A proppant injection method based on particle size distribution optimization

Abstract

The present invention discloses a proppant injection method based on particle size distribution optimization, comprising: comprehensively analyzing the geometric parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, and determining the proppant particle size distribution range corresponding to different areas of the fracture through a data analysis model and algorithm; dividing the proppant particle size distribution range into a plurality of particle size adaptation intervals according to the width measurement values ​​and permeability test results of different areas of the fracture; during the proppant injection process, using downhole sensors to perform real-time data monitoring, and judging the areas in the fracture where proppant injection and distribution abnormalities exist based on the real-time data analysis, and performing dynamic particle size adjustment. The present invention solves the problem of uneven distribution of traditional proppant injection methods in complex underground environments. By constructing a multi-factor analysis model, key factors such as fracture geometry, rock properties, and fracturing pressure are accurately controlled to achieve refined regulation of proppant particle size distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a proppant injection method based on particle size distribution optimization. Background Art

[0002] In fracturing operations for oil and gas extraction, effective proppant injection is crucial for increasing well productivity. However, traditional proppant injection methods often rely on a single particle size or a simple combination of particle sizes. This approach presents numerous shortcomings in complex and changing underground environments.

[0003] The geometry of underground fractures is extremely complex, ranging in length from tens to hundreds of meters, with widths varying on the millimeter scale or even smaller, significant height variations, and frequent bends and branches. Rock properties also vary widely. Harder rocks hinder proppant insertion, while softer rocks easily trap proppant, compromising support effectiveness. Brittle rock is prone to numerous irregular cracks during fracturing, resulting in complex pore structures that place higher demands on proppant filling and distribution. Furthermore, the pressure during fracturing is not constant; it fluctuates dynamically as the fractures expand and extend, making the proppant's motion during injection difficult to predict.

[0004] Under these complex conditions, proppants with a single or simple combination of particle sizes are difficult to achieve uniform distribution within the fracture. For example, in narrow fractures, large-particle proppants cannot penetrate smoothly, resulting in insufficient support in that area. Conversely, in wider and more permeable fractures, small-particle proppants are easily carried out by the high-speed flow of the fracturing fluid, resulting in uneven proppant distribution. This uneven distribution severely affects the fracture's conductivity, significantly compromising the effectiveness of fracturing, making it difficult to increase oil and gas well production, while maintaining high production costs. Therefore, the development of a proppant injection method that can adapt to complex underground environments is urgent.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a proppant injection method based on particle size distribution optimization, which fundamentally solves the problem of uneven distribution of traditional proppant injection methods in complex underground environments. It is of key significance for improving oil and gas extraction efficiency, reducing extraction costs, and promoting the sustainable development of the energy industry.

[0007] Specifically, the following technical solutions are adopted:

[0008] A proppant injection method based on particle size distribution optimization, comprising:

[0009] By comprehensively analyzing the geometric parameters of the fracture, the properties of the rock, and the pressure parameters that change in real time during the fracturing process, the proppant particle size distribution range corresponding to different areas of the fracture is determined using data analysis models and algorithms;

[0010] Based on the width measurements of different fracture regions and the permeability test results, the proppant particle size distribution range is divided into multiple particle size adaptation intervals;

[0011] During the proppant injection process, downhole sensors are used to monitor real-time data. Based on real-time data analysis, areas in the fracture where proppant injection and distribution are abnormal are determined, and dynamic particle size adjustment is performed.

[0012] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, the proppant particle size distribution range corresponding to different regions of the fracture is determined by comprehensively analyzing the geometric parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, including:

[0013] Obtaining the geometric parameters of the crack, including the length, width, height, and curvature of the crack;

[0014] Obtain rock properties, including rock hardness, brittleness, and pore structure parameters;

[0015] Obtain real-time changing pressure parameters during fracturing;

[0016] Based on the geometric parameters of the fracture, the properties of the rock, and the pressure parameters that change in real time during the fracturing process, a multivariate analysis is performed using a data analysis model to determine the proppant particle size distribution range corresponding to different areas of the fracture: di =f(L,W,H, Cr , Rh , Pt ),in, di is the target particle size range of the proppant in the i-th region, L, W, and H correspond to the length, width, and height of the fracture, respectively. Cr is the crack curvature parameter, which is determined by the crack curvature radius Rc and bending angle θ Calculated, is the rock hardness parameter, measured by indentation test, Pt It is the real-time pressure dynamic parameter.

[0017] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, the fracture curvature parameter in the fracture geometric parameters obtained includes:

[0018] Use logging imaging technology and image processing algorithms to measure the curvature radius of fractures Rc and bending angle θ, and input them into the data analysis model to calculate and analyze the proppant particle size distribution range corresponding to different areas of the fracture;

[0019] Radius of curvature Rc Calculated by the three-point arc method in well logging imaging technology , where Δs is the lateral offset of the crack and Δh is the longitudinal offset;

[0020] The bending angle θ is calculated using the vector angle formula after the crack contour line is extracted through image processing algorithm: ,in, represents the angle between two vectors, v1 and v2 represent two vectors, · represents the dot product operation, |v1| and |v2| represent the modulus lengths of v1 and v2 respectively.

[0021] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, the rock property parameters obtained also include the mechanical parameters of the rock, and the mechanical parameters include elastic modulus and Poisson's ratio. When determining the influence of the rock properties on the proppant particle size distribution, the influence of the rock mechanical parameters on the proppant embedment depth and support stability is analyzed through rock mechanics experiments and numerical simulations, thereby optimizing the proppant particle size distribution.

[0022] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, analyzing the influence of rock mechanical parameters on proppant embedment depth and proppant stability through rock mechanics experiments and numerical simulations, and then optimizing the proppant particle size distribution includes:

[0023] Rock mechanical parameter optimization formula: proppant embedment depth The relationship between the rock elastic modulus E is: , where σ is the proppant bearing stress, is the proppant particle size, k is the experimental fitting coefficient;

[0024] The rock elastic modulus E and Poisson's ratio v are obtained by triaxial rock test; different Next and support stability; choose to use And the particle size range in which the support stress is evenly distributed.

[0025] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, dividing the optimal particle size distribution of proppant particles into multiple particle size adaptation intervals based on the width measurement values ​​of different fracture regions and the permeability test results includes:

[0026] For crack width greater than the preset crack width threshold W th and the permeability result is greater than the preset permeability threshold K In the first fracture region of th, large-size proppant is used. The particle size distribution range of the large-size proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the first fracture region, the properties of the rock, and the pressure parameters that change in real time during the fracturing process;

[0027] For the second fracture area where the fracture width is less than the preset fracture width threshold or the permeability result is less than the preset permeability threshold, small-particle proppant is used. The particle size distribution range of the small-particle proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the second fracture area, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

[0028] As an optional embodiment of the present invention, a proppant injection method based on particle size distribution optimization of the present invention comprises:

[0029] Conducting simulation experiments based on the particle size adaptation interval and the corresponding crack width region;

[0030] By comparing the simulation experimental results with the historical data of the crack width area, the accuracy of the data analysis model is evaluated, and the parameters of the data analysis model are adjusted according to the error.

[0031] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, during the proppant injection process, downhole sensors are used to monitor real-time data, and based on real-time data analysis, areas in the fracture where proppant injection and distribution are abnormal are determined, and dynamic particle size adjustment is performed, including:

[0032] During the injection process, downhole sensors are used to monitor the real-time data of pressure, flow rate, proppant concentration and distribution image of proppant in the fracture in real time;

[0033] According to the real-time data analysis, it is determined that there are abnormal areas in the fracture where proppant injection and distribution are abnormal. Based on the causes of the abnormal proppant injection and distribution in the abnormal areas, a corresponding adjustment strategy is selected to perform dynamic particle size adjustment.

[0034] As an optional embodiment of the present invention, in a proppant injection method based on particle size distribution optimization of the present invention, the abnormal area where proppant injection and distribution are abnormal in the fracture is determined based on real-time data analysis includes:

[0035] Analyzing and determining whether there is an abnormal area of ​​local blockage based on whether the pressure in the crack in the real-time data is abnormal;

[0036] Determining whether there is an abnormal area with abnormal proppant concentration fluctuation based on the proppant concentration in the fracture in the real-time data;

[0037] It is determined whether there is an abnormal area with abnormal proppant settling velocity based on the distribution image of the proppant in the fracture in the real-time data.

[0038] As an optional embodiment of the present invention, a proppant injection method based on particle size distribution optimization of the present invention comprises:

[0039] Preset corresponding adjustment strategies according to different abnormal situations;

[0040] When real-time data analysis determines that there are abnormal areas of proppant injection and distribution in the fracture, the corresponding adjustment strategy is selected to automatically adjust the parameters of the proppant injection equipment, including but not limited to the discharge rate of the proppant storage tanks with different particle sizes and the opening of the mixing ratio control valve.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a proppant injection method based on particle size distribution optimization, which fundamentally solves the problem of uneven distribution of traditional proppant injection methods in complex underground environments. By constructing a multi-factor analysis model, key factors such as fracture geometry, rock properties and fracturing pressure are accurately controlled to achieve fine-grained regulation of proppant particle size distribution. It not only effectively improves the fracture conductivity, significantly increases the production of oil and gas wells, and reduces mining costs, but also reduces resource waste and environmental risks caused by improper proppant distribution. The present invention provides a proppant injection method based on particle size distribution optimization, which is expected to reshape the oil and gas extraction operation process and provide strong support for the industry's efficient, green and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Process of a proppant injection method based on particle size distribution optimization in an embodiment of the present invention Figure 1 ;

[0044] Figure 2 Process of a proppant injection method based on particle size distribution optimization in an embodiment of the present invention Figure 2 ;

[0045] Figure 3 Process of a proppant injection method based on particle size distribution optimization in an embodiment of the present invention Figure 3 . DETAILED DESCRIPTION

[0046] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0047] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0048] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0049] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0050] In the description of the present invention, it should be noted that the terms "upper" and "lower" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is typically placed when in use, or the orientations or positional relationships commonly understood by those skilled in the art. Such terms are intended solely to facilitate the description of the present invention and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" and the like are used solely for distinction and should not be construed as indicating or implying relative importance.

[0051] See also Figure 1 As shown, a proppant injection method based on particle size distribution optimization of this embodiment includes:

[0052] Particle size distribution analysis: Through comprehensive analysis of fracture geometry parameters, rock properties, and real-time pressure parameters during fracturing, data analysis models and algorithms are used to determine the proppant particle size distribution range corresponding to different fracture regions;

[0053] Particle size distribution optimization plan: Based on the width measurements and permeability test results of different fracture areas, the proppant particle size distribution range is divided into multiple particle size adaptation intervals;

[0054] Dynamic particle size adjustment mechanism: During the proppant injection process, downhole sensors are used to monitor real-time data. Based on real-time data analysis, areas with proppant injection and abnormal distribution in the fracture are determined, and dynamic particle size adjustment is performed.

[0055] This embodiment of a proppant injection method based on particle size distribution optimization achieves uniform and efficient proppant distribution within fractures through precise proppant particle size distribution analysis, a targeted proppant size distribution optimization scheme, and a dynamic particle size adjustment mechanism. This effectively enhances fracture conductivity, significantly increases oil and gas well production and recovery efficiency, and significantly reduces production costs. This method brings positive economic benefits and broad application prospects to the oil and gas extraction industry, promoting technological advancement and sustainable development across the industry.

[0056] As an optional implementation of this embodiment, see Figure 2 As shown, in a proppant injection method based on particle size distribution optimization of this embodiment, the proppant particle size distribution range corresponding to different areas of the fracture is determined by comprehensively analyzing the geometric parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

[0057] Obtaining the geometric parameters of the crack, including the length, width, height, and curvature of the crack;

[0058] Obtain rock properties, including rock hardness, brittleness, and pore structure parameters;

[0059] Obtain real-time changing pressure parameters during fracturing;

[0060] Based on the geometric parameters of the fracture, the properties of the rock, and the pressure parameters that change in real time during the fracturing process, a multivariate analysis is performed using a data analysis model to determine the proppant particle size distribution range corresponding to different areas of the fracture: di =f(L,W,H, Cr , Rh , Pt ),in, di is the target particle size range of the proppant in the i-th region (unit: mm), L, W, and H correspond to the length, width, and height of the fracture, respectively (unit: m, mm, m). Cr is the crack curvature parameter, which is determined by the crack curvature radius Rc and the bending angle θ is calculated , is the rock hardness parameter, measured by indentation test, unit: MPa, Pt is the real-time pressure dynamic parameter (unit: MPa).

[0061] The specific implementation method is to pre-process the data to analyze the fracture geometry (such as curvature) Cr ), rock properties (hardness Rh , Poisson's ratio ν) for normalization. Model training then uses a random forest algorithm, inputting historical fracturing data (including fracture parameters, rock parameters, pressure data, and the corresponding optimal particle size distribution) to train a multivariate prediction model. Finally, dynamic optimization uses real-time pressure data to correct the model output and generate a dynamic particle size distribution range.

[0062] The data analysis models and algorithms used in this embodiment include random forests and support vector machines, which are widely used in petroleum engineering. The particle size distribution analysis process requires accurate parameter values ​​for each fracture region and is validated and optimized using historical fracturing data and simulation results. The accuracy of the data analysis model is evaluated by comparing historical data with simulation results, and the model parameters are adjusted based on the errors to ensure that the predicted particle size distribution meets actual requirements.

[0063] As an optional implementation of this embodiment, in a proppant injection method based on particle size distribution optimization of this embodiment, the fracture curvature parameter in the fracture geometric parameters obtained includes:

[0064] Use logging imaging technology and image processing algorithms to measure the curvature radius of fractures Rc and bending angle θ, which are input into the data analysis model to calculate and analyze the proppant particle size distribution range corresponding to different areas of the fracture.

[0065] Radius of curvature Rc Calculated by the three-point arc method in well logging imaging technology , where Δs is the lateral offset of the crack and Δh is the longitudinal offset;

[0066] The bending angle θ is calculated using the vector angle formula after the crack contour line is extracted through image processing algorithm: ,in, represents the angle between two vectors, v1 and v2 represent two vectors, · represents the dot product operation, and |v1| and |v2| represent the modulus lengths of v1 and v2, respectively. In this embodiment, a proppant injection method based on particle size distribution optimization incorporates fracture geometry parameters into a data analysis model to calculate and analyze the proppant particle size distribution range corresponding to different fracture regions based on fracture geometry, resulting in a more accurate proppant particle size distribution.

[0067] Furthermore, in a proppant injection method based on particle size distribution optimization of this embodiment, the rock property parameters obtained also include rock mechanical parameters, and the mechanical parameters include elastic modulus and Poisson's ratio. When determining the influence of rock properties on proppant particle size distribution, rock mechanics experiments and numerical simulations are used to analyze the influence of rock mechanical parameters on proppant embedment depth and support stability, thereby optimizing proppant particle size distribution.

[0068] Specifically, in a proppant injection method based on particle size distribution optimization of this embodiment, analyzing the influence of rock mechanical parameters on proppant embedment depth and proppant stability through rock mechanics experiments and numerical simulations, and then optimizing the proppant particle size distribution includes:

[0069] Rock mechanical parameter optimization formula: proppant embedment depth The relationship between the rock elastic modulus E is: , where σ is the proppant bearing stress, is the proppant particle size, k is the experimental fitting coefficient;

[0070] The rock elastic modulus E and Poisson's ratio v are obtained by triaxial rock test; different Next and support stability; choose to use And the particle size range in which the support stress is evenly distributed.

[0071] As an optional implementation of this embodiment, see Figure 2 As shown, in a proppant injection method based on particle size distribution optimization of this embodiment, the optimal particle size distribution of proppant particles is divided into multiple particle size adaptation intervals according to the width measurement values ​​of different fracture regions and the permeability test results, including:

[0072] For the first fracture region where the fracture width is greater than a preset fracture width threshold and the permeability result is greater than a preset permeability threshold, a large-particle proppant is used. The particle size distribution range of the large-particle proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the first fracture region, the rock properties, and the pressure parameters that change in real time during the fracturing process.

[0073] For the second fracture area where the fracture width is less than the preset fracture width threshold or the permeability result is less than the preset permeability threshold, small-particle proppant is used. The particle size distribution range of the small-particle proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the second fracture area, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

[0074] In the proppant injection method based on particle size distribution optimization of this embodiment, although the method for determining the particle size distribution range of the proppant in the first fracture region and the second fracture region is the same, the threshold value is dynamically adjusted (such as the preset fracture width threshold value). W th and permeability threshold Kth), and combined with a multi-factor model to calculate the adaptive particle size, the following technical effects are achieved: efficiency improvement avoids repeated modeling for different areas and simplifies the calculation process; resource optimization dynamically matches proppants through a unified algorithm to reduce equipment loss caused by particle size switching; precise control of parameters under the same model can be differentiated to ensure optimal filling effects in both narrow areas (small particle size) and wide fracture areas (large particle size).

[0075] This embodiment of a proppant injection method based on particle size distribution optimization optimizes the proppant particle size distribution to ensure uniform proppant distribution across different areas and avoid particle aggregation. The threshold value is determined based on extensive experimental data and actual engineering experience. The threshold value is manually set based on experimental data and engineering experience, including historical fracturing results and proppant performance, to ensure that the threshold value is reasonable and effective.

[0076] Furthermore, a proppant injection method based on particle size distribution optimization of this embodiment includes:

[0077] Conducting simulation experiments based on the particle size adaptation interval and the corresponding crack width region;

[0078] By comparing the simulation experimental results with the historical data of the crack width area, the accuracy of the data analysis model is evaluated, and the parameters of the data analysis model are adjusted according to the error, so as to achieve data analysis model optimization and ensure the reliability of the data analysis model.

[0079] As an optional implementation of this embodiment, see Figure 3 As shown, in a proppant injection method based on particle size distribution optimization of this embodiment, during the proppant injection process, downhole sensors are used to perform real-time data monitoring, and based on real-time data analysis, areas in the fracture where proppant injection and distribution are abnormal are determined, and dynamic particle size adjustment is performed, including:

[0080] During the injection process, downhole sensors are used to monitor the real-time data of pressure, flow rate, proppant concentration and distribution image of proppant in the fracture in real time;

[0081] According to the real-time data analysis, it is determined that there are abnormal areas in the fracture where proppant injection and distribution are abnormal. Based on the causes of the abnormal proppant injection and distribution in the abnormal areas, a corresponding adjustment strategy is selected to perform dynamic particle size adjustment.

[0082] Specifically, during the injection process, downhole sensors monitor real-time data such as fracture pressure, flow rate, proppant concentration, and proppant distribution within the fracture. This data is then fed back to the ground control center via a data transmission system. If the monitoring data indicates suboptimal proppant distribution in a particular area, such as abnormal concentration fluctuations, localized blockages, or abnormal proppant settling velocity, the real-time monitoring system collects data and utilizes existing technologies (such as data analysis and pattern recognition) to determine if an abnormality has occurred. The ground control center then applies pre-set adjustment strategies and algorithms based on the specific abnormality to ensure optimal proppant distribution within the fracture.

[0083] In this embodiment, a proppant injection method based on particle size distribution optimization is provided. The dynamic particle size adjustment mechanism evaluates whether the proppant distribution achieves the expected effect through real-time monitoring and data analysis, and makes adjustments when necessary.

[0084] The data transmission system in this embodiment is responsible for collecting and transmitting real-time data, ensuring the system can respond and adjust promptly. It must include data filtering, noise reduction, and real-time transmission capabilities to ensure that the data transmitted to the ground control center is accurate and reliable, and that transmission delays do not exceed a set time threshold to ensure the timeliness and effectiveness of dynamic particle size adjustments.

[0085] Furthermore, in a proppant injection method based on particle size distribution optimization of this embodiment, the abnormal area in the fracture where proppant injection and distribution are abnormal is determined based on real-time data analysis, including:

[0086] Analyzing and determining whether there is an abnormal area of ​​local blockage based on whether the pressure in the crack in the real-time data is abnormal;

[0087] Determining whether there is an abnormal area with abnormal proppant concentration fluctuation based on the proppant concentration in the fracture in the real-time data;

[0088] It is determined whether there is an abnormal area with abnormal proppant settling velocity based on the distribution image of the proppant in the fracture in the real-time data.

[0089] A proppant injection method based on particle size distribution optimization in this embodiment includes:

[0090] Preset corresponding adjustment strategies according to different abnormal situations;

[0091] When real-time data analysis determines that there are abnormal areas of proppant injection and distribution in the fracture, the corresponding adjustment strategy is selected to automatically adjust the parameters of the proppant injection equipment, including but not limited to the discharge rate of the proppant storage tanks with different particle sizes and the opening of the mixing ratio control valve.

[0092] This embodiment of a proppant injection method based on particle size distribution optimization automatically adjusts injection equipment parameters to dynamically change the proppant particle size distribution in the injected fluid, ensuring that the particles maintain an optimized distribution within the fracture. By presetting corresponding adjustment strategies and algorithms based on different abnormal conditions, the proppant maintains an optimized distribution within the fracture. Real-time monitoring and data analysis assess whether the proppant distribution achieves the desired effect, and adjustments are made if necessary.

[0093] In summary, the proppant injection method based on particle size distribution optimization provided in this embodiment fundamentally solves the problem of uneven distribution of traditional proppant injection methods in complex underground environments. By constructing a multi-factor analysis model, key factors such as fracture geometry, rock properties and fracturing pressure are accurately controlled to achieve fine-grained regulation of proppant particle size distribution. It not only effectively improves the fracture conductivity, significantly increases the production of oil and gas wells, and reduces mining costs, but also reduces resource waste and environmental risks caused by improper proppant distribution. The proppant injection method based on particle size distribution optimization of this embodiment is expected to reshape the oil and gas extraction operation process and provide strong support for the industry's efficient, green and sustainable development.

[0094] This embodiment also provides a computer-readable recording medium storing a computer-executable program. When the computer-executable program is executed, the proppant injection method based on particle size distribution optimization is implemented.

[0095] The computer-readable recording medium described in this embodiment may include a data signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable recording medium may also be any readable medium other than a readable recording medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable recording medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof. Example

[0096] Basic information of shale gas well: A shale gas well was fractured, with a fracture length of 180 meters and an average width of 3 mm. The rock hardness was low and the porosity was large, and the fracturing pressure was 28 MPa.

[0097] Particle size analysis and plan formulation: Considering that the low hardness of the rock makes it easy for proppants to embed, and the large porosity may cause proppant loss, 0.5-0.9 mm surface-coated proppants are used in the initial section of the fracture (0-60 meters) to enhance support stability; 0.3-0.5 mm high-strength proppants are used in the middle section (60-120 meters) to ensure support strength; and 0.1-0.3 mm low-density proppants are used in the terminal section (120-180 meters) to facilitate filling in low-pressure areas.

[0098] Injection Process and Results: During injection, the proppant settling rate and pressure changes within the fractures were monitored to promptly adjust the injection rate of proppant of different particle sizes. After fracturing, shale gas well production increased by 28% compared to traditional methods, and long-term gas production stability was enhanced. Example

[0099] Basic information of heavy oil well: A heavy oil well was fractured, with a crack length of 160 meters and an average width of 6 mm. The rock has low brittleness and high plasticity, and the fracturing pressure is 32 MPa.

[0100] Particle size analysis and plan formulation: In view of the strong plasticity of rock that easily closes cracks, 1.0-1.5 mm large-particle high-strength proppants are used in the initial section of the crack (0-40 meters); 0.6-1.0 mm toughness proppants are used in the middle section (40-120 meters); and 0.4-0.6 mm anti-deformation proppants are used in the terminal section (120-160 meters).

[0101] Injection Process and Results: During injection, the proppant particle size combination was dynamically adjusted based on real-time monitoring of the heavy oil flow resistance and proppant distribution. After fracturing, the heavy oil well production increased by 35%, effectively reducing the difficulty and cost of extraction.

[0102] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above embodiments, the present invention is not limited to the above specific implementation methods. Therefore, any modification or equivalent replacement of the present invention; and all technical solutions and improvements thereof that do not depart from the spirit and scope of the invention are included in the scope of the claims of the present invention.

Claims

1. A proppant injection method based on particle size distribution optimization, characterized in that: include: By comprehensively analyzing the geometric parameters of the fracture, the properties of the rock, and the pressure parameters that change in real time during the fracturing process, the proppant particle size distribution range corresponding to different areas of the fracture is determined using data analysis models and algorithms; Based on the width measurements of different fracture regions and the permeability test results, the proppant particle size distribution range is divided into multiple particle size adaptation intervals; During the proppant injection process, downhole sensors are used to monitor real-time data. Based on real-time data analysis, areas with proppant injection and abnormal distribution in the fracture are determined, and dynamic particle size adjustment is performed. The proppant particle size distribution range corresponding to different areas of the fracture is determined by comprehensively analyzing the geometric parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process. Obtaining the geometric parameters of the crack, including the length, width, height, and curvature of the crack; Obtain rock properties, including rock hardness, brittleness, and pore structure parameters; Obtain real-time changing pressure parameters during fracturing; Based on the geometric parameters of the fracture, the properties of the rock, and the pressure parameters that change in real time during the fracturing process, a multivariate analysis is performed using a data analysis model to determine the proppant particle size distribution range corresponding to different areas of the fracture: di =f(L,W,H, Cr , Rh , Pt ),in, di is the target particle size range of the proppant in the i-th region, L, W, and H correspond to the length, width, and height of the fracture, respectively. Cr is the crack curvature parameter, which is determined by the crack curvature radius R c and bending angle θ Calculated , Rc is the rock hardness parameter, measured by indentation test, Pt is the real-time pressure dynamic parameter; The optimal particle size distribution of proppant particles is divided into multiple particle size adaptation intervals according to the width measurement values ​​of different fracture regions and the permeability test results, including: For cracks with a width greater than the preset crack width threshold W th and the permeability result is greater than the preset permeability threshold K In the first fracture region of th, large-size proppant is used. The particle size distribution range of the large-size proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the first fracture region, the properties of the rock, and the pressure parameters that change in real time during the fracturing process; For the second fracture area where the fracture width is less than the preset fracture width threshold or the permeability result is less than the preset permeability threshold, small-particle proppant is used. The particle size distribution range of the small-particle proppant is determined by multivariate analysis using a data analysis model based on the geometric parameters of the second fracture area, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

2. A proppant injection method based on particle size distribution optimization according to claim 1, characterized in that: The crack curvature parameters in the obtained crack geometric parameters include: Use logging imaging technology and image processing algorithms to measure the curvature radius of fractures R c and bending angle θ, and input them into the data analysis model to calculate and analyze the proppant particle size distribution range corresponding to different areas of the fracture; Radius of curvature R c Calculated by the three-point arc method in well logging imaging technology , where Δs is the lateral offset of the crack and Δh is the longitudinal offset; The bending angle θ is calculated using the vector angle formula after the crack contour line is extracted through image processing algorithm: ,in, represents the angle between two vectors, v1 and v2 represent two vectors, · represents the dot product operation, |v1| and |v2| represent the modulus lengths of v1 and v2 respectively.

3. The proppant injection method based on particle size distribution optimization according to claim 1, characterized in that: The rock property parameters obtained also include the mechanical parameters of the rock, which include the elastic modulus and Poisson's ratio. When determining the influence of the rock properties on the proppant particle size distribution, rock mechanics experiments and numerical simulations are used to analyze the influence of the rock mechanical parameters on the proppant embedment depth and support stability, thereby optimizing the proppant particle size distribution.

4. A proppant injection method based on particle size distribution optimization according to claim 3, characterized in that: The above mentioned rock mechanics experiments and numerical simulations are used to analyze the influence of rock mechanical parameters on proppant embedment depth and proppant stability, thereby optimizing the proppant particle size distribution, including: Rock mechanical parameter optimization formula: proppant embedment depth The relationship between the rock elastic modulus E is: , where σ is the proppant bearing stress, is the proppant particle size, k is the experimental fitting coefficient; The rock elastic modulus E and Poisson's ratio v are obtained by triaxial rock test; different Next and support stability; choose to use And the particle size range in which the support stress is evenly distributed.

5. The proppant injection method based on particle size distribution optimization according to claim 1, characterized in that: include: Conducting simulation experiments based on the particle size adaptation interval and the corresponding crack width region; By comparing the simulation experimental results with the historical data of the crack width area, the accuracy of the data analysis model is evaluated, and the parameters of the data analysis model are adjusted according to the error.

6. The proppant injection method based on particle size distribution optimization according to claim 1, characterized in that: During the proppant injection process, downhole sensors are used to monitor real-time data, and based on real-time data analysis, areas in the fracture where proppant injection and distribution are abnormal are determined, and dynamic particle size adjustment is performed, including: During the injection process, downhole sensors are used to monitor the real-time data of pressure, flow rate, proppant concentration and distribution image of proppant in the fracture in real time; According to the real-time data analysis, it is determined that there are abnormal areas in the fracture where proppant injection and distribution are abnormal. Based on the causes of the abnormal proppant injection and distribution in the abnormal areas, a corresponding adjustment strategy is selected to perform dynamic particle size adjustment.

7. The proppant injection method based on particle size distribution optimization according to claim 6, characterized in that: The abnormal areas where proppant injection and distribution abnormalities are determined in the fractures based on real-time data analysis include: Analyzing and determining whether there is an abnormal area of ​​local blockage based on whether the pressure in the crack in the real-time data is abnormal; Determining whether there is an abnormal area with abnormal proppant concentration fluctuation based on the proppant concentration in the fracture in the real-time data; It is determined whether there is an abnormal area with abnormal proppant settling velocity based on the distribution image of the proppant in the fracture in the real-time data.

8. The proppant injection method based on particle size distribution optimization according to claim 7, characterized in that: include: Preset corresponding adjustment strategies according to different abnormal situations; When real-time data analysis determines that there are abnormal areas of proppant injection and distribution in the fracture, the corresponding adjustment strategy is selected to automatically adjust the parameters of the proppant injection equipment, including but not limited to the discharge rate of the proppant storage tanks with different particle sizes and the opening of the mixing ratio control valve.

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