Real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs

Abstract

The present invention relates to the technical field of oil and natural gas exploitation, and discloses a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, including: a real-time monitoring module, which is used to collect crack geometric morphology parameters and construction operation parameters in real time during crack expansion; a nonlinear modeling module, which is connected to the real-time monitoring module, and is used to construct a nonlinear dynamic model of crack expansion according to the collected crack expansion parameters and construction operation parameters, and output a predicted value of crack morphology; an optimization control module, which is connected to the nonlinear modeling module, and is used to calculate the optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of fracturing fluid. The present invention realizes precise control of crack expansion, intelligent optimization of construction parameters and efficiency improvement through the closed-loop linkage of real-time monitoring, nonlinear modeling, optimization control and feedback execution.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and natural gas exploitation, and in particular to a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs. Background Art

[0002] As global energy demand continues to grow, traditional fossil fuels such as oil and natural gas will continue to occupy an important position in the future energy structure. However, the development of conventional oil and gas resources has gradually become saturated, and the development demand for unconventional oil and gas resources such as low-permeability reservoirs and shale gas fields has become increasingly prominent. Hydraulic fracturing technology, as an efficient production-increasing measure, greatly improves the permeability and oil and gas recovery rate of the reservoir by injecting high-pressure fluids into the reservoir and forming a fracture network. Therefore, how to accurately control the fracture morphology under complex reservoir conditions, optimize the conductivity of the fracture, and reduce construction energy consumption has become an important issue in improving the efficiency of hydraulic fracturing technology.

[0003] In the existing hydraulic fracturing technology, the monitoring and construction control of fracture geometry have made certain progress. For example, microseismic monitoring technology can accurately capture the expansion path and length of the fracture through seismic wave signals, optical fiber sensing technology can obtain the temperature field and strain field distribution in the fracture in real time, and the monitoring method of injection pressure and flow rate can directly reflect the dynamic changes of the internal pressure of the fracture. The application of these technologies has significantly improved the accuracy of fracture expansion monitoring and the integrity of information, providing the necessary data support for the construction control of fracture morphology.

[0004] Although existing technologies have made some progress in fracture extension monitoring and parameter control, there are still many shortcomings that cannot be ignored. First, existing monitoring methods mostly rely on a single data source. Although they have high accuracy in specific directions, they cannot fully reflect the dynamic expansion characteristics of fracture geometry. For example, microseismic monitoring cannot directly measure fracture width and conductivity, and fiber optic sensing has limited feedback on fracture complexity. This data limitation greatly affects the accuracy of construction regulation. Second, existing fracture morphology prediction technologies are mostly based on static models or empirical formulas, which fail to consider the nonlinear coupling characteristics of reservoir conditions and fracture extension, resulting in low adaptability and prediction accuracy of the model. Finally, the regulation of construction parameters usually adopts fixed preset values, which cannot be adjusted in real time to respond to the dynamic changes of fracture extension. This lag directly leads to problems such as difficulty in optimizing fracture morphology, high construction energy consumption, and uneven distribution of proppant. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, which solves the problems in the prior art of single fracture geometry monitoring data, lack of nonlinear dynamic adaptability of the prediction model, and lagging construction parameter control.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, comprising:

[0007] Real-time monitoring module, used to collect crack geometry parameters and construction operation parameters in real time during crack expansion;

[0008] A nonlinear modeling module, connected to the real-time monitoring module, is used to construct a nonlinear dynamic model of crack extension according to the collected crack extension parameters and construction operation parameters, and output a predicted value of crack morphology;

[0009] An optimization and control module, connected to the nonlinear modeling module, for calculating optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of the fracturing fluid;

[0010] The feedback execution module is connected to the optimization control module and is used to dynamically adjust the fracturing construction equipment to optimize the crack morphology according to the construction parameters output by the optimization control module.

[0011] Preferably, the real-time monitoring module includes:

[0012] The microseismic monitoring submodule is used to collect microseismic event data generated during the crack expansion process to determine the expansion path and length of the crack;

[0013] The optical fiber sensing submodule is used to collect the temperature field and strain field information inside the crack in real time to obtain the change of the crack width;

[0014] The pressure and flow rate monitoring submodule is used to obtain real-time data of injection pressure and injection flow rate.

[0015] Preferably, the nonlinear modeling module includes:

[0016] The physical modeling submodule is used to construct a nonlinear dynamic model of crack propagation, including the coupling relationship between crack length, width, and complexity with time and space;

[0017] A parameter updating submodule, used to update key parameters in the dynamic model according to the data collected by the real-time monitoring module, including formation elastic modulus, fluid viscosity, net pressure and conductivity;

[0018] The dynamic prediction submodule is used to calculate the predicted values ​​of crack length, width and complexity based on the updated dynamic model.

[0019] Preferably, the physical modeling submodule of the nonlinear modeling module constructs a nonlinear dynamic model of crack propagation, wherein:

[0020] The change in fracture length is calculated based on the relationship between the formation elastic modulus, injection pressure, and geostress;

[0021] The change in fracture width is calculated based on the conductivity of the fracture net pressure field and the reservoir stress distribution;

[0022] The variation in fracture complexity is calculated using the spatial gradients of fracture length and width.

[0023] Preferably, the optimization and control module includes:

[0024] The objective function design submodule is used to construct an objective function for comprehensive crack morphology optimization and construction cost control, wherein the objective function includes optimization targets for crack length, width, complexity, and energy consumption;

[0025] Constraint module, used to set physical constraints and stability constraints on injection pressure, flow rate, fracture width;

[0026] The control strategy solving submodule is used to calculate the optimal injection pressure, flow rate and fracturing fluid viscosity based on the optimal control theory, and output the time series of construction parameters.

[0027] Preferably, the objective function constructed by the objective function design submodule in the optimization and control module includes:

[0028] Maximizing crack length, width, and complexity;

[0029] Minimize the impact of injection pressure and flow rate on construction energy consumption;

[0030] The dynamic changes of comprehensive construction costs and the optimal relationship between crack morphology.

[0031] Preferably, the feedback execution module includes:

[0032] The construction control submodule is used to dynamically adjust the pump pressure and injection flow rate according to the construction parameters output by the optimization control module;

[0033] The fracturing fluid preparation submodule is used to adjust the viscosity and sand carrying ratio of the fracturing fluid to optimize the supporting effect of the fracture morphology;

[0034] The feedback monitoring submodule is used to feed back the results of the construction adjustment to the real-time monitoring module in real time to achieve closed-loop control.

[0035] Preferably, the data collected by the real-time monitoring module is used to correct the reservoir parameters in the nonlinear modeling module through the parameter updating submodule, wherein the reservoir parameters include:

[0036] Reservoir elastic modulus, used to characterize the mechanical response of rocks;

[0037] Fluid viscosity, which is used to describe the internal friction properties of the fracturing fluid;

[0038] Net pressure, which describes the difference between the pressure inside the fracture and the ground stress;

[0039] Fracture conductivity is used to characterize the resistance of the fracture to the flow of fracturing fluid.

[0040] Preferably, the step of adjusting the pump pressure, injection flow rate and fracturing fluid viscosity by the feedback execution module includes:

[0041] Adjust the injection pressure and injection flow rate according to the time series output by the optimization control module;

[0042] Dynamically optimize the viscosity of the fracturing fluid based on real-time crack morphology monitoring results;

[0043] The distribution of proppant in the fracture is controlled by adjusting the sand carrying ratio, thus improving the conductivity of the fracture.

[0044] Preferably, the oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system constitutes a closed-loop system, wherein:

[0045] The monitoring data provided by the real-time monitoring module updates the crack propagation model in real time through the nonlinear modeling module;

[0046] The optimization and control module generates optimal construction parameters according to the modeling results;

[0047] The feedback execution module dynamically adjusts the construction operation according to the output of the optimization and control module, and feeds back the construction results to the real-time monitoring module in real time to achieve real-time optimization and control.

[0048] The present invention provides a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs. It has the following beneficial effects:

[0049] 1. The present invention combines real-time monitoring, nonlinear modeling and dynamic optimization and control to continuously obtain accurate geometric morphology information during the crack expansion process, predict the dynamic behavior of the crack, and optimize the construction parameters to achieve precise real-time control. Compared with the existing technology, it breaks through the limitations of parameter hysteresis and insufficient model adaptability, and greatly improves the accuracy of crack morphology prediction and construction efficiency.

[0050] 2. The present invention links the optimization parameters with the actual construction through the feedback execution module to form a closed-loop control system. The construction parameters can be adjusted in real time, including injection pressure, flow rate and fracturing fluid viscosity, directly acting on the key nodes of crack expansion. Different from the existing one-way control construction mode, this solution significantly improves the fracture conductivity and support effect, ensuring the stability and accuracy of the construction process.

[0051] 3. The present invention balances the optimization of fracture geometry and the control of construction energy consumption by constructing a multi-objective optimization function. The construction parameters are dynamically optimized to significantly improve the complexity and conductivity of the fractures while effectively reducing the construction cost. Compared with the traditional method of adjusting parameters based on experience, it breaks through the bottleneck of excessive construction energy consumption and uncontrollable fracture morphology, and realizes an efficient and economical hydraulic fracturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a module architecture diagram of the present invention;

[0053] Figure 2 This is a module architecture diagram of the real-time monitoring module of the present invention;

[0054] Figure 3 A module framework diagram for nonlinear modeling of the present invention;

[0055] Figure 4 The module architecture diagram of the optimization control module of the present invention;

[0056] Figure 5 This is a module architecture diagram of the feedback execution module of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Please refer to the attached Figure 1 -Attached Figure 5 The embodiment of the present invention provides a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, including:

[0059] Real-time monitoring module, used to collect crack geometry parameters and construction operation parameters in real time during crack expansion;

[0060] During the hydraulic fracturing construction process, the real-time monitoring module is used to obtain key geometric parameters (such as crack length, width, complexity, etc.) and construction parameters (such as injection pressure, flow rate, etc.) in the dynamic process of crack expansion. These data provide direct input for crack dynamic modeling and subsequent optimization and regulation, and form an important data source for closed-loop control. In general, the design of the real-time monitoring module must meet the requirements of high precision, real-time and data integrity to ensure the accuracy of crack expansion prediction and construction regulation. In the technical solution of the present invention, the real-time monitoring module combines a variety of monitoring methods to achieve multi-dimensional capture of dynamic changes in cracks.

[0061] In this embodiment, the real-time monitoring module mainly includes a microseismic monitoring submodule, an optical fiber sensing submodule, and a pressure and flow rate monitoring submodule. Each submodule obtains real-time data during the construction process from different angles and transmits the data to the subsequent modeling module through a unified interface. The following is a detailed description of it.

[0062] In the microseismic monitoring submodule, this submodule is used to capture microseismic events caused by formation rupture at the crack tip and inside the crack network during hydraulic fracturing. Microseismic signals are recorded by seismic detectors (such as ground detector arrays or downhole detectors) arranged near the well site, and the spatial position of crack extension is obtained using event location algorithms. Specifically, the dynamic change of the crack length L(t) can be inferred based on the source mechanism analysis method of microseismic. In general, the real-time estimation of the crack length can be obtained by the formula:

[0063]

[0064] Among them, x1, y1 and x2, y2 are the two boundary coordinates of the microseismic event. By calculating the cumulative number of microseismic events, the complexity of the fracture network C(t) can also be inferred; as an option, the microseismic signal can be further enhanced through waveform inversion technology to improve the measurement accuracy of the fracture geometry.

[0065] The fiber optic sensing submodule uses distributed fiber optic sensing technology and is installed in or around the wellbore to obtain the temperature field and strain field distribution information inside the crack. Specifically, the fiber optic sensor monitors the formation temperature change and strain field gradient caused by the crack expansion, and inverts the real-time change of the crack width W(t). As an implementation method, fiber Bragg grating (FBG) technology or distributed acoustic sensing (DAS) technology can be used. For example, in fiber Bragg grating technology, the crack width can be calculated by the following formula:

[0066] W(t)=k·Δ∈

[0067] Where k is the sensor calibration coefficient, and Δ∈ represents the change in strain in the optical fiber. In general, the change in strain is a direct reflection of the change in fracture aperture. As an extension, the temperature gradient information obtained in real time by the optical fiber sensor can also be combined with the reservoir geological model to estimate the change in fracture conductivity.

[0068] The pressure and flow rate monitoring submodule obtains the injection pressure P through sensors installed in the surface pipeline system or wellhead. frac(t) and injection flow rate Q(t). Among them, the injection pressure reflects the driving effect of the fracturing fluid at the crack tip and inside the crack, and is usually collected by a high-frequency pressure sensor. The injection flow rate is a direct expression of the fracturing fluid injection rate. Combining the two, the net pressure of the crack can be calculated:

[0069] P net (t) = P frac (t)-P closure

[0070] Among them, P closure is the closure pressure of the formation, and its value can be predetermined according to the reservoir stress state. As an option, the injection flow rate Q(t) data can be further combined with the optical fiber sensing data to verify whether the flow of the fracturing fluid in the fracture meets the design requirements.

[0071] The real-time monitoring module in this embodiment realizes all-round monitoring of the crack geometry and construction parameters during the crack expansion process through the collaborative work of the above three sub-modules. In one possible implementation, the collected data can be integrated and preliminarily processed by the data processing system, thereby reducing the data processing pressure of the subsequent modeling module. For example, the positioning results of microseismic events can be used in conjunction with the strain field distribution of optical fiber sensing to form a more complete description of the crack morphology. For another example, the injection pressure and flow rate monitoring data can be used to verify whether the results of the optical fiber sensor conform to the laws of physics.

[0072] As an extension, in this embodiment, the collection of reservoir geological characteristic data can also be added to the monitoring module. For example, the elastic modulus E and permeability κ of the reservoir are obtained through well wall sampling or formation resistivity imaging. These data can be used as initial input parameters of the physical model in the subsequent modeling module, thereby improving the modeling accuracy. Generally, the initial values ​​of these reservoir parameters can be determined by geological modeling before construction, and updated in real time according to the monitoring data during the construction process.

[0073] A nonlinear modeling module is connected to the real-time monitoring module and is used to construct a nonlinear dynamic model of crack extension based on the collected crack extension parameters and construction operation parameters, and output a predicted value of the crack morphology;

[0074] The nonlinear modeling module is used to construct a nonlinear dynamic model of the fracture extension process based on the data provided by the real-time monitoring module. Its main task is to describe the dynamic evolution of the fracture geometry (such as length, width and complexity) over time and space through physical modeling methods, and continuously update the model parameters in combination with real-time monitoring data to improve the accuracy and adaptability of the model. In general, the modeling process of this module needs to comprehensively consider the elastic mechanical properties of the reservoir, the fluid dynamics behavior of the fracturing fluid, and the distribution characteristics of the net pressure during the fracture extension process. The output of this module directly provides the required fracture morphology prediction data for the optimization and control module, thereby realizing the precise optimization of construction parameters.

[0075] In this embodiment, the nonlinear modeling module includes a physical modeling submodule, a parameter updating submodule and a dynamic prediction submodule, and its specific implementation is described in detail below.

[0076] The physical modeling submodule in this embodiment is mainly used to construct a nonlinear dynamic model of crack expansion to describe the dynamic changes of crack geometry at different time points. The expansion speed of the crack length L(t,x) can be described by the following equation:

[0077]

[0078] Where L(t,x) represents the fracture length, which is a dynamic variable of the fracture with time t and spatial position x; E is the elastic modulus of the reservoir (unit: GPa), which is used to describe the deformation capacity of the reservoir rock; P net is the net pressure of the fracture (unit: MPa), that is, the fracturing fluid pressure in the fracture minus the reservoir closure pressure; σx is the minimum horizontal ground stress of the reservoir (unit: MPa), representing the original stress state of the reservoir; μ is the viscosity of the fracturing fluid (unit: Pa·s), reflecting the flow resistance of the fracturing fluid; It is a spatial gradient operator, which is used to describe the spatial variation of crack extension.

[0079] The change of fracture width W(t,x) and the net fracture pressure P net It is directly related to the conductivity of the crack, η, and can be described by the following equation:

[0080]

[0081] Where W(t,x) represents the crack width (unit: mm), which is a dynamic variable of the crack opening changing with time and space; u represents the displacement field of the crack (unit: mm), which describes the specific spatial distribution of the crack opening; η is the conductivity of the crack, which describes the transmission performance of the fluid in the crack.

[0082] The complexity of the fracture, C(t), is mainly used to describe the branching structure of the fracture network and the complexity of the spatial intersection. In general, the fracture complexity is closely related to the spatial gradient of the fracture length and width, and its variation can be expressed by the following formula:

[0083]

[0084] Where: C(t) represents the complexity of the crack, which is a dimensionless parameter; β1 and β2 are weight parameters in the model, reflecting the influence weights of the crack length and width on the complexity respectively;

[0085] As a possible implementation method, the physical modeling submodule discretizes the above equations and numerically solves the dynamic process of crack extension through the finite difference method (FDM) or finite element method (FEM). This processing method can significantly improve the efficiency of calculation and ensure the stability of the solution.

[0086] The parameter update submodule is mainly used to dynamically adjust the parameters in the physical model according to the real-time monitoring data. For example, during the crack expansion process, the elastic modulus E of the reservoir, the viscosity μ of the fracturing fluid, and the net pressure P net Parameters such as and conductivity η may change due to changes in the construction environment or crack conditions. In general, these parameters can be estimated and corrected using the temperature, strain and pressure data collected by the real-time monitoring module through the least squares method or Kalman filter:

[0087] The dynamic prediction submodule calculates the changing trend of the fracture geometry in the future based on the updated physical model. By solving the above dynamic equations, the fracture length L(t+Δt), width W(t+Δt) and complexity C(t+Δt) can be predicted. As an option, the prediction results can be smoothed using a sliding window to reduce the impact of the noise of the monitoring data on the prediction results.

[0088] In one possible implementation, the dynamic prediction results can be directly connected to the optimization control module to form real-time feedback control of the fracture geometry. In addition, the prediction results of the fracture length and width can also be used to estimate the conductivity of the fracture and provide a reference for the adjustment of the fracturing fluid sand ratio; through the nonlinear modeling module in this embodiment, high-precision modeling and prediction of the fracture extension process can be achieved.

[0089] The optimization and control module is connected to the nonlinear modeling module and is used to calculate the optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of the fracturing fluid;

[0090] The optimization and control module is one of the core modules of the present invention for realizing intelligent control of hydraulic fracturing. It generates the optimal control strategy of construction parameters by combining the fracture extension prediction results and real-time monitoring data provided by the nonlinear modeling module. In general, this module needs to design an optimization objective function, combine construction constraints, and use optimal control theory to dynamically calculate the optimal injection pressure, flow rate, fracturing fluid viscosity and other construction parameters. Through real-time adjustment of these parameters, the optimization and control module can dynamically adapt to the nonlinear behavior of fracture extension, improve the controllability of fracture morphology, and provide efficient guidance for construction.

[0091] In this embodiment, the optimization and control module includes an objective function design submodule, a constraint condition module and a control strategy solution submodule. The functions and implementation methods of each submodule will be described in detail below in conjunction with the specific technical content of the present invention.

[0092] The objective function design submodule in this embodiment is used to construct a comprehensive optimization target. The objective function considers both crack morphology optimization and construction energy consumption control. Specifically, crack morphology optimization includes maximizing crack length L(t), crack width W(t) and crack complexity C(t), while energy consumption control includes minimizing injection pressure P frac (t) and injection velocity Q(t) on construction energy consumption. The mathematical form of the objective function can be expressed as:

[0093]

[0094] Among them, J is the total optimization target value of the objective function; α1, α2, α3 are the weight coefficients for the optimization of fracture length, width and complexity respectively; L(t) is the fracture length (unit: m), provided by the nonlinear modeling module; W(t) is the fracture width (unit: mm), provided by the nonlinear modeling module; C(t) is the fracture complexity (dimensionless), provided by the nonlinear modeling module; γ1, γ2 are the weight coefficients for the injection pressure and flow rate energy consumption respectively; P frac (t) is the real-time injection pressure (unit: MPa), which is dynamically adjusted by the optimization control module; Q(t) is the real-time injection flow rate (unit: m 3 / s), which is dynamically adjusted by the optimization control module; T is the construction time range (unit: s).

[0095] As an option, the weight coefficients α1, α2, α3, γ1, and γ2 can be preset according to the construction objectives. For example, in a scenario where the production increase objective is the main objective, the weight α3 of the crack complexity can be increased, and in a scenario where the energy saving and consumption reduction objective is the main objective, the energy consumption control weights γ1 and γ2 can be increased.

[0096] The constraint module in this embodiment is used to set physical and engineering constraints during the construction process. Generally, crack expansion needs to meet the following constraints:

[0097] P min ≤P frac (t)≤P max

[0098] Where: P min is the minimum pressure for fracture rupture (unit: MPa), which needs to be higher than the formation fracture pressure; P max is the maximum pump pressure allowed by the equipment (unit: MPa), which is limited by the performance of the fracturing equipment; P frac (t) is the real-time injection pressure (unit: MPa).

[0099] The constraint condition of injection flow rate Q(t) can be expressed as:

[0100] 0≤Q(t)≤Q max

[0101] Where: Q(t) is the real-time injection velocity (unit: m 3 / s), which indicates the injection rate of fracturing fluid at a certain moment; Q max The maximum injection flow rate allowed by the equipment (unit: m 3 / s).

[0102] In addition, in order to ensure the stability of the crack morphology, it is also necessary to set the constraints of the crack width W(t) and complexity C(t):

[0103] W(t)>0,C(t)>0

[0104] Wherein, W(t) is the crack width (unit: mm), which indicates the crack opening and is the output of the nonlinear modeling module; C(t) is the crack complexity (dimensionless), which is used to describe the crack branching and crossing characteristics and is a quantitative indicator of the complexity of the crack network;

[0105] In a possible implementation, the constraint condition module can also dynamically adjust the constraint range according to real-time monitoring data. For example, when the formation conditions are monitored to change, P can be adjusted accordingly. min and P max The value range of .

[0106] The control strategy solving submodule in this embodiment calculates the time series solution of the construction parameters based on the optimal control theory. Specifically, by constructing the Hamiltonian function H, the comprehensive relationship between the optimization objective and the constraint conditions is described:

[0107]

[0108] Where: H is the Hamiltonian function; λ L ,λ W are the adjoint variables of crack length and width, respectively, used to describe the gradient direction of the optimization objective; and are the time derivatives of the crack length and width, respectively, provided by the nonlinear modeling module.

[0109] According to the Pontryagin extreme value principle, the optimal injection pressure and flow rate Q * The solution of (t) must satisfy the following conditions:

[0110]

[0111] in, Optimal injection pressure (unit: MPa), which indicates the optimized construction pressure at a certain moment; Q * (t): Optimal injection velocity (unit: m 3 / s), represents the optimized fracturing fluid flow rate at a certain moment; λ L : concomitant variable of crack length; λ W : Concomitant variable of fracture width; γ1, γ2: Energy consumption weight coefficients, which respectively control the influence weights of injection pressure and flow rate on energy consumption;

[0112] In a possible implementation, the above optimization problem can be solved by using a gradient descent method or a dynamic programming algorithm, and the optimal time series of the construction parameters can be output. These time series will be used as inputs of the feedback execution module to adjust the fracturing construction equipment in real time.

[0113] In some embodiments, in order to further improve the computational efficiency, a decomposition-based multi-objective optimization method (such as the MOEA / D algorithm) can be used to decompose the objective function and transform the multi-objective optimization problem into a series of single-objective optimization problems to be solved separately. This method can significantly reduce the complexity of high-dimensional optimization problems while ensuring the global optimality of the optimization results;

[0114] A feedback execution module is connected to the optimization control module and is used to dynamically adjust the fracturing construction equipment to optimize the crack morphology according to the construction parameters output by the optimization control module;

[0115] The feedback execution module is used to convert the optimal construction parameters generated by the optimization control module into actual construction operations, and ensure that the fracture morphology is consistent with the optimization target by adjusting the construction parameters such as pump pressure, flow rate, and fracturing fluid viscosity in real time. At the same time, the module transmits the feedback results of the construction operation to the real-time monitoring module to form a closed-loop control system. In general, the feedback execution module needs to have a high response speed and precise control capabilities to adapt to the dynamic and nonlinear characteristics of fracture expansion.

[0116] In this embodiment, the feedback execution module includes a construction control submodule, a fracturing fluid preparation submodule and a feedback monitoring submodule. The specific implementation of each submodule is described in detail below.

[0117] The construction control submodule in this embodiment is mainly used to dynamically adjust the pump pressure and injection flow rate according to the construction parameters output by the optimization control module. Specifically, this submodule controls the injection pressure P frac (t) and injection flow rate Q(t), and the construction parameters can be changed in real time through the execution equipment of the pump station. As an implementation method, the working pressure of the pump can be adjusted through a proportional integral differential (PID) controller to achieve precise control of the injection pressure. Injection pressure P frac The control target of (t) is determined by the following formula:

[0118] P frac (t) = P net (t)+P closure

[0119] Where: P frac (t) is the real-time injection pressure (unit: MPa), which indicates the actual pressure of the fracturing fluid injected into the fracture; P net (t) is the net pressure (unit: MPa), which is output by the optimization control module and represents the pressure required for crack expansion; P closure is the formation closure pressure (unit: MPa), which is used to describe the critical pressure when the fracture is closed.

[0120] The control target of injection flow rate Q(t) is described by the following formula:

[0121]

[0122] Where: Q(t) is the real-time injection velocity (unit: m 3 / s), which indicates the injection rate of the fracturing fluid at a certain moment; V inj is the volume of fracturing fluid per unit time (unit: m 3 ), calculated by the optimization control module; Δt is the time interval (unit: s), indicating the cycle of construction parameter adjustment.

[0123] In a possible implementation, the construction control submodule can also achieve precise control of the injection flow rate Q(t) through a flow control valve. Specifically, when the crack expands too fast, the control valve will reduce the flow rate to stabilize the expansion; when the crack expands too slowly, the control valve will increase the flow rate to accelerate the formation of the crack.

[0124] The fracturing fluid allocation submodule in this embodiment is used to adjust the viscosity and sand carrying ratio of the fracturing fluid according to the dynamic requirements of the fracture, thereby optimizing the conductivity and support effect of the fracture. In general, the viscosity μ of the fracturing fluid is of great significance to the control of fracture expansion, and its adjustment formula is:

[0125]

[0126] Where: μ is the real-time adjusted fracturing fluid viscosity (unit: Pa·s); μ0 is the basic fracturing fluid viscosity (unit: Pa·s), which is determined by the pre-construction design; β1 is the viscosity adjustment coefficient (dimensionless), which is pre-set according to the formation conditions; is the fracturing fluid pressure gradient (unit: MPa / m), which indicates the rate of change of the fluid pressure in the fracture along the length of the fracture.

[0127] As an implementation method, the fracturing fluid mixing submodule can adjust the sand carrying ratio φ in real time through the automated mixing equipment to meet the distribution requirements of the proppant in the fracture. The control formula of the sand carrying ratio is:

[0128]

[0129] Among them, φ is the sand carrying ratio (dimensionless) adjusted in real time, which represents the ratio of sand mass to total mass; m s is the mass of the proppant (unit: kg); m f is the mass of fracturing fluid (unit: kg).

[0130] In one possible implementation, when it is monitored in real time that the fracture width W(t) is too small or the fracture complexity C(t) increases, the sand carrying ratio φ will be dynamically increased to increase the proppant deposition amount, thereby improving the conductivity of the fracture.

[0131] The feedback monitoring submodule in this embodiment is used to obtain the results of construction adjustments in real time and feed them back to the real-time monitoring module. In general, the feedback results include the actual values ​​of pump pressure, flow rate, viscosity and sand carrying ratio, as well as the monitoring results of fracture geometry. For example, when the injection pressure P is monitored frac (t) When it exceeds the set range, the feedback monitoring submodule will immediately trigger an alarm and notify the construction control submodule to make corrections. In a possible implementation, this submodule can directly interact with the real-time monitoring module through the data bus to achieve high-speed data transmission and dynamic update

[0132] Through the collaborative work of the above functions, the feedback execution module in this embodiment can achieve real-time adjustment and dynamic optimization of construction parameters. At the same time, the feedback results provide the necessary data support for closed-loop control for the real-time monitoring module and the optimization and control module, thereby ensuring the accuracy of the crack expansion process and the safety of construction.

[0133] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, characterized in that: include: Real-time monitoring module, used to collect crack geometry parameters and construction operation parameters in real time during crack expansion; A nonlinear modeling module, connected to the real-time monitoring module, is used to construct a nonlinear dynamic model of crack extension according to the collected crack extension parameters and construction operation parameters, and output a predicted value of crack morphology; An optimization and control module, connected to the nonlinear modeling module, for calculating optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of the fracturing fluid; The feedback execution module is connected to the optimization control module and is used to dynamically adjust the fracturing construction equipment to optimize the crack morphology according to the construction parameters output by the optimization control module.

2. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1 is characterized in that: The real-time monitoring module comprises: The microseismic monitoring submodule is used to collect microseismic event data generated during the crack expansion process to determine the expansion path and length of the crack; The optical fiber sensing submodule is used to collect the temperature field and strain field information inside the crack in real time to obtain the change of crack width; The pressure and flow rate monitoring submodule is used to obtain real-time data of injection pressure and injection flow rate.

3. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1 is characterized in that: The nonlinear modeling module includes: The physical modeling submodule is used to construct a nonlinear dynamic model of crack propagation, including the coupling relationship between crack length, width, and complexity with time and space; A parameter updating submodule, used to update key parameters in the dynamic model according to the data collected by the real-time monitoring module, including formation elastic modulus, fluid viscosity, net pressure and conductivity; The dynamic prediction submodule is used to calculate the predicted values ​​of crack length, width and complexity based on the updated dynamic model.

4. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The physical modeling submodule of the nonlinear modeling module constructs a nonlinear dynamic model of crack propagation, wherein: The change in fracture length is calculated based on the relationship between the formation elastic modulus, injection pressure, and geostress; The change in fracture width is calculated based on the conductivity of the fracture net pressure field and the reservoir stress distribution; The variation in fracture complexity is calculated using the spatial gradients of fracture length and width.

5. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The optimization and control module includes: The objective function design submodule is used to construct an objective function for comprehensive crack morphology optimization and construction cost control, wherein the objective function includes optimization targets for crack length, width, complexity, and energy consumption; Constraint module, used to set physical constraints and stability constraints on injection pressure, flow rate, fracture width; The control strategy solving submodule is used to calculate the optimal injection pressure, flow rate and fracturing fluid viscosity based on the optimal control theory, and output the time series of construction parameters.

6. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 5, characterized in that: The objective function constructed by the objective function design submodule in the optimization and control module includes: Maximizing crack length, width, and complexity; Minimize the impact of injection pressure and flow rate on construction energy consumption; The dynamic changes of comprehensive construction costs and the optimal relationship between crack morphology.

7. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The feedback execution module comprises: The construction control submodule is used to dynamically adjust the pump pressure and injection flow rate according to the construction parameters output by the optimization control module; The fracturing fluid preparation submodule is used to adjust the viscosity and sand carrying ratio of the fracturing fluid to optimize the supporting effect of the fracture morphology; The feedback monitoring submodule is used to feed back the results of the construction adjustment to the real-time monitoring module in real time to achieve closed-loop control.

8. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The data collected by the real-time monitoring module is used to correct the reservoir parameters in the nonlinear modeling module through the parameter updating submodule, wherein the reservoir parameters include: Reservoir elastic modulus, used to characterize the mechanical response of rocks; Fluid viscosity, which is used to describe the internal friction properties of the fracturing fluid; Net pressure, which describes the difference between the pressure inside the fracture and the ground stress; Fracture conductivity is used to characterize the resistance of the fracture to the flow of fracturing fluid.

9. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The steps of adjusting the pump pressure, injection flow rate and fracturing fluid viscosity by the feedback execution module include: Adjust the injection pressure and injection flow rate according to the time series output by the optimization control module; Dynamically optimize the viscosity of the fracturing fluid based on real-time crack morphology monitoring results; The distribution of proppant in the fracture is controlled by adjusting the sand carrying ratio, thus improving the conductivity of the fracture.

10. The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system according to claim 1, characterized in that: The oil reservoir hydraulic fracturing crack real-time monitoring and intelligent control system constitutes a closed-loop system, wherein: The monitoring data provided by the real-time monitoring module updates the crack propagation model in real time through the nonlinear modeling module; The optimization and control module generates optimal construction parameters according to the modeling results; The feedback execution module dynamically adjusts the construction operation according to the output of the optimization and control module, and feeds back the construction results to the real-time monitoring module in real time to achieve real-time optimization and control.

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