Intelligent digital implementation method and control method for oil field heating furnace

Abstract

The invention relates to the field of oil field heating furnace control, and discloses an oil field heating furnace intelligent digital implementation method and control method.The control method comprises the following steps that S1, a temperature sensor is used for monitoring the temperature of an oil field heating furnace in real time; s2, calculating a temperature error between the upper limit and the lower limit of the real-time temperature and the set temperature according to the monitoring data of the temperature sensor; s3, a self-adaptive PID control algorithm is applied, and heating power is calculated based on the temperature error; and S4, dynamically adjusting the heating power to control the temperature of the oil field heating furnace. The precision and stability of temperature control can be improved, the oil field heating furnace can control the target temperature more accurately, and the production efficiency is improved. Meanwhile, the self-adaptive PID control algorithm can automatically adjust parameters according to actual working conditions and environment changes, the adaptability and robustness of the system are improved, and the requirements for manual intervention and adjustment are reduced. In addition, the stable and efficient heating process can be achieved by dynamically adjusting the heating power, and energy and cost are saved.

Description

Technical Field

[0001] This invention relates to the field of control technology for oilfield heating furnaces, specifically to intelligent digital implementation methods and control methods for oilfield heating furnaces. Background Technology

[0002] Oilfield heaters play a crucial role in oil extraction and processing. They are used to heat oil wells and equipment in oilfields to improve crude oil flowability and extraction efficiency. Traditional oilfield heaters typically employ simple control methods, such as manual control or timer-based control, which suffer from problems such as temperature instability, energy waste, and operational complexity. To address these issues, we propose an intelligent digital control method for oilfield heaters. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent digital implementation method and control method for oilfield heating furnaces. By monitoring the furnace temperature in real time and adjusting the heating power and strategy through an adaptive PID control algorithm, temperature stability and improved energy efficiency can be achieved.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent digital control method for oilfield heating furnaces, comprising the following steps:

[0005] Use temperature sensors to monitor the temperature of oilfield heating furnaces in real time;

[0006] Based on the monitoring data from the temperature sensor, calculate the temperature error between the real-time temperature and the set upper and lower temperature limits;

[0007] An adaptive PID control algorithm is applied to calculate the heating power based on the temperature error;

[0008] The heating power is dynamically adjusted to control the temperature of the oilfield heating furnace.

[0009] Preferably, the expression for the adaptive PID control algorithm is as follows:

[0010]

[0011] Where u(t) is the output of the controller, K p K i and K d These are the proportional, integral, and derivative parameters of the PID controller, respectively, and e(t) is the temperature error.

[0012] Preferably, the step of dynamically adjusting the heating power further includes:

[0013] Automatically reduce heating power when the temperature rises rapidly;

[0014] The heating power is automatically increased when the temperature drops to prevent the temperature from falling below the set lower limit.

[0015] Preferably, the PID parameter K is adaptively adjusted. p K i and K d The steps include:

[0016] Based on the statistical data of control error, K is automatically adjusted. p K i and K d To improve control performance.

[0017] Preferably, the method also includes using a humidity sensor to monitor the humidity inside the oilfield heating furnace and adjusting the heating strategy based on the humidity information.

[0018] Preferably, it also includes establishing a data acquisition system for collecting and storing historical operating data of oilfield heating furnaces, and combining the historical data for energy utilization optimization.

[0019] Preferably, it also includes using predictive models to predict heating demand based on historical data and real-time environmental information, and adjusting the heating strategy accordingly.

[0020] Preferably, it also includes a remote monitoring system to enable remote monitoring, operation, and fault diagnosis of oilfield heating furnaces.

[0021] This invention also provides an intelligent digital implementation system for oilfield heating furnaces, including:

[0022] Temperature and humidity sensors are used to monitor the temperature and humidity inside the heating furnace;

[0023] The controller uses the aforementioned control method to control the heating power.

[0024] A data acquisition system is used to collect and store operational data;

[0025] The remote monitoring system is used to enable remote monitoring and operation of the heating furnace.

[0026] Preferably, the controller further includes:

[0027] One or more processors;

[0028] A memory for storing program code for the control method;

[0029] Interfaces connected to the temperature sensor and humidity sensor;

[0030] Output interface used to send control signals to the heating furnace.

[0031] This invention provides a method for the intelligent digital implementation and control of oilfield heating furnaces. It offers the following advantages:

[0032] 1. This invention improves the accuracy and stability of temperature control, enabling oilfield heating furnaces to more accurately control target temperatures, thereby increasing production efficiency and product quality. Simultaneously, the adaptive PID control algorithm automatically adjusts parameters based on actual operating conditions and environmental changes, enhancing the adaptability and robustness of the control system and reducing the need for manual intervention and adjustments. Furthermore, dynamic adjustment of heating power allows for flexible control based on actual temperature changes and the magnitude of temperature errors, achieving a more stable and efficient heating process while saving energy and costs.

[0033] 2. By using a humidity sensor to monitor the humidity inside the oilfield heater and adjusting the heating strategy based on the humidity information, the present invention can more accurately control the humidity of the oilfield heater, meet specific heating and humidity control requirements, improve the heating efficiency of the oilfield heater, avoid energy waste and excessive dryness, enhance the adaptability of the oilfield heater, and enable it to adapt to heating needs under different humidity conditions.

[0034] 3. By combining historical data for energy utilization optimization, this invention can understand the operating characteristics and energy utilization status of oilfield heating furnaces, providing a basis and reference for optimization, identifying potential problems and areas for improvement in energy utilization, proposing corresponding optimization measures, reducing energy waste, thereby improving energy utilization efficiency, reducing energy costs, and enhancing economic benefits.

[0035] 4. This invention uses a predictive model to predict heating demand based on historical data and real-time environmental information, and adjusts the heating strategy accordingly. This allows for advance adjustments to the heating strategy to meet future heating demands and improve energy efficiency. Furthermore, predicting future heating demands helps in planning and optimizing the operation of the heating furnace, improving production efficiency, reducing energy waste and excessive consumption, and lowering energy costs.

[0036] 5. This invention uses a remote monitoring system to remotely monitor, operate, and diagnose faults in oilfield heating furnaces. It can monitor the operating status and parameters of oilfield heating furnaces in real time, promptly identify and resolve problems, and improve the reliability and stability of the equipment. At the same time, it can remotely operate the heating furnace, realize remote adjustment and control, improve the flexibility and efficiency of operation and maintenance, and through fault diagnosis and early warning functions, it can promptly detect and handle equipment faults, reducing downtime and production losses. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the control method of the present invention;

[0038] Figure 2 This is a schematic diagram of a method flow according to one embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the method flow according to the second embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the method flow of the third embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram of the method flow of the fourth embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only 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.

[0044] Please see the appendix Figure 1 The present invention provides an intelligent digital control method for oilfield heating furnaces, including steps S1 to S4.

[0045] Among them, step S1 is to use a temperature sensor to monitor the temperature of the oilfield heating furnace in real time;

[0046] In this step, the temperature of the oilfield heater is monitored in real time using temperature sensors. These sensors can be installed at key locations within the heater to accurately obtain internal temperature information. Various types of sensors can be used, such as thermocouples, thermistors, or infrared sensors. The sensors continuously measure the temperature and transmit the data to the control system for further processing.

[0047] In this embodiment, a temperature sensor is installed inside the furnace of the oilfield heater to ensure accurate measurement of the heater's temperature. The sensor transmits the temperature data to the control system for processing.

[0048] In step S2, the temperature error between the real-time temperature and the set upper and lower temperature limits is calculated based on the monitoring data of the temperature sensor.

[0049] In this step, based on the real-time temperature data monitored by the temperature sensor, the temperature error between the real-time temperature and the set upper and lower temperature limits is calculated. The set upper and lower temperature limits can be set according to specific needs and operational requirements. The temperature error can be calculated using a simple subtraction operation, that is, comparing the real-time temperature with the set upper and lower temperature limits to obtain the temperature error value.

[0050] In this embodiment, based on the real-time temperature data monitored by the temperature sensor, the control system calculates the temperature error between the real-time temperature and the set upper and lower temperature limits. For example, if the set upper temperature limit is 100℃ and the lower limit is 80℃, and the real-time temperature is 90℃, then the temperature error is 10℃.

[0051] In step S3, an adaptive PID control algorithm is applied to calculate the heating power based on the temperature error.

[0052] In this step, an adaptive PID (Proportional-Integral-Derivative) control algorithm is applied to calculate the heating power based on the temperature error. PID control is a classic control method that comprehensively considers the contributions of the proportional, integral, and derivative components to achieve precise temperature control. By adjusting the parameters of the PID controller, the response speed, stability, and anti-interference capability of the control system can be optimized.

[0053] In this embodiment, an adaptive PID control algorithm is used, and the control system calculates the heating power based on the temperature error. The parameters of the PID control algorithm can be adjusted according to actual needs.

[0054] Specifically, the expression for the adaptive PID control algorithm used in this embodiment is as follows:

[0055]

[0056] Where u(t): the controller output (heating power)

[0057] e(t): Temperature error (set temperature - actual temperature)

[0058] K p : Proportion coefficient

[0059] K i Integral coefficient

[0060] K d : Differential coefficient.

[0061] In the adaptive PID control algorithm, parameter K p K i and K d It is adaptive and can automatically adjust according to the system's real-time response and performance requirements. The implementation method of the adaptive PID control algorithm in this embodiment is as follows:

[0062] 1. Initialize PID parameter K p K i and K d The initial value of .

[0063] 2. Set the sampling time interval Δt.

[0064] 3. At each sampling time point t, perform the following steps:

[0065] 3.1 Read the current temperature value and the set temperature value, and calculate the temperature error e(t).

[0066] 3.2 Calculate the PID controller output u(t) based on the current temperature error and historical error information.

[0067] 3.3 Calculate the control error based on the actual output u(t) and the set output value.

[0068] 3.4 Update PID parameter K p K i and K d :

[0069] 3.4.1K p =K p +ΔK p

[0070] 3.4.2K i =K i +ΔK i

[0071] 3.4.3K d =K d +ΔK d

[0072] (where ΔK) p ΔK i and ΔK d It is an adaptive adjustment amount calculated based on control error and historical error information.

[0073] 3.5 Save the current PID parameters and controller output for the next sampling calculation.

[0074] The key to adaptive PID control algorithms lies in how to calculate the control error based on the actual output and the set output, and update the PID parameters through adaptive adjustment.

[0075] In step S4, the heating power is dynamically adjusted to control the temperature of the oilfield heating furnace.

[0076] In this step, the heating power is dynamically adjusted based on the heating power calculated by the PID control algorithm to control the temperature of the oilfield heater. The heating power can be controlled by adjusting the input current of the heating element (such as a resistance heater or burner) or the fuel supply. Based on the real-time temperature error and the output of the PID control algorithm, the control system can automatically adjust the heating power to gradually bring the actual temperature closer to the set temperature and maintain it near the set temperature.

[0077] In this embodiment, the control system dynamically adjusts the heating power calculated by the PID control algorithm to control the temperature of the oilfield heater. For example, if the current temperature is lower than the set temperature, the control system will increase the heating power; if the current temperature is higher than the set temperature, the control system will decrease the heating power. By continuously adjusting the heating power, the control system gradually brings the actual temperature closer to the set temperature and keeps it stable.

[0078] In summary, the intelligent digital control method for oilfield heaters of this invention improves the accuracy and stability of temperature control, enabling the heaters to more accurately control target temperatures, thereby increasing production efficiency and product quality. Simultaneously, the adaptive PID control algorithm automatically adjusts parameters based on actual operating conditions and environmental changes, enhancing the adaptability and robustness of the control system and reducing the need for manual intervention and adjustments. Furthermore, the dynamic adjustment of heating power allows for flexible control based on actual temperature changes and the magnitude of temperature errors, achieving a more stable and efficient heating process while saving energy and costs.

[0079] Please see the appendix Figure 2 As one embodiment of the present invention, the method further includes using a humidity sensor to monitor the humidity inside the oilfield heating furnace and adjusting the heating strategy based on the humidity information.

[0080] Specifically, the following is a further description of this implementation method:

[0081] Step S10: Use a temperature sensor to monitor the temperature of the oilfield heater in real time.

[0082] As described previously, temperature sensors are used to monitor the temperature of the oilfield heater in real time.

[0083] Step S11: Monitor the humidity of the oilfield heater in real time using a humidity sensor.

[0084] In this embodiment, an additional humidity sensor is installed to monitor the humidity inside the oilfield heater in real time. The humidity sensor can be placed in a suitable location within the heater to obtain accurate humidity data. The humidity sensor can be a capacitive humidity sensor, a resistive humidity sensor, or other types of humidity sensors. The sensor will continuously measure the humidity and transmit the humidity data to the control system for further processing.

[0085] Step S12: Calculate the temperature error and humidity information based on the monitoring data from the temperature sensor and humidity sensor.

[0086] In this embodiment, based on monitoring data from the temperature and humidity sensors, the control system calculates the temperature error between the real-time temperature and the set upper and lower temperature limits, and acquires real-time humidity information. The calculation method for the temperature error is the same as described previously, while the humidity information can be directly obtained from the humidity sensor.

[0087] Step S13: Adjust the heating strategy based on temperature error and humidity information.

[0088] In this implementation, the control system adjusts the heating strategy based on temperature error and humidity information. The heating strategy may include controlling the heating power and adjusting the heating time. By combining temperature error and humidity information, the control system can optimize the heating strategy according to specific heating and humidity control requirements, achieving more precise and stable temperature and humidity control.

[0089] For example, if the humidity is high, the control system can increase the heating power or extend the heating time accordingly to provide more heat to dry the air inside the oilfield heating furnace. Conversely, if the humidity is low, the heating power can be reduced or the heating time shortened to avoid over-drying or energy waste.

[0090] By using humidity sensors to monitor the humidity inside oilfield heaters and adjusting heating strategies based on this information, the humidity of oilfield heaters can be controlled more precisely, meeting specific heating and humidity control requirements, improving heating efficiency, avoiding energy waste and excessive dryness, and enhancing the adaptability of oilfield heaters to meet heating needs under different humidity conditions.

[0091] Please see the appendix Figure 3 As one embodiment of the present invention, the method further includes establishing a data acquisition system for collecting and storing historical operating data of oilfield heating furnaces, and combining the historical data for energy utilization optimization.

[0092] The following is a further description of this implementation:

[0093] Step S20: Establish a data acquisition system

[0094] In this embodiment, a data acquisition system is established to collect and store historical operating data of the oilfield heater. The data acquisition system may include sensors, data acquisition equipment, data storage equipment, and data processing software. Sensors are used to collect various operating parameters of the oilfield heater, such as temperature, humidity, and heating power. The data acquisition equipment is responsible for collecting and transmitting the data collected by the sensors. The data storage equipment is used to store the collected historical operating data. The data processing software is used to process, analyze, and optimize the collected data.

[0095] Step S21: Collect and store historical operating data of oilfield heating furnaces

[0096] The data acquisition system continuously collects various operating parameter data of the oilfield heater and stores them in the data storage device. This data includes temperature, humidity, heating power, heating time, humidity adjustment strategies, etc. Historical operating data can be collected continuously or sampled at time intervals.

[0097] Step S22: Optimize energy utilization by combining historical data

[0098] By utilizing collected historical operational data, energy utilization can be optimized. Data processing software can analyze and model historical data to understand the operating characteristics and energy utilization of oilfield heaters. By analyzing historical data, potential problems and areas for improvement in energy utilization can be identified, and corresponding optimization measures can be proposed.

[0099] For example, historical data analysis can determine the optimal heating power and heating time under different temperature and humidity conditions to maximize energy efficiency. Furthermore, statistical and trend analysis of historical data can identify the causes and influencing factors of energy waste, and propose corresponding improvement measures, such as optimizing heating strategies and improving equipment operating efficiency.

[0100] By combining historical data for energy utilization optimization, we can understand the operating characteristics and energy utilization status of oilfield heating furnaces, providing a basis and reference for optimization, identifying potential problems and areas for improvement in energy utilization, proposing corresponding optimization measures, reducing energy waste, thereby improving energy utilization efficiency, reducing energy costs, and enhancing economic benefits.

[0101] Please see the appendix Figure 4 As one embodiment of the present invention, the method further includes using a predictive model to predict heating demand based on historical data and real-time environmental information, and adjusting the heating strategy accordingly.

[0102] The following is a further description of this implementation:

[0103] Step S30: Collect historical data and real-time environmental information

[0104] In this implementation, historical operating data of the oilfield heater is first collected and recorded, including parameters such as temperature, humidity, and heating power. Simultaneously, real-time environmental information related to heating needs, such as weather conditions and oilfield production, is also required.

[0105] Step S31: Establish a prediction model

[0106] A predictive model is built using collected historical data and real-time environmental information. This model can employ various machine learning algorithms or statistical methods, such as regression analysis, time series analysis, and neural networks. The goal of the predictive model is to predict future heating demand based on historical data and real-time environmental information.

[0107] Step S32: Predict heating demand

[0108] Using established predictive models, heating demand over a future period is forecasted based on current environmental information and historical data. The forecast results can include temperature demand, humidity demand, or heating power demand at a future point in time.

[0109] Step S33: Adjust the heating strategy

[0110] Based on predicted heating demand, heating strategies are adjusted. These strategies can include controlling heating power and adjusting heating time. By proactively adjusting heating strategies based on predicted demand, future heating needs can be met and energy efficiency improved.

[0111] For example, if predicted heating demand indicates a need for higher temperatures in the near future, heating power can be increased or heating time extended in advance to ensure that the required temperature can be reached in time when needed. Conversely, if predicted heating demand indicates a need for lower temperatures in the near future, heating power can be reduced or heating time shortened to avoid overheating and energy waste.

[0112] By using predictive models to forecast heating demand based on historical data and real-time environmental information, and adjusting heating strategies accordingly, adjustments can be made in advance to meet future heating needs and improve energy efficiency. Furthermore, forecasting future heating demand helps in planning and optimizing furnace operation schedules, improving production efficiency, reducing energy waste and excessive consumption, and lowering energy costs.

[0113] Please see the appendix Figure 5 As one embodiment of the present invention, the method also includes a remote monitoring system to realize remote monitoring, operation and fault diagnosis of oilfield heating furnaces.

[0114] The following is a further description of this implementation:

[0115] Step S40: Establish a remote monitoring system

[0116] In this implementation, a remote monitoring system is established for remote monitoring, operation, and fault diagnosis of the oilfield heater. The remote monitoring system may include sensors, data transmission equipment, monitoring software, and a user interface. Sensors are used to collect various operating parameters of the oilfield heater in real time, such as temperature, humidity, and heating power. The data transmission equipment is responsible for transmitting the data collected by the sensors to the monitoring center. The monitoring software is used to process, analyze, and display the transmitted data. The user interface can be a control console in the monitoring center or a web interface accessed remotely via the internet.

[0117] Step S41: Remote monitoring and operation

[0118] The remote monitoring system allows for real-time monitoring of the oilfield heater's operating status and parameters. The monitoring software displays and analyzes data collected by sensors in real time, providing a comprehensive understanding of the heater's operation. The system monitors trends in parameters such as temperature, humidity, and heating power, as well as the equipment's working status and performance. Furthermore, it allows for remote operation of the heater, such as adjusting heating power and setting heating times.

[0119] Step S42: Fault Diagnosis and Early Warning

[0120] The remote monitoring system can detect faults in oilfield heating furnaces in real time, and perform fault diagnosis and early warning. The monitoring software can analyze and compare the data collected by sensors in real time according to the set fault diagnosis rules and algorithms. Once an anomaly or fault is detected, the system can automatically generate an alarm and send it to relevant personnel so that timely measures can be taken to troubleshoot and repair the fault.

[0121] Step S43: Remote Support and Maintenance

[0122] The remote monitoring system also provides remote support and maintenance capabilities. Through this system, maintenance personnel can remotely access the operating data and status information of the oilfield heating furnace, enabling real-time monitoring and analysis of equipment operation. In the event of a malfunction or abnormal situation, maintenance personnel can remotely diagnose and resolve the problem, reducing troubleshooting time and maintenance costs.

[0123] By using a remote monitoring system to remotely monitor, operate, and diagnose faults in oilfield heating furnaces, the operating status and parameters of the furnaces can be monitored in real time, problems can be identified and resolved in a timely manner, and the reliability and stability of the equipment can be improved. At the same time, remote operation of the heating furnaces enables remote adjustment and control, improving the flexibility and efficiency of operation and maintenance. Furthermore, through fault diagnosis and early warning functions, equipment faults can be detected and dealt with in a timely manner, reducing downtime and production losses.

[0124] Please see the appendix Figure 6The present invention also provides an intelligent digital implementation system for oilfield heating furnaces, comprising:

[0125] Temperature and humidity sensors are used to monitor the temperature and humidity inside the heating furnace, specifically in oilfield heating furnaces. These sensors can collect and report temperature and humidity data inside the furnace in real time, providing accurate input for subsequent control and adjustments.

[0126] The controller uses the above control method to control the heating power. Based on real-time data from temperature and humidity sensors, the controller can adaptively adjust the heating power and strategy to meet the heating demand and keep the temperature and humidity within the set range.

[0127] The data acquisition system is used to collect and store operational data. It can record the operating parameters of the heating furnace, heating power adjustment records, temperature and humidity data, etc. This data can be used for subsequent analysis, optimization, and fault diagnosis.

[0128] The remote monitoring system enables remote monitoring and operation of the heating furnace. Through this system, users can remotely access the furnace's real-time data, operating status, and parameters, as well as perform remote operation and control. This allows maintenance personnel to monitor the furnace's operation anytime, anywhere, and to perform remote adjustments and fault diagnosis.

[0129] Furthermore, the controller includes:

[0130] One or more processors, which are the core components of the controller and are responsible for executing the program code of the control methods described above. The processor can be a single processor or a system of multiple processors, used to process sensor data, execute algorithms, and generate control signals;

[0131] The memory stores the program code used in the aforementioned control method. This program code may include adaptive PID control algorithms, predictive models, fault diagnosis algorithms, etc. The memory may be flash memory, a solid-state drive, or other forms of non-volatile storage devices.

[0132] An interface for connecting temperature and humidity sensors. This interface connects the controller to the temperature and humidity sensors to receive temperature and humidity data collected by the sensors. The interface can be analog or digital, adapted according to the sensor type and communication protocol.

[0133] An output interface is used to send control signals to the heating furnace, adjusting heating power and strategies. The output interface can be either an analog or digital signal output interface, adapted to the heating furnace's control interface.

[0134] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent digital control of oilfield heating furnaces, characterized in that, The method comprises the following steps: Real-time monitoring the temperature of the oilfield heating furnace using temperature sensors; Calculating the temperature error between the real-time temperature and the upper and lower limits of the set temperature based on the monitoring data of the temperature sensors; Applying an adaptive PID control algorithm to calculate the heating power based on the temperature error; Dynamically adjusting the heating power to control the temperature of the oilfield heating furnace.

2. The intelligent digital control method for oilfield furnace according to claim 1, characterized in that, The expression of the adaptive PID control algorithm is as follows: where u(t) is the output of the controller, K p , K i , and K d are the proportional, integral, and derivative parameters of the PID controller, respectively, and e(t) is the temperature error.

3. The intelligent digital control method for oilfield furnace as claimed in claim 1, wherein, The step of dynamically adjusting the heating power further comprises: Automatically reducing the heating power when the temperature rises rapidly; Automatically increasing the heating power when the temperature drops to prevent the temperature from falling below the set lower limit.

4. The intelligent digital control method of oilfield furnace according to claim 3, characterized in that, the step of adaptively adjusting the PID parameters K p , K i , and K d comprises: K is automatically adjusted according to the statistics of control error p , K i and K d to improve control performance.

5. The intelligent digital control method for oilfield furnace as claimed in claim 1, wherein, Further comprising monitoring the humidity in the oilfield heating furnace using humidity sensors and adjusting the heating strategy in combination with the humidity information.

6. The intelligent digital control method for oilfield furnace as claimed in claim 1, wherein, Further comprising establishing a data acquisition system for collecting and storing historical operation data of the oilfield heating furnace and optimizing energy utilization in combination with the historical data.

7. The intelligent digital control method for oilfield furnace as claimed in claim 1, wherein, Further comprising using a prediction model to predict heating demand based on historical data and real-time environmental information and adjusting the heating strategy accordingly.

8. The intelligent digital control method for oilfield furnace of claim 1, wherein, Further comprising a remote monitoring system to realize remote monitoring, operation and fault diagnosis of the oilfield heating furnace.

9. An intelligent digital implementation system of oilfield heating furnace based on the intelligent digital implementation method and control method of oilfield heating furnace according to any one of claims 1-8, characterized in that, The system comprises: Temperature sensors and humidity sensors for monitoring the temperature and humidity in the heating furnace; A controller applying the control method of any one of claims 1-8 to control the heating power; A data acquisition system for collecting and storing operation data; A remote monitoring system for realizing remote monitoring and operation of the heating furnace.

10. The intelligent digital implementation system of an oilfield furnace of claim 9, wherein, The controller further comprises: One or more processors; Memory storing program code for executing the control method of any one of claims 1-8; An interface connected with the temperature sensors and humidity sensors; An output interface for sending control signals to the heating furnace.

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