.

[0057] The application discloses a low-permeability reservoir sweet spot identification method combining dynamic and static parameters, which screens out the best sweet spot reservoir with high yield and developed fracturing fractures after fracturing by combining the yield contribution rate of each cluster (section) after fracturing and the development degree of the fractures after fracturing, improves the identification precision of the low-permeability reservoir sweet spot, matches the development degree of the screened sweet spot reservoir, the pressureability of the reservoir and the productivity after fracturing, effectively improves the drilling rate of the sweet spot reservoir, and improves the productivity of a single well.

[0058] Example 2: as shown in the accompanying Figure 2 The application embodiment discloses a low-permeability reservoir sweet spot identification method combining dynamic and static parameters, comprising:

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

[0060] As shown in the accompanying Figure 3 The above step S210 comprises:

[0061] Step S211, dipole acoustic logging is performed before fracturing to obtain formation anisotropy G n ;

[0062]

[0063] Wherein, 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. the first cluster, the second cluster... the nth cluster;

[0064] Step S212, fracturing is performed on the well, and after fracturing, production test is performed, the wellbore is cleaned, and dipole acoustic logging is performed after fracturing to obtain formation anisotropy G n ′ after fracturing;

[0065] Step S213, the change amount E n of the formation anisotropy before and after fracturing is determined;

[0066] E n =G n ′-G n

[0067] Step S214, according to the change amount of the formation anisotropy before and after fracturing, the fracture development degree of each cluster is evaluated;

[0068]

[0069] Wherein, E min is the minimum value of the change amount of the anisotropy; E max is the maximum value of the change amount of the anisotropy.

[0070] In each cluster, the maximum value E max of the change amount of the anisotropy is X = 100 points, the minimum value E min of the change amount of the anisotropy is X = 0 points, and the fracture development degree in each cluster is scored as shown in the above formula.

[0071] Step S220, according to the production of each cluster, the production contribution rate of each cluster is calculated;

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

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

[0074] Step S222, the production D n of each cluster and the total production D of the day are used to calculate the production contribution rate C n x of each cluster;

[0075]

[0076] Step S230, using the evaluation results of the fracture development degree of each cluster and the yield contribution rate of each cluster, determining the reservoir quality score of each cluster, and screening out all clusters with a reservoir quality score greater than or equal to a threshold score, which are considered as the best sweet spot reservoir sections;

[0077] Q n =C n x ×X n

[0078] wherein Q n is the reservoir quality score of a single cluster; X n is the evaluation result of the fracture development degree of a single cluster; C n x is the yield contribution rate of each cluster, and n is the cluster number, i.e., the first cluster, the second cluster,..., the nth cluster, and x is a coefficient;

[0079] In this step, the threshold score Q 门槛 of the best sweet spot reservoir is as follows:

[0080] Q 门槛 =C 平均 ×X 平均

[0081] wherein C 平均 is the average yield contribution rate of a cluster; X 平均 is the average fracture development score of a cluster,

[0082] Step S240, in combination with the grey correlation method, screening out the main control factors affecting the best sweet spot reservoir, including:

[0083] (1) For a certain best sweet spot reservoir, in the same development horizon, multiple wells are selected for fluid production profile and dipole acoustic logging, multiple best sweet spot reservoir samples are screened out, the geological parameters of each best sweet spot reservoir sample are taken as independent variables, and the best sweet spot reservoir is taken as a dependent variable, and the grey correlation method is combined to select the independent variables with a weight coefficient greater than the average value of the independent variable weight coefficient as the main control factors affecting the best sweet spot reservoir;

[0084] (2) The lower limit value of the main control factor is taken as the lower limit standard of the best sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor.

[0085] The present application combines the yield contribution rate of each cluster after fracturing and the fracture development degree after fracturing to screen out the best sweet spot reservoir with high yield and developed fracturing cracks, improves the sweet spot identification precision of low permeability reservoirs, and can also combine the gray correlation method to reversely analyze the main control factors of the best sweet spot reservoir on logging and logging data, guide the later drilling geosteering, improve the drilling rate of the best sweet spot reservoir, guide the fracturing segmentation and clustering, and improve the producing rate of the best sweet spot reservoir.

[0086] Example 4: Taking horizontal well XJHW061 as an example, the method of the present application is used for low permeability reservoir sweet spot identification, the well has a vertical depth of 4100 meters and a horizontal section length of 800 meters, and specifically includes:

[0087] Firstly, the horizontal well XJHW061 has a vertical depth of 4100 meters and a horizontal section length of 800 meters, and dipole acoustic logging is performed before fracturing to obtain the anisotropy G n of the formation before fracturing, as shown in Table 1.

[0088] Secondly, the XJHW061 well is perforated and segmented by bridge plug fracturing, and the fracturing technology and construction parameters of each cluster are basically the same, and the production test is performed after fracturing and drainage, and the bridge plug is cleaned up to clean the wellbore, and the dipole acoustic logging is performed after fracturing to obtain the anisotropy G n of the formation after fracturing, and the change E n of the anisotropy of the formation before and after fracturing is calculated, E n =G n ′-G n . The change E n of the anisotropy before and after fracturing is used to quantitatively evaluate the fracture development degree X n of each cluster, as shown in Table 1.

[0089] Table 1: Anisotropy data of the formation before and after fracturing of the XJHW061 well and fracture development scoring table

[0090]

[0091]

[0092] Thirdly, the array fluid production profile instrument is used to measure the fluid production profile of each section after fracturing to obtain the yield contribution rate C n of each cluster of the XJHW061 well, as shown in Table 2.

[0093] Fourthly, the best sweet spot reservoir is screened. Considering that the region is in the early exploration stage, x is 2, the reservoir quality score Q n of each cluster of the XJHW061 well is calculated, the average yield contribution rate is 10%, the average fracture development score of the cluster is 56, as shown in Table 2, and the threshold score of the best sweet spot reservoir is 0.560, so Q nThe cluster of ≥0.560 corresponds to the best dessert reservoir, which is the first, third, fourth, seventh, eighth and tenth cluster respectively, as shown in Table 2.

[0094] Table 2: Calculation table of yield contribution rate of each cluster of XJHW061 well and best dessert reservoir

[0095]

[0096] In the fifth step, in the same development layer, five horizontal well fluid production profiles and dipole acoustic logging are respectively carried out, and 30 best dessert reservoir sections are screened out. Through the best dessert reservoir samples screened out, the attribute characteristics on the logging data are analyzed, in which the thickness, the nuclear magnetic effective porosity, the dolomitic rock content, the oil saturation, the brittleness index and the organic matter content have certain relationships with the best dessert reservoir section Q n . Taking these geological parameters as independent variables and the best dessert reservoir Q n as dependent variable, the grey correlation method is used to calculate the grey correlation degree and weight coefficient, and the weight coefficients obtained are respectively: 0.31, 0.24, 0.19, 0.11, 0.08, 0.07, and the corresponding geological parameters are respectively: thickness, nuclear magnetic effective porosity, brittleness index, oil saturation, organic matter content and dolomitic rock content. The average value of the weight coefficient is 0.17, and the geological parameters greater than the average value of the weight coefficient are respectively: thickness, nuclear magnetic effective porosity and brittleness index, that is, the main control factors of the best dessert reservoir.

[0097] In the sixth step, in the 30 best dessert reservoir sections, the minimum values of the main control factors of thickness, nuclear magnetic effective porosity and brittleness index are respectively: 3 meters, 8% and 12, so the lower limit standard of the best dessert reservoir is: thickness of 3 meters, nuclear magnetic effective porosity of 8% and brittleness index of 12.

[0098] Example 5: as shown in the accompanying Figure 5 , the embodiment of the present application discloses a low-permeability reservoir dessert identification device combining dynamic and static parameters, which comprises:

[0099] The first processing unit evaluates the fracture development degree of each cluster according to the change amount of anisotropy of the formation 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] The identification unit determines 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, screens out the clusters with reservoir quality scores greater than or equal to the threshold score, and regards them as the best dessert reservoir sections;

[0102] Q n = C n x Xn

[0103] Q = C x X x Xn n is the reservoir quality score of a single cluster; X n is the evaluation result of the fracture development degree of a single cluster; C n x is the yield contribution rate of each cluster, n is the cluster number, i.e., the first cluster, the second cluster,..., the nth cluster, and x is a coefficient.

[0104] Embodiment 6: as shown in the accompanying Figure 6 Embodiments of the present application disclose a low-permeability reservoir sweet spot identification device combining dynamic and static parameters, which comprises:

[0105] a first processing unit configured to evaluate the fracture development degree of each cluster according to the change in anisotropy of the formation before and after fracturing;

[0106] a second processing unit configured to calculate the yield contribution rate of each cluster according to the yield of each cluster;

[0107] an identification unit configured to determine 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 screen out the clusters with a reservoir quality score greater than or equal to a threshold value as the best sweet spot reservoirs;

[0108] Q = C x X x Xn n n x x X n

[0109] Q = C x X x Xn n is the reservoir quality score of a single cluster; X n is the evaluation result of the fracture development degree of a single cluster; C n x is the yield contribution rate of each cluster, n is the cluster number, i.e., the first cluster, the second cluster,..., the nth cluster, and x is a coefficient.

[0110] a main control factor finding unit configured to screen out the main control factors affecting the best sweet spot reservoir by using the grey correlation method, including:

[0111] a first finding module configured to, for a certain best sweet spot reservoir, select multiple wells for fluid production profile and dipole acoustic logging in the same development layer, screen out multiple best sweet spot reservoir samples, take the geological parameters of each best sweet spot reservoir sample as the independent variables and the best sweet spot reservoir as the dependent variable, and select the independent variables with a weight coefficient greater than the average value of the weight coefficients of the independent variables as the main control factors affecting the best sweet spot reservoir by using the grey correlation method;

[0112] a second finding module configured to take the lower limit value of the main control factors as the lower limit standard of the best sweet spot reservoir, wherein the lower limit value of the main control factors is the minimum value of the main control factors. ​

[0113] Embodiment 7: The embodiment of the present application discloses a storage medium, and the storage medium stores a computer program readable by a computer, and the computer program is arranged to execute a low-permeability reservoir sweet spot identification method combined with dynamic and static parameters when running.

[0114] The storage medium can include, but is not limited to, a U disk, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing a computer program.

[0115] Embodiment 8: The embodiment of the present application discloses an electronic device, which comprises a processor and a memory, and the memory stores a computer program, and the computer program is loaded and executed by the processor to realize a low-permeability reservoir sweet spot identification method combined with dynamic and static parameters.

[0116] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. It can also be a combination of computing functions, such as one or more microprocessor combinations, DSP and microprocessor combinations, etc. The memory can include, but is not limited to, a U disk, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing a computer program.

[0117] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.). The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting language JavaScript.

[0118] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems) and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the functions described in the flowcharts and / or block diagrams. Figure 1apparatuses that implement the functions specified in the flowchart or flowcharts and / or blocks. Figure 1

[0119] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart or flowcharts and / or blocks. Figure 1 apparatuses that implement the functions specified in the flowchart or flowcharts and / or blocks. Figure 1

[0120] The above technical features constitute the best embodiments of the present application, which have strong adaptability and best implementation effects. Non-essential technical features can be added or removed according to actual needs to meet the needs of different situations.​​

Claims

1. A method for identifying sweet spots in low permeability reservoirs combining dynamic and static parameters, characterized in that, The method comprises the following steps: According to the change amount of the anisotropy of the formation before and after fracturing, the development degree of each cluster of fractures is evaluated; According to the production of each cluster, the production contribution rate of each cluster is calculated; The reservoir quality score of each cluster is determined by using the evaluation result of the development degree of each cluster of fractures and the production contribution rate of each cluster, and all clusters with a reservoir quality score greater than or equal to a threshold value are selected as the best sweet spot reservoir section; Q n = C n x x X n wherein Q n is the quality score of the reservoir for a single cluster; X n is the evaluation result of the development degree of a single cluster fracture; C n x is the yield contribution rate of each cluster, n is the cluster number, i.e., the first cluster, the second cluster,..., the nth cluster, and x is a coefficient. According to the change amount of the anisotropy of the formation before and after fracturing, the development degree of each cluster of fractures is evaluated; A dipole acoustic log is performed prior to fracturing to obtain the pre-fracture formation anisotropy G n ; Wherein, S1 is the fast transverse wave velocity, S2 is the slow transverse wave velocity, and ΔS = S1-S2; G n is the formation anisotropy, and n is the cluster number, i.e., the 1st cluster, the 2nd cluster,..., and the nth cluster. The well is fractured, and after fracturing, the production test is carried out, the wellbore is cleaned, the dipole acoustic logging after fracturing is carried out, and the anisotropy G of the formation after fracturing is obtained n ′; Determining the amount of change in formation anisotropy E before and after fracturing n ; E n = G n '-G n The method further comprises the following steps: wherein E min is the minimum value of the anisotropic change amount; E max is the maximum value of the anisotropic change amount.

2. The low permeability reservoir sweet spot identification method combining dynamic and static parameters of claim 1, wherein, For a certain best sweet spot reservoir, in the same development layer, multiple wells are selected to perform production fluid profile and dipole acoustic logging, multiple best sweet spot reservoir samples are selected, the geological parameters of each best sweet spot reservoir sample are taken as independent variables, and the best sweet spot reservoir is taken as a dependent variable; and the gray correlation method is used to select independent variables with a weight coefficient greater than the average value of the weight coefficients of the independent variables as the main control factors affecting the best sweet spot reservoir; The lower limit value of the main control factor is taken as the lower limit standard of the best sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor. The method further comprises the following steps:

3. The low permeability reservoir sweet spot identification method combining dynamic and static parameters according to claim 1 or 2, characterized in that, The method comprises the following steps: The post-fracturing liquid production profile of each cluster is measured to obtain the production D of each cluster n ; The yield D of each cluster is calculated using the yield of each cluster n and the total yield D of the day to calculate the yield contribution rate C of each cluster n x ; 4. The low permeability reservoir sweet spot identification method combining dynamic and static parameters according to claim 1 or 2, characterized in that, The threshold score Q of the optimal dessert reservoir 门槛 As follows: Q 门槛 = C 平均 x X 平均 wherein C 平均 is the average contribution of the cluster to the yield; X 平均 is the average score of the cluster for the crack development, 5. A low permeability reservoir sweet spot identification device incorporating dynamic and static parameters using the method of any one of claims 1 to 4, characterized in that, The first processing unit evaluates the development degree of each cluster of fractures according to the change amount of the anisotropy of the formation before and after fracturing, which comprises the following steps: According to the change amount of the anisotropy of the formation before and after fracturing, the development degree of each cluster of fractures is evaluated; A dipole acoustic log is run prior to fracturing to obtain the pre-fracture formation anisotropy G n ; wherein S1 is the fast shear wave velocity, S2 is the slow shear wave velocity, and ΔS = S1 - S2; G n is the formation anisotropy, and n is the cluster number, i.e., 1st cluster, 2nd cluster,... nth cluster. The well is fractured, and after fracturing, the production test is carried out, the well bore is cleaned, the dipole acoustic logging after fracturing is carried out, and the anisotropy G of the formation after fracturing is obtained n ′; Determining the amount of change in formation anisotropy E before and after fracturing n ; E n = G n '- G n The second processing unit calculates the production contribution rate of each cluster according to the production of each cluster. E min is the minimum value of the anisotropic change amount; and max is the maximum value of the anisotropic change amount. The identification unit determines the reservoir quality score of each cluster by using the evaluation result of the development degree of each cluster of fractures and the production contribution rate of each cluster, and selects all clusters with a reservoir quality score greater than or equal to a threshold value as the best sweet spot reservoir section. The method further comprises the following steps: Q n = C n x x X n wherein Q n is the quality score of the reservoir for a single cluster; X n is the evaluation result of the development degree of a single cluster fracture; C n x is the yield contribution rate of each cluster, n is the cluster number, i.e. the 1st cluster, the 2nd cluster... the nth cluster, and x is a coefficient.

6. The low permeability reservoir sweet spot identification device incorporating dynamic and static parameters of claim 5, wherein, The first finding module selects multiple wells to perform production fluid profile and dipole acoustic logging in the same development layer for a certain best sweet spot reservoir, selects multiple best sweet spot reservoir samples, takes the geological parameters of each best sweet spot reservoir sample as independent variables, takes the best sweet spot reservoir as a dependent variable, and uses the gray correlation method to select independent variables with a weight coefficient greater than the average value of the weight coefficients of the independent variables as the main control factors affecting the best sweet spot reservoir. The second finding module takes the lower limit value of the main control factor as the lower limit standard of the best sweet spot reservoir, wherein the lower limit value of the main control factor is the minimum value of the main control factor. The storage medium stores a computer program readable by a computer, and the computer program is configured to execute the low-permeability reservoir sweet spot identification method combining dynamic and static parameters as claimed in any one of claims 1 to 4 when running.

7. A storage medium, characterized by The method comprises a processor and a memory, and the memory stores a computer program which is loaded and executed by the processor to realize the low-permeability reservoir sweet spot identification method combining dynamic and static parameters as claimed in any one of claims 1 to 4.

8. An electronic device, comprising: ​

Images