Proppant injection method based on particle size distribution optimization

Abstract

The invention discloses a propping agent injection method based on particle size distribution optimization, which comprises the following steps: comprehensively analyzing geometrical morphology parameters of cracks, property parameters of rocks and pressure parameters changing in real time in a fracturing process; determining propping agent particle size distribution ranges corresponding to different areas of the crack through a data analysis model and an algorithm; dividing the particle size distribution range of the proppant particles into a plurality of particle size adaptation intervals according to the width measurement values and permeability test results of different areas of the crack; in the proppant injection process, real-time data monitoring is conducted through an underground sensor, the area with abnormal proppant injection and distribution in the crack is analyzed and judged according to real-time data, and dynamic particle size adjustment is conducted. According to the method, the problem that a traditional propping agent injection mode is uneven in distribution in a complex underground environment is solved, key factors such as the geometrical morphology of a crack, rock properties and fracturing pressure are accurately controlled by constructing a multi-factor analysis model, and fine regulation and control of particle size distribution of the propping agent are achieved.

Description

Technical Field

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

[0002] In the fracturing operation of oil and gas extraction, the effective injection of proppants is a key link to improve the productivity of oil and gas wells. However, most traditional proppant injection methods rely on a single particle size or a simple particle size combination. When faced with complex and variable underground environments, this injection method exposes many deficiencies.

[0003] The geometric shape of underground fractures is extremely complex, with lengths ranging from dozens of meters to hundreds of meters, widths that can vary at the millimeter level or even on a smaller scale, significant height differences, and often bending, branching, etc. Rock properties also vary greatly. Hard rocks will hinder the embedding of proppants, while soft rocks are prone to causing proppants to sink into them, affecting the support effect; brittle rocks are prone to generating a large number of irregular fractures during fracturing, and the pore structure is complex, which poses higher requirements for the filling and distribution of proppants. At the same time, the pressure during the fracturing process is not stable. As the fracture expands and extends, the pressure will fluctuate dynamically, making it difficult to predict the movement state of proppants during injection.

[0004] Under such complex conditions, proppants with a single or simple combined particle size are difficult to achieve uniform distribution in fractures. For example, in narrow fractures, large-sized proppants cannot enter smoothly, resulting in insufficient support in this area; while in wide and highly permeable fracture areas, small-sized proppants are easily carried out by the high-speed flowing fracturing fluid, causing uneven distribution of proppants. This uneven distribution seriously affects the fracture conductivity, greatly reducing the fracturing effect, making it difficult to improve the production of oil and gas wells, and the production cost remains high. Therefore, it is urgent to develop a proppant injection method that can adapt to complex underground environments.

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

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

[0007] Specifically, the following technical solutions are adopted: A proppant injection method based on optimized particle size distribution, comprising: By comprehensively analyzing the geometric shape 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 range of proppant particle sizes corresponding to different regions of the fracture through a data analysis model and algorithm; According to the width measurement values and permeability test results of different regions of the fracture, divide the range of proppant particle sizes into multiple particle size adaptation intervals; During the proppant injection process, use downhole sensors for real-time data monitoring, and based on real-time data analysis, judge the regions where proppant injection and distribution are abnormal in the fracture, and perform dynamic particle size adjustment.

[0008] As an alternative implementation of the present invention, in a proppant injection method based on optimized particle size distribution of the present invention, the comprehensive analysis of the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process to determine the range of proppant particle sizes corresponding to different regions of the fracture includes: Obtain the geometric shape parameters of the fracture, including the length, width, height, and curvature parameter of the fracture; Obtain the property parameters of the rock, including the hardness, brittleness, and pore structure parameters of the rock; Obtain the pressure parameters that change in real time during the fracturing process; Based on the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, perform multivariate analysis using a data analysis model to determine the range of proppant particle sizes corresponding to different regions of the fracture: di =f(L, W, H, Cr , Rh , Pt ), where, di is the target proppant particle size range for the i-th region, L, W, and H respectively correspond to the length, width, and height of the fracture, Cr is the fracture curvature parameter, which is calculated from the curvature radius Rc and the bending angle θ of the fracture, is the rock hardness parameter, measured through an indentation experiment, Pt is the real-time pressure dynamic parameter.

[0009] As an alternative implementation of the present invention, in a proppant injection method based on optimized particle size distribution of the present invention, the fracture curvature parameter in the acquisition of the geometric shape parameters of the fracture includes: Adopt well logging imaging technology and image processing algorithm to measure the curvature radius Rc and the bending angle θ of the fracture, and input them into the data analysis model to calculate and analyze the range of proppant particle sizes corresponding to different regions of the fracture; The curvature radiusRc Calculated by the three-point circular arc method in logging imaging technology , where Δs is the transverse offset of the fracture, and Δh is the longitudinal offset; After extracting the fracture contour line through the image processing algorithm, the bending angle θ is calculated using the vector included angle formula: , where represents the included angle between two vectors, v1 and v2 respectively represent two vectors, · represents the dot product operation, and |v1| and |v2| respectively represent the magnitudes of v1 and v2.

[0010] As an alternative embodiment of the present invention, in a proppant injection method based on optimizing particle size distribution, the acquisition of the rock property parameters further includes the mechanical parameters of the rock. The mechanical parameters include the elastic modulus and Poisson's ratio. When determining the influence of the rock properties on the proppant particle size distribution, through rock mechanics experiments and numerical simulations, analyze the influence of the rock's mechanical parameters on the proppant embedment depth and support stability, and then optimize the proppant particle size distribution.

[0011] As an alternative embodiment of the present invention, in a proppant injection method based on optimizing particle size distribution, the analysis of the influence of the rock's mechanical parameters on the proppant embedment depth and support stability through rock mechanics experiments and numerical simulations, and then optimizing the proppant particle size distribution includes: Optimization formula for the mechanical parameters of the rock: The relationship between the proppant embedment depth and the rock elastic modulus E is , where σ is the proppant bearing stress, is the proppant particle size, and k is the experimental fitting coefficient; Obtain the rock elastic modulus E and Poisson's ratio v through rock triaxial experiments; use finite element simulation to calculate different under the and support stability; select the particle size range that makes and the support stress distribution is uniform.

[0012] As an alternative embodiment of the present invention, in a proppant injection method based on optimizing particle size distribution, the division of the optimal proppant particle size distribution into multiple particle size adaptation intervals according to the width measurement values and permeability test results of different regions of the fracture includes: For the first fracture region where the fracture width is greater than the preset fracture width threshold W th and the permeability result is greater than the preset permeability threshold K th, use large-size proppants. The particle size distribution range of the large-size proppants is determined by multivariate analysis using a data analysis model based on the geometric shape parameters of the first fracture region, the rock property parameters, and the pressure parameters that change in real time during the fracturing process. For a second fracture area where the fracture width is less than a preset fracture width threshold or the permeability result is less than a preset permeability threshold, small-sized proppants are used. The particle size distribution range of the small-sized proppants is determined by multivariate analysis using a data analysis model based on the geometric shape 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.

[0013] As an alternative implementation of the present invention, a proppant injection method based on optimized particle size distribution includes: Conducting simulation experiments based on the particle size adaptation interval and the corresponding fracture width area; By comparing the simulation experiment results with the historical data of the fracture width area, evaluating the accuracy of the data analysis model, and adjusting the parameters of the data analysis model according to the error.

[0014] As an alternative implementation of the present invention, in a proppant injection method based on optimized particle size distribution, during the proppant injection process, downhole sensors are used for real-time data monitoring, and according to the real-time data analysis, areas with abnormal proppant injection and distribution in the fracture are judged, and the dynamic particle size adjustment includes: During the injection process, downhole sensors are used to monitor in real time the real-time data of the pressure, flow rate, proppant concentration in the fracture, and the distribution image of the proppant in the fracture; According to the real-time data analysis, judge the abnormal areas where there are abnormal proppant injection and distribution in the fracture, select corresponding adjustment strategies based on the reasons for the abnormal proppant injection and distribution in the abnormal areas, and perform dynamic particle size adjustment.

[0015] As an alternative implementation of the present invention, in a proppant injection method based on optimized particle size distribution, the judging of the abnormal areas where there are abnormal proppant injection and distribution in the fracture according to the real-time data analysis includes: Analyze and judge whether there is an abnormal area of local blockage according to whether the pressure in the fracture in the real-time data is abnormal; Judge whether there is an abnormal area with abnormal fluctuation of proppant concentration according to the proppant concentration in the fracture in the real-time data; Judge whether there is an abnormal area with abnormal proppant settlement speed according to the distribution image of the proppant in the fracture in the real-time data.

[0016] As an alternative implementation of the present invention, a proppant injection method based on optimized particle size distribution includes: Presetting corresponding adjustment strategies according to different abnormal situations; When it is judged according to real-time data analysis that there is an abnormal area with abnormal proppant injection and distribution in the fracture, select the corresponding adjustment strategy and automatically adjust the parameters of the proppant injection equipment, including but not limited to the discharge speed of the storage tanks of proppants with different particle sizes and the opening degree of the mixing ratio control valve.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: A proppant injection method based on particle size distribution optimization provided by the present invention 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, realizing refined control of the particle size distribution of proppants. It not only effectively improves the fracture conductivity, significantly increases the oil and gas well production, reduces the production cost, but also reduces resource waste and environmental risks caused by improper proppant distribution. The proppant injection method based on particle size distribution optimization of the present invention is expected to reshape the oil and gas exploitation operation process and provide strong support for the efficient, green, and sustainable development of the industry. Description of the Drawings

[0018] Figure 1 Flow chart of a proppant injection method based on particle size distribution optimization according to an embodiment of the present invention Figure 1 ; Figure 2 Flow chart of a proppant injection method based on particle size distribution optimization according to an embodiment of the present invention Figure 2 ; Figure 3 Flow chart of a proppant injection method based on particle size distribution optimization according to an embodiment of the present invention Figure 3 . Detailed Embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some, but not all, of the embodiments of the present invention.

[0020] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed present invention, but merely represents some embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] It should be noted that, without conflict, the embodiments in the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0022] It should be noted that like reference numerals and letters refer to like items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the inventive product is customarily placed during use, or the orientation or positional relationship commonly understood by those skilled in the art. Such terms are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0024] See Figure 1 As shown, a proppant injection method based on particle size distribution optimization in this embodiment includes: Analysis of particle size distribution: By comprehensively analyzing the geometric shape 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 range of proppant particle size distribution corresponding to different regions of the fracture through a data analysis model and algorithm; Particle size distribution optimization plan: According to the width measurement values and permeability test results of different regions of the fracture, divide the range of proppant particle size distribution into multiple particle size adaptation intervals; Dynamic particle size adjustment mechanism: During the proppant injection process, use downhole sensors to monitor real-time data, and based on the real-time data analysis, judge the regions where there are abnormal proppant injection and distribution in the fracture, and perform dynamic particle size adjustment.

[0025] A proppant injection method based on particle size distribution optimization in this embodiment realizes the uniform and efficient distribution of proppant in the fracture through precise analysis of proppant particle size distribution, targeted proppant particle size distribution optimization plan, and dynamic particle size adjustment mechanism. It effectively enhances the conductivity of the fracture, significantly improves the production and recovery efficiency of oil and gas wells, greatly reduces the production cost, brings good economic benefits and broad application prospects to the oil and gas extraction industry, and promotes the technological progress and sustainable development of the entire industry.

[0026] As an alternative implementation of this embodiment, see Figure 2 As shown, in a proppant injection method based on particle size distribution optimization in this embodiment, the determination of the range of proppant particle size distribution corresponding to different regions of the fracture by comprehensively analyzing the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process includes: Obtain the geometric shape parameters of the fracture, including the length, width, height, and curvature parameter of the fracture; Obtain the property parameters of the rock, including the hardness, brittleness, and pore structure parameters of the rock; Obtain the pressure parameters that change in real time during the fracturing process; Based on the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, perform multivariate analysis using a data analysis model to determine the range of proppant particle sizes corresponding to different regions of the fracture: di =f(L,W,H, Cr , Rh , Pt ), where, di is the target proppant particle size range for the i-th region (unit: mm), L, W, and H respectively correspond to the length, width, and height of the fracture (unit: m, mm, m), Cr is the fracture curvature parameter, which is calculated from the curvature radius Rc and the bending angle θ , is the rock hardness parameter, measured through an indentation experiment, unit: MPa, Pt is the real-time pressure dynamic parameter (unit: MPa).

[0027] The specific implementation method is to perform normalization processing on the fracture geometry (such as the degree of curvature Cr ), and the rock properties (hardness Rh , Poisson's ratio ν ) through data preprocessing. Then, for model training, the random forest algorithm is adopted, and historical fracturing data (including fracture parameters, rock parameters, pressure data, and corresponding optimal particle size distribution results) is input to train the multivariate prediction model. Finally, for dynamic optimization, the model output is corrected through real-time pressure data to generate the dynamic particle size distribution range.

[0028] The data analysis models and algorithms adopted in this embodiment include random forest, support vector machine, etc., which have been widely used in petroleum engineering. The particle size distribution analysis process needs to be accurate to the specific parameter values of each fracture region, and be verified and optimized in combination with historical fracturing data and simulation experiment results. By comparing historical data and simulation results, the accuracy of the data analysis model is evaluated, and the data analysis model parameters are adjusted according to the error to ensure that the predicted particle size distribution meets the actual requirements.

[0029] As an alternative implementation of this embodiment, in a proppant injection method based on particle size distribution optimization of this embodiment, the fracture curvature parameter in the obtained geometric shape parameters of the fracture includes: Adopt well logging imaging technology and image processing algorithms to measure the curvature radius of the fracture RcAnd the bending angle θ, and input it into the data analysis model to calculate the range of proppant particle size distribution corresponding to different regions of the fracture.

[0030] Radius of curvature Rc Calculated by the three-point arc method in logging imaging technology , where Δs is the lateral offset of the fracture and Δh is the longitudinal offset; After extracting the fracture contour line by the image processing algorithm, the bending angle θ is calculated using the vector included angle formula: , where represents the included angle between two vectors, v1 and v2 represent two vectors respectively, · represents the dot product operation, and |v1| and |v2| represent the magnitudes of v1 and v2 respectively. In a proppant injection method based on particle size distribution optimization according to this embodiment, the geometric shape parameters of the fracture are added to the data analysis model to realize the calculation and analysis of the range of proppant particle size distribution corresponding to different regions of the fracture based on the geometric shape of the fracture, and the proppant particle size distribution is more accurate.

[0031] Furthermore, in a proppant injection method based on particle size distribution optimization according to this embodiment, the acquisition of the rock property parameters further includes the mechanical parameters of the rock. The mechanical parameters include elastic modulus and Poisson's ratio. When determining the influence of the rock properties on the proppant particle size distribution, through rock mechanics experiments and numerical simulations, analyze the influence of the mechanical parameters of the rock on the proppant embedment depth and support stability, and then optimize the proppant particle size distribution.

[0032] Specifically, in a proppant injection method based on particle size distribution optimization according to this embodiment, the analysis of the influence of the mechanical parameters of the rock on the proppant embedment depth and support stability through rock mechanics experiments and numerical simulations, and then optimizing the proppant particle size distribution includes: Optimization formula for the mechanical parameters of the rock: Proppant embedment depth The relationship with the rock elastic modulus E is , where σ is the proppant bearing stress, is the proppant particle size, and k is the experimental fitting coefficient; Obtain the rock elastic modulus E and Poisson's ratio v through rock triaxial experiments; use finite element simulation to calculate different under and support stability; select the particle size range that makes and the support stress distribution is uniform.

[0033] As an alternative implementation of this embodiment, see Figure 2As shown, in a proppant injection method based on particle size distribution optimization in this embodiment, dividing the optimal particle size distribution of proppant particles into multiple particle size adaptation intervals according to the width measurement values and permeability test results of different regions of the fracture includes: For the first fracture region where the fracture width is greater than the preset fracture width threshold and the permeability result is greater than the preset permeability threshold, large particle size proppant is used. The particle size distribution range of the large particle size proppant is determined by multivariate analysis using a data analysis model based on the geometric shape parameters of the first fracture region, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process; For the second fracture region 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 size proppant is used. The particle size distribution range of the small particle size proppant is determined by multivariate analysis using a data analysis model based on the geometric shape parameters of the second fracture region, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

[0034] In a proppant injection method based on particle size distribution optimization in this embodiment, although the methods for determining the particle size distribution ranges of the proppant in the first fracture region and the second fracture region are the same, by dynamically adjusting the thresholds (such as the preset fracture width threshold W th and the permeability threshold K th), and combining a multi-factor model to calculate the adapted particle size, the following technical effects are achieved: efficiency improvement, avoiding repeated modeling for different regions and simplifying the calculation process; resource optimization, dynamically matching the proppant through a unified algorithm and reducing equipment losses caused by particle size switching; precise control, the parameters can be differentially adjusted under the same model to ensure that both narrow regions (small particle size) and wide fracture regions (large particle size) obtain the optimal filling effect.

[0035] In a proppant injection method based on particle size distribution optimization in this embodiment, the particle size distribution of the proppant particles is optimized to ensure that the proppant can be evenly distributed in different regions and avoid particle aggregation. The set thresholds need to be determined based on a large amount of experimental data and actual engineering experience. The set thresholds are artificially set according to experimental data and engineering experience, and the basis includes historical fracturing effects, proppant performance, etc., to ensure that the thresholds are reasonable and effective.

[0036] Furthermore, a proppant injection method based on particle size distribution optimization in this embodiment includes: Conducting simulation experiments based on the particle size adaptation intervals and the corresponding fracture width regions; By comparing the simulation experiment results with the historical data of the fracture width regions, evaluating the accuracy of the data analysis model, and adjusting the parameters of the data analysis model according to the errors, thereby realizing the optimization of the data analysis model and ensuring the reliability of the data analysis model.

[0037] As an alternative implementation of this embodiment, refer to Figure 3 As shown in Figure 3 , in a proppant injection method based on particle size distribution optimization according to this embodiment, during the proppant injection process, downhole sensors are used for real-time data monitoring. According to the real-time data analysis, it is judged whether there are areas with abnormal proppant injection and distribution in the fracture, and the dynamic particle size adjustment includes: During the injection process, downhole sensors are used to monitor in real time the real-time data of the pressure, flow rate, proppant concentration in the fracture, and the distribution image of the proppant in the fracture; According to the real-time data analysis, it is judged whether there are abnormal areas with abnormal proppant injection and distribution in the fracture. Based on the reasons for the abnormal proppant injection and distribution in the abnormal area, corresponding adjustment strategies are selected for dynamic particle size adjustment.

[0038] Specifically, during the injection process, downhole sensors are used to monitor in real time data such as the pressure, flow rate, proppant concentration in the fracture, and the distribution image of the proppant in the fracture. These data are transmitted in real time to the ground control center through a data transmission system. When the monitored data shows that the proppant distribution in a certain area is not ideal, such as abnormal concentration fluctuations, local blockage, or abnormal proppant settlement speed, etc., data is collected through the real-time monitoring system, and existing technologies (such as data analysis and pattern recognition) are used to judge whether abnormal conditions occur. The ground control center, based on the preset adjustment strategies and algorithms, presets corresponding adjustment strategies and algorithms according to different abnormal conditions to ensure that the proppant always maintains an optimized distribution state in the fracture.

[0039] In a proppant injection method based on particle size distribution optimization according to this embodiment, the dynamic particle size adjustment mechanism evaluates whether the proppant distribution reaches the expected effect through real-time monitoring and data analysis, and makes adjustments if necessary.

[0040] The data transmission system of this embodiment is responsible for collecting and transmitting real-time data to ensure that the system can respond and adjust in a timely manner. It needs to have data filtering, noise reduction, and real-time transmission functions to ensure that the data transmitted to the ground control center is accurate, reliable, and the transmission delay does not exceed the set time threshold to ensure the timeliness and effectiveness of dynamic particle size adjustment.

[0041] Furthermore, in a proppant injection method based on particle size distribution optimization according to this embodiment, the judging of the abnormal areas with abnormal proppant injection and distribution in the fracture according to the real-time data analysis includes: Judging whether there are abnormal areas with local blockage according to whether the pressure in the fracture in the real-time data is abnormal; Judging whether there are abnormal areas with abnormal fluctuations in proppant concentration according to the proppant concentration in the fracture in the real-time data; Judge whether there is an abnormal area with abnormal proppant settlement velocity according to the distribution image of proppant in the fracture in the real-time data.

[0042] A proppant injection method based on particle size distribution optimization in this embodiment includes: Preset corresponding adjustment strategies according to different abnormal situations; When it is judged according to the real-time data analysis that there is an abnormal area with abnormal proppant injection and distribution in the fracture, select the corresponding adjustment strategy and automatically adjust the parameters of the proppant injection equipment, including but not limited to the discharge speed of storage tanks for proppants with different particle sizes and the opening degree of the mixing ratio control valve.

[0043] A proppant injection method based on particle size distribution optimization in this embodiment automatically adjusts the parameters of the injection equipment, thereby dynamically changing the particle size distribution of proppant in the injection fluid to ensure that the particles always maintain an optimized distribution state in the fracture. By presetting corresponding adjustment strategies and algorithms according to different abnormal situations, it is ensured that the proppant always maintains an optimized distribution state in the fracture, and through real-time monitoring and data analysis, it is evaluated whether the proppant distribution reaches the expected effect, and adjustments are made if necessary.

[0044] In summary, a 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, realizing fine control of the proppant particle size distribution. It not only effectively improves the fracture conductivity, significantly increases the oil and gas well production, reduces the production cost, but also reduces the resource waste and environmental risks caused by improper proppant distribution. A proppant injection method based on particle size distribution optimization in this embodiment is expected to reshape the oil and gas exploitation operation process and provide strong support for the efficient, green, and sustainable development of the industry.

[0045] This embodiment also provides a computer-readable recording medium storing a computer-executable program, and when the computer-executable program is executed, the proppant injection method based on particle size distribution optimization is implemented.

[0046] The computer-readable recording medium described in this embodiment may include a data signal propagated in a baseband or as part of a carrier wave, which carries readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable recording medium may also be any readable medium other than the readable recording medium, and the readable medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable recording medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above. Example

[0047] Basic situation of a shale gas well: A shale gas well was fractured. The fracture length was 180 meters, the average width was 3 millimeters, the rock hardness was low and the porosity was large, and the fracturing pressure was 28 MPa.

[0048] Particle size analysis and scheme formulation: Considering that the low rock hardness is likely to cause proppants to embed and the large porosity may lead to proppant loss, 0.5-0.9 millimeter surface-coated proppants were used in the initial section (0-60 meters) of the fracture to enhance the support stability; 0.3-0.5 millimeter high-strength proppants were used in the middle section (60-120 meters) to ensure the support strength; 0.1-0.3 millimeter low-density proppants were used in the end section (120-180 meters) to facilitate filling in the low-pressure area.

[0049] Injection process and effect: During injection, by monitoring the settlement speed of proppants in the fracture and the pressure change, the injection volume of proppants with different particle sizes was adjusted in a timely manner. After fracturing, the production of the shale gas well increased by 28% compared with the traditional method, and the long-term gas production stability was enhanced. Example

[0050] Basic situation of a heavy oil well: A heavy oil well was fractured. The fracture length was 160 meters, the average width was 6 millimeters, the rock brittleness was low and the plasticity was strong, and the fracturing pressure was 32 MPa.

[0051] Particle size analysis and scheme formulation: In view of the strong plasticity of the rock, which is likely to cause the fracture to close, 1.0-1.5 millimeter large-particle-size high-strength proppants were used in the initial section (0-40 meters) of the fracture; 0.6-1.0 millimeter ductile proppants were used in the middle section (40-120 meters); 0.4-0.6 millimeter anti-deformation proppants were used in the end section (120-160 meters).

[0052] Injection process and effect: During injection, according to the real-time monitored heavy oil flow resistance and the proppant distribution image, the particle size combination of proppants was dynamically adjusted. After fracturing, the production of the heavy oil well increased by 35%, effectively reducing the mining difficulty and cost.

[0053] The above embodiments are only used to illustrate the present invention and do not limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above respective embodiments, the present invention is not limited to the above specific implementation manners. Therefore, any modification or equivalent replacement to the present invention; and all technical solutions and their improvements that do not depart from the spirit and scope of the invention are covered within the scope of the claims of the present invention.

Claims

1. A proppant injection method optimized based on particle size distribution, characterized in that Comprising: By comprehensively analyzing the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, determining the range of proppant particle size distributions corresponding to different regions of the fracture through a data analysis model and algorithm; According to the width measurement values and permeability test results of different regions of the fracture, dividing the range of proppant particle size distributions into multiple particle size adaptation intervals; During the proppant injection process, using downhole sensors for real-time data monitoring, judging the regions where there are abnormal proppant injection and distribution in the fracture based on real-time data analysis, and performing dynamic particle size adjustment.

2. The proppant injection method based on particle size distribution optimization according to claim 1, wherein The determining of the range of proppant particle size distributions corresponding to different regions of the fracture by comprehensively analyzing the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process includes: Obtaining the geometric shape parameters of the fracture, including the length, width, height, and curvature degree parameters of the fracture; Obtaining the property parameters of the rock, including the hardness, brittleness, and pore structure parameters of the rock; Obtaining the pressure parameters that change in real time during the fracturing process; Based on the geometric shape parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, a multivariate analysis is carried out using a data analysis model to determine the range of proppant particle sizes corresponding to different regions of the fracture: di =f(L,W,H, Cr , Rh , Pt ), where di is the target proppant particle size range for the i-th region, L, W, and H respectively correspond to the length, width, and height of the fracture, Cr is the fracture curvature parameter, which is calculated from the curvature radius Rc and the bending angle θ of the fracture , Rc is the rock hardness parameter, which is measured through an indentation experiment, Pt is the real-time pressure dynamic parameter.

3. The proppant injection method based on particle size distribution optimization according to claim 2, wherein The curvature degree parameter of the fracture in the obtaining of the geometric shape parameters of the fracture includes: Using logging imaging technology and image processing algorithms, measure the radius of curvature of the fracture Rc and the bending angle θ, and input them into the data analysis model to calculate and analyze the range of proppant particle sizes corresponding to different regions of the fracture; Radius of curvature Rc Calculated by the three-point circular arc method in logging imaging technology , where Δs is the lateral offset of the fracture and Δh is the longitudinal offset; After the bending angle θ is extracted from the crack contour line by the image processing algorithm, it is calculated using the vector included angle formula: , where represents the included angle between two vectors, v1 and v2 represent the two vectors respectively, · represents the dot product operation, and |v1| and |v2| represent the magnitudes of v1 and v2 respectively.

4. A proppant injection method optimized based on particle size distribution according to claim 2, characterized in that, The obtaining of the property parameters of the rock further includes the mechanical parameters of the rock. The mechanical parameters include the elastic modulus and Poisson's ratio. When determining the influence of the rock properties on the proppant particle size distribution, through rock mechanics experiments and numerical simulations, analyzing the influence of the mechanical parameters of the rock on the proppant embedding depth and support stability, and then optimizing the proppant particle size distribution.

5. A proppant injection method optimized based on particle size distribution according to claim 4, characterized in that, The analyzing of the influence of the mechanical parameters of the rock on the proppant embedding depth and support stability through rock mechanics experiments and numerical simulations, and then optimizing the proppant particle size distribution includes: Optimization formula for mechanical parameters of rock: Proppant embedment depth The relationship with the elastic modulus E of the rock is , where σ is the proppant bearing stress, is the proppant particle size, and k is the experimental fitting coefficient; Obtain the elastic modulus E and Poisson's ratio v of the rock through triaxial rock experiments; use finite element simulation to calculate different under and support stability; select the particle size range that makes and the support stress distribution is uniform.

6. The proppant injection method based on particle size distribution optimization according to claim 1, wherein The dividing of the optimal proppant particle size distribution into multiple particle size adaptation intervals according to the width measurement values and permeability test results of different regions of the fracture includes: For a first fracture region where the fracture width is greater than a preset fracture width threshold W th and the permeability result is greater than a preset permeability threshold K th, large-size proppants are used. The particle size distribution range of the large-size proppants is determined by performing multivariate analysis using a data analysis model based on the geometric shape parameters of the first fracture region, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process; For the second fracture region where the fracture width is less than the preset fracture width threshold or the permeability result is less than the preset permeability threshold, using small-sized proppants. The range of particle size distributions of the small-sized proppants is determined by performing multivariate analysis using a data analysis model based on the geometric shape parameters of the second fracture region, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process.

7. A proppant injection method optimized based on particle size distribution according to claim 6, characterized in that, Comprising: Performing simulation experiments based on the particle size adaptation intervals and the corresponding fracture width regions; By comparing the simulation experiment results with the historical data of the fracture width regions, evaluating the accuracy of the data analysis model, and adjusting the parameters of the data analysis model according to the error.

8. A proppant injection method optimized based on particle size distribution according to claim 1, characterized in that, The performing of dynamic particle size adjustment by using downhole sensors for real-time data monitoring during the proppant injection process, judging the regions where there are abnormal proppant injection and distribution in the fracture based on real-time data analysis includes: During the injection process, using downhole sensors to monitor in real time the in-fracture pressure, flow rate, proppant concentration, and real-time data of the proppant distribution image in the fracture; Based on the real-time data analysis, an abnormal area with abnormal proppant injection and distribution in the fracture is determined, and corresponding adjustment strategies are selected according to the reasons for the abnormal proppant injection and distribution in the abnormal area, and dynamic particle size adjustment is carried out.

9. A proppant injection method optimized based on particle size distribution according to claim 8, characterized in that, The determination of the abnormal area with abnormal proppant injection and distribution in the fracture according to the real-time data analysis includes: Analyzing and determining whether there is an abnormal area of local blockage according to whether the pressure in the fracture in the real-time data is abnormal; Judging whether there is an abnormal area with abnormal fluctuation of proppant concentration according to the proppant concentration in the fracture in the real-time data; Judging whether there is an abnormal area with abnormal settling velocity of proppant according to the distribution image of proppant in the fracture in the real-time data.

10. A proppant injection method optimized based on particle size distribution according to claim 9, characterized in that, Including: Corresponding adjustment strategies are preset according to different abnormal situations; When an abnormal area with abnormal proppant injection and distribution in the fracture is determined according to the real-time data analysis, the corresponding adjustment strategy is selected, and the parameters of the proppant injection equipment are automatically adjusted, including but not limited to the discharging speed of the storage tank of proppants with different particle sizes and the opening degree of the mixing ratio control valve.

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