A method and system for proppant transport optimization based on multi-scale model

Abstract

The application discloses a proppant migration optimization method and system based on a multi-scale model, and the method comprises the following steps: a macro-micro combined proppant multi-scale migration model is constructed; numerical simulation of coupling of a fracture network and fluid behavior is carried out based on the model; field data of a target well section is acquired, and an initial prediction model is established; a special physical experiment device is used for experiment, and an initial model is inverted and checked by using experimental results, so that a high-precision prediction model is obtained; different injection schemes are simulated based on the model, and a scheme that enables proppant placement to reach an optimization target is selected as an optimized injection strategy. The application overcomes the limitation of traditional single-scale simulation, ensures the prediction accuracy of the model by physical experiment calibration, realizes intelligent and quantitative optimization of the proppant injection strategy, and can effectively improve the proppant placement uniformity, fracture conductivity and oil and gas recovery.

Description

Technical Field

[0001] This invention relates to the field of oil extraction technology, specifically to a method and system for optimizing proppant transport based on a multi-scale model. Background Technology

[0002] In today's key technology system for oil extraction, hydraulic fracturing technology, with its significant production-enhancing effect, has become one of the core means to improve the productivity of oil and gas wells. This technology injects high-pressure fluid into the formation to fracture the rock, and then proppant is injected into the fractures to maintain their open state, thereby creating channels for oil and gas flow. The quality of its effect directly has a decisive impact on the productivity of oil and gas wells.

[0003] As a key element of hydraulic fracturing technology, proppant's migration trajectory and distribution within fractures play a crucial role in the post-fracturing conductivity of the fractures. The conductivity of the fractures directly determines the smoothness of oil and gas flow from the formation to the wellbore, thus affecting oil and gas production. However, traditional proppant migration simulation methods have significant limitations. These methods often analyze proppant movement only at a single scale, such as the macroscopic fracture scale or the microscopic particle scale, failing to comprehensively and accurately reflect the true movement behavior of proppant in complex fracture systems.

[0004] In actual fracturing operations, fracture systems are extremely complex, containing not only main fractures of varying widths, lengths, and orientations, but also numerous intricate branch fractures. In such an environment, the limitations of traditional simulation methods make it difficult to achieve uniform proppant distribution, often resulting in localized accumulation and resource waste. Simultaneously, insufficient proppant at the distal ends of fractures severely restricts the improvement of fracture conductivity, thereby significantly limiting the production growth of oil and gas wells.

[0005] Therefore, developing an innovative method that can comprehensively and accurately simulate the proppant migration process and optimize the injection strategy based on the simulation results has become a key issue that urgently needs to be addressed in the field of oil extraction, and it has important practical significance for promoting the efficient development of oil and gas resources.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] To address the technical challenge of effectively monitoring pressure drop during proppant placement and migration in existing research, this invention proposes a proppant migration optimization method and system based on a multi-scale model. Specifically, the following technical solution is adopted:

[0008] A method for optimizing proppant transport based on a multi-scale model includes:

[0009] A multi-scale proppant transport model combining macroscopic fracture scale and microscopic particle scale was constructed to simulate the flow, deposition and aggregation of proppant within fractures.

[0010] Based on the aforementioned multi-scale proppant transport model, a coupled numerical simulation of fracture network and fluid behavior is performed to predict the motion trajectory and distribution of proppant particles in the fracture network.

[0011] Acquire the field downhole data of the target fracturing section and input it into the proppant multi-scale migration model to establish the initial prediction model of the target section;

[0012] A physical experiment was conducted using a multi-scale proppant placement and transport simulation physical experimental device, and the initial prediction model was inverted and verified using the physical experiment results data to obtain a calibrated high-precision prediction model.

[0013] Based on the high-precision prediction model, the proppant placement morphology under different injection parameter schemes is simulated. Based on the simulation results, the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network is selected as the optimized injection strategy.

[0014] As an optional embodiment of the present invention, in a proppant transport optimization method based on a multi-scale model, the construction of a proppant multi-scale transport model combining macroscopic crack scale and microscopic particle scale includes:

[0015] Based on the geometry and size of the fractures, a macroscopic fracture scale model is constructed using the fluid dynamics continuity equation to describe the overall pressure and velocity field distribution of the fracturing fluid in the fracture network.

[0016] Based on the discrete element method, a micro-particle scale model is constructed by establishing a contact mechanics model and motion equations of proppant particles to simulate the translation, rotation, collision and force behavior of particles in fluids.

[0017] The macroscopic crack-scale model is coupled with the microscopic particle-scale model to form a multi-scale proppant transport model, which is used to simulate proppant transport across scales from crack-level hydrodynamics to particle-level kinematics.

[0018] As an optional embodiment of the present invention, in a proppant transport optimization method based on a multi-scale model, the macroscopic crack-scale model and the microscopic particle-scale model are coupled through a bidirectional coupling mechanism, including:

[0019] The fluid velocity field and pressure field calculated by the macroscopic crack-scale model are dynamically applied as boundary conditions to each proppant particle in the microscopic particle-scale model to calculate the drag force and lift force on the particle.

[0020] The local concentration of proppant particles and momentum exchange source terms obtained from the micro-particle scale model are fed back into the fluid control equations of the macro-crack scale model to correct the fluid density, viscosity and flow resistance.

[0021] As an optional embodiment of the present invention, a proppant transport optimization method based on a multi-scale model is provided. When constructing the multi-scale proppant transport model, the micro-particle scale model considers the following physical properties of the proppant particles: particle shape, particle size distribution, density, elastic modulus, and friction coefficient and recovery coefficient between particles and between particles and crack walls.

[0022] As an optional embodiment of the present invention, in a proppant migration optimization method based on a multi-scale model, the construction of the macro-fracture scale model includes: establishing a complex fracture network geometric model containing a main fracture and multi-level branch fractures based on the heterogeneous geological characteristics of the target fractured well section, and discretizing the geometric model into a computational grid as the computational domain of the macro-fracture scale model.

[0023] The initialization of the micro-particle scale model includes: at the crack inlet boundary defined in the computational domain of the macro-crack scale model, dynamically generating a particle population with corresponding physical properties according to the set proppant particle size distribution and pumping procedure.

[0024] As an optional embodiment of the present invention, in a proppant transport optimization method based on a multi-scale model, the step of performing coupled numerical simulation of fracture network and fluid behavior based on the proppant multi-scale transport model to predict the motion trajectory and distribution of proppant particles in the fracture network includes:

[0025] The proppant multiscale transport model is spatiotemporally discretized, and the time step of the coupled calculation is set.

[0026] Within each time step, perform the following operations sequentially:

[0027] Step a: Call the macroscopic crack scale model to solve for the fluid pressure field and velocity field distribution in the current step;

[0028] Step b: Using the fluid pressure field and velocity field obtained in step a as input, call the micro-particle scale model to calculate the forces and motions of all proppant particles under the current flow field;

[0029] Step c: Based on the particle motion results calculated by the micro-particle scale model, the local concentration distribution and momentum exchange source terms of the proppant phase are statistically obtained.

[0030] Step d: Feed the local concentration distribution of the proppant phase and the momentum exchange source term obtained in step c back to the macroscopic crack-scale model to update the fluid property parameters and flow control equations for the next time step.

[0031] The above steps a to d are executed iteratively until the simulation ends, thereby obtaining the motion trajectory and spatiotemporal distribution of proppant particles in the entire fracture network.

[0032] As an optional embodiment of the present invention, in a proppant migration optimization method based on a multi-scale model, the acquisition of on-site downhole data of the target fracturing well section includes:

[0033] Geological parameters of the target fractured well section, including reservoir stress, rock mechanical properties, natural fracture development and producing layer thickness;

[0034] Fracturing construction design parameters, including design displacement, pumping procedure, and fracturing fluid performance parameters;

[0035] Prop material parameters, including proppant particle size distribution, density, and sphericity;

[0036] The establishment of the initial prediction model for the target well section includes:

[0037] Based on the geological parameters and construction design parameters in the field downhole data, the macroscopic fracture scale model in the proppant multi-scale migration model is called to construct and initialize the basic fracture network and flow calculation domain that reflect the geological characteristics and construction plan of the target well section.

[0038] Based on the proppant material parameters and construction design parameters in the downhole data, the micro-particle scale model in the multi-scale transport model of the proppant is invoked to set the initial physical properties of the particle population and the injection conditions at the inlet of the foundation fracture network.

[0039] As an optional embodiment of the present invention, in a multi-scale model-based proppant transport optimization method, the step of inverting and verifying the initial prediction model using physical experimental results data includes:

[0040] The crack geometry, construction parameters, and proppant properties from the physical experiment are simultaneously set as input conditions for the initial prediction model.

[0041] Run the initial prediction model to obtain the corresponding numerical simulation results of proppant migration;

[0042] By comparing and analyzing the numerical simulation results with the physical experiment results, the differences in key indicators were obtained.

[0043] Based on the differences in the key indicators, the preset key parameters of the micro-model in the initial prediction model are inverted and adjusted by the optimization algorithm until the differences in the key indicators meet the preset tolerance range, thereby obtaining the calibrated high-precision prediction model.

[0044] Key indicators include the leading edge transport distance of the proppant sand embankment, the equilibrium height, the morphology of the proppant profile, and the concentration distribution along the crack length.

[0045] As an optional embodiment of the present invention, in a proppant transport optimization method based on a multi-scale model, the step of selecting an injection parameter scheme that enables the proppant to achieve a preset optimization target in the fracture network, based on simulation results, as an optimized injection strategy, includes:

[0046] Based on the high-precision prediction model, multiple preset injection parameter schemes are simulated in batches to obtain the proppant placement morphology prediction results for each scheme.

[0047] Based on the preset optimization objective, an evaluation function is constructed to quantitatively score the prediction results of each scheme;

[0048] The injection parameter scheme with the highest quantitative score is selected as the optimized injection strategy;

[0049] The preset optimization objective is at least one of the following:

[0050] Maximize the uniformity of proppant distribution in the fracture network;

[0051] Maximize the coverage area of ​​the proppant on the effective fractures in the producing formation;

[0052] The overall flow-carrying capacity of the fracture network is maximized after the pump is shut down.

[0053] This invention also provides a proppant transport optimization system based on a multi-scale model, comprising:

[0054] The model building module is used to construct a multi-scale proppant transport model that combines macroscopic crack scale and microscopic particle scale.

[0055] The numerical simulation module, connected to the model building module, is used to perform coupled numerical simulation of fracture network and fluid behavior based on the proppant multi-scale transport model, so as to predict the motion trajectory and distribution of proppant particles in the fracture network.

[0056] The data acquisition and modeling module is used to acquire field downhole data of the target fracturing well section and to establish an initial prediction model of the target well section using the data and the proppant multi-scale migration model.

[0057] The physical experiment calibration module includes a multi-scale proppant placement and transport simulation physical experiment device, used to conduct physical experiments and acquire experimental result data, as well as to invert and verify the initial prediction model to obtain a calibrated high-precision prediction model.

[0058] The optimization decision module, connected to the numerical simulation module and the physical experiment calibration module, is used to simulate the proppant placement morphology under different injection parameter schemes based on the high-precision prediction model, and select the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network based on the simulation results, as the output of the optimized injection strategy.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] A high-precision, full-scale simulation of the entire proppant migration process has been achieved: by combining the macroscopic fracture scale with the microscopic particle scale, the limitations of traditional single-scale simulations have been overcome. This method can not only describe the overall flow law within the fracture, but also finely characterize the microscopic movement, interaction, and accumulation morphology of proppant particles, thus comprehensively and realistically reflecting the actual migration behavior of proppant in complex fracture networks, significantly improving the accuracy of simulation predictions.

[0061] This significantly improves the reliability and prediction confidence of the numerical model: An innovative multi-scale proppant placement and transport simulation physical experimental device was introduced to invert and verify the initial prediction model. By using high-fidelity physical experimental data as a calibration benchmark, optimization algorithms were employed to correct key parameters of the multi-scale proppant transport model, resulting in a high-precision prediction model that closely matches physical reality. This process ensures the prediction accuracy of the multi-scale proppant transport model under real geological and engineering conditions, laying a reliable foundation for subsequent optimization decisions.

[0062] This method achieves intelligent, quantitative, and optimized proppant injection strategies: based on a calibrated high-precision prediction model, it can quickly and cost-effectively simulate and evaluate a large number of different injection parameter schemes. By defining clear optimization objectives (such as proppant uniformity and conductivity) and constructing a quantitative evaluation function, it can automatically select the injection parameter combination that optimizes proppant placement, forming a scientifically optimized injection strategy. This method transforms fracturing design from qualitative judgment based on experience to quantitative optimization based on data and models, making the decision-making process more objective and efficient.

[0063] This method effectively improves the efficiency and economic benefits of fracturing operations: the optimized injection strategy generated by this method guides proppant to be more evenly and effectively distributed in the target fracture area during actual construction, especially achieving effective support in complex fracture networks and distal fractures. This directly translates into a significant increase in fracture conductivity, thereby improving oil and gas well productivity and recovery. Simultaneously, precise optimization avoids ineffective proppant accumulation and waste, reducing material costs and operational risks.

[0064] In summary, this invention forms a complete technical system of "precise modeling - experimental verification - intelligent optimization", providing a powerful solution to the industry problem of effective proppant placement in hydraulic fracturing, and has important practical significance for promoting the efficient and economical development of oil and gas resources. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the coupling of the macroscopic crack-scale model and the microscopic particle-scale model in a proppant transport optimization method based on a multi-scale model according to an embodiment of the present invention.

[0066] Figure 2 This is a flowchart illustrating the proppant injection optimization and strategy adjustment process in a multi-scale model-based proppant transport optimization method according to an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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 some embodiments of the present invention, and not all embodiments.

[0068] Therefore, the following detailed description of embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0069] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In the description of this invention, it should be noted that the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. These terms are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0072] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a proppant transport optimization method based on a multi-scale model, comprising:

[0073] A multi-scale proppant transport model combining macroscopic fracture scale and microscopic particle scale was constructed to simulate the flow, deposition and aggregation of proppant within fractures.

[0074] Based on the aforementioned multi-scale proppant transport model, a coupled numerical simulation of fracture network and fluid behavior is performed to predict the motion trajectory and distribution of proppant particles in the fracture network.

[0075] Acquire the field downhole data of the target fracturing section and input it into the proppant multi-scale migration model to establish the initial prediction model of the target section;

[0076] A physical experiment was conducted using a multi-scale proppant placement and transport simulation physical experimental device, and the initial prediction model was inverted and verified using the physical experiment results data to obtain a calibrated high-precision prediction model.

[0077] Based on the high-precision prediction model, the proppant placement morphology under different injection parameter schemes is simulated. Based on the simulation results, the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network is selected as the optimized injection strategy.

[0078] This invention provides a multi-scale model-based proppant transport optimization method. By constructing and applying a multi-scale proppant transport model that combines macroscopic crack scale and microscopic particle scale, and introducing physical experimental inversion calibration and intelligent optimization decision-making, significant technical effects are achieved, specifically reflected in the following aspects:

[0079] A high-precision, full-scale simulation of the entire proppant migration process has been achieved: by combining the macroscopic fracture scale with the microscopic particle scale, the limitations of traditional single-scale simulations have been overcome. This method can not only describe the overall flow law within the fracture, but also finely characterize the microscopic movement, interaction, and accumulation morphology of proppant particles, thus comprehensively and realistically reflecting the actual migration behavior of proppant in complex fracture networks, significantly improving the accuracy of simulation predictions.

[0080] This significantly improves the reliability and prediction confidence of the numerical model: An innovative multi-scale proppant placement and transport simulation physical experimental device was introduced to invert and verify the initial prediction model. By using high-fidelity physical experimental data as a calibration benchmark, optimization algorithms were employed to correct key parameters of the multi-scale proppant transport model, resulting in a high-precision prediction model that closely matches physical reality. This process ensures the prediction accuracy of the multi-scale proppant transport model under real geological and engineering conditions, laying a reliable foundation for subsequent optimization decisions.

[0081] This method achieves intelligent, quantitative, and optimized proppant injection strategies: based on a calibrated high-precision prediction model, it can quickly and cost-effectively simulate and evaluate a large number of different injection parameter schemes. By defining clear optimization objectives (such as proppant uniformity and conductivity) and constructing a quantitative evaluation function, it can automatically select the injection parameter combination that optimizes proppant placement, forming a scientifically optimized injection strategy. This method transforms fracturing design from qualitative judgment based on experience to quantitative optimization based on data and models, making the decision-making process more objective and efficient.

[0082] This method effectively improves the efficiency and economic benefits of fracturing operations: the optimized injection strategy generated by this method guides proppant to be more evenly and effectively distributed in the target fracture area during actual construction, especially achieving effective support in complex fracture networks and distal fractures. This directly translates into a significant increase in fracture conductivity, thereby improving oil and gas well productivity and recovery. Simultaneously, precise optimization avoids ineffective proppant accumulation and waste, reducing material costs and operational risks.

[0083] In summary, this invention forms a complete technical system of "precise modeling - experimental verification - intelligent optimization", providing a powerful solution to the industry problem of effective proppant placement in hydraulic fracturing, and has important practical significance for promoting the efficient and economical development of oil and gas resources.

[0084] See Figure 1 As shown in this embodiment, in a proppant transport optimization method based on a multi-scale model, the construction of a proppant multi-scale transport model combining macroscopic crack scale and microscopic particle scale includes:

[0085] Based on the geometry and size of the fractures, a macroscopic fracture scale model is constructed using the fluid dynamics continuity equation to describe the overall pressure and velocity field distribution of the fracturing fluid in the fracture network.

[0086] Based on the discrete element method, a micro-particle scale model is constructed by establishing a contact mechanics model and motion equations of proppant particles to simulate the translation, rotation, collision and force behavior of particles in fluids.

[0087] The macroscopic crack-scale model is coupled with the microscopic particle-scale model to form a multi-scale proppant transport model, which is used to simulate proppant transport across scales from crack-level hydrodynamics to particle-level kinematics.

[0088] Specifically, the macroscopic crack-scale model and the microscopic particle-scale model described in this embodiment are coupled through a two-way coupling mechanism, including:

[0089] The fluid velocity field and pressure field calculated by the macroscopic crack-scale model are dynamically applied as boundary conditions to each proppant particle in the microscopic particle-scale model to calculate the drag force and lift force on the particle.

[0090] The local concentration of proppant particles and momentum exchange source terms obtained from the micro-particle scale model are fed back into the fluid control equations of the macro-crack scale model to correct the fluid density, viscosity and flow resistance.

[0091] In the proppant transport optimization method of this embodiment, when constructing the multi-scale proppant transport model, the micro-particle scale model considers the following proppant particle physical properties: particle shape, particle size distribution, density, elastic modulus, and friction coefficient and recovery coefficient between particles and between particles and crack walls.

[0092] Optionally, the bidirectional coupling mechanism described in this embodiment is implemented through a coupled solver, which performs an iterative process of solving the macroscopic flow field, calculating the force on particles and updating their motion, calculating the momentum exchange between phases, and correcting the flow field in each computation time step.

[0093] Furthermore, in the proppant migration optimization method of this embodiment, the physical phenomena that the multi-scale proppant migration model can simulate also include: the bridging effect of proppant at narrow fractures, the migration reversal at fracture junctions, and the time-varying concentration effect under the influence of fracturing fluid filtration.

[0094] At the on-site implementation level, in a proppant migration optimization method based on a multi-scale model in this embodiment, the construction of the macro-fracture scale model includes: establishing a complex fracture network geometric model containing main fractures and multi-level branch fractures based on the heterogeneous geological characteristics of the target fractured well section, and discretizing the geometric model into a computational grid as the computational domain of the macro-fracture scale model.

[0095] The initialization of the micro-particle scale model includes: at the crack inlet boundary defined in the computational domain of the macro-crack scale model, dynamically generating a particle population with corresponding physical properties according to the set proppant particle size distribution and pumping procedure.

[0096] In this embodiment, a proppant transport optimization method based on a multi-scale model includes performing coupled numerical simulations of fracture network and fluid behavior based on the proppant multi-scale transport model to predict the trajectory and distribution of proppant particles in the fracture network. This includes:

[0097] The proppant multiscale transport model is spatiotemporally discretized, and the time step of the coupled calculation is set.

[0098] Within each time step, perform the following operations sequentially:

[0099] Step a: Call the macroscopic crack scale model to solve for the fluid pressure field and velocity field distribution in the current step;

[0100] Step b: Using the fluid pressure field and velocity field obtained in step a as input, call the micro-particle scale model to calculate the forces and motions of all proppant particles under the current flow field;

[0101] Step c: Based on the particle motion results calculated by the micro-particle scale model, the local concentration distribution and momentum exchange source terms of the proppant phase are statistically obtained.

[0102] Step d: Feed the local concentration distribution of the proppant phase and the momentum exchange source term obtained in step c back to the macroscopic crack-scale model to update the fluid property parameters and flow control equations for the next time step.

[0103] The above steps a to d are executed iteratively until the simulation ends, thereby obtaining the motion trajectory and spatiotemporal distribution of proppant particles in the entire fracture network.

[0104] Specifically, the coupled numerical simulation described in this embodiment dynamically incorporates the changes in fluid volume within the fracture caused by fracturing fluid loss, the changes in local flow resistance caused by fracture wall roughness and tortuosity, and the effects of proppant concentration on the viscosity of the mixed fluid and particle settling velocity during the simulation process.

[0105] The proppant migration behavior that can be predicted by the coupled numerical simulation described in this embodiment includes:

[0106] The proppant causes bridging and blockage effects in narrow cracks due to the ratio of particle size to crack width;

[0107] Propionage shifts and redistributions at crack branch junctions due to abrupt changes in the flow field;

[0108] The layered placement phenomenon along the crack due to the difference in settling velocity of proppant with different particle sizes.

[0109] See Figure 2 As shown in this embodiment, in a proppant migration optimization method based on a multi-scale model, the acquisition of on-site downhole data for the target fracturing well section includes:

[0110] Geological parameters of the target fractured well section, including reservoir stress, rock mechanical properties, natural fracture development and producing layer thickness;

[0111] Fracturing construction design parameters, including design displacement, pumping procedure, and fracturing fluid performance parameters;

[0112] Prop material parameters, including proppant particle size distribution, density, and sphericity;

[0113] The establishment of the initial prediction model for the target well section includes:

[0114] Based on the geological parameters and construction design parameters in the field downhole data, the macroscopic fracture scale model in the proppant multi-scale migration model is called to construct and initialize the basic fracture network and flow calculation domain that reflect the geological characteristics and construction plan of the target well section.

[0115] Based on the proppant material parameters and construction design parameters in the downhole data, the micro-particle scale model in the multi-scale transport model of the proppant is invoked to set the initial physical properties of the particle population and the injection conditions at the inlet of the foundation fracture network.

[0116] Specifically, the downhole data mentioned in this embodiment includes data monitored in real time by downhole sensors during fracturing operations; the establishment of the initial prediction model is a dynamic process, which includes continuously inputting the real-time monitoring data into the proppant multi-scale transport model to update the fluid pressure field, fracture width field, and the position and concentration of the pumped proppant in the model in real time.

[0117] In the proppant transport optimization method of this embodiment, the multi-scale proppant placement and transport simulation physical experimental device can simulate a real crack environment, including:

[0118] Two-dimensional or three-dimensional crack networks with specific widths, roughness, and tortuosity paths can be formed through adjustable transparent crack plates.

[0119] The formation filtration effect is simulated by a filtration control system connected to the fracture plate wall.

[0120] The device is equipped with a high-speed camera and particle image velocimetry system, which is used to observe and record the proppant's migration trajectory, placement morphology and concentration distribution in the crack in real time, so as to obtain the physical experiment results data.

[0121] See Figure 2 As shown in this embodiment, in a proppant transport optimization method based on a multi-scale model, the step of inverting and verifying the initial prediction model using physical experimental results data includes:

[0122] The crack geometry, construction parameters, and proppant properties from the physical experiment are simultaneously set as input conditions for the initial prediction model.

[0123] Run the initial prediction model to obtain the corresponding numerical simulation results of proppant migration;

[0124] By comparing and analyzing the numerical simulation results with the physical experiment results, the differences in key indicators were obtained.

[0125] Based on the differences in the key indicators, the preset key parameters of the micro-model in the initial prediction model are inverted and adjusted by the optimization algorithm until the differences in the key indicators meet the preset tolerance range, thereby obtaining the calibrated high-precision prediction model.

[0126] Key indicators include the leading edge transport distance of the proppant sand embankment, the equilibrium height, the morphology of the proppant profile, and the concentration distribution along the crack length.

[0127] Optionally, the preset tolerance range is such that the relative error of the key indicator is less than 5%.

[0128] See Figure 2 As shown in this embodiment, in a proppant transport optimization method based on a multi-scale model, the step of selecting an injection parameter scheme that enables the proppant to achieve a preset optimization target in the fracture network, based on simulation results, as an optimized injection strategy, includes:

[0129] Based on the high-precision prediction model, multiple preset injection parameter schemes are simulated in batches to obtain the proppant placement morphology prediction results for each scheme.

[0130] Based on the preset optimization objective, an evaluation function is constructed to quantitatively score the prediction results of each scheme;

[0131] The injection parameter scheme with the highest quantitative score is selected as the optimized injection strategy;

[0132] The preset optimization objective is at least one of the following:

[0133] Maximize the uniformity of proppant distribution in the fracture network;

[0134] Maximize the coverage area of ​​the proppant on the effective fractures in the producing formation;

[0135] The overall flow-carrying capacity of the fracture network is maximized after the pump is shut down.

[0136] Optionally, the multiple preset injection parameter schemes described in this embodiment are generated through orthogonal experimental design or parameter scanning. The variables include construction discharge rate, average particle size of proppant, ratio of pre-treatment liquid to sand-carrying liquid in the stage, and step change program of proppant concentration in the sand-carrying liquid stage.

[0137] The optimized injection strategy described in this embodiment includes a dynamically adjusted pumping procedure that specifies real-time changes in the required injection rate, proppant type, and sand ratio during different time periods of fracturing operations.

[0138] This invention also provides a proppant transport optimization system based on a multi-scale model, comprising:

[0139] The model building module is used to construct a multi-scale proppant transport model that combines macroscopic crack scale and microscopic particle scale.

[0140] The numerical simulation module, connected to the model building module, is used to perform coupled numerical simulation of fracture network and fluid behavior based on the proppant multi-scale transport model, so as to predict the motion trajectory and distribution of proppant particles in the fracture network.

[0141] The data acquisition and modeling module is used to acquire field downhole data of the target fracturing well section and to establish an initial prediction model of the target well section using the data and the proppant multi-scale migration model.

[0142] The physical experiment calibration module includes a multi-scale proppant placement and transport simulation physical experiment device, used to conduct physical experiments and acquire experimental result data, as well as to invert and verify the initial prediction model to obtain a calibrated high-precision prediction model.

[0143] The optimization decision module, connected to the numerical simulation module and the physical experiment calibration module, is used to simulate the proppant placement morphology under different injection parameter schemes based on the high-precision prediction model, and select the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network based on the simulation results, as the output of the optimized injection strategy.

[0144] To address the technical challenge of effectively monitoring pressure drop during proppant placement and migration in existing research, this invention innovatively proposes a multi-scale model-based method and system for optimizing proppant migration. Breaking away from the limitations of traditional single-scale simulations, it constructs a multi-scale proppant migration model combining macroscopic fracture scale and microscopic particle scale. Combined with a multi-fracture, multi-scale proppant migration plate device capable of simulating realistic filtration and tortuous processes, it performs inversion and process optimization of proppant accumulation in real fractures. Through precise simulation and optimized injection strategies, it significantly improves the efficiency and effectiveness of hydraulic fracturing operations, reduces proppant waste, and lowers operating costs. Simultaneously, it increases oil and gas well productivity and enhances oil and gas recovery rates, providing strong technical support for the efficient and sustainable development of oil and gas resources and helping the petroleum extraction industry move towards a more efficient and environmentally friendly direction.

[0145] Example 1: Homogeneous stratum with a single crack.

[0146] Data details: In an oil and gas well in a homogeneous formation, the target fracture is a single vertical fracture, 100 meters long, with an average width of 5 millimeters. The rock's elastic modulus is 30 GPa, and Poisson's ratio is 0.25. The selected proppant has a particle size of 0.5-0.8 mm, a density of 2.6 g / cm³, a flow rate of 8 m³ / min, and is a medium-viscosity fracturing fluid.

[0147] Multi-scale physical model inversion: Downhole proppant placement inversion is achieved using a multi-scale proppant placement and migration simulation physical experimental device.

[0148] Model Coupling: Based on the collected downhole data of the target fractured well section, the data is input into the aforementioned multi-scale proppant migration model to establish an initial prediction model for the target well section. At the macroscopic fracture scale, fluid flow within the fracture is described using hydrodynamic equations, while at the microscopic particle scale, the discrete element method is used to simulate proppant particle movement. Simulation results show that, under conventional injection parameters, proppant accumulation is minimal at the distal fracture end.

[0149] Strategy optimization and results: By adjusting the injection strategy through model adjustments, the pumping rate was increased by 20%. After actual fracturing operations, microseismic monitoring and production data analysis showed that the fracture conductivity improved by 30%, and oil and gas production increased by 25% compared to expectations.

[0150] Example 2: Complex fracture network in heterogeneous strata.

[0151] Data characteristics: The heterogeneous strata contain a complex fracture network with varied fracture orientations and numerous branches; some fractures vary in width between 1 and 3 mm. Rock mechanical parameters differ across regions, with elastic modulus ranging from 20 to 40 GPa and Poisson's ratio from 0.2 to 0.3. Different proppant particle size combinations were selected, and high-viscosity fracturing fluid was chosen to adapt to the complex fractures.

[0152] Multi-scale physical model inversion: Downhole proppant placement inversion is achieved using a multi-scale proppant placement and migration simulation physical experimental device.

[0153] Model Construction and Simulation: The crack network of the proppant multi-scale transport model was discretized using the finite element method (FEM), and coupled simulations were performed using fluid flow equations and the discrete element method. The simulation revealed that under conventional injection strategies, the proppant is unevenly distributed in narrow cracks and branches.

[0154] Strategy Optimization and Results: The injection strategy was optimized by dynamically adjusting the pumping rate and proppant particle size based on fracture width and orientation. In narrow fracture zones, the pumping rate was reduced and smaller-diameter proppant was used, while in wide fractures and the main fracture zone, the pumping rate was increased and larger-diameter proppant was used. After actual fracturing, the overall conductivity of the fracture network increased by 40%, and oil and gas production significantly improved, verifying the effectiveness of the method under complex geological conditions.

[0155] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and 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 method for optimizing proppant transport based on a multi-scale model, characterized in that, include: Based on the geometry and size of the fractures, a macroscopic fracture scale model is constructed using the fluid dynamics continuity equation to describe the overall pressure and velocity field distribution of the fracturing fluid in the fracture network. The construction of the macroscopic fracture scale model includes: establishing a complex fracture network geometric model containing the main fracture and multi-level branch fractures according to the heterogeneous geological characteristics of the target fracturing well section, and discretizing the geometric model into a computational grid as the computational domain of the macroscopic fracture scale model. Based on the discrete element method, a micro-particle scale model is constructed by establishing a contact mechanics model and motion equations of proppant particles to simulate the translation, rotation, collision and force behavior of particles in fluid. The initialization of the micro-particle scale model includes: at the crack inlet boundary defined in the computational domain of the macro-crack scale model, a particle group with corresponding physical properties is dynamically generated according to the set proppant particle size distribution and pumping program. The macroscopic fracture-scale model is coupled with the microscopic particle-scale model to form a multi-scale proppant transport model, which simulates the flow, deposition and aggregation of proppant in fractures, and is used to realize cross-scale proppant transport simulation from fracture-level fluid dynamics to particle-level kinematics. Based on the aforementioned multi-scale proppant transport model, a coupled numerical simulation of fracture network and fluid behavior is performed to predict the motion trajectory and distribution of proppant particles in the fracture network. This includes: spatiotemporally discretizing the multi-scale proppant transport model, setting the time step for the coupled calculation, and performing the following operations sequentially within each time step: Step a, calling the macroscopic fracture-scale model to solve for the fluid pressure and velocity field distribution of the current step; Step b, using the fluid pressure and velocity fields obtained in Step a as input, calling the microscopic particle-scale model to calculate the forces and motions of all proppant particles under the current flow field; Step c, based on the particle motion results calculated by the microscopic particle-scale model, statistically obtaining the local concentration distribution and momentum exchange source term of the proppant phase; Step d, feeding back the local concentration distribution and momentum exchange source term of the proppant phase obtained in Step c to the macroscopic fracture-scale model to update the fluid property parameters and flow control equations for the next time step. The above steps a to d are iteratively executed until the simulation ends, thereby obtaining the motion trajectory and spatiotemporal distribution of proppant particles in the entire fracture network. Acquire the field downhole data of the target fracturing section and input it into the proppant multi-scale migration model to establish the initial prediction model of the target section; A physical experiment was conducted using a multi-scale proppant placement and transport simulation physical experimental device, and the initial prediction model was inverted and verified using the physical experiment results data to obtain a calibrated high-precision prediction model. Based on the high-precision prediction model, the proppant placement morphology under different injection parameter schemes is simulated. Based on the simulation results, the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network is selected as the optimized injection strategy.

2. The proppant transport optimization method based on a multi-scale model according to claim 1, characterized in that, The macroscopic crack-scale model and the microscopic particle-scale model are coupled through a two-way coupling mechanism, including: The fluid velocity field and pressure field calculated by the macroscopic crack-scale model are dynamically applied as boundary conditions to each proppant particle in the microscopic particle-scale model to calculate the drag force and lift force on the particle. The local concentration of proppant particles and momentum exchange source terms obtained from the micro-particle scale model are fed back into the fluid control equations of the macro-crack scale model to correct the fluid density, viscosity and flow resistance.

3. The proppant transport optimization method based on a multi-scale model according to claim 1, characterized in that, The micro-particle scale model considers the following physical properties of the proppant particles: particle shape, particle size distribution, density, elastic modulus, and friction coefficients and recovery coefficients between particles and between particles and crack walls.

4. The proppant transport optimization method based on a multi-scale model according to claim 1, characterized in that, The acquisition of on-site downhole data for the target fracturing section includes: Geological parameters of the target fractured well section, including reservoir stress, rock mechanical properties, natural fracture development and producing layer thickness; Fracturing construction design parameters, including design displacement, pumping procedure, and fracturing fluid performance parameters; Prop material parameters, including proppant particle size distribution, density, and sphericity; The establishment of the initial prediction model for the target well section includes: Based on the geological parameters and construction design parameters in the field downhole data, the macroscopic fracture scale model in the proppant multi-scale migration model is called to construct and initialize the basic fracture network and computational domain that reflect the geological characteristics and construction plan of the target well section. Based on the proppant material parameters and construction design parameters in the downhole data, the micro-particle scale model in the multi-scale transport model of the proppant is invoked to set the initial physical properties of the particle population and the injection conditions at the inlet of the foundation fracture network.

5. The proppant transport optimization method based on a multi-scale model according to claim 4, characterized in that, The process of inverting and verifying the initial prediction model using physical experiment results data includes: The crack geometry, construction parameters, and proppant properties from the physical experiment are simultaneously set as input conditions for the initial prediction model. Run the initial prediction model to obtain the corresponding numerical simulation results of proppant migration; By comparing and analyzing the numerical simulation results with the physical experiment results, the differences in key indicators were obtained. Based on the differences in the key indicators, the preset key parameters of the micro-model in the initial prediction model are inverted and adjusted by the optimization algorithm until the differences in the key indicators meet the preset tolerance range, thereby obtaining the calibrated high-precision prediction model. Key indicators include the leading edge transport distance of the proppant sand embankment, the equilibrium height, the morphology of the proppant profile, and the concentration distribution along the crack length.

6. The proppant transport optimization method based on a multi-scale model according to claim 5, characterized in that, The step of selecting an injection parameter scheme that enables the proppant to achieve a preset optimization target in the fracture network based on simulation results, as an optimized injection strategy, includes: Based on the high-precision prediction model, multiple preset injection parameter schemes are simulated in batches to obtain the proppant placement morphology prediction results for each scheme. Based on the preset optimization objective, an evaluation function is constructed to quantitatively score the prediction results of each scheme; The injection parameter scheme with the highest quantitative score is selected as the optimized injection strategy; The preset optimization objective is at least one of the following: Maximize the uniformity of proppant distribution in the fracture network; Maximize the coverage area of ​​the proppant on the effective fractures in the producing formation; The overall flow-carrying capacity of the fracture network is maximized after the pump is shut down.

7. A proppant transport optimization system based on a multi-scale model, characterized in that, include: The model building module, based on the geometry and size of the fractures, uses the fluid dynamics continuity equation to construct a macroscopic fracture-scale model to describe the overall pressure and velocity field distribution of the fracturing fluid in the fracture network. The construction of the macroscopic fracture-scale model includes: establishing a complex fracture network geometric model containing the main fracture and multi-level branch fractures according to the heterogeneous geological characteristics of the target fracturing well section, and discretizing the geometric model into a computational grid as the computational domain of the macroscopic fracture-scale model. Based on the discrete element method, a micro-particle scale model is constructed by establishing a contact mechanics model and motion equations of proppant particles to simulate the translation, rotation, collision and force behavior of particles in fluid. The initialization of the micro-particle scale model includes: at the crack inlet boundary defined in the computational domain of the macro-crack scale model, a particle group with corresponding physical properties is dynamically generated according to the set proppant particle size distribution and pumping program. The macroscopic fracture-scale model is coupled with the microscopic particle-scale model to form a multi-scale proppant transport model, which simulates the flow, deposition and aggregation of proppant in fractures, and is used to realize cross-scale proppant transport simulation from fracture-level fluid dynamics to particle-level kinematics. The numerical simulation module, connected to the model building module, is used to perform coupled numerical simulation of fracture network and fluid behavior based on the proppant multi-scale transport model, so as to predict the motion trajectory and distribution of proppant particles in the fracture network. The data acquisition and modeling module is used to acquire on-site downhole data of the target fractured well section and establish an initial prediction model of the target well section using the data and the proppant multi-scale migration model. This includes: spatiotemporally discretizing the proppant multi-scale migration model, setting the time step for coupled calculations, and performing the following operations sequentially within each time step: Step a, calling the macroscopic fracture-scale model to solve for the fluid pressure and velocity field distributions of the current step; Step b, using the fluid pressure and velocity fields obtained in Step a as input, calling the microscopic particle-scale model to calculate the forces and motions of all proppant particles under the current flow field; Step c, based on the particle motion results calculated by the microscopic particle-scale model, statistically obtaining the local concentration distribution and momentum exchange source terms of the proppant phase; Step d, feeding back the local concentration distribution and momentum exchange source terms of the proppant phase obtained in Step c to the macroscopic fracture-scale model to update the fluid property parameters and flow control equations for the next time step. Steps a to d are iteratively executed until the simulation ends, thereby obtaining the motion trajectory and spatiotemporal distribution of proppant particles in the entire fracture network. The physical experiment calibration module includes a multi-scale proppant placement and transport simulation physical experiment device, used to conduct physical experiments and acquire experimental result data, as well as to invert and verify the initial prediction model to obtain a calibrated high-precision prediction model. The optimization decision module, connected to the numerical simulation module and the physical experiment calibration module, is used to simulate the proppant placement morphology under different injection parameter schemes based on the high-precision prediction model, and select the injection parameter scheme that enables the proppant to achieve the preset optimization target in the fracture network based on the simulation results, as the output of the optimized injection strategy.

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