Modularization-based oil field gathering and transportation intelligent operation and maintenance system and method

Abstract

The invention discloses an oil field gathering and transportation intelligent operation and maintenance system and method based on modularization, and relates to the technical field of oil field gathering and transportation operation and maintenance coordinated control, and the method comprises the following steps: collecting oil field gathering and transportation inter-well station data under the area through a sensor, and combining a current inter-well station crude oil reserve prediction value and a crude oil collection condition; establishing an inter-well station daily oil production prediction digital twinborn model, and obtaining an oil quantity acquisition prediction period; for inter-well stations under the oil transfer stations, the inter-well stations connected with the oil transfer stations in an area are allocated according to the oil mass collection prediction periods of different inter-well stations and the transfer capacity of the oil transfer stations, meanwhile, the AI algorithm is used for optimizing the liquid volume, pressure and temperature parameters in real time, and the mining and transportation process coordination of the oil transfer stations and the inter-well stations is achieved. According to the method, the digital twinborn model is established for the inter-well station in the area, intelligent operation and maintenance and coordinated control of oil field gathering and transportation in the area are facilitated, and practicability and functionality are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of oilfield gathering and transportation operation and maintenance, and in particular to a modular-based intelligent operation and maintenance system and method for oilfield gathering and transportation. Background Art

[0002] In the process of oilfield gathering and transportation, one oil transfer station often corresponds to multiple well stations in one area. Therefore, ensuring the efficient and stable operation of the oil transfer station and the well station is crucial to the entire oilfield production. Therefore, it is necessary to coordinate and control the oilfield gathering and transportation well stations and oil transfer stations in the area to ensure the smooth progress of oilfield production. Traditional oilfield gathering, transportation and operation and maintenance systems mostly rely on a single model for data prediction to make macro adjustments to oilfield gathering and transportation. Although physical models alone can build a basic framework based on geological conditions, it is difficult to adapt to complex changes in actual production in real time. Using only data-driven models may lack compliance with the physical characteristics and geological laws of the reservoir, resulting in a large deviation between the predicted results and the actual results. The limitations of this single model make the predicted data inaccurate and unable to provide a reliable basis for production decisions. It is also impossible to reasonably plan and allocate the oil volume processing cycle of the oil transfer station within the jurisdiction, resulting in low practicality and functionality.

[0003] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0004] In view of the problems in the related technology, the present invention proposes a modular-based intelligent operation and maintenance system and method for oilfield gathering and transportation to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] To this end, the specific technical solution adopted by the present invention is as follows: The modular intelligent operation and maintenance method for oilfield gathering and transportation includes the following steps: S1. Collect data from the well stations of the oil fields in the region through sensors, combine the current predicted crude oil reserves of the well stations and the crude oil collection situation, establish a digital twin model for predicting daily oil production at the well stations, and obtain the oil collection prediction cycle; S2. For the inter-well stations under the jurisdiction of the oil transfer station, according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer station, the inter-well stations connected to the oil transfer stations in the region are deployed, and the AI ​​algorithm is used to optimize the liquid volume, pressure, and temperature parameters in real time to achieve coordination of the production and transportation process of the oil transfer station and the inter-well stations.

[0006] As a preferred embodiment, the S1 comprises the following steps: S11. Install sensors in the oilfield gathering and transportation well stations under the jurisdiction of the region, including flow sensors, pressure sensors, and temperature sensors, to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process at the well stations; S12. By combining the physical model and the data-driven model, a digital twin model of the well stations under the jurisdiction of the region is constructed. The physical model provides a basic description of the oilfield gathering and transportation process. The data-driven model uses historical and real-time data to optimize the parameters of the digital twin model and obtain the oil volume collection prediction cycle.

[0007] As a preferred implementation, the S12 comprises the following steps: S121. Collect the daily oil production, wellhead pressure, oil well production, and oil well production history data of different well stations in the region, combine the geological data of different well stations, build a three-dimensional geological model of the reservoir, describe the distribution, structure and properties of the reservoir, and Simulate the flow process of fluid in the reservoir, simulate the theoretical reservoir pressure in different time periods and the predicted value of oil production of the physical model; S122. Based on the historical production data of the well station, a data-driven model is constructed to optimize the parameters of the physical model. The historical production data of the well station is collected and normalized, including the historical daily oil production, oil well pressure, temperature, water content, equipment operation time, valve opening, and the Pearson correlation coefficient between each feature and the daily oil production is calculated. The algorithm formula is: ; in, For different eigenvalues, is the daily oil production, and represent the mean of characteristic and daily oil production, Represents the number of samples, retained The features of are used as correlation features; Based on the correlation characteristics, a linear regression equation is constructed as a data-driven model to obtain the predicted value of daily oil production: ; in, is the intercept, The coefficients representing different features, is the error term, is the predicted value of oil production on that day, Represents the characteristic parameter value of the day; Use historical data to train the data-driven model and optimize the model's hyperparameters. The specific steps are: The historical data is divided into a training set and a validation set, where the training set contains 70% of the historical data and the validation set contains 30% of the historical data. The mean square error is used as the loss function: ; in, Respectively represent The predicted value at time and Real daily oil production at all times, Represents the number of samples, and updates the model coefficients iteratively through the gradient descent method : ; in, Represents the learning rate, the model is validated by the validation set, and evaluated by the mean absolute error. represent The coefficient value at the moment; S123. Based on the weight ratio, the predicted value of oil production from the comprehensive physical model and the predicted value of oil production from the data-driven model are used to obtain the output value of oil production from the digital twin model: ; in, They represent the current daily oil production output value of the digital twin model of the well station, the daily oil production prediction value of the physical model, and the daily oil production prediction value of the data-driven model. represents the weight ratio, .

[0008] As a preferred implementation, the S123 includes the following steps: S1231, based on the current daily digital twin model oil production output value of the well station ,Establish Daily oil production output value set , with the date as the horizontal axis and the daily oil production output value of the digital twin model of the inter-well station as the vertical axis, a line chart of the current inter-well station oil production collection prediction period is drawn.

[0009] As a preferred embodiment, S2 comprises the following steps: S21. Oil volume collection prediction cycle of each well station under the jurisdiction of the oil transfer station in the collection area ,in represents the date, Represents the number of inter-well stations under the current oil transfer station, obtains the current oil volume processing cycle of the oil transfer station by statistics, and coordinates and optimizes it in combination with the processing capacity of the subordinate oil transfer stations; S22. For well stations and oil transfer stations that cannot be deployed, AI algorithms are used to coordinate and optimize the production and transportation process while meeting the transfer capacity of the oil transfer stations and the production requirements of each well station.

[0010] As a preferred embodiment, the S21 comprises the following steps: S211, the oil volume collection prediction cycle of each well station under the oil transfer station is accumulated to obtain the oil volume processing cycle of the oil transfer station: The predicted oil volume of each well station under the daily oil transfer station in the cycle is accumulated to obtain the predicted oil volume processing value of the daily oil transfer station ,in represents the date, The number of the current oil transfer station; S212, based on the maximum value of the oil volume processing at the current oil transfer station , statistical abnormal date: when ≥0, indicating that the oil volume at the oil transfer station is normal on the current date and no oil transfer is required; when <0, indicating that the oil volume at the transfer station is abnormal on the current date and needs to be allocated; S213. Count the abnormal dates of oil volume processing at all oil transfer stations. When the abnormal dates are consecutive dates, calculate the daily deficit value. , and calculate the surplus value of the normal oil transfer stations under its jurisdiction ,in For abnormal dates, when At the same time, the oilfield gathering and transportation pipelines of the two current oil transfer stations will be re-planned.

[0011] As a preferred embodiment, the S22 comprises the following steps: S221, when the abnormal date is a discontinuous date, the current oil transfer station is recorded as an oil transfer station that cannot be deployed, and the production and transportation parameters of the oil transfer station and the inter-well station are adjusted through a multi-layer perceptron, and the steps are as follows: The difference data between the historical oil volume processing value of the oil transfer station and the oil volume collection value of the inter-well station is collected as the input of the input layer. After the nonlinear transformation of the hidden layer, the output layer outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station. The model is trained using historical data, and the weight and bias parameters of the model are adjusted by minimizing the objective function. S222. On the abnormal date, the difference between the oil transfer station and the subordinate inter-well stations on that day is input into the multi-layer perceptron, and the model outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station.

[0012] The modularized intelligent operation and maintenance system for oilfield gathering and transportation includes data acquisition module, digital twin model building module, cycle prediction module, and site deployment module: The data acquisition module includes a flow sensor, a pressure sensor, and a temperature sensor, which are used to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process of the well station, and collect historical operation data of each well station and transmit it to the subsequent module; The digital twin model building module constructs a digital twin model of the well station under the jurisdiction of the region through the combination of physical model and data-driven model, provides a basic description of the oilfield gathering and transportation process through the physical model, and optimizes the parameters of the digital twin model by using historical and real-time data through the data-driven model; The cycle prediction module is established based on the current daily digital twin model oil production output value of the well station. The daily oil production output value set is taken as the horizontal coordinate, and the daily digital twin model oil production output value of the well station is taken as the vertical coordinate. A line chart of the current well station oil production collection prediction cycle is drawn; The site allocation module allocates the inter-well stations connected to the oil transfer stations in the region according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer stations. At the same time, it uses AI algorithms to optimize the liquid volume, pressure, and temperature parameters in real time to achieve coordination of the production and transportation processes of the oil transfer stations and inter-well stations.

[0013] The beneficial effects of the present invention are: 1. The present invention establishes a digital twin model for predicting daily oil production for the inter-well stations based on the real-time parameters and historical data of the oil field gathering and transportation transfer stations and inter-well stations in the region, establishes a prediction cycle for the daily oil production of the inter-well stations under its jurisdiction, predicts the operation of the transfer stations, and controls the gathering and transportation of the transfer stations and inter-well stations in the region, thereby enhancing the functionality of the method; 2. The present invention combines the daily oil production forecast value of the physical model, considers the changes in geological parameters caused by the changes in oil reserves during the oil field acquisition process, and integrates the daily oil production forecast value of the data-driven model to construct a line chart of the current inter-well station oil volume acquisition forecast cycle. The physical model provides a basic framework and constraints based on geological conditions to ensure that the prediction is consistent with the physical characteristics and geological laws of the reservoir. The digital model can supplement and correct the physical model, and use the information in real-time data and historical data to adjust the prediction results to make them more in line with the actual production situation, thereby enhancing the data accuracy of the control adjustment between sites; 3. The present invention accumulates the oil volume collection prediction cycles of each well station under the oil transfer station, clarifies the oil volume processing cycle of the oil transfer station itself, and accumulates the daily oil volume prediction values ​​within the cycle to obtain the daily processing prediction value, providing basic data for subsequent analysis, so as to plan the production arrangement and resource reserve of the oil transfer station in advance, and performs pairing processing of oil transfer stations for abnormalities on consecutive dates, and coordinates and optimizes the operation and maintenance of abnormal oil transfer stations and well stations for abnormalities on non-consecutive dates, which can eliminate deviations caused by occasional abnormalities and avoid unnecessary waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a modularized intelligent operation and maintenance method for oilfield gathering and transportation according to an embodiment of the present invention.

[0016] Figure 2 It is a flow chart of a modularized intelligent operation and maintenance method for oilfield gathering and transportation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0018] According to an embodiment of the present invention, a modular-based intelligent operation and maintenance system and method for oilfield gathering and transportation are provided.

[0019] The present invention is further described with reference to the accompanying drawings and specific embodiments: Example 1: Figure 1 As shown, according to the modularized intelligent operation and maintenance method for oilfield gathering and transportation according to an embodiment of the present invention, the method comprises the following steps: S1. Collect data from the well stations of the oil fields in the region through sensors, combine the current predicted crude oil reserves of the well stations and the crude oil collection situation, establish a digital twin model for predicting daily oil production at the well stations, and obtain the oil collection prediction cycle; S11. Install sensors in the oilfield gathering and transportation well stations under the jurisdiction of the region, including flow sensors, pressure sensors, and temperature sensors, to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process at the well stations; S12. By combining the physical model and the data-driven model, a digital twin model of the well stations under the jurisdiction of the region is constructed. The physical model provides a basic description of the oilfield gathering and transportation process. The data-driven model uses historical and real-time data to optimize the parameters of the digital twin model and obtain the oil volume collection prediction cycle. S121. Collect the daily oil production, wellhead pressure, oil well production, and oil well production history data of different well stations in the region, combine the geological data of different well stations, build a three-dimensional geological model of the reservoir, describe the distribution, structure and properties of the reservoir, and Simulate the flow process of fluid in the reservoir, simulate the theoretical reservoir pressure in different time periods and the predicted value of oil production of the physical model; It should be noted that the geological data of the well station include reservoir geological characteristics, reservoir parameters, etc., among which reservoir parameters include permeability, porosity, oil saturation, etc. When constructing the physical model of the well station, it is necessary to further improve the physical model through equations, describe the mass transfer of crude oil in the pipeline based on the mass conservation equation, describe the temperature change of crude oil in the pipeline based on the energy conservation equation, and describe the flow characteristics of crude oil in the pipeline through the Darcy-Weisbach equation.

[0020] S122. Based on the historical production data of the well station, a data-driven model is constructed to optimize the parameters of the physical model. The historical production data of the well station is collected and normalized, including the historical daily oil production, oil well pressure, temperature, water content, equipment operation time, valve opening, and the Pearson correlation coefficient between each feature and the daily oil production is calculated. The algorithm formula is: ; in, For different eigenvalues, is the daily oil production, and represent the mean of characteristic and daily oil production, Represents the number of samples, retained The features of are used as correlation features; Based on the correlation characteristics, a linear regression equation is constructed as a data-driven model to obtain the predicted value of daily oil production: ; in, is the intercept, The coefficients representing different features, is the error term, is the predicted value of oil production on that day, Represents the characteristic parameter value of the day; Use historical data to train the data-driven model and optimize the model's hyperparameters. The specific steps are: The historical data is divided into a training set and a validation set, where the training set contains 70% of the historical data and the validation set contains 30% of the historical data. The mean square error is used as the loss function: ; in, Respectively represent The predicted value at time and Real daily oil production at all times, Represents the number of samples, and updates the model coefficients iteratively through the gradient descent method : ; in, Represents the learning rate, the model is validated by the validation set, and evaluated by the mean absolute error. represent The coefficient value at the moment; It should be noted that the updated coefficient value is obtained by subtracting the product of the learning rate and the partial derivative of the mean square error with respect to the coefficient from the current coefficient value. Each coefficient is iteratively updated to gradually find the coefficient value combination that minimizes the loss function MSE. The calculation formula for the mean absolute error is: ; The smaller the MAE is, the smaller the average error between the model's predicted value and the true value is, and the better the model's prediction performance is. By setting the judgment threshold based on the magnitude of the collected data, the model's prediction performance can be further judged.

[0021] S123. Based on the weight ratio, the predicted value of oil production from the comprehensive physical model and the predicted value of oil production from the data-driven model are used to obtain the output value of oil production from the digital twin model: ; in, They represent the current daily oil production output value of the digital twin model of the well station, the daily oil production prediction value of the physical model, and the daily oil production prediction value of the data-driven model. represents the weight ratio, .

[0022] It should be noted that The weight ratio is set to 0.6 and 0.4, and the weight ratio can also be adjusted according to the actual situation to meet the actual situation of different well stations; S1231, based on the current daily digital twin model oil production output value of the well station ,Establish Daily oil production output value set , with the date as the horizontal axis and the daily oil production output value of the digital twin model of the inter-well station as the vertical axis, a line chart of the current inter-well station oil production collection prediction period is drawn.

[0023] It should be noted that by combining the daily oil production forecast of the physical model, the changes in geological parameters caused by changes in oil reserves during oil field collection can be considered, and the daily oil production forecast of the comprehensive data-driven model can be used to construct a line chart of the current inter-well station oil production forecast cycle. The physical model provides a basic framework and constraints based on geological conditions to ensure that the prediction is consistent with the physical characteristics and geological laws of the reservoir. The digital model can supplement and correct the physical model, and use the information in real-time data and historical data to adjust the prediction results to make them more in line with actual production conditions.

[0024] Embodiment 2: S2. For the inter-well stations under the jurisdiction of the oil transfer station, according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer station, the inter-well stations connected to the oil transfer station in the region are deployed, and at the same time, the AI ​​algorithm is used to optimize the liquid volume, pressure, and temperature parameters in real time to achieve the coordination of the oil transfer station and the inter-well station production and transportation process; S21. Oil volume collection prediction cycle of each well station under the jurisdiction of the oil transfer station in the collection area ,in represents the date, Represents the number of inter-well stations under the current oil transfer station, obtains the current oil volume processing cycle of the oil transfer station by statistics, and coordinates and optimizes it in combination with the processing capacity of the subordinate oil transfer stations; S21 includes the following steps: S211, the oil volume collection prediction cycle of each well station under the oil transfer station is accumulated to obtain the oil volume processing cycle of the oil transfer station: The predicted oil volume of each well station under the daily oil transfer station in the cycle is accumulated to obtain the predicted oil volume processing value of the daily oil transfer station ,in represents the date, The number of the current oil transfer station; S212, based on the maximum value of the oil volume processing at the current oil transfer station , statistical abnormal date: when ≥0, indicating that the oil volume at the oil transfer station is normal on the current date and no oil transfer is required; when <0, indicating that the oil volume at the transfer station is abnormal on the current date and needs to be allocated; S213. Count the abnormal dates of oil volume processing at all oil transfer stations. When the abnormal dates are consecutive dates, calculate the daily deficit value. , and calculate the surplus value of the normal oil transfer stations under its jurisdiction ,in For abnormal dates, when At the same time, the oilfield gathering and transportation pipelines of the two current oil transfer stations will be re-planned.

[0025] It should be noted that by accumulating the oil volume collection prediction cycle of each well station under the oil transfer station, the oil volume processing cycle of the oil transfer station itself is clarified. At the same time, the daily oil volume prediction values ​​within the cycle are accumulated to obtain the daily processing prediction value, which provides basic data for subsequent analysis, so as to plan the production arrangement and resource reserve of the oil transfer station in advance. The oil transfer station pairing processing is carried out for the abnormalities of consecutive dates, which can eliminate the deviation caused by occasional abnormalities and avoid unnecessary waste of resources. S22. For well stations and oil transfer stations that cannot be deployed, AI algorithms are used to coordinate and optimize the production and transportation process while meeting the transfer capacity of the oil transfer stations and the production requirements of each well station; S221, when the abnormal date is a discontinuous date, the current oil transfer station is recorded as an oil transfer station that cannot be deployed, and the production and transportation parameters of the oil transfer station and the inter-well station are adjusted through a multi-layer perceptron, and the steps are as follows: The difference data between the historical oil volume processing value of the oil transfer station and the oil volume collection value of the inter-well station is collected as the input of the input layer. After the nonlinear transformation of the hidden layer, the output layer outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station. The model is trained using historical data, and the weight and bias parameters of the model are adjusted by minimizing the objective function. S222. On the abnormal date, the difference between the oil transfer station and the subordinate inter-well stations on that day is input into the multi-layer perceptron, and the model outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station.

[0026] It should be noted that the number of neurons in the output layer is determined according to needs. If the oil production rate of the inter-well station is to be output, assuming that there are 10 inter-well stations, the output layer can have 10 neurons, corresponding to the oil production rate adjustment value of each inter-well station. If the valve opening of the oil transfer station is to be output, the number of neurons in the output layer can be set according to the number of valves in the oil transfer station. The output value of the neurons in the output layer is the oil production rate of the inter-well station or the valve opening of the oil transfer station predicted by the model.

[0027] Example 3: Figure 2 As shown in the figure, the modular intelligent operation and maintenance system for oilfield gathering and transportation includes data acquisition module, digital twin model building module, cycle prediction module and site deployment module: The data acquisition module includes a flow sensor, a pressure sensor, and a temperature sensor, which are used to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process at the well station, and to collect historical operation data of each well station and transmit it to subsequent modules; The digital twin model building module builds the digital twin model of the well stations under the jurisdiction of the region through the combination of physical models and data-driven models. The physical model provides a basic description of the oilfield gathering and transportation process, and the data-driven model uses historical and real-time data to optimize the parameters of the digital twin model. The cycle prediction module is established based on the daily oil production output value of the digital twin model of the current well station. The daily oil production output value set is taken as the horizontal coordinate, and the daily digital twin model oil production output value of the well station is taken as the vertical coordinate. A line chart of the current well station oil production collection prediction cycle is drawn; The site allocation module allocates the inter-well stations connected to the oil transfer stations in the region according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer stations. At the same time, it uses AI algorithms to optimize the liquid volume, pressure, and temperature parameters in real time to achieve coordination of the production and transportation processes of the oil transfer stations and inter-well stations.

[0028] In summary, the present invention establishes a digital twin model for predicting daily oil production for the inter-well stations based on the real-time parameters and historical data of the oil field gathering and transportation transfer stations and inter-well stations in the region, establishes a prediction cycle for the daily oil production of the inter-well stations under its jurisdiction, and predicts the operation of the transfer stations, so as to control the gathering and transportation of the transfer stations and inter-well stations in the region, thereby enhancing the functionality of the method; By combining the daily oil production forecast value of the physical model, considering the changes in geological parameters caused by the changes in oil reserves during the oil field acquisition process, and integrating the daily oil production forecast value of the data-driven model, a line chart of the current inter-well station oil collection forecast cycle is constructed. The physical model provides a basic framework and constraints based on geological conditions to ensure that the prediction conforms to the physical characteristics and geological laws of the reservoir. The digital model can supplement and correct the physical model, and use the information in real-time data and historical data to adjust the prediction results to make it more in line with the actual production situation, thereby enhancing the data accuracy of the control adjustment between stations. By accumulating the oil collection forecast cycle of each inter-well station under the oil transfer station, the oil processing cycle of the oil transfer station itself is clarified. At the same time, the daily oil volume forecast values ​​within the cycle are accumulated to obtain the daily processing forecast value, providing basic data for subsequent analysis, so as to plan the production arrangement and resource reserves of the oil transfer station in advance, and pairing the oil transfer station for abnormalities on consecutive dates, and coordinate and optimize the operation and maintenance of abnormal oil transfer stations and inter-well stations for abnormalities on non-continuous dates, which can eliminate the deviation caused by occasional abnormalities and avoid unnecessary waste of resources.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A modularized intelligent operation and maintenance method for oilfield gathering and transportation, characterized in that: The method comprises the following steps: S1. Collect data from the well stations of the oil fields in the region through sensors, combine the current predicted crude oil reserves of the well stations and the crude oil collection situation, establish a digital twin model for predicting daily oil production at the well stations, and obtain the oil collection prediction cycle; S2. For the inter-well stations under the jurisdiction of the oil transfer station, according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer station, the inter-well stations connected to the oil transfer stations in the region are deployed, and the AI ​​algorithm is used to optimize the liquid volume, pressure, and temperature parameters in real time to achieve coordination of the production and transportation process of the oil transfer station and the inter-well stations.

2. The modular-based intelligent operation and maintenance method for oilfield gathering and transportation according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Install sensors in the oilfield gathering and transportation well stations under the jurisdiction of the region, including flow sensors, pressure sensors, and temperature sensors, to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process at the well stations; S12. By combining the physical model and the data-driven model, a digital twin model of the well stations under the jurisdiction of the region is constructed. The physical model provides a basic description of the oilfield gathering and transportation process. The data-driven model uses historical and real-time data to optimize the parameters of the digital twin model and obtain the oil volume collection prediction cycle.

3. The modular-based intelligent operation and maintenance method for oilfield gathering and transportation according to claim 2 is characterized in that: The S12 comprises the following steps: S121. Collect the daily oil production, wellhead pressure, oil well production, and oil well production history data of different well stations in the region, combine the geological data of different well stations, build a three-dimensional geological model of the reservoir, describe the distribution, structure and properties of the reservoir, and Simulate the flow process of fluid in the reservoir, simulate the theoretical reservoir pressure in different time periods and the predicted value of oil production of the physical model; S122. Based on the historical production data of the well station, a data-driven model is constructed to optimize the parameters of the physical model. The historical production data of the well station is collected and normalized, including the historical daily oil production, oil well pressure, temperature, water content, equipment operation time, valve opening, and the Pearson correlation coefficient between each feature and the daily oil production is calculated. The algorithm formula is: ； in, For different eigenvalues, is the daily oil production, and represent the mean of characteristic and daily oil production, Represents the number of samples, retained The features of are used as correlation features; Based on the correlation characteristics, a linear regression equation is constructed as a data-driven model to obtain the daily oil production forecast value: ； in, is the intercept, The coefficients representing different features, is the error term, is the predicted value of oil production on that day, Represents the characteristic parameter value of the day; Use historical data to train the data-driven model and optimize the model's hyperparameters. The specific steps are: The historical data is divided into a training set and a validation set, where the training set contains 70% of the historical data and the validation set contains 30% of the historical data. The mean square error is used as the loss function: ； in, Respectively represent The predicted value at the moment and Real daily oil production at all times, Represents the number of samples, and updates the model coefficients iteratively through the gradient descent method : ； in, Represents the learning rate, the model is validated by the validation set, and evaluated by the mean absolute error. represent The coefficient value at the moment; S123. Based on the weight ratio, the predicted value of oil production from the comprehensive physical model and the predicted value of oil production from the data-driven model are used to obtain the output value of oil production from the digital twin model: ； in, They represent the current daily oil production output value of the digital twin model of the well station, the daily oil production prediction value of the physical model, and the daily oil production prediction value of the data-driven model. represents the weight ratio, .

4. The modularized intelligent operation and maintenance method for oilfield gathering and transportation according to claim 3 is characterized in that: The S123 comprises the following steps: S1231, based on the current daily digital twin model oil production output value of the well station ,Establish Daily oil production output value set , with the date as the horizontal axis and the daily oil production output value of the digital twin model of the inter-well station as the vertical axis, a line chart of the current inter-well station oil production collection prediction period is drawn.

5. The modular-based intelligent operation and maintenance method for oilfield gathering and transportation according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Oil volume collection prediction cycle of each well station under the jurisdiction of the oil transfer station in the collection area ,in represents the date, Represents the number of inter-well stations under the current oil transfer station, obtains the current oil volume processing cycle of the oil transfer station by statistics, and coordinates and optimizes it in combination with the processing capacity of the subordinate oil transfer stations; S22. For well stations and oil transfer stations that cannot be deployed, AI algorithms are used to coordinate and optimize the production and transportation process while meeting the transfer capacity of the oil transfer stations and the production requirements of each well station.

6. The modular-based intelligent operation and maintenance method for oilfield gathering and transportation according to claim 5 is characterized in that: The S21 comprises the following steps: S211, the oil volume collection prediction cycle of each well station under the oil transfer station is accumulated to obtain the oil volume processing cycle of the oil transfer station: The predicted oil volume of each well station under the daily oil transfer station in the cycle is accumulated to obtain the predicted oil volume processing value of the daily oil transfer station ,in Represents the date, The number of the current oil transfer station; S212, based on the maximum value of the oil volume processing at the current oil transfer station , statistical abnormal date: when ≥0, indicating that the oil volume at the oil transfer station is normal on the current date and no oil transfer is required; when <0, indicating that the oil volume at the transfer station is abnormal on the current date and needs to be allocated; S213. Count the abnormal dates of oil volume processing at all oil transfer stations. When the abnormal dates are consecutive dates, calculate the daily deficit value. , and calculate the surplus value of the normal oil transfer stations under its jurisdiction ,in For abnormal dates, when At the same time, the oilfield gathering and transportation pipelines of the two current oil transfer stations will be re-planned.

7. The modular-based intelligent operation and maintenance method for oilfield gathering and transportation according to claim 6 is characterized in that: The S22 comprises the following steps: S221, when the abnormal date is a discontinuous date, the current oil transfer station is recorded as an oil transfer station that cannot be deployed, and the production and transportation parameters of the oil transfer station and the inter-well station are adjusted through a multi-layer perceptron, and the steps are as follows: The difference data between the historical oil volume processing value of the oil transfer station and the oil volume collection value of the inter-well station is collected as the input of the input layer. After the nonlinear transformation of the hidden layer, the output layer outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station. The model is trained using historical data, and the weight and bias parameters of the model are adjusted by minimizing the objective function. S222. On the abnormal date, the difference between the oil transfer station and the subordinate inter-well stations on that day is input into the multi-layer perceptron, and the model outputs the adjusted production and transportation parameters, including the oil production rate adjustment value of the inter-well station and the valve opening adjustment value of the oil transfer station.

8. The modularized intelligent operation and maintenance system for oilfield gathering and transportation is characterized by: The system adopts the modularized intelligent operation and maintenance method for oilfield gathering and transportation as described in any one of claims 1 to 7, including a data acquisition module, a digital twin model establishment module, a cycle prediction module, and a site deployment module: The data acquisition module includes a flow sensor, a pressure sensor, and a temperature sensor, which are used to collect real-time flow, wellhead pressure, and temperature during the crude oil collection process of the well station, and collect historical operation data of each well station and transmit it to the subsequent module; The digital twin model building module constructs a digital twin model of the well station under the jurisdiction of the region through the combination of physical model and data-driven model, provides a basic description of the oilfield gathering and transportation process through the physical model, and optimizes the parameters of the digital twin model by using historical and real-time data through the data-driven model; The cycle prediction module is established based on the current daily digital twin model oil production output value of the well station. The daily oil production output value set is taken as the horizontal coordinate, and the daily digital twin model oil production output value of the well station is taken as the vertical coordinate. A line chart of the current well station oil production collection prediction cycle is drawn; The site allocation module allocates the inter-well stations connected to the oil transfer stations in the region according to the oil volume collection prediction cycle of different inter-well stations and the transfer capacity of the oil transfer stations. At the same time, it uses AI algorithms to optimize the liquid volume, pressure, and temperature parameters in real time to achieve coordination of the production and transportation processes of the oil transfer stations and inter-well stations.

Images