Optimization Design Method for Perforation Cluster Location, Processor, and Machine-Readable Storage Medium

Abstract

The present application discloses an optimization design method for perforation cluster positions, a processor, and a machine-readable storage medium, belonging to the technical field of oil and gas well development. The optimization design method includes: obtaining the oil saturation and the oil layer thickness; determining the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore; determining the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters; constructing a three-dimensional spatial segment cluster optimization model based on the initial perforation cluster positions to perform fracture morphology simulation and single-well productivity simulation through the three-dimensional spatial segment cluster optimization model to obtain an initial simulation result; adjusting the initial perforation cluster positions according to the oil saturation and the oil layer thickness so that the increased production of the obtained target simulation result compared with the initial simulation result reaches a preset percentage. The present application can adjust the position design scheme of the perforation clusters according to the simulation results output by the three-dimensional spatial segment cluster optimization model, make the position design of the perforation clusters more reasonable, and thus improve the production of oil and gas wells.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas well development, and particularly to an optimized design method for perforation cluster positions, a processor, and a machine-readable storage medium. Background Art

[0002] Staged multi-cluster fracturing in horizontal wells is a key link in the fracturing construction process of oil and gas wells. When determining the fracturing sections and clusters for oil and gas wells, currently, the reservoir type, average section length, number of sections, number of perforation clusters, and casing joint positions are usually used to constrain the staged multi-cluster fracturing, so as to determine the optimal fracturing sections and clusters. However, after studying a large amount of on-site data of oil and gas wells, it is found that the perforation clusters determined in this way are prone to problems with unreasonable position design, resulting in situations such as non-production of perforation clusters and uneven production profiles in horizontal wells during oil and gas well development. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide an optimized design method for perforation cluster positions, a processor, and a machine-readable storage medium, so as to solve the problems in the prior art where, due to unreasonable perforation cluster position design, non-production of perforation clusters and uneven production profiles in horizontal wells occur during oil and gas well development.

[0004] To achieve the above purpose, the first aspect of the embodiments of this application provides an optimized design method for perforation cluster positions, including:

[0005] Obtain the oil saturation and oil layer thickness;

[0006] Determine the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore;

[0007] Determine the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters;

[0008] Construct a three-dimensional spatial section-cluster optimization model based on the initial perforation cluster positions, so as to simulate the fracture morphology and the single-well productivity through the three-dimensional spatial section-cluster optimization model, and obtain the initial simulation results;

[0009] Adjust the initial perforation cluster positions according to the oil saturation and the oil layer thickness, so that the increase in production of the obtained target simulation results compared with the initial simulation results reaches a preset percentage.

[0010] In the embodiments of this application, the reservoir physical property parameters include the in-situ stress along the wellbore, the brittleness index, and the mechanical specific energy. Determining the reservoir physical property parameters includes: obtaining logging data and drilling time data, where the logging data includes the in-situ stress along the wellbore and the brittleness index, and the drilling time data includes the drilling pressure, the bit area, the rotary table speed, the bit torque, and the mechanical drilling rate; determining the mechanical specific energy according to the drilling pressure, the bit area, the rotary table speed, the bit torque, and the mechanical drilling rate.

[0011] In the embodiments of the present application, the mechanical specific energy satisfies formula (1):

[0012]

[0013] where MSE is the mechanical specific energy, WOB is the weight on bit, A b is the bit area, N is the rotary table speed, T is the bit torque, and ROP is the rate of penetration.

[0014] In the embodiments of the present application, determining the profile of the compressibility factor distributed along the wellbore includes: constructing a compressibility factor calculation model; and determining the profile of the compressibility factor distributed along the wellbore through the compressibility factor calculation model.

[0015] In the embodiments of the present application, constructing the compressibility factor calculation model includes: obtaining a plurality of control parameters related to the compressibility factor calculation model, where the plurality of control parameters include compensated neutron, gas logging value, brittleness index, and shale content; respectively determining the weights corresponding to the respective control parameters through a random forest algorithm according to the correlation between each control parameter in the plurality of control parameters and the liquid intake condition of the perforation cluster; and constructing the compressibility factor calculation model based on the plurality of control parameters and the weights corresponding to the respective control parameters.

[0016] In the embodiments of the present application, the compressibility factor calculation model satisfies formula (2):

[0017] FSI = ω 1 *CNL + ω 2 *QL + ω 3 *BI - ω 4 *S h ; (2)

[0018] where FSI is the compressibility factor, CNL is the compensated neutron, ω 1 is the weight corresponding to the compensated neutron, QL is the gas logging value, ω 2 is the weight corresponding to the gas logging value, BI is the brittleness index, ω 3 is the weight corresponding to the brittleness index, S h is the shale content, and ω 4 is the weight corresponding to the shale content.

[0019] In the embodiments of the present application, determining the initial perforation cluster position according to the profile of the compressibility factor and the reservoir physical property parameters includes: determining the compressibility factors at a plurality of positions according to the profile of the compressibility factor; respectively determining whether the compressibility factor at each position is greater than a preset compressibility factor threshold, and whether the reservoir physical property parameters at the plurality of positions are peaks; and uniformly arranging perforation clusters at the positions where the compressibility factor is greater than the preset compressibility factor threshold or the reservoir physical property parameters are peaks to determine the initial perforation cluster position.

[0020] In the embodiments of the present application, constructing a spatial three-dimensional segment cluster optimization model for the initial perforation cluster position includes: importing the initial perforation cluster position into a three-dimensional geological model to establish a spatial three-dimensional segment cluster optimization model.

[0021] The second aspect of the embodiments of the present application provides a processor configured to execute the above-mentioned optimization design method for the perforation cluster position.

[0022] The third aspect of the embodiments of the present application provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause the machine to execute the above-mentioned optimization design method for the perforation cluster position.

[0023] In the above technical solution, by determining the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore, and then determining the initial perforation cluster position according to the profile of the compressibility factor and the reservoir physical property parameters, and further constructing a spatial three-dimensional segment cluster optimization model based on the initial perforation cluster position to perform fracture morphology simulation and single-well productivity simulation through the spatial three-dimensional segment cluster optimization model to obtain an initial simulation result. Finally, adjust the initial perforation cluster position according to the oil saturation and the oil layer thickness, so that the obtained target simulation result increases the production of the oil and gas well by a preset percentage compared with the initial simulation result. In this way, the position design scheme of the perforation cluster can be adjusted according to the simulation result output by the spatial three-dimensional segment cluster optimization model, making the position design of the perforation cluster more reasonable, and thus increasing the production of the oil and gas well.

[0024] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used to explain the embodiments of the present application together with the following specific implementation, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0026] Figure 1 Schematically shows a flowchart of an optimization design method for a perforation cluster position according to an embodiment of the present application;

[0027] Figure 2 Schematically shows a schematic diagram of the perforation cluster position of a horizontal section in the horizontal and vertical profile directions of a reservoir according to a specific embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. It should be understood that the specific embodiments described herein are only for explaining and interpreting the embodiments of this application, and are not used to limit the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by this application.

[0029] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of this application, then such directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If this specific posture changes, then the directional indications will also change accordingly.

[0030] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of this application, then such descriptions of "first", "second", etc. are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0031] Figure 1 The flowchart of an optimization design method for perforation cluster positions according to an embodiment of this application is schematically shown. As Figure 1 shown, the embodiments of this application provide an optimization design method for perforation cluster positions. Taking the application of this optimization design method to a processor as an example, this optimization design method may include the following steps:

[0032] Step S101: Obtain the oil saturation and oil layer thickness.

[0033] Step S102: Determine the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore.

[0034] Step S103: Determine the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters.

[0035] Step S104: Construct a three-dimensional spatial segment cluster optimization model based on the initial perforation cluster positions to perform fracture morphology simulation and single-well productivity simulation through the three-dimensional spatial segment cluster optimization model, and obtain the initial simulation results.

[0036] Step S105: Adjust the initial perforation cluster positions according to the oil saturation and the oil layer thickness, so that the increased production in the obtained target simulation result compared with the initial simulation result reaches a preset percentage.

[0037] In the embodiments of the present application, the processor can adjust the position design scheme of the perforation clusters according to the simulation results output by the spatial three-dimensional segment cluster optimization model. Specifically, the processor can first determine the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore. The reservoir physical property parameters can include parameters such as the in-situ stress along the wellbore, the brittleness index, and the mechanical specific energy. Among them, the in-situ stress along the wellbore and the brittleness index can be directly obtained through measurement, while the mechanical specific energy needs to be determined according to the drilling time data. The profile of the compressibility factor consists of the compressibility factors at multiple positions distributed along the wellbore. The compressibility factor is the weighted sum of multiple control parameters and can be used to quantitatively measure the fracturing performance. The processor can evenly set the perforation clusters at the positions where the compressibility factor is relatively large or at the peaks of the reservoir physical property parameters, thereby obtaining the initial perforation cluster positions. Subsequently, the processor can construct a spatial three-dimensional segment cluster optimization model based on the initial perforation cluster positions, and through the spatial three-dimensional segment cluster optimization model, perform fracture morphology simulation and single-well productivity simulation on the Petrel and Kinetix platforms under the same pumping program, so as to obtain the initial simulation result.

[0038] Subsequently, the processor can adjust the initial perforation cluster positions according to the measured oil saturation and the oil layer thickness. When adjusting the initial perforation cluster positions, if the oil saturation and the oil layer thickness are relatively large, perforation clusters are added at this position to make the distribution of the perforation clusters denser; if the oil saturation and the oil layer thickness are relatively small, perforation clusters are reduced at this position. In this way, the processor can obtain the adjusted perforation cluster positions, and input the adjusted perforation cluster positions into the spatial three-dimensional segment cluster optimization model, and perform fracture morphology simulation and single-well productivity simulation on the Petrel and Kinetix platforms under the same pumping program to obtain the target simulation result. Further, the processor can compare the initial simulation result with the target simulation result. When the increased production in the target simulation result compared with the initial simulation result reaches the preset percentage, the processor determines the adjusted perforation cluster positions as the target perforation cluster positions, and at the same time ends the optimization process of the perforation cluster positions. The preset percentage can be determined according to actual needs, for example, it can be 5%. In this way, fracturing construction of the oil and gas well according to the target perforation cluster positions can improve the fracturing effect of the oil and gas well and increase the production of the oil and gas well.

[0039] In the above technical solution, by determining the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore, and then determining the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters, a three-dimensional spatial segment cluster optimization model is constructed based on the initial perforation cluster positions, so as to simulate the fracture morphology and the single-well productivity through the three-dimensional spatial segment cluster optimization model to obtain the initial simulation results. Finally, the initial perforation cluster positions are adjusted according to the oil saturation and the oil layer thickness, so that the increased production of the obtained target simulation results compared with the initial simulation results reaches a preset percentage. In this way, the position design scheme of the perforation clusters can be adjusted according to the simulation results output by the three-dimensional spatial segment cluster optimization model, making the position design of the perforation clusters more reasonable, and thus improving the production of the oil and gas wells.

[0040] In the embodiments of the present application, the reservoir physical property parameters may include the in-wellbore ground stress, the brittleness index, and the mechanical specific energy. Determining the reservoir physical property parameters may include: obtaining logging data and drilling time data, where the logging data includes the in-wellbore ground stress and the brittleness index, and the drilling time data includes the weight on bit, the bit area, the rotary table speed, the bit torque, and the rate of penetration; and determining the mechanical specific energy according to the weight on bit, the bit area, the rotary table speed, the bit torque, and the rate of penetration.

[0041] In the embodiments of the present application, the reservoir physical property parameters include parameters such as the in-wellbore ground stress, the brittleness index, and the mechanical specific energy. When determining the initial perforation cluster positions, the reservoir physical property parameters in the embodiments of the present application mainly consider the in-wellbore ground stress, the brittleness index, and the mechanical specific energy. Among them, the in-wellbore ground stress and the brittleness index can be directly obtained by measurement, while the mechanical specific energy needs to be determined according to the drilling time data. The drilling time data may include data such as the well depth, the rate of penetration, the weight on bit, the drilling time, the bit torque, the bit area, and the rotary table speed. According to the weight on bit, the bit area, the rotary table speed, the bit torque, and the rate of penetration, the processor can determine the mechanical specific energy. In this way, the processor can determine the reservoir physical property parameters, which is convenient for subsequently determining the initial perforation cluster positions in combination with the profile of the compressibility factor.

[0042] In the embodiments of the present application, the mechanical specific energy may satisfy formula (1):

[0043]

[0044] where MSE is the mechanical specific energy, WOB is the weight on bit, A b is the bit area, N is the rotary table speed, T is the bit torque, and ROP is the rate of penetration.

[0045] In the embodiments of the present application, the drilling time data may include data such as the well depth, the rate of penetration, the weight on bit, the drilling time, the bit torque, the bit area, and the rotary table speed. According to the weight on bit, the bit area, the rotary table speed, the bit torque, and the rate of penetration, the processor can determine the mechanical specific energy.

[0046] In an embodiment of the present application, determining a profile of compressibility factors distributed along a wellbore may include: constructing a compressibility factor calculation model; and determining a profile of compressibility factors distributed along the wellbore through the compressibility factor calculation model.

[0047] In an embodiment of the present application, a processor may determine a profile of compressibility factors distributed along a wellbore. There are multiple control parameters that have a great influence on the compressibility factors. The processor may construct a compressibility factor calculation model according to the multiple control parameters and the weight corresponding to each control parameter. Thus, through the compressibility factor calculation model and the multiple control parameters corresponding to different positions, the processor may determine the compressibility factors at different positions, and then obtain a profile of compressibility factors distributed along the wellbore. In this way, the processor may determine the profile of compressibility factors, which is convenient for subsequently determining the initial perforation cluster position in combination with reservoir physical property parameters.

[0048] In an embodiment of the present application, constructing a compressibility factor calculation model may include: obtaining multiple control parameters related to the compressibility factor calculation model, where the multiple control parameters include compensated neutron, gas logging value, brittleness index, and shale content; through the random forest algorithm, respectively determining the weight corresponding to each control parameter according to the correlation between each control parameter in the multiple control parameters and the fluid intake condition of the perforation cluster; and constructing a compressibility factor calculation model based on the multiple control parameters and the weight corresponding to each control parameter.

[0049] In an embodiment of the present application, before optimizing the perforation cluster position, the processor may obtain logging data, mud logging data, drilling time data, and sand-liquid diversion data of an oil and gas well. The logging data, mud logging data, drilling time data, and sand-liquid diversion data include various types of data. The processor may determine multiple control parameters with a relatively high correlation with the compressibility factor calculation model from the various types of data, namely compensated neutron, gas logging value, brittleness index, and shale content. Among them, compensated neutron is commonly used to calculate and represent the oil saturation of a formation. When its value is relatively large, it indicates that the fracturing potential at this position is better. The gas logging value represents the oil and gas flow rate entering the drilling fluid per unit reservoir volume. When its value is relatively large, it represents that the oil and gas characteristics and reservoir geological characteristics at this position are better. The brittleness index represents the mechanical properties of a reservoir. The possibility of the perforation cluster at a position with a higher brittleness index starting and propagating hydraulic fractures is higher, and it is positively correlated with the fracturing stimulation effect. The shale content is negatively correlated with the fracturing stimulation effect. Fractures are more difficult to start and propagate at positions with a larger shale content.

[0050] The random forest method is a comprehensive algorithm of multiple decision trees, which can measure the importance of variables by judging the contribution of each feature to the decision tree. Through the random forest algorithm, the processor can respectively determine the weights corresponding to each control parameter according to the correlation between each control parameter among multiple control parameters and the liquid intake condition of the perforation cluster. After determining the weights corresponding to multiple control parameters, the processor can construct a compressibility factor calculation model based on multiple control parameters and the weights corresponding to each control parameter. In this way, the processor can obtain the compressibility factor profile distributed along the wellbore through the constructed compressibility factor calculation model.

[0051] In the embodiment of the present application, the compressibility factor calculation model can satisfy formula (2):

[0052] FSI = ω 1 *CNL + ω 2 *QL + ω 3 BI - ω 4 *S h ; (2)

[0053] Wherein, FSI is the compressibility factor, CNL is the compensated neutron, ω 1 is the weight corresponding to the compensated neutron, QL is the gas logging value, ω 2 is the weight corresponding to the gas logging value, BI is the brittleness index, ω 3 is the weight corresponding to the brittleness index, S h is the shale content, ω 4 is the weight corresponding to the shale content.

[0054] In the embodiment of the present application, multiple control parameters include compensated neutron, gas logging value, brittleness index, and shale content. Through the random forest algorithm, the processor can respectively determine the weights corresponding to each control parameter according to the correlation between each control parameter among multiple control parameters and the liquid intake condition of the perforation cluster. In one example, the weight corresponding to the compensated neutron can be 0.24878, the weight corresponding to the gas logging value can be 0.4561, the weight corresponding to the brittleness index can be 0.1302, and the weight corresponding to the shale content can be 0.16492. After determining the weights corresponding to multiple control parameters, the processor can construct a compressibility factor calculation model based on multiple control parameters and the weights corresponding to each control parameter. The compressibility factor calculation model satisfies formula (2). In this way, the processor can obtain the compressibility factor profile distributed along the wellbore through the compressibility factor calculation model.

[0055] In the embodiments of the present application, determining the initial perforation cluster positions according to the compressibility factor profile and reservoir physical property parameters may include: determining the compressibility factors at multiple positions according to the compressibility factor profile; respectively determining whether the compressibility factors at each position are greater than a preset compressibility factor threshold, and whether the reservoir physical property parameters at the multiple positions are peaks; and uniformly arranging perforation clusters at the positions where the compressibility factor is greater than the preset compressibility factor threshold or the reservoir physical property parameters are peaks, so as to determine the initial perforation cluster positions.

[0056] In the embodiments of the present application, taking into account both balanced stimulation and fracturing sweet spots, after obtaining the compressibility factor profile through the compressibility factor calculation model and the reservoir physical property parameters at multiple positions, the processor may determine the compressibility factors at the multiple positions, and then respectively determine whether the compressibility factors at each position are greater than a preset compressibility factor threshold, and whether the reservoir physical property parameters at the multiple positions are peaks. The preset compressibility factor threshold may be determined according to factors such as the actual mechanical properties of the oil and gas well. If the compressibility factor is greater than the preset compressibility factor threshold, or the reservoir physical property parameters are peaks, then perforation clusters are uniformly arranged at the positions where the compressibility factor is greater than the preset compressibility factor threshold or the reservoir physical property parameters are peaks, so that the initial perforation cluster positions can be obtained.

[0057] In the embodiments of the present application, constructing a spatial three-dimensional segment cluster optimization model for the initial perforation cluster positions may include: importing the initial perforation cluster positions into a three-dimensional geological model to establish a spatial three-dimensional segment cluster optimization model.

[0058] In the embodiments of the present application, after determining the initial perforation cluster positions, the processor may import the initial perforation cluster positions into a three-dimensional geological model to establish a spatial three-dimensional segment cluster optimization model. In this way, fracture morphology simulation and single-well productivity simulation can be carried out through the spatial three-dimensional segment cluster optimization model.

[0059] In a specific embodiment, the optimization design method for the perforation cluster positions includes:

[0060] (1) Obtaining the logging data, mud logging data, drilling time data, and sand-liquid diversion data of the oil and gas well.

[0061] The processor collects and processes the logging data, mud logging data, drilling time data, and sand-liquid diversion data of the oil and gas well. Among them, the logging data includes parameters such as well depth, density, porosity, Poisson's ratio, Young's modulus, minimum horizontal principal stress, maximum horizontal principal stress, and brittleness index. The drilling time data includes parameters such as well depth, mechanical drilling rate, drilling pressure, drilling time, bit torque, and bit diameter.

[0062] (2) Determining the in-situ stress, brittleness index, and mechanical specific energy along the wellbore in combination with the logging data, mud logging data, drilling time data, and sand-liquid diversion data.

[0063] (3) Fit the compressibility factor calculation model by the random forest method.

[0064] Use the random forest method to assign weights. The random forest method is a comprehensive algorithm of multiple decision trees. It can measure the importance of variables by judging the contribution of each feature to the decision tree. Based on the random forest algorithm, the strong and weak relationships between each parameter and the fluid injection situation of each cluster can be evaluated, so as to determine the weights of each factor.

[0065] (4) Calculate the compressibility factor profile along the wellbore and preliminarily determine the position of the stage clusters.

[0066] Calculate the compressibility factor profile, and determine the initial perforation cluster position distribution based on the compressibility factor and reservoir physical property parameters. Among them, the perforation clusters are mostly evenly distributed at the positions with better compressibility and the peaks of reservoir physical property parameters, taking into account both balanced stimulation and fracturing sweet spots.

[0067] (5) Import the initial perforation cluster positions into the three-dimensional geological model to establish a spatial three-dimensional stage cluster optimization model.

[0068] Import the initial perforation cluster positions into the three-dimensional geological model, and simulate the fracture morphology and single-well productivity based on the Petrel and Kinetix platforms under the same pumping program.

[0069] (6) Adjust the initial perforation cluster positions according to the oil saturation and oil layer thickness distribution.

[0070] Figure 2 Schematically shows a schematic diagram of the horizontal section perforation cluster positions in the horizontal and vertical section directions of a reservoir according to a specific embodiment of the present application. As Figure 2 shown, spread the horizontal section perforation cluster positions in the horizontal and vertical sections of the three-dimensional geological model, and adjust the initial perforation cluster positions based on the oil saturation and oil layer thickness. Optimize the perforation cluster position design scheme, and the target perforation cluster positions as shown in Figure 2 can be obtained.

[0071] (7) Conduct fracture morphology and single-well productivity simulations to ensure that the production increase of the scheme is 5% or more.

[0072] By adjusting the positions and numbers of the perforation clusters, the fracture lengths of the segmented and clustered design schemes are balanced, the drainage area is increased, and the production increase of the optimized scheme is 5% or more.

[0073] In summary, compared with the prior art, the beneficial effect of the technical solution provided by the embodiment of the present application is that it can make full use of the test data, combine the machine learning method and the three-dimensional geological model, and can obtain a more reasonable fracturing scheme compared with the traditional one-dimensional stage cluster combination optimization method, maximizing the oil and gas production of a single well.

[0074] An embodiment of the present application further provides a processor configured to execute the above-mentioned optimization design method for the perforation cluster position.

[0075] Specifically, in the embodiment of the present application, the processor may be configured to: acquire the oil saturation and the oil layer thickness; determine the reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore; determine the initial perforation cluster position according to the profile of the compressibility factor and the reservoir physical property parameters; construct a three-dimensional spatial segment cluster optimization model based on the initial perforation cluster position to perform fracture morphology simulation and single-well productivity simulation through the three-dimensional spatial segment cluster optimization model to obtain an initial simulation result; adjust the initial perforation cluster position according to the oil saturation and the oil layer thickness so that the increased production of the obtained target simulation result compared with the initial simulation result reaches a preset percentage.

[0076] In one embodiment, the processor is further configured to: acquire logging data and drilling time data, where the logging data includes the in-situ stress and the brittleness index along the wellbore, and the drilling time data includes the weight on bit, the bit area, the rotary speed of the turntable, the bit torque, and the rate of penetration; determine the mechanical specific energy according to the weight on bit, the bit area, the rotary speed of the turntable, the bit torque, and the rate of penetration.

[0077] In one embodiment, the mechanical specific energy satisfies formula (1):

[0078]

[0079] where MSE is the mechanical specific energy, WOB is the weight on bit, ROP b is the bit area, N is the rotary speed of the turntable, T is the bit torque, and ROP is the rate of penetration.

[0080] In one embodiment, the processor is further configured to: construct a compressibility factor calculation model; determine the profile of the compressibility factor distributed along the wellbore through the compressibility factor calculation model.

[0081] In one embodiment, the processor is further configured to: acquire a plurality of control parameters related to the compressibility factor calculation model, where the plurality of control parameters include compensated neutron, gas logging value, brittleness index, and shale content; respectively determine the weights corresponding to the control parameters according to the correlation between each control parameter in the plurality of control parameters and the liquid intake condition of the perforation cluster through the random forest algorithm; construct a compressibility factor calculation model based on the plurality of control parameters and the weights corresponding to the control parameters.

[0082] In one embodiment, the compressibility factor calculation model satisfies formula (2):

[0083] FSI = ω 1 *CNL + ω 2 *QL + ω 3 *BI - ω 4 *Sh ; (2)

[0084] wherein, FSI is the compressibility factor, CNL is the compensated neutron, ω 1 is the weight corresponding to the compensated neutron, QL is the gas logging value, ω 2 is the weight corresponding to the gas logging value, BI is the brittleness index, ω 3 is the weight corresponding to the brittleness index, S h is the shale content, ω 4 is the weight corresponding to the shale content.

[0085] In one embodiment, the processor is further configured to: determine the compressibility factors at multiple positions according to the compressibility factor profile; respectively determine whether the compressibility factor at each position is greater than a preset compressibility factor threshold, and whether the reservoir physical property parameters at the multiple positions are peaks; uniformly set perforation clusters at the positions where the compressibility factor is greater than the preset compressibility factor threshold or the reservoir physical property parameters are peaks, so as to determine the initial perforation cluster positions.

[0086] In one embodiment, the processor is further configured to: import the initial perforation cluster positions into a three-dimensional geological model to establish a spatial three-dimensional segment cluster optimization model.

[0087] In the above technical solution, by determining the reservoir physical property parameters and the compressibility factor profile distributed along the wellbore, then determining the initial perforation cluster positions according to the compressibility factor profile and the reservoir physical property parameters, and further constructing a spatial three-dimensional segment cluster optimization model based on the initial perforation cluster positions, so as to perform fracture morphology simulation and single-well productivity simulation through the spatial three-dimensional segment cluster optimization model to obtain an initial simulation result. Finally, adjust the initial perforation cluster positions according to the oil saturation and the oil layer thickness, so that the increased production of the obtained target simulation result compared with the initial simulation result reaches a preset percentage. In this way, the position design scheme of the perforation clusters can be adjusted according to the simulation result output by the spatial three-dimensional segment cluster optimization model, making the position design of the perforation clusters more reasonable, and thus improving the production of oil and gas wells.

[0088] An embodiment of the present application further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above-mentioned optimization design method for perforation cluster positions.

[0089] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. 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 memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0090] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented 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 devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0094] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0095] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0096] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0097] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An optimization design method for perforation cluster positions, characterized in that, it includes: Obtain oil saturation and oil layer thickness; Determine reservoir physical property parameters and the profile of the compressibility factor distributed along the wellbore; Determine the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters; Construct a three-dimensional spatial segment cluster optimization model based on the initial perforation cluster positions to perform fracture morphology simulation and single-well productivity simulation through the three-dimensional spatial segment cluster optimization model, and obtain the initial simulation results; Adjust the initial perforation cluster positions according to the oil saturation and the oil layer thickness so that the increased production of the obtained target simulation results compared with the initial simulation results reaches a preset percentage; Determining the profile of the compressibility factor distributed along the wellbore includes: Construct a compressibility factor calculation model; Determine the profile of the compressibility factor distributed along the wellbore through the compressibility factor calculation model; The construction of the compressibility factor calculation model includes: Obtain multiple control parameters related to the compressibility factor calculation model, where the multiple control parameters include compensated neutron, gas logging value, brittleness index, and shale content; Through the random forest algorithm, determine the weights corresponding to each control parameter according to the correlation between each control parameter in the multiple control parameters and the liquid intake condition of the perforation cluster; Construct a compressibility factor calculation model based on the multiple control parameters and the weights corresponding to each control parameter; The determination of the initial perforation cluster positions according to the profile of the compressibility factor and the reservoir physical property parameters includes: Determine the compressibility factors at multiple positions according to the profile of the compressibility factor; Respectively judge whether the compressibility factor at each position is greater than the preset compressibility factor threshold, and whether the reservoir physical property parameters at multiple positions are peaks; Uniformly set perforation clusters at the positions where the compressibility factor is greater than the preset compressibility factor threshold or the reservoir physical property parameters are peaks to determine the initial perforation cluster positions.

2. The optimization design method according to claim 1, characterized in that, the reservoir physical property parameters include in-situ stress along the wellbore, brittleness index, and mechanical specific energy, and the determination of the reservoir physical property parameters includes: Obtain logging data and drilling time data, the logging data includes the in-situ stress along the wellbore and the brittleness index, and the drilling time data includes drilling pressure, bit area, rotary table speed, bit torque, and rate of penetration; Determine the mechanical specific energy according to the drilling pressure, the bit area, the rotary table speed, the bit torque, and the rate of penetration.

3. The optimization design method according to claim 2, characterized in that, the mechanical specific energy satisfies formula (1): Wherein, MSE is the mechanical specific energy, WOB is the weight on bit, A b is the bit area, N is the rotary speed of the rotary table, T is the bit torque, and ROP is the rate of penetration.

4. The optimization design method according to claim 1, characterized in that, the compressibility factor calculation model satisfies formula (2): FSI = ω 1 *CNL + ω 2 *QL + ω 3 *BI - ω 4 *S h ; (2) Among them, FSI is the compressibility factor, CNL is the compensated neutron, ω 1 is the weight corresponding to the compensated neutron, QL is the gas logging value, ω 2 is the weight corresponding to the gas logging value, BI is the brittleness index, ω 3 is the weight corresponding to the brittleness index, S h is the shale content, ω 4 is the weight corresponding to the shale content.

5. The optimization design method according to claim 1, characterized in that, Constructing a three-dimensional spatial segment cluster optimization model based on the initial perforation cluster positions includes: Import the initial perforation cluster positions into a three-dimensional geological model to establish the three-dimensional spatial segment cluster optimization model.

6. A processor configured to execute the optimization design method for perforation cluster positions according to any one of claims 1 to 5.

7. A machine-readable storage medium, characterized in that, Instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the method for optimizing the design of perforation cluster positions according to any one of claims 1 to 5.

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