Horizontal well perforation optimization design method and system based on Qt frame

Abstract

The invention belongs to the field of geological engineering, and discloses a horizontal well perforation optimization design method and system based on a Qt framework, and the method comprises the steps: selecting reservoir classification alternative parameters; establishing a quasi-three-dimensional model of the target horizontal well according to the reservoir classification alternative parameters; according to the basic parameters of the various drilling reservoirs, establishing a perforation parameter flow-limiting fracturing model of each cluster of the target horizontal well; dividing fracturing sections of the target horizontal well according to the similarity of the reservoir classification alternative parameters; determining the hole number and cluster length of each perforation cluster based on the quasi-three-dimensional model and the perforation parameter current-limiting fracturing model of each cluster; and calculating the uniform development degree index of each cluster of hydraulic fractures under different perforation parameters, and preferably selecting a perforation parameter scheme. According to the method, horizontal section fracturing process parameters and geological differences are combined, multiple cracks are opened at the same time through large-displacement volume fracturing to form a crack net, and the single well yield is increased.

Description

Technical Field

[0001] The present application belongs to the field of geological engineering, and in particular, relates to a method and system for optimizing the design of horizontal well perforation based on a Qt framework. Background Art

[0002] Multi-cluster fracturing in horizontal wells is the main means to effectively develop horizontal wells in tight reservoirs. Ensuring that each cluster in the segment is opened and evenly filled with fluid, thereby increasing the contact area between the fracture and the reservoir is the key to this process. The perforation scheme of each cluster in the segment is crucial to improving the efficiency of multi-cluster fracturing. The existing multi-cluster perforation method has the following problems: the number of perforations is determined only based on the thickness of each cluster, and then the amount of fluid and sand in each layer is optimized based on the number of perforations; the existing multi-stage fracturing segmentation design of horizontal wells is mainly completed by equal spacing or based on engineering experience, and the cluster design is still mainly based on qualitative judgment, without combining the key parameters of fracturing transformation and formation characteristics, and generally has problems such as low efficiency, poor accuracy, and low degree of automation.

[0003] Therefore, it is necessary to find a method to effectively initiate fracturing of multiple clusters by combining the horizontal section fracturing process parameters and geological differences. To this end, a method for optimizing the perforation design of horizontal wells based on the Qt framework is proposed. Through large-volume fracturing, multiple fractures can be opened simultaneously to form a fracture network, thereby increasing the production of a single well. Summary of the invention

[0004] In order to overcome the defects of the above-mentioned prior art, the purpose of this application is to provide a method and system for horizontal well perforation optimization design based on the Qt framework, combining the horizontal section fracturing process parameters and geological differences, and realizing the simultaneous opening of multiple fractures to form a fracture network through large-volume fracturing, thereby increasing the single well production.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A method for optimizing horizontal well perforation design based on a Qt framework, comprising:

[0007] Collect basic parameters from reservoir logging curves, and select candidate parameters for reservoir classification based on the correlation between the basic parameters;

[0008] A quasi-3D model of a target horizontal well is established according to reservoir classification candidate parameters, and the flow rate in each fracture of the target horizontal well and the entrance pressure of each fracture are determined according to the quasi-3D model;

[0009] According to the clustering results of reservoir classification candidate parameters, the reservoirs encountered by each target horizontal well are classified, and the perforation parameter flow limiting fracturing model of each cluster of the target horizontal well is established according to the basic parameters of each type of reservoir encountered;

[0010] Divide the target horizontal well into fracturing sections according to the similarity of reservoir classification candidate parameters, and determine the length of each fracturing section;

[0011] Based on the pseudo-3D model and the flow-limiting fracturing model of each cluster perforation parameter, the number of holes and cluster length of each perforation cluster are determined;

[0012] The uniform development index of each cluster of hydraulic fractures under different perforation parameters is calculated to optimize the perforation parameter scheme.

[0013] Furthermore, basic parameters in the reservoir logging curve are collected, and candidate parameters for reservoir classification are selected according to the correlation between the basic parameters, including:

[0014] The Pearson correlation coefficient analysis method was used to analyze the correlation between basic parameters, and basic parameters or basic parameter combinations with correlation coefficients less than 0.5 were selected as candidate parameters for reservoir classification.

[0015] Furthermore, the basic parameters in the reservoir logging curve include geophysical parameters and geomechanical parameters;

[0016] Geophysical parameters include porosity, oil saturation and permeability;

[0017] Geomechanical parameters include minimum horizontal principal stress, brittleness index, Young's modulus and Poisson's ratio.

[0018] Furthermore, candidate parameters for reservoir classification include the product of porosity and oil saturation, brittleness index and minimum horizontal principal stress.

[0019] Further, a quasi-3D model of the target horizontal well is established according to the reservoir classification candidate parameters, and the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture are determined according to the quasi-3D model, including:

[0020] The quasi-three-dimensional model is established by combining the continuity equation of the fluid in the fracture and the pressure drop equation of the fluid in the fracture.

[0021] The inlet pressure of each fracture is determined according to the pressure drop equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is:

[0022]

[0023]

[0024]

[0025] Where q is the flow rate of a single crack wing, m 3 / s; W(x,z), W(x,0) are the half-width of the crack at a certain point z and at 0 in the crack height direction, m; z, x are the quantities in the crack height direction and crack length direction, m; H(x) is the half-height of the crack at the coordinate x position, m; n is the number of perforation clusters;

[0026] The flow rate in each fracture is determined according to the continuity equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is:

[0027]

[0028] Where: A(x,t) is the cross-sectional area of ​​the crack length x at time t, m 2 ; hu(x,t), hd(x,t) are the upper and lower fracture heights at x in the fracture at time t, respectively, m; Γ(x) is the time required for the fracturing fluid to move from the fracture mouth to x, s; C(x,t) is the filtration coefficient, m·min -0.5 .

[0029] Furthermore, the reservoirs encountered by each target horizontal well are classified according to the clustering results of the reservoir classification candidate parameters, including:

[0030] Clustering algorithm is used to cluster reservoir classification parameters, and reservoirs encountered by each target horizontal well are classified according to the clustering results;

[0031] The reservoirs encountered with the product of porosity and oil saturation greater than 650 are classified as Class A; the reservoirs encountered with the product of porosity and oil saturation greater than 560 and less than or equal to 650 are classified as Class B; the reservoirs encountered with the product of porosity and oil saturation greater than 440 and less than or equal to 560 are classified as Class C; the reservoirs encountered with the product of porosity and oil saturation less than or equal to 440 are classified as Class D.

[0032] Furthermore, the flow limiting fracturing model of each cluster perforation parameter of the target horizontal well is established according to the basic parameters of various drilled reservoirs, including:

[0033] Obtain the average values ​​of basic parameters for each type of reservoir encountered;

[0034] Based on the average value of each basic parameter, combined with the pressure balance principle, mass balance principle and perforation hole friction calculation formula, the flow limiting fracturing model of each cluster perforation parameter is established. The parameters of the flow limiting fracturing model of each cluster perforation parameter include the equivalent diameter of the hole, the number of holes and the cluster length.

[0035] Numerical simulations were performed using the flow-limiting fracturing model with perforation parameters of each cluster to obtain the oil production of single cluster fractures in various types of drilled reservoirs under different injection scales and different cluster spacing conditions;

[0036] Determine the type of reservoir encountered by each fracturing stage, calculate the theoretical benefits of each fracturing stage under different injection scales and different cluster spacing conditions, and determine the optimal cluster spacing and optimized injection scale of each fracturing stage based on the theoretical benefits;

[0037] The perforation clusters are equally divided and placed in each fracturing stage according to the optimized cluster spacing, and the perforation cluster positions are optimized using the interior point method;

[0038] Based on the average values ​​of basic parameters of each fracturing stage corresponding to the encountered reservoir type and the optimized perforation cluster positions, a single-stage multi-cluster fracturing numerical model was established to determine the optimized perforation number of each perforation cluster in each fracturing stage.

[0039] Furthermore, the target horizontal well is divided into fracturing stages according to the similarity of the reservoir classification candidate parameters, including:

[0040] The target horizontal well is divided into a plurality of initial small sections, and the average change rate of the reservoir classification candidate parameters of two adjacent initial small sections is calculated;

[0041] Merge two initial segments with an average change rate less than 20%. When the average change rates on both sides of any initial segment are less than 20%, select the adjacent initial segment with a smaller average change rate for merging. The two merged initial segments are used as a new initial segment. The average value of the reservoir classification candidate parameters of the two merged initial segments is used as the reservoir classification candidate parameter value of the new initial segment. Continue to calculate the average change rate of the two adjacent initial segments and merge them until the algorithm stops or the segment length constraint is reached.

[0042] When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is less than the preset segment length, the initial small segment is merged with the adjacent initial small segment on the side with the smaller average change rate. When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is greater than or equal to the preset segment length, the initial small segment is used as an independent fracturing segment.

[0043] Furthermore, the uniform development index of each cluster of hydraulic fractures under different perforation parameters is calculated, including:

[0044] The relationship between stress and fracture width is described by using the displacement discontinuity method, and the fluid flow equations in the wellbore and fractures are coupled. The effects of fracturing fluid loss and pore friction are considered at the same time, and a horizontal well dense cutting fracturing multi-fracture synchronous expansion model with full fluid-solid coupling is established.

[0045] The displacement discontinuity method and finite volume method are used to discretize the multi-crack propagation model, and the Newton-Raphson iteration method is used to solve the global nonlinear coupling equations and compile a calculation program.

[0046] On the other hand, the present application discloses a system for horizontal well perforation optimization design based on the Qt framework, comprising:

[0047] A reservoir classification candidate parameter acquisition unit is used to collect basic parameters in reservoir logging curves and select reservoir classification candidate parameters according to the correlation between the basic parameters;

[0048] A quasi-3D model building unit is used to build a quasi-3D model of a target horizontal well according to reservoir classification candidate parameters, and determine the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture according to the quasi-3D model;

[0049] Each cluster perforation parameter flow limiting fracturing model establishment unit is used to classify the reservoirs encountered by each target horizontal well according to the clustering results of the reservoir classification candidate parameters, and establish each cluster perforation parameter flow limiting fracturing model of the target horizontal well according to the basic parameters of each type of reservoir encountered;

[0050] A fracturing section division unit is used to divide the target horizontal well into fracturing sections according to the similarity of the reservoir classification candidate parameters and determine the length of each fracturing section;

[0051] A perforation number and cluster length determination unit, used to determine the perforation number and cluster length of each perforation cluster based on the pseudo three-dimensional model and the flow limiting fracturing model of the perforation parameters of each cluster;

[0052] The perforation parameter optimization unit is used to calculate the uniform development index of each cluster of hydraulic fractures under different perforation parameters and optimize the perforation parameter scheme.

[0053] Technical effects and advantages of this application:

[0054] 1. This application uses a pseudo-three-dimensional model to obtain the displacement absorbed by each fracture in each cluster in the horizontal well section and the fracture mouth pressure based on the formation logging data and fracturing parameters of the horizontal well section, and then combines the flow balance and pressure balance relationship of the flow-limiting fracturing to obtain the relationship between the effective hole equivalent and the diameter of each perforation cluster, thereby accurately obtaining the number of holes and cluster length corresponding to each cluster, so as to achieve uniform fracturing and fluid initiation of multiple clusters in the horizontal well section, increase the contact area between the fracture and the reservoir, and achieve the purpose of increasing the single well production.

[0055] 2. This application proposes to divide the fracturing sections based on the similarity of logging curves, starting from the actual fracturing conditions, considering the feasibility of the fracturing site, selecting the optimal fracturing construction parameters, and proposing a reference balanced fracturing distribution principle. It can quickly and effectively realize the design of a cluster combination scheme for fracturing and perforating sections in horizontal wells in unconventional reservoirs, reduce the probability of unbalanced fracturing within the section, and improve the production efficiency of a single well. It is of great significance to the fracturing design and production increase and efficiency improvement of unconventional oil reservoirs.

[0056] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures indicated in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1This is a flow chart of a method for horizontal well perforation optimization design based on the Qt framework for this application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] like Figure 1 As shown, the present application provides a method for horizontal well perforation optimization design based on the Qt framework, including:

[0060] Collect basic parameters from reservoir logging curves, and select candidate parameters for reservoir classification based on the correlation between the basic parameters;

[0061] A quasi-3D model of a target horizontal well is established according to reservoir classification candidate parameters, and the flow rate in each fracture of the target horizontal well and the entrance pressure of each fracture are determined according to the quasi-3D model;

[0062] According to the clustering results of reservoir classification candidate parameters, the reservoirs encountered by each target horizontal well are classified, and the perforation parameter flow limiting fracturing model of each cluster of the target horizontal well is established according to the basic parameters of each type of reservoir encountered;

[0063] Divide the target horizontal well into fracturing sections according to the similarity of reservoir classification candidate parameters, and determine the length of each fracturing section;

[0064] Based on the pseudo-3D model and the flow-limiting fracturing model of each cluster perforation parameter, the number of holes and cluster length of each perforation cluster are determined;

[0065] The uniform development index of each cluster of hydraulic fractures under different perforation parameters is calculated to optimize the perforation parameter scheme.

[0066] In some embodiments of the present application, basic parameters in reservoir logging curves are collected, and candidate reservoir classification parameters are selected according to the correlation between the basic parameters, including:

[0067] The Pearson correlation coefficient analysis method was used to analyze the correlation between basic parameters, and basic parameters or basic parameter combinations with correlation coefficients less than 0.5 were selected as candidate parameters for reservoir classification.

[0068] In some embodiments of the present application, the basic parameters in the reservoir logging curve include geophysical parameters and geomechanical parameters;

[0069] Geophysical parameters include porosity, oil saturation and permeability;

[0070] Geomechanical parameters include minimum horizontal principal stress, brittleness index, Young's modulus and Poisson's ratio.

[0071] In some embodiments of the present application, candidate parameters for reservoir classification include the product of porosity and oil saturation, a brittleness index, and a minimum horizontal principal stress.

[0072] In some embodiments of the present application, a quasi-3D model of a target horizontal well is established according to the reservoir classification candidate parameters, and the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture are determined according to the quasi-3D model, including:

[0073] The quasi-three-dimensional model is established by combining the continuity equation of the fluid in the fracture and the pressure drop equation of the fluid in the fracture.

[0074] The inlet pressure of each fracture is determined according to the pressure drop equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is:

[0075]

[0076]

[0077]

[0078] Where q is the flow rate of a single crack wing, m 3 / s; W(x,z), W(x,0) are the half-width of the crack at a certain point z and at 0 in the crack height direction, m; z, x are the quantities in the crack height direction and crack length direction, m; H(x) is the half-height of the crack at the coordinate x position, m; n is the number of perforation clusters;

[0079] The flow rate in each fracture is determined according to the continuity equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is:

[0080]

[0081] Where: A(x,t) is the cross-sectional area of ​​the crack length x at time t, m 2 ; hu(x,t), hd(x,t) are the upper and lower fracture heights at x in the fracture at time t, respectively, m; Γ(x) is the time required for the fracturing fluid to move from the fracture mouth to x, s; C(x,t) is the filtration coefficient, m·min -0.5 .

[0082] In some embodiments of the present application, the reservoirs encountered by each target horizontal well are classified according to the clustering results of the reservoir classification candidate parameters, including:

[0083] Clustering algorithm is used to cluster reservoir classification parameters, and reservoirs encountered by each target horizontal well are classified according to the clustering results;

[0084] The reservoirs encountered with the product of porosity and oil saturation greater than 650 are classified as Class A; the reservoirs encountered with the product of porosity and oil saturation greater than 560 and less than or equal to 650 are classified as Class B; the reservoirs encountered with the product of porosity and oil saturation greater than 440 and less than or equal to 560 are classified as Class C; the reservoirs encountered with the product of porosity and oil saturation less than or equal to 440 are classified as Class D.

[0085] In some embodiments of the present application, a flow limiting fracturing model of each cluster perforation parameter of a target horizontal well is established according to basic parameters of various drilled reservoirs, including:

[0086] Obtain the average values ​​of basic parameters for each type of reservoir encountered;

[0087] Based on the average value of each basic parameter, combined with the pressure balance principle, mass balance principle and perforation hole friction calculation formula, the flow limiting fracturing model of each cluster perforation parameter is established. The parameters of the flow limiting fracturing model of each cluster perforation parameter include the equivalent diameter of the hole, the number of holes and the cluster length.

[0088] Numerical simulations were performed using the flow-limiting fracturing model with perforation parameters of each cluster to obtain the oil production of single cluster fractures in various types of drilled reservoirs under different injection scales and different cluster spacing conditions;

[0089] Determine the type of reservoir encountered by each fracturing stage, calculate the theoretical benefits of each fracturing stage under different injection scales and different cluster spacing conditions, and determine the optimal cluster spacing and optimized injection scale of each fracturing stage based on the theoretical benefits;

[0090] The perforation clusters are equally divided and placed in each fracturing stage according to the optimized cluster spacing, and the perforation cluster positions are optimized using the interior point method;

[0091] Based on the average values ​​of basic parameters of each fracturing stage corresponding to the encountered reservoir type and the optimized perforation cluster positions, a single-stage multi-cluster fracturing numerical model was established to determine the optimized perforation number of each perforation cluster in each fracturing stage.

[0092] It should be noted that this application has completed a complete set of perforation section cluster parameter combination optimization design for horizontal wells, proposed a method for dividing fracturing sections based on the similarity of logging curves, selected the optimal fracturing construction parameters based on the actual fracturing conditions and considered the feasibility of the fracturing site, and proposed a reference principle of balanced fracturing and seam distribution. It can quickly and effectively realize the design of perforation section cluster combination schemes for fracturing horizontal wells in unconventional reservoirs, reduce the probability of non-balanced fracturing within the section, and improve the production efficiency of a single well, which is of great significance to the fracturing design and production increase and efficiency improvement of unconventional oil reservoirs.

[0093] In some embodiments of the present application, the target horizontal well is divided into fracturing stages according to the similarity of the reservoir classification candidate parameters, including:

[0094] The target horizontal well is divided into a plurality of initial small sections, and the average change rate of the reservoir classification candidate parameters of two adjacent initial small sections is calculated;

[0095] Merge two initial segments with an average change rate less than 20%. When the average change rates on both sides of any initial segment are less than 20%, select the adjacent initial segment with a smaller average change rate for merging. The two merged initial segments are used as a new initial segment. The average value of the reservoir classification candidate parameters of the two merged initial segments is used as the reservoir classification candidate parameter value of the new initial segment. Continue to calculate the average change rate of the two adjacent initial segments and merge them until the algorithm stops or the segment length constraint is reached.

[0096] When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is less than the preset segment length, the initial small segment is merged with the adjacent initial small segment on the side with the smaller average change rate. When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is greater than or equal to the preset segment length, the initial small segment is used as an independent fracturing segment.

[0097] In some embodiments of the present application, the uniform development index of each cluster of hydraulic fractures under different perforation parameters is calculated, including:

[0098] The relationship between stress and fracture width is described by using the displacement discontinuity method, and the fluid flow equations in the wellbore and fractures are coupled. The effects of fracturing fluid loss and pore friction are considered at the same time, and a horizontal well dense cutting fracturing multi-fracture synchronous expansion model with full fluid-solid coupling is established.

[0099] The displacement discontinuity method and finite volume method are used to discretize the multi-crack propagation model, and the Newton-Raphson iteration method is used to solve the global nonlinear coupling equations and compile a calculation program.

[0100] On the other hand, the present application discloses a system for horizontal well perforation optimization design based on the Qt framework, comprising:

[0101] A reservoir classification candidate parameter acquisition unit is used to collect basic parameters in reservoir logging curves and select reservoir classification candidate parameters according to the correlation between the basic parameters;

[0102] A quasi-3D model building unit is used to build a quasi-3D model of a target horizontal well according to reservoir classification candidate parameters, and determine the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture according to the quasi-3D model;

[0103] Each cluster perforation parameter flow limiting fracturing model establishment unit is used to classify the reservoirs encountered by each target horizontal well according to the clustering results of the reservoir classification candidate parameters, and establish each cluster perforation parameter flow limiting fracturing model of the target horizontal well according to the basic parameters of each type of reservoir encountered;

[0104] A fracturing section division unit is used to divide the target horizontal well into fracturing sections according to the similarity of the reservoir classification candidate parameters and determine the length of each fracturing section;

[0105] A perforation number and cluster length determination unit, used to determine the perforation number and cluster length of each perforation cluster based on the pseudo three-dimensional model and the flow limiting fracturing model of the perforation parameters of each cluster;

[0106] The perforation parameter optimization unit is used to calculate the uniform development index of each cluster of hydraulic fractures under different perforation parameters and optimize the perforation parameter scheme.

[0107] In summary, this application uses a pseudo-three-dimensional model to obtain the displacement and fracture mouth pressure absorbed by each fracture in each cluster in the segment based on the formation logging data and fracturing parameters of the horizontal well section, and then combines the flow balance and pressure balance relationship of the flow-limiting fracturing to obtain the relationship between the equivalent diameters of the effective holes of each perforation cluster, thereby accurately obtaining the number of holes and cluster length corresponding to each cluster, so as to achieve uniform fracturing of multiple clusters in the horizontal well section, increase the contact area between the fracture and the reservoir by fluid injection, and achieve the purpose of increasing the production of a single well.

[0108] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for horizontal well perforation optimization design based on Qt framework, characterized in that: include: Collect basic parameters from reservoir logging curves, and select candidate parameters for reservoir classification based on the correlation between the basic parameters; Establishing a pseudo three-dimensional model of the target horizontal well according to the reservoir classification candidate parameters, and determining the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture according to the pseudo three-dimensional model; Classifying the reservoirs encountered by each target horizontal well according to the clustering results of the reservoir classification candidate parameters, and establishing the flow limiting fracturing model of each cluster perforation parameter of the target horizontal well according to the basic parameters of each type of drilled reservoir; Dividing the target horizontal well into fracturing sections according to the similarity of the candidate reservoir classification parameters, and determining the length of each fracturing section; Determine the number of holes and cluster length of each perforation cluster based on the pseudo three-dimensional model and the flow-limiting fracturing model of each cluster perforation parameter; The uniform development index of each cluster of hydraulic fractures under different perforation parameters is calculated to optimize the perforation parameter scheme.

2. According to a method for horizontal well perforation optimization design based on Qt framework according to claim 1, it is characterized in that: The method of collecting basic parameters in reservoir logging curves and selecting candidate reservoir classification parameters according to the correlation between the basic parameters includes: The Pearson correlation coefficient analysis method is used to analyze the correlation between the basic parameters, and the basic parameters or basic parameter combinations with correlation coefficients less than 0.5 are selected as candidate parameters for reservoir classification.

3. The method for horizontal well perforation optimization design based on Qt framework according to claim 2, characterized in that: The basic parameters in the reservoir logging curve include geophysical parameters and geomechanical parameters; The geological physical parameters include porosity, oil saturation and permeability; The geomechanical parameters include minimum horizontal principal stress, brittleness index, Young's modulus and Poisson's ratio.

4. The method for horizontal well perforation optimization design based on Qt framework according to claim 3 is characterized in that: The candidate parameters for reservoir classification include the product of porosity and oil saturation, a brittleness index and a minimum horizontal principal stress.

5. The method for horizontal well perforation optimization design based on Qt framework according to claim 1, characterized in that: The method of establishing a quasi-3D model of the target horizontal well according to the reservoir classification candidate parameters, and determining the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture according to the quasi-3D model, comprises: The pseudo three-dimensional model is established by combining the continuity equation of the fluid in the fracture and the pressure drop equation of the fluid in the fracture; The inlet pressure of each fracture is determined according to the pressure drop equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is: Where q is the flow rate of a single crack wing, m 3 / s; W(x,z), W(x,0) are the half-width of the crack at a certain point z and at 0 in the crack height direction, m; z, x are the quantities in the crack height direction and crack length direction, m; H(x) is the half-height of the crack at the coordinate x position, m; n is the number of perforation clusters; The flow rate in each fracture is determined according to the continuity equation of the fluid in the fracture. The pressure drop equation of the fluid in the fracture is: Where: A(x,t) is the cross-sectional area of ​​the crack length x at time t, m 2 ; hu(x,t), hd(x,t) are the upper and lower fracture heights at x in the fracture at time t, respectively, m; Γ(x) is the time required for the fracturing fluid to move from the fracture mouth to x, s; C(x,t) is the filtration coefficient, m·min -0.5 .

6. The method for optimizing horizontal well perforation design based on Qt framework according to claim 4, characterized in that: The method of classifying the reservoirs encountered by each target horizontal well according to the clustering result of the reservoir classification candidate parameters includes: Clustering the reservoir classification parameters using a clustering algorithm, and classifying the reservoirs encountered by each target horizontal well according to the clustering results; The reservoirs encountered with the product of porosity and oil saturation greater than 650 are classified as Class A; the reservoirs encountered with the product of porosity and oil saturation greater than 560 and less than or equal to 650 are classified as Class B; the reservoirs encountered with the product of porosity and oil saturation greater than 440 and less than or equal to 560 are classified as Class C; the reservoirs encountered with the product of porosity and oil saturation less than or equal to 440 are classified as Class D.

7. A method for horizontal well perforation optimization design based on Qt framework according to claim 6, characterized in that: The method of establishing the flow limiting fracturing model of each cluster perforation parameter of the target horizontal well according to the basic parameters of various drilled reservoirs includes: Obtain the average values ​​of basic parameters for each type of reservoir encountered; Based on the average values ​​of the basic parameters, the pressure balance principle, the mass balance principle and the perforation hole friction calculation formula are combined to establish the perforation parameter flow limiting fracturing model of each cluster, wherein the parameters of the perforation parameter flow limiting fracturing model of each cluster include the equivalent diameter of the hole, the number of holes and the cluster length; Numerical simulations were performed using the flow-limiting fracturing model with perforation parameters of each cluster to obtain the oil production of single cluster fractures in various types of drilled reservoirs under different injection scales and different cluster spacing conditions; Determine the type of reservoir encountered by each fracturing stage, calculate the theoretical benefits of each fracturing stage under different injection scales and different cluster spacing conditions, and determine the optimal cluster spacing and optimized injection scale of each fracturing stage based on the theoretical benefits; The perforation clusters are equally placed in each fracturing stage according to the optimized cluster spacing, and the positions of the perforation clusters are optimized using the interior point method; Based on the average values ​​of basic parameters of each fracturing stage corresponding to the encountered reservoir type and the optimized perforation cluster positions, a single-stage multi-cluster fracturing numerical model was established to determine the optimized perforation number of each perforation cluster in each fracturing stage.

8. The method for horizontal well perforation optimization design based on Qt framework according to claim 1, characterized in that: The method of dividing the target horizontal well into fracturing stages according to the similarity of the reservoir classification candidate parameters includes: The target horizontal well is divided into a plurality of initial small sections, and the average change rate of the reservoir classification candidate parameters of two adjacent initial small sections is calculated; Merge two initial segments with an average change rate less than 20%. When the average change rates on both sides of any initial segment are less than 20%, select the adjacent initial segment with a smaller average change rate for merging. The two merged initial segments are used as a new initial segment. The average value of the reservoir classification candidate parameters of the two merged initial segments is used as the reservoir classification candidate parameter value of the new initial segment. Continue to calculate the average change rate of the two adjacent initial segments and merge them until the algorithm stops or the segment length constraint is reached. When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is less than the preset segment length, the initial small segment is merged with the adjacent initial small segment on the side with the smaller average change rate. When the average change rates on both sides of any initial small segment are greater than or equal to 20%, and the segment length of the initial small segment is greater than or equal to the preset segment length, the initial small segment is used as an independent fracturing segment.

9. The method for horizontal well perforation optimization design based on Qt framework according to claim 1, characterized in that: The calculation of the uniform development index of each cluster of hydraulic fractures under different perforation parameters includes: The relationship between stress and fracture width is described by using the displacement discontinuity method, and the fluid flow equations in the wellbore and fractures are coupled. The effects of fracturing fluid loss and pore friction are considered at the same time, and a horizontal well dense cutting fracturing multi-fracture synchronous expansion model with full fluid-solid coupling is established. The displacement discontinuity method and finite volume method are used to discretize the multi-crack propagation model, and the Newton-Raphson iteration method is used to solve the global nonlinear coupling equations and compile a calculation program.

10. A system for horizontal well perforation optimization design based on Qt framework, characterized in that: include: A reservoir classification candidate parameter acquisition unit is used to collect basic parameters in reservoir logging curves and select reservoir classification candidate parameters according to the correlation between the basic parameters; A quasi-3D model building unit is used to build a quasi-3D model of the target horizontal well according to the reservoir classification candidate parameters, and determine the flow rate in each fracture of the target horizontal well and the inlet pressure of each fracture according to the quasi-3D model; Each cluster perforation parameter flow limiting fracturing model establishment unit is used to classify the reservoirs encountered by each target horizontal well according to the clustering results of the reservoir classification candidate parameters, and establish each cluster perforation parameter flow limiting fracturing model of the target horizontal well according to the basic parameters of each type of reservoir encountered; A fracturing section division unit, used to divide the target horizontal well into fracturing sections according to the similarity of the reservoir classification candidate parameters, and determine the length of each fracturing section; A perforation number and cluster length determination unit, used to determine the perforation number and cluster length of each perforation cluster based on the pseudo three-dimensional model and the flow limiting fracturing model of each cluster perforation parameter; The perforation parameter optimization unit is used to calculate the uniform development index of each cluster of hydraulic fractures under different perforation parameters and optimize the perforation parameter scheme.

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