Underground electric heater temperature control method based on model predictive control

Abstract

The invention provides an underground electric heater temperature control method based on model predictive control, and belongs to the technical field of temperature control, and the method comprises the steps: obtaining the temperature of produced liquid, the environment temperature and the current of an electric heater; according to the produced liquid temperature, the environment temperature and the electric heater current, iterative prediction is carried out based on a pre-constructed temperature prediction model, and a produced liquid temperature prediction value of a preset prediction step number is obtained; and constructing a target function by minimizing the current of the electric heater in a period of time in the future, enabling the temperature of the produced liquid to reach a target set temperature, and solving to obtain an optimal current sequence of the target function under a control constraint condition. The electric heating temperature control method has the beneficial effects that electric heating temperature control is realized on the basis of a model prediction control strategy in combination with a prediction model, optimization calculation and constraint processing, and the temperature control accuracy, the system response sensitivity and the energy consumption economy are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a method for temperature control of a downhole electric heater based on model predictive control. Background Technology

[0002] In the oil extraction industry, when oil wells are producing oil, the temperature of the produced fluid flowing out of the formation outlet is relatively high. However, during the process of raising the fluid to the wellhead and transporting it to the gathering and transportation station, factors such as low ambient formation temperature, large wellbore depth, and long flow time significantly exacerbate heat loss, leading to a series of serious problems, among which wax deposition is one of the most prominent. If the wellbore wall temperature or the temperature of the produced fluid drops below the wax point, wax deposition is highly likely to occur, causing problems such as pipeline blockage, equipment wear, and well shutdown. Therefore, wellbore electric heating or pipeline heating is commonly used in the field to maintain stable fluid temperature and achieve temperature control of the produced fluid during oil well production.

[0003] Currently, the mainstream control method in the field is Proportional-Integral-Derivative (PID) regulation. This method uses the difference between the produced fluid temperature and the target set temperature as an error signal, and outputs a heating current control signal through a linear regulator to adjust the power of the electric heater, thereby controlling the produced fluid temperature. While simple and effective, this method has drawbacks such as response lag, sensitivity to external disturbances, difficulty in handling power supply fluctuations (e.g., photovoltaic output), and lack of predictive power trends. These drawbacks are particularly pronounced in winter or under high-frequency load fluctuations, often leading to temperature control instability, high energy consumption, and even control overshoot, which can damage the equipment. Summary of the Invention

[0004] To address the above technical problems, this invention provides a method for controlling the temperature of a downhole electric heater based on model predictive control.

[0005] The technical problem solved by this invention can be achieved by the following technical solutions: A method for controlling the temperature of a downhole electric heater based on model predictive control, comprising: Step S1: Obtain the temperature of the produced fluid, the ambient temperature, and the current of the electric heater; Step S2: Based on the produced fluid temperature, the ambient temperature, and the electric heater current, perform iterative prediction based on a pre-built temperature prediction model to obtain a predicted value of the produced fluid temperature with a preset number of prediction steps. Step S3: Construct an objective function by minimizing the electric heater current over a future period of time, so that the produced fluid temperature reaches the target set temperature, and solve for the optimal current sequence of the objective function under control constraints.

[0006] Preferably, step S2 includes: A temperature prediction model is established, and the temperature prediction model is as follows:

[0007] in, t Indicates time; express t The temperature of the produced fluid at any given time; express t The current of the electric heater at all times; express t The ambient temperature at that moment; a,b,c,d These represent the identification coefficients; express t Predicted temperature of produced fluid at time +1.

[0008] Preferably, step S2 further includes: The temperature prediction model is extended based on a preset number of prediction steps, resulting in the following extended temperature prediction model:

[0009] in, k Indicates step index, , N This indicates the preset prediction step number; express t + k The temperature of the produced fluid at any given time; express t + k The current of the electric heater at all times; express t + k The ambient temperature at that moment; express t + k Predicted temperature of produced fluid at time +1.

[0010] Preferably, the objective function is:

[0011] in, Indicates the target set temperature; express t + k The predicted temperature of the produced fluid at time +1; λ represents the weighting coefficient; express t+ k The current of the electric heater at all times; express t + k The current of the electric heater at time -1; These represent the electric heater current over a future period of time.

[0012] Preferably, the control constraints include upper and lower current limits, current regulation range constraints, and power supply capacity constraints. The current upper and lower limit constraints are as follows:

[0013] in, Indicates the lower limit of the current; Indicates the upper limit of the current; k Indicates step index, , N This indicates the preset prediction step number; express t + k The current of the electric heater at all times; The current regulation amplitude constraint condition is:

[0014] in, express t + k The current of the electric heater at time -1; Indicates the current adjustment amplitude threshold; The energy supply capacity constraint is as follows:

[0015] in, R Indicates the resistance of the electric heater cable; Indicates the time control step size; express t + k Photovoltaic output capacity at all times; express t + k Energy storage and output capability at all times.

[0016] Preferably, after step S3, the method further includes: Step S4: In each control cycle, a current control command is output according to the current value of the first step in the optimal current sequence to adjust the electric heater according to the current control command, and steps S1-S3 are re-executed when entering the next control cycle until operation stops.

[0017] Preferably, in step S4, the output current control command is followed by: Step S41: Check whether the risk control verification conditions are met; Step S42: When the risk control verification conditions are met, switch to safe mode.

[0018] Preferably, the risk control verification conditions include: Temperature sensor failure; and / or The current value drifts beyond the preset range; and / or The temperature rise of the electric heater cable exceeds the preset temperature rise threshold.

[0019] Preferably, the safe mode performs one of the following two operation methods: Lock the electric heater current value to the preset safe current value; The electric heater is subjected to a soft start and slow rise process according to the current value of the first step in the optimal current sequence.

[0020] Preferably, it further includes: recording log data, the log data including one or more combinations of the predicted value of the produced fluid temperature, the current change amplitude, the target set temperature, and whether the boundary is triggered for each prediction step.

[0021] The advantages or beneficial effects of the technical solution of this invention are as follows: This invention is based on a model predictive control strategy, which combines predictive models, optimization calculations and constraint processing to achieve electric heating temperature control, significantly improving temperature control accuracy, system response sensitivity and energy economy. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a preferred embodiment of the downhole electric heater temperature control method based on model predictive control. Figure 2 This is a schematic diagram of the model predictive control process in a preferred embodiment of the present invention; Figure 3 This is a flowchart illustrating the risk control verification process in a preferred embodiment of the present invention. Detailed Implementation

[0023] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0025] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0026] In a preferred embodiment of the present invention, based on the above-mentioned problems existing in the prior art, a method for temperature control of downhole electric heaters based on model predictive control (MPC) is provided. The present invention is based on the model predictive control strategy, and combines predictive models, optimization calculations and constraint processing to achieve electric heating control, which significantly improves the accuracy of temperature control, system response sensitivity and energy economy.

[0027] like Figure 1 As shown, the method includes: Step S1: Obtain the temperature of the produced fluid, the ambient temperature, and the current of the electric heater; Step S2: Based on the produced fluid temperature, ambient temperature and electric heater current, perform iterative prediction based on the pre-built temperature prediction model to obtain the produced fluid temperature prediction value for the preset prediction steps. To achieve forward-looking and precise temperature regulation control, this invention first establishes a temperature prediction model for the state of the temperature-controlled object. The temperature of the extracted fluid is denoted as... The predicted temperature of the produced fluid at the next moment is denoted as This predicted value is affected by the temperature of the produced fluid. Factors affecting the temperature include the electric heater current, electric heating power, ambient temperature, and flow rate. By systematically identifying or simplifying the heat transfer theory, the following dynamic temperature prediction model can be constructed:

[0028] in, t Indicates time; express t The temperature of the produced fluid at any given time, in °C; express t The current of the electric heater at any given time, in amperes (A). This reflects the relationship between electric heating power and the square of current, assuming a constant cable resistance. express t The ambient temperature at any given time, in °C; a,b,c,d These represent the identification coefficients, which are either empirical fitting results or model identification results; express t The predicted temperature of the produced fluid at time +1, in °C.

[0029] Furthermore, this temperature prediction model can be extended to a multi-step prediction form. Specifically, based on a preset number of prediction steps, the temperature prediction model is extended, resulting in the following extended temperature prediction model:

[0030] in, k Indicates step index, , N Indicates the preset prediction steps; express t + k The temperature of the produced fluid at any given time; express t + k The current of the electric heater at all times; express t + k The ambient temperature at that moment; express t + k Predicted temperature of produced fluid at time +1.

[0031] Within each control cycle, the controller will perform iterative predictions based on the extended temperature prediction model according to the current temperature status, and obtain a preset number of prediction steps. N The predicted value of the produced fluid temperature, to obtain future N Temperature trajectory of the extracted liquid.

[0032] Step S3: Construct an objective function by minimizing the electric heater current over a future period of time, so that the produced fluid temperature reaches the target set temperature, and solve for the optimal current sequence of the objective function under control constraints.

[0033] Within each control cycle, the controller predicts future temperature conditions based on the current temperature state. N The temperature trajectory of the produced fluid is determined, and the optimal current sequence for the future is calculated. To bring the temperature of the produced fluid to a certain level. As close as possible to the target set temperature And limit current fluctuations. The objective function is:

[0034] In the formula, These represent the electric heater current over a future period of time; This is the sum of the squares of the temperature errors, used to approximate the set temperature. Wherein, This indicates the target set temperature, for example, 70℃. Of course, it is not limited to this; it can be set as needed during the actual oilfield generation process. express t + k Predicted temperature of produced fluid at time +1; The current regulation smoothing penalty term controls the rate of change of current; where, express t +k The current of the electric heater at all times; express t + k The current of the electric heater at time -1; λ represents the weighting coefficient, which reflects the degree of importance that the current regulation smoothing penalty term places on equipment wear and power grid impact. λ can be set to 0.1 ~ 0.5.

[0035] The preset prediction steps can cover a preset time period in the future, which is divided into multiple sub-time periods, each representing a step size. Preferably, the preset time period can be set to 1 hour, for example, the preset prediction steps. N The optimal value is 4 to 6, which covers the operating trend for the next hour, with each step being 15 minutes. Of course, in actual oilfield deployment, this is not the only option; the value can be set according to actual needs.

[0036] To ensure the safe and stable operation of power equipment and its compatibility with the power grid, this embodiment optimizes the constraint expressions and actual boundary conditions to ensure that the equipment operates within a safe range and can better adapt to the characteristics of the power grid, avoiding unnecessary interference to the power grid.

[0037] The system needs to simultaneously satisfy a set of control constraints, including the following constraints: upper and lower current limits, current regulation range, and power supply capacity.

[0038] In power systems, the magnitude of the current directly affects the operating status and lifespan of equipment. If the current is too low, the equipment may fail to start or operate unstablely; conversely, if the current is too high, it may cause overheating, damage, or even safety accidents. Therefore, upper and lower limits are imposed on the current. The conditions for these current limits are as follows:

[0039] in, Indicates the lower limit of the current; Indicates the upper limit of the current; k Indicates step index, , N Indicates the preset prediction steps; express t + k The current of the electric heater at all times.

[0040] Taking electric heating cables as an example, the rated minimum current of the cable is 30A and the maximum current is 150A. When the controller is solving the problem, it will force the current to be kept within the range of 30A to 150A to ensure that the cable operates within a safe current range and avoids damage caused by excessive or insufficient current, thereby ensuring the stable operation of the entire power system.

[0041] In power systems, sudden and significant changes in current can trigger a series of problems, such as bus voltage fluctuations and circuit breaker tripping, affecting the normal operation of equipment, impacting the power grid, and even causing power outages. Therefore, constraints are imposed on the current regulation amplitude. The current regulation amplitude constraint conditions are as follows:

[0042] in, express t + k The current of the electric heater at time -1; This indicates the threshold value for current adjustment.

[0043] Under normal circumstances, Set to 10A. When the current regulation range exceeds 10A, the system will make corresponding adjustments to ensure smooth current changes, thereby avoiding problems such as bus voltage fluctuations or circuit breaker tripping caused by sudden large currents, and thus preventing impact on the system.

[0044] Furthermore, if the system adopts a combined photovoltaic and energy storage power supply method, due to the limited output capacity of photovoltaics and energy storage, insufficient power supply will occur when the system's power demand exceeds the power supply capacity, affecting the normal operation of the equipment. Therefore, the power supply side power must also meet the power supply capacity constraint condition, the mathematical expression of which is:

[0045] in, R Indicates the resistance of the electric heater cable; Indicates the time control step size, in hours; express t + k Photovoltaic output capacity at any given time, in kW; express t + k Real-time energy storage output capacity, measured in kW.

[0046] When the system's power demand exceeds the power supply capacity, the system will automatically reduce the target current to ensure stable operation. By reducing the current, the power consumption is reduced, matching the system's power demand with the power supply's capacity, thereby preventing equipment failure or system crashes due to insufficient power supply.

[0047] In a preferred embodiment, such as Figure 2 As shown, after step S3, the following steps are also included: Step S4: In each control cycle, output a current control command based on the current value of the first step in the optimal current sequence, so as to adjust the electric heater according to the current control command, and repeat steps S1-S3 when entering the next control cycle until the operation stops.

[0048] In actual deployment, the control method of the present invention is scheduled to run with a fixed control cycle, for example, a control cycle of 15 minutes, that is, scheduling once every 15 minutes, so as to respond to changes in the system in a timely manner, without increasing the operating burden of the system due to excessively frequent scheduling.

[0049] At the beginning of each control cycle, the controller first reads the produced fluid temperature data collected by the temperature sensor, and receives the ambient temperature data uploaded by the scheduling system. Photovoltaic output capacity Input variables include State of Charge (SOC).

[0050] Next, the future is calculated iteratively based on the temperature prediction model. N The temperature evolution trajectory is analyzed step by step, and the objective function and constraints are combined to form a standard constrained optimization problem. This type of problem can be solved by a quadratic programming (QP) solver, which uses advanced algorithms and optimization techniques to quickly and accurately select the optimal solution from many possible solutions and obtain the optimal current sequence.

[0051] During the temperature control execution phase, the controller selects only the current value of the first step in the sequence and uses it to adjust the working state of the electric heater, so as to respond more flexibly to real-time changes.

[0052] After completing the current regulation for the current control cycle, the controller enters the next control cycle. During the next control cycle, the controller repeats the data acquisition, temperature prediction, and constraint optimization solution process described above. This cycle repeats continuously to achieve rolling optimization control, enabling the system to constantly adjust its control strategy based on real-time changes and maintain optimal operating conditions. This improves the system's operating efficiency and stability, ensuring that the system better meets the needs of practical applications.

[0053] Specifically, to further enhance system robustness and field adaptability, the control system incorporates a multi-level protection mechanism. Before issuing an output current command, the controller performs a complete risk control verification process. As a preferred implementation, such as... Figure 3 As shown, in step S4, the output current control command is preceded by: Step S41: Check whether the risk control verification conditions are met; wherein, the risk control verification conditions include: Temperature sensor failure; and / or The current value drifts beyond the preset range; and / or The temperature rise of the electric heater cable exceeds the preset temperature rise threshold.

[0054] Step S42: When the risk control verification conditions are met, switch to safe mode.

[0055] Specifically, during the risk control verification process, if conditions such as temperature sensor failure, excessive current drift, or excessively rapid cable temperature rise are detected, the system will immediately switch to safety mode and take corresponding protective measures to ensure that the on-site equipment is not damaged.

[0056] In a preferred embodiment, one of the following two operating methods is performed in safe mode: Lock the electric heater current value to the preset safe current value; The electric heater is subjected to a soft start and slow rise process according to the current value of the first step in the optimal current sequence.

[0057] Specifically, in safety mode, the current value is locked to a preset safe current value. Alternatively, a soft start and slow rise process can be performed to ensure that the on-site equipment is not damaged.

[0058] Furthermore, when the power source is a photovoltaic array and an energy storage system, the MPC controller also has an energy dispatch interface. This interface can automatically determine whether to reduce power consumption or enter sleep mode based on the state of charge (SOC) of the energy storage system and the expected photovoltaic output, thus avoiding power-side risks such as deep discharge of the energy storage and inverter overload, and improving the energy utilization efficiency and reliability of the system.

[0059] In a preferred embodiment, the method further includes: recording log data, which includes one or more combinations of the following: predicted temperature of the produced fluid, current change amplitude, target set temperature, and whether a boundary is triggered for each prediction step.

[0060] Specifically, the controller also provides an internal logging function to record parameters such as predicted temperature, current changes, optimized target values, and boundary triggering in real time. This facilitates source analysis and parameter tuning by operators. By viewing the log data, operators can understand the system's operating status, promptly identify potential problems, and make corresponding adjustments and optimizations.

[0061] The model predictive control-based downhole electric heater temperature control method of this invention has been tested in multiple oilfield well sites. In complex scenarios involving photovoltaic fluctuations and frequent start-ups and shutdowns of waxy oil wells, the temperature error of this control method is kept within ±0.8℃, significantly reducing temperature deviation and ensuring the stability of the produced fluid temperature. Simultaneously, it reduces energy waste by more than 30%, effectively improving energy utilization efficiency.

[0062] Compared to traditional PID control schemes, the system response time of this invention is reduced by approximately 25%, enabling faster response to system changes and improving system real-time performance and stability. Furthermore, this algorithm reduces the fluctuation amplitude of heating current, minimizing impact on equipment, extending equipment lifespan, and also enhancing system compatibility with the power grid.

[0063] This invention combines model prediction capabilities, optimized solution logic, and actual physical boundaries to maximize system energy efficiency and operational safety while ensuring stable produced fluid temperature in oil wells. It is particularly suitable for deployment in smart oilfield scenarios with energy storage and renewable energy access, and has broad engineering application prospects and significant industrialization value.

[0064] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included within the protection scope of the present invention.

Claims

1. A method for temperature control of a downhole electric heater based on model predictive control, characterized in that, include: Step S1: Obtain the temperature of the produced fluid, the ambient temperature, and the current of the electric heater; Step S2: Based on the produced fluid temperature, the ambient temperature, and the electric heater current, perform iterative prediction based on a pre-built temperature prediction model to obtain a predicted value of the produced fluid temperature with a preset number of prediction steps. Step S3: Construct an objective function by minimizing the electric heater current over a future period of time, so that the produced fluid temperature reaches the target set temperature, and solve for the optimal current sequence of the objective function under control constraints.

2. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, Step S2 includes: A temperature prediction model is established, and the temperature prediction model is as follows: ； Where t represents time; This represents the temperature of the extracted fluid at time t; This represents the current of the electric heater at time t; The ambient temperature at time t is represented; a, b, c, and d represent the identification coefficients, respectively. This represents the predicted temperature of the produced fluid at time t+1.

3. The downhole electric heater temperature control method based on model predictive control according to claim 2, characterized in that, Step S2 further includes: The temperature prediction model is extended based on a preset number of prediction steps, resulting in the following extended temperature prediction model: ； Where k represents the step index, N represents the preset prediction step number; This represents the temperature of the produced fluid at time t+k; This represents the current of the electric heater at time t+k; This represents the ambient temperature at time t+k; This represents the predicted temperature of the produced fluid at time t+k+1.

4. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, The objective function is: ； in, Indicates the target set temperature; λ represents the predicted temperature of the produced fluid at time t+k+1; λ represents the weighting coefficient. This represents the current of the electric heater at time t+k; This represents the electric heater current at time t+k-1; These represent the electric heater current over a future period of time.

5. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, The control constraints include upper and lower current limits, current regulation range constraints, and power supply capacity constraints. The current upper and lower limit constraints are as follows: ； in, Indicates the lower limit of the current; This indicates the upper limit of the current; k indicates the step index. N represents the preset prediction step number; This represents the current of the electric heater at time t+k; The current regulation amplitude constraint condition is: ； in, This represents the electric heater current at time t+k-1; Indicates the current adjustment amplitude threshold; The energy supply capacity constraint is as follows: ； Where R represents the resistance of the electric heater cable; Indicates the time control step size; This represents the photovoltaic output capacity at time t+k; This represents the energy storage output capacity at time t+k.

6. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, Following step S3, the following is also included: Step S4: In each control cycle, a current control command is output according to the current value of the first step in the optimal current sequence to adjust the electric heater according to the current control command, and steps S1-S3 are re-executed when entering the next control cycle until operation stops.

7. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, In step S4, the following is included before the output current control command: Step S41: Check whether the risk control verification conditions are met; Step S42: When the risk control verification conditions are met, switch to safe mode.

8. The downhole electric heater temperature control method based on model predictive control according to claim 7, characterized in that, The risk control verification conditions include: Temperature sensor failure; and / or The current value drifts beyond the preset range; and / or The temperature rise of the electric heater cable exceeds the preset temperature rise threshold.

9. The downhole electric heater temperature control method based on model predictive control according to claim 7, characterized in that, In the safe mode, one of the following two operation methods can be performed: Lock the electric heater current value to the preset safe current value; The electric heater is subjected to a soft start and slow rise process according to the current value of the first step in the optimal current sequence.

10. The downhole electric heater temperature control method based on model predictive control according to claim 1, characterized in that, Also includes: Record log data, which includes one or more combinations of the following for each prediction step: predicted temperature of produced fluid, current change amplitude, target set temperature, and whether the boundary is triggered.

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