Green energy heating method and system for oil field produced liquid

Abstract

The invention relates to the technical field of oilfield exploitation, in particular to a green energy heating method and system for oilfield produced liquid, and the method comprises the following steps: selecting each treatment point, needing to be heated, of the produced liquid as a heating demand node; selecting available heating equipment as a heat supply node; generating a predicted heating demand of each heating demand node; calculating the schedulable heating capacity of each heat supply node; obtaining a priority calling index of each heat supply node, and obtaining a heat supply node calling sequence; the schedulable heating capacity is adjusted, and the actual heat supply capacity is obtained; on the basis of the predicted heating requirement and the actual heat supply amount, the heat supply nodes are called in sequence for heating according to the heat supply node calling sequence sorting result; by scientifically predicting the heating demand of each treatment point and reasonably planning and calling available heating resources, precise heat supply is achieved, the energy utilization rate is increased, the oil gas treatment cost is reduced, and the safety and stability of the heating process are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of oilfield exploitation, and in particular to a green energy heating method and system for oilfield produced fluid. Background Art

[0002] In the process of oil production, the liquid produced from the oil well (referred to as "produced fluid") is usually composed of crude oil, natural gas, water and trace impurities. In cold climate conditions or in order to reduce the viscosity of the produced fluid for transportation and processing, the produced fluid needs to be heated.

[0003] In the oil extraction industry, green energy is used as a source of heat energy for produced fluids. Traditional produced fluid heating methods and technologies have many limitations. On the one hand, due to the lack of accurate prediction and management of heating demand, traditional methods often lead to a mismatch between heat supply and actual demand, which not only causes energy waste and increases costs, but also affects the continuity and efficiency of production due to slow response speed. On the other hand, the existing green energy heating equipment configuration is usually fixed, and it is difficult to make flexible adjustments according to changes in oil field layout or demand characteristics in different seasons and time periods, which limits the adaptability of the system and leads to higher operating costs in the long run. In addition, traditional solutions do not fully consider the energy loss factor in the heating process, and a large amount of heat energy may be lost during the transmission process, reducing the overall energy utilization efficiency. Moreover, most traditional heating solutions rely on manual operation and fixed modes, lack intelligent analysis and automated scheduling mechanisms, which is not conducive to improving the accuracy of management and control. When multiple heating demand points operate at the same time, if there is no reasonable priority planning, there may be a shortage of supply or overload, which will affect the safety and stability of the entire system. Summary of the invention

[0004] The first purpose of the present invention is to provide a green energy heating method for oilfield produced fluid, which can achieve precise heating, improve energy utilization, reduce operating costs, and ensure the safety and stability of the heating process by scientifically predicting the heating needs of each processing point, rationally planning and calling available heating resources.

[0005] A green energy heating method for oilfield produced fluid comprises the following steps:

[0006] According to the oilfield layout, each processing point where the produced fluid needs to be heated is selected as the heating demand node;

[0007] Select available heating equipment as heat supply node;

[0008] Based on a preset time interval, traverse all heating demand nodes to perform heating demand prediction, and generate an estimated heating demand for each heating demand node, wherein the estimated heating demand corresponds to the heating demand node one by one;

[0009] According to the preset time interval, all heat supply nodes are traversed, the heat supply nodes that can provide heat are screened out, and the schedulable heating amount of each heat supply node is calculated;

[0010] Setting a priority call attribute, performing a priority call evaluation on the heat supply node according to the priority call attribute, obtaining a priority call index of each heat supply node, and using the priority call index to sort the heat supply nodes in descending order to obtain a heat supply node call sequence;

[0011] Traverse the heat supply node call sequence, analyze the energy loss during the heating process, adjust the schedulable heating amount, and obtain the actual available heat;

[0012] Based on the estimated heating demand and the actual available heat, the heat supply nodes are called in sequence according to the heat supply node call sequence sorting results to perform heating operations until the heating demand of each heating demand node is met.

[0013] As a preferred solution of the present invention, according to the oil field layout, a method for selecting each processing point where produced fluid needs to be heated as a heating demand node includes:

[0014] Conduct a comprehensive layout analysis of the oil field, including its geographical location, geological structure, oil well distribution, process flow and location of gathering and transportation stations;

[0015] Evaluate the characteristics of the fluids produced from different oil wells. For oil wells with low produced fluid temperature or high viscosity, heating treatment is required;

[0016] Make a selection among all potential heating demand points and determine the final heating demand node.

[0017] As a preferred embodiment of the present invention, a method for predicting heating demand includes:

[0018] Collect historical data, including meteorological information and seasonal change information;

[0019] Remove noise and outliers from historical data and organize the data in a time series format;

[0020] Extract features from the processed data, including: lag features, rolling window statistics, and external variables;

[0021] Select matching mathematical modeling methods or machine learning algorithms to build a heating demand forecasting model;

[0022] The extracted features are input into the heating demand prediction model for prediction, and the model parameters are continuously optimized.

[0023] As a preferred solution of the present invention, a method for screening out heat supply nodes that can provide heat includes:

[0024] Check all heat supply nodes to understand their current status and technical parameters, including: equipment status, power output, energy source and geographical location;

[0025] Based on the current status and technical parameters, select the equipment that can provide the required heat within the specified time.

[0026] As a preferred solution of the present invention, a method for calculating the dispatchable heating amount of each heat supply node comprises the following steps:

[0027] Consult the equipment's technical manual or the information provided by the manufacturer to obtain its rated output power;

[0028] According to the preset time interval, determine the actual running time of the equipment within the time period;

[0029] The dispatchable heating capacity is calculated using the following formula: Q t =P*T;

[0030] Among them, Q t is the dispatchable heating amount, P is the rated output power, and T is the actual operating time.

[0031] As a preferred solution of the present invention, the priority calling attributes include: energy efficiency and transmission loss;

[0032] The method of evaluating the priority calling of the heat supply nodes according to the priority calling attribute to obtain the priority calling index of each heat supply node includes:

[0033] Determine the weights of energy efficiency and transmission losses;

[0034] Determine scoring rules for energy efficiency and transmission losses;

[0035] Score the energy efficiency and transmission loss of each heat supply node to obtain an energy efficiency score and a transmission loss score;

[0036] The energy efficiency score and transmission loss score of each heat supply node are weighted and calculated to obtain the priority call index. The calculation formula is as follows:

[0037] ,P i =(E i ×W1)+(T i ×W2);

[0038] Among them, P i is the priority call index of the i-th heat supply node;

[0039] Ei is the energy efficiency score of the ith heat supply node;

[0040] W1 is the weight of energy efficiency;

[0041] T i is the transmission loss score of the ith heat supply node;

[0042] W2 is the weight of transmission loss.

[0043] As a preferred solution of the present invention, an energy loss analysis is performed during the heating process, including: transmission loss, internal equipment loss and external conditions;

[0044] The calculation formula for the dispatchable heating capacity is:

[0045] Q s =Q t *(1-L1-L2-L3);

[0046] Among them, Q s is the actual heat supply; Q t is the dispatchable heating amount, L1 is the heat loss caused by transmission loss, L2 is the heat loss caused by internal loss of equipment, and L3 is the heat loss caused by external conditions.

[0047] The second object of the present invention is to provide a green energy heating system for oilfield produced fluid, which realizes automation and intelligent management through modular design, and has the advantages of high efficiency and energy saving, intelligent optimization, flexible adaptability and simplified operation.

[0048] The present invention discloses a green energy heating system for oilfield produced fluid, the system comprising:

[0049] Demand node selection module: used to select various processing points where produced fluid needs to be heated as heating demand nodes according to the oil field layout;

[0050] Supply node selection module: selects available heating equipment as heat supply node;

[0051] Demand forecasting module: based on the preset time interval, traverses all heating demand nodes to forecast heating demand and generates the estimated heating demand for each heating demand node;

[0052] Scheduling heating calculation module: according to the preset time interval, traverse all heat supply nodes, screen out the heat supply nodes that can provide heat, and calculate the schedulable heating of each heat supply node;

[0053] Priority call evaluation module: set the priority call attribute, perform priority call evaluation on the heat supply node according to the priority call attribute, obtain the priority call index of each heat supply node, and use the priority call index to sort the heat supply nodes in descending order to form a heat supply node call sequence;

[0054] Energy loss analysis module: traverses the heat supply node call sequence, analyzes the energy loss during the heating process, adjusts the schedulable heating amount, and obtains the actual available heat;

[0055] Heating scheduling execution module: Based on the estimated heating demand and the actual available heat, the heat supply nodes are called in sequence according to the heat supply node call sequence sorting results to perform heating operations until the heating demand of each heating demand node is met.

[0056] An oilfield produced fluid green energy heating electronic device of the present invention comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor. The transceiver, the memory and the processor are connected via the bus, and the steps in the method are implemented when the computer program is executed by the processor.

[0057] A computer-readable storage medium of the present invention stores a computer program, which implements the steps in the method when executed by a processor.

[0058] Compared with the prior art, the present invention has the following beneficial effects: (1) by predicting heating demand based on a preset time interval, the expected heating demand of each heating demand node can be accurately estimated, so that the heat supply is more closely matched with the actual demand, thus avoiding energy waste and reducing unnecessary cost expenditure; at the same time, the present invention can quickly respond to changing demand, timely adjust the heating strategy, ensure that the continuity and efficiency of production are not affected, select available heating equipment as heat supply nodes, and sort the heat supply nodes according to the priority call attribute, so as to ensure that the most effective resources are used first, thereby improving the adaptability of the system;

[0059] (2) Through intelligent scheduling of heat supply nodes, additional maintenance and operating costs caused by equipment overload or shortage are reduced, achieving long-term stable low-cost operation. At the same time, the present invention also takes energy loss factors into consideration, further improves energy utilization efficiency, reduces heat energy loss during transmission, effectively prevents the occurrence of supply shortage or overload, and ensures the safe and stable operation of the entire heating system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a schematic flow chart of a method for heating oilfield produced fluid using green energy. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example

[0065] Reference Figure 1 This embodiment provides a method for heating oilfield produced fluid with green energy, comprising the following steps:

[0066] S1. According to the oilfield layout, each processing point where the produced fluid needs to be heated is selected as the heating demand node;

[0067] Specifically, a comprehensive layout analysis of the oil field is required; this includes but is not limited to understanding the geographical location, geological structure, oil well distribution, process flow, location of gathering and transportation stations and other information of the oil field; through this information, it can be determined which areas or facilities will involve the treatment of produced fluids, thereby preliminarily delineating possible heating demand points.

[0068] Next, the characteristics of the liquid produced by different oil wells should be evaluated. Because of the different formation conditions and production stages, the temperature, viscosity, water content and other physical properties of the produced liquid (oil, water and gas mixture) of different oil wells may vary greatly. For those oil wells with low temperature or high viscosity of the produced liquid, heating treatment is usually required to ensure that the fluid can maintain good fluidity during transportation;

[0069] Finally, make a choice among all potential heating demand points and determine the final heating demand node. This decision-making process should not only take into account the current actual needs, but also reserve a certain degree of flexibility for possible changes in the future. For example, as the oil field development progresses, new oil wells may be drilled and the original production facilities may be adjusted. Therefore, when selecting the heating demand node, a certain degree of foresight should be possessed to facilitate the subsequent system expansion and optimization.

[0070] S2, selecting an available heating device as a heat supply node;

[0071] Conduct a preliminary screening of all possible heating equipment and select those that can meet the basic heating requirements. This includes checking whether the equipment is in good working condition and has sufficient power to meet the expected maximum heating demand.

[0072] S3. Based on a preset time interval, traverse all heating demand nodes to perform heating demand prediction, and generate an estimated heating demand for each heating demand node, wherein the estimated heating demand corresponds to the heating demand node one by one;

[0073] Specifically, a reasonable time interval needs to be set based on production plans and historical data; this time interval can be hours, days, weeks or months, depending on the operation cycle and management needs of the oil field. Shorter time intervals (such as every hour) can provide more precise demand forecasts, but also increase the computational complexity; longer time intervals help identify long-term trends and simplify scheduling;

[0074] Methods for forecasting heating demand, including:

[0075] Collect historical data, including production records such as oil well output, temperature, pressure, meteorological information such as temperature, wind speed, precipitation, and seasonal changes;

[0076] Remove noise and outliers from historical data and organize the data in a time series format to prepare for subsequent analysis;

[0077] Perform feature extraction on the processed data, which includes:

[0078] Lag features: Create new features based on data from a certain period of time in the past. For example, if we want to predict today's demand, we can add the demand from yesterday, the day before yesterday, or even earlier as new features. Lag features help capture time dependencies and delay effects, allowing the model to understand the relationship between the current state and the past state, thereby improving the modeling capabilities of complex dynamic systems.

[0079] Rolling window statistics: Calculate statistical data, such as moving average and standard deviation, within a fixed-length time window. As new data arrives, old data will be removed from the window to keep the data within the window up to date. Rolling window statistics can smooth short-term fluctuations, identify long-term trends and seasonal components, provide stable signals, and improve the robustness of the model.

[0080] External variables: Introduce factors that are not directly derived from the target variable itself but have an important impact on it, such as meteorological data (temperature, precipitation), market information (crude oil prices), holiday arrangements, etc.; through external variables, increase relevance, improve generalization ability, respond to emergencies, make the model more comprehensively consider all relevant factors, and improve the ability to predict unknown situations in the future;

[0081] Select appropriate mathematical modeling methods or machine learning algorithms to build a heating demand forecasting model; common models include time series analysis (such as ARIMA), machine learning algorithms (such as random forest, support vector machine SVM), deep learning models (such as LSTM or GRU), and ensemble learning (such as XGBoost or LightGBM);

[0082] The extracted features are input into the model, and the model parameters are continuously optimized to improve the prediction accuracy. As new data is continuously added, the model is automatically updated and optimized to ensure that it always reflects the latest actual situation.

[0083] For each heating demand node, the specific estimated heating demand is calculated using the constructed heating demand prediction model within a preset time interval.

[0084] This step can significantly improve the accuracy of prediction and the flexibility of the system. Specifically, by introducing rich feature engineering such as lag features, rolling window statistics and external variables, the model can more comprehensively capture time dependence, long-term trends and the influence of external factors, thereby generating more accurate heating demand forecasts; the optimized model parameters and automatic update mechanism ensure that the model always reflects the latest actual situation and improves the ability to predict future unknown situations; selecting the appropriate time interval can not only provide precise demand forecasts, but also identify long-term trends and simplify the scheduling process; the data preprocessing step of removing noise and outliers ensures data quality and lays a solid foundation for subsequent analysis; finally, for each heating demand node, the constructed heating demand prediction model is used to calculate the specific estimated heating demand within the preset time interval, ensuring the rationality and efficiency of resource allocation and scheduling, and achieving the goals of energy conservation, emission reduction and cost reduction as a whole.

[0085] S4. According to a preset time interval, traverse all heat supply nodes, select heat supply nodes that can provide heat, and calculate the schedulable heating amount of each heat supply node;

[0086] Based on the time interval (such as hours, days, weeks or months) determined in step S3, the specific time period for which the evaluation needs to be conducted is clarified. This step ensures the pertinence and timeliness of the evaluation.

[0087] Perform a comprehensive inspection of all heat supply nodes to understand their current status and technical parameters, including but not limited to:

[0088] Equipment status: confirm whether the equipment is in normal working condition and whether there is any maintenance requirement;

[0089] Power output: record the maximum power output capability of each device;

[0090] Energy source: Understand the type of energy used by the equipment (such as wind power, solar power, etc.) and its supply;

[0091] Geographic location: Consider the distance between the equipment and the heating demand node, as this affects energy transmission efficiency and losses;

[0092] Based on the above information, select the devices that can provide the required heat within the specified time; specifically:

[0093] Exclude unavailable devices: If a device is in maintenance or cannot meet the power requirements, remove it from the candidate list;

[0094] Assess the stability of energy supply: ensure that the energy supply of the selected equipment is stable and reliable to avoid insufficient heating due to energy shortage;

[0095] Consider transmission loss: For devices that are farther away, additional redundant capacity may be required to account for heat loss during transmission.

[0096] The method for calculating the dispatchable heating amount of each heat supply node comprises the following steps:

[0097] Consult the equipment's technical manual or the information provided by the manufacturer to obtain its rated output power;

[0098] According to the preset time interval, determine the actual running time of the equipment within the time period;

[0099] The dispatchable heating capacity is calculated using the following formula: Q t =P*T;

[0100] Among them, Q t is the dispatchable heating amount, P is the rated output power, and T is the actual operating time;

[0101] Through the detailed evaluation and calculation methods in the above S4 step, we can accurately screen out the heating equipment that can provide the required heat within the specified time, and accurately calculate the dispatchable heating capacity of each device. This approach not only ensures the flexibility and responsiveness of the system, but also effectively reduces energy waste and reduces operating costs while ensuring production continuity and efficiency. Specifically, by comprehensively checking information such as equipment status, power output, energy source, and geographical location, we are able to exclude unavailable equipment, ensure that the energy supply of the selected equipment is stable and reliable, and consider transmission losses to optimize thermal energy utilization. Finally, using formula Q t =P*T calculates the dispatchable heating amount, provides a solid data foundation for intelligent dispatch, improves the accuracy of management and control, and enhances the safety and stability of the system.

[0102] S5. Setting priority call attributes, the priority call attributes include: energy efficiency and transmission loss; performing priority call evaluation on the heat supply nodes according to the priority call attributes to obtain a priority call index of each heat supply node, and using the priority call index to sort the heat supply nodes in descending order to obtain a heat supply node call sequence;

[0103] Priority call attributes include: energy efficiency and transmission losses;

[0104] Energy efficiency is a measure of a device’s ability to convert input energy into useful heat;

[0105] Transmission loss considers the distance between the equipment and the heating demand node and the resulting energy transmission loss;

[0106] The method of evaluating the priority call of the heat supply node according to the priority call attribute to obtain the priority call index of each heat supply node includes:

[0107] Determine the weights of energy efficiency and transmission losses;

[0108] Determine scoring rules for energy efficiency and transmission losses;

[0109] Score the energy efficiency and transmission loss of each heat supply node to obtain an energy efficiency score and a transmission loss score;

[0110] The energy efficiency score and transmission loss score of each heat supply node are weighted and calculated to obtain the priority call index. The calculation formula is as follows:

[0111] ,P i =(E i ×W1)+(T i ×W2);

[0112] Among them, P i is the priority call index of the i-th heat supply node;

[0113] E i is the energy efficiency score of the ith heat supply node;

[0114] W1 is the weight of energy efficiency;

[0115] T i is the transmission loss score of the ith heat supply node;

[0116] W2 is the weight of transmission loss.

[0117] All heat supply nodes are sorted in descending order using the calculated priority call index to form a final heat supply node call sequence, ensuring that high-priority nodes are called first;

[0118] Through this step, by comprehensively considering the two key priority call attributes of energy efficiency and transmission loss, and conducting priority call evaluation on the heat supply nodes, the priority call index of each node can be obtained; this index not only reflects the ability of the device to convert input energy into effective heat, but also takes into account the distance between the device and the heating demand node and the resulting energy transmission loss. Subsequently, all heat supply nodes are sorted in descending order using these priority call indices to form a more scientific and reasonable heat supply node call sequence; the advantage of this sorting method is that it can ensure that those nodes with high energy efficiency and low transmission loss are called first; in this way, not only can the energy utilization efficiency be improved and unnecessary waste be reduced, but also the energy loss in the transmission process can be reduced, further improving the energy efficiency of the entire system.

[0119] S6, traverse the heat supply node call sequence, analyze the energy loss in the heating process, adjust the schedulable heating amount, and obtain the actual available heat;

[0120] The method for analyzing energy loss during heating includes:

[0121] Transmission loss: Calculate the heat energy loss from the heat supply node to the heating demand node. This depends on factors such as the length of the pipeline, the quality of the insulation material, and the ambient temperature;

[0122] Internal losses: Evaluate the energy conversion efficiency of the equipment itself, including losses during startup and operation; for example, an electric heater may have efficiency losses when converting electricity to heat energy;

[0123] Impact of external conditions: Consider the impact of weather conditions (such as wind speed and temperature) on heat transfer; cold weather may lead to higher heat losses, while extreme heat may reduce equipment efficiency.

[0124] The calculation formula for the dispatchable heating capacity is:

[0125] Q s =Q t *(1-L1-L2-L3);

[0126] Among them, Q s is the actual heat supply; Q t is the dispatchable heating amount, L1 is the heat loss caused by transmission loss, L2 is the heat loss caused by internal loss of equipment, and L3 is the heat loss caused by external conditions;

[0127] This method significantly improves the flexibility and response speed of the system, optimizes resource allocation and reduces costs, enhances the safety and stability of the system, and reduces the risk of failure through precise energy loss analysis and dynamic adjustment of heating strategies. At the same time, it improves the overall economic benefits, achieves energy conservation and emission reduction goals, ensures efficient use of energy, and provides a reliable scheduling basis for the green energy heating system of oilfield produced fluids, thereby achieving higher operating efficiency and lower operating costs.

[0128] S7, based on the estimated heating demand and the actual available heat, calling the heat supply nodes in sequence according to the heat supply node calling sequence sorting result to perform heating operations until the heating demand of each heating demand node is met;

[0129] Specifically, according to the heat supply node calling sequence, the heat supply node with the highest priority calling index is called first;

[0130] Matching the actual available heat of the heat supply node with the estimated heating demand of the corresponding heating demand node;

[0131] If the actual available heat is greater than or equal to the estimated heating demand, the heating operation is directly performed; if it is less than the estimated heating demand, the shortfall is recorded and the next heat supply node is called;

[0132] This step is a key link in the green energy heating method of oilfield produced fluid. It is necessary to comprehensively consider multiple factors such as heating demand, heat supply, energy loss, etc. to ensure the efficiency, safety and stability of the heating process. By optimizing the heating strategy and equipment configuration, the heating efficiency and energy utilization efficiency can be further improved, providing strong support for oilfield exploitation.

[0133] The present invention discloses a green energy heating system for oilfield produced fluid, the system comprising:

[0134] Demand node selection module: used to select various processing points where produced fluid needs to be heated as heating demand nodes according to the oil field layout;

[0135] Supply node selection module: selects available heating equipment as heat supply node;

[0136] Demand forecasting module: based on the preset time interval, traverses all heating demand nodes to forecast heating demand and generates the estimated heating demand for each heating demand node;

[0137] Scheduling heating calculation module: according to the preset time interval, traverse all heat supply nodes, screen out the heat supply nodes that can provide heat, and calculate the schedulable heating of each heat supply node;

[0138] Priority call evaluation module: set the priority call attribute, perform priority call evaluation on the heat supply node according to the priority call attribute, obtain the priority call index of each heat supply node, and use the priority call index to sort the heat supply nodes in descending order to form a heat supply node call sequence;

[0139] Energy loss analysis module: traverses the heat supply node call sequence, analyzes the energy loss during the heating process, adjusts the schedulable heating amount, and obtains the actual available heat;

[0140] Heating scheduling execution module: Based on the estimated heating demand and the actual available heat, the heat supply nodes are called in sequence according to the heat supply node call sequence sorting results to perform heating operations until the heating demand of each heating demand node is met.

[0141] The green energy heating system for oilfield produced fluids has realized the automation and intelligent management from demand analysis to supply scheduling through modular design. Its main advantages are high efficiency and energy saving, intelligent optimization, flexible adaptation and simplified operation: accurate prediction of heating demand and effective scheduling of heat supply reduce unnecessary energy consumption and improve energy utilization efficiency; priority call evaluation and energy loss analysis ensure that energy loss is minimized while meeting heating demand and optimizing the entire heating process; the system can be flexibly adjusted according to different oilfield layouts and conditions to be suitable for a variety of operating environments, enhancing the adaptability and practicality of the system; modular system design simplifies the operating process, reduces dependence on manual intervention, improves work efficiency and also reduces the possibility of human error. In short, this system can not only effectively improve heating efficiency but also significantly reduce operating costs, providing a more scientific and reasonable solution for oilfield produced fluid treatment.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A green energy heating method for oilfield produced fluid, characterized in that: The following steps are involved: According to the oilfield layout, each processing point where the produced fluid needs to be heated is selected as the heating demand node; Select available heating equipment as heat supply node; Based on a preset time interval, traverse all heating demand nodes to perform heating demand prediction, and generate an estimated heating demand for each heating demand node, wherein the estimated heating demand corresponds to the heating demand node one by one; According to the preset time interval, traverse all heat supply nodes, screen out heat supply nodes that can provide heat, and calculate the schedulable heating amount of each heat supply node; Setting a priority call attribute, performing a priority call evaluation on the heat supply node according to the priority call attribute, obtaining a priority call index of each heat supply node, and using the priority call index to sort the heat supply nodes in descending order to obtain a heat supply node call sequence; Traverse the heat supply node call sequence, analyze the energy loss during the heating process, adjust the schedulable heating amount, and obtain the actual available heat; Based on the estimated heating demand and the actual available heat, the heat supply nodes are called in sequence according to the heat supply node call sequence sorting results to perform heating operations until the heating demand of each heating demand node is met.

2. The green energy heating method for oilfield produced fluid according to claim 1, characterized in that: The method of selecting various processing points where produced fluid needs to be heated as heating demand nodes according to the oil field layout includes: Conduct a comprehensive layout analysis of the oil field, including its geographical location, geological structure, oil well distribution, process flow and location of gathering and transportation stations; Evaluate the characteristics of the fluids produced from different oil wells. For oil wells with low produced fluid temperature or high viscosity, heating treatment is required; Make a selection among all potential heating demand points and determine the final heating demand node.

3. The green energy heating method for oilfield produced fluid according to claim 1, characterized in that: The method for predicting heating demand comprises: Collect historical data, including meteorological information and seasonal change information; Remove noise and outliers from historical data and organize the data in a time series format; Extract features from the processed data, including: lag features, rolling window statistics, and external variables; Select matching mathematical modeling methods or machine learning algorithms to build a heating demand forecasting model; The extracted features are input into the heating demand prediction model for prediction, and the model parameters are continuously optimized.

4. The green energy heating method for oilfield produced fluid according to claim 1, characterized in that: The method for screening out heat supply nodes that can provide heat includes: Check all heat supply nodes to understand their current status and technical parameters, including: equipment status, power output, energy source and geographical location; Based on the current status and technical parameters, select the devices that can provide the required heat within the specified time.

5. The green energy heating method for oilfield produced fluid according to claim 4, characterized in that: The method for calculating the schedulable heating amount of each heat supply node comprises the following steps: Consult the equipment's technical manual or the information provided by the manufacturer to obtain its rated output power; According to the preset time interval, determine the actual running time of the equipment within the time period; The dispatchable heating capacity is calculated using the following formula: Q t =P*T; Among them, Q t is the dispatchable heating amount, P is the rated output power, and T is the actual operating time.

6. The green energy heating method for oilfield produced fluid according to claim 1, characterized in that: Priority call attributes include: energy efficiency and transmission losses; The method of performing priority call evaluation on the heat supply nodes according to the priority call attribute to obtain the priority call index of each heat supply node includes: Determine the weights of energy efficiency and transmission losses; Determine scoring rules for energy efficiency and transmission losses; Score the energy efficiency and transmission loss of each heat supply node to obtain an energy efficiency score and a transmission loss score; The energy efficiency score and transmission loss score of each heat supply node are weighted and calculated to obtain the priority call index. The calculation formula is as follows: P i =(E i ×W1)+(T i ×W2); Among them, P i is the priority call index of the i-th heat supply node; E i is the energy efficiency score of the ith heat supply node; W1 is the weight of energy efficiency; T i is the transmission loss score of the ith heat supply node; W2 is the weight of transmission loss.

7. The green energy heating method for oilfield produced fluid according to claim 5, characterized in that: The energy loss analysis during the heating process includes: transmission loss, internal equipment loss and external conditions; The calculation formula of the dispatchable heating amount is: <h2 style=";text-align:left;direction:ltr">Q<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> =Q<h2 style=";text-align:left;direction:ltr"> t <h2 style=";text-align:left;direction:ltr"> *(1-L1-L2-L3); Among them, Q s is the actual heat supply; Q t is the dispatchable heating amount, L1 is the heat loss caused by transmission loss, L2 is the heat loss caused by internal loss of equipment, and L3 is the heat loss caused by external conditions.

8. An oilfield produced fluid green energy heating system, characterized in that: The system comprises: Demand node selection module: used to select various processing points where produced fluid needs to be heated as heating demand nodes according to the oil field layout; Supply node selection module: selects available heating equipment as heat supply node; Demand forecasting module: based on the preset time interval, traverses all heating demand nodes to forecast heating demand and generates the estimated heating demand for each heating demand node; Scheduling heating calculation module: according to the preset time interval, traverse all heat supply nodes, screen out the heat supply nodes that can provide heat, and calculate the schedulable heating of each heat supply node; Priority call evaluation module: set the priority call attribute, perform priority call evaluation on the heat supply node according to the priority call attribute, obtain the priority call index of each heat supply node, and use the priority call index to sort the heat supply nodes in descending order to form a heat supply node call sequence; Energy loss analysis module: traverses the heat supply node call sequence, analyzes the energy loss during the heating process, adjusts the schedulable heating amount, and obtains the actual available heat; Heating scheduling execution module: Based on the estimated heating demand and the actual available heat, the heat supply nodes are called in sequence according to the heat supply node call sequence sorting results to perform heating operations until the heating demand of each heating demand node is met.

9. An oilfield produced fluid green energy heating electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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