Oil and gas field electric energy metering control method and system based on localization of swan mongolian system

Abstract

The invention discloses an oil and gas field electric energy metering control method and system based on the localization of a swan-gap system, and the electric energy metering control system constructed based on the swan-gap system achieves the electric energy metering and distribution of the oil and gas field. Firstly, the minimum expected operation duration of the oil well production unit under the current condition is obtained through the pre-trained back propagation neural network, and the electric energy influence degree parameter corresponding to the oil well production unit and the expected power consumption required under the expected operation condition are further determined, so that the accuracy of oil and gas field electric energy metering can be improved; all the oil well production units are subjected to correlation degree division to obtain a plurality of oil well operation groups, weight parameters corresponding to the oil well operation groups are determined in a weight assignment mode, and the weight parameters represent the electric energy demand load conditions of the oil well operation groups; and finally, according to the weight parameters corresponding to the oil well operation group and the expected power consumption of each oil well production unit, accurate electric energy distribution of the oil well operation group is realized, and the overall production efficiency of an oil-gas field is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield production management, and in particular to an oil and gas field electricity metering control method and system based on the localization of the Hongmeng system, and a computer-readable storage medium. Background Art

[0002] Since oil and gas field production operations are relatively complicated and the process cycle is not short, multiple operating systems are often required to cooperate with each other to effectively complete the production operations. At present, most of the oil and gas field production on the market has been carried out in a coordinated operation mode. Although the energy consumption of each operating system can be monitored in real time during the production process, considering the actual influence of factors such as the differences in the oil and gas field mining environment, the insufficient energy utilization rate of the operating system itself, or the inability to grasp the operating time of the operating system, the energy consumption of the monitored operating system may not enable the operating system to operate in the expected good working condition, resulting in the overall production efficiency of the oil and gas field cannot be guaranteed. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present invention proposes an oil and gas field power metering control method and system based on the localization of the Hongmeng system, which can improve the accuracy of oil and gas field power metering and ensure the overall production efficiency of the oil and gas field.

[0004] In the first aspect, an embodiment of the present invention provides an oil and gas field electric energy metering control method based on the localization of the Hongmeng system, which is applied to an electric energy metering control system built based on the Hongmeng system. The oil and gas field is configured with multiple oil well production units, and the electric energy metering control system is respectively connected to each of the oil well production units; the method comprises the following steps: Step S1, traversing the operation status of the oil well production unit in a preset historical period, thereby obtaining multiple groups of historical operation data, wherein each group of the historical operation data includes the historical power consumption, historical operation power and historical effective operation time of one of the oil well production units in the preset historical period; inputting each group of the historical operation data as training data into a back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network; Step S2: for each of the oil well production units, obtain the current power consumption and the current operating power of the oil well production unit, use the current power consumption and the current operating power as input data, and input them into the pre-trained back propagation neural network, so as to obtain the minimum expected operating time of the oil well production unit; determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, and make an expected correction to the current power consumption according to the power influence degree parameter to obtain the expected power consumption; Step S3, according to the matching relationship between each of the electric energy impact parameters and the pre-configured multiple electric energy impact characteristic intervals, all the oil well production units are divided according to the correlation degree, so as to obtain multiple oil well operation groups, wherein each of the oil well operation groups includes a plurality of the oil well production units; Step S4: for each of the oil well operation groups, weighting the oil well operation group is performed according to the power influence parameters and the current operating power of all the oil well production units in the oil well operation group, thereby obtaining a weight parameter corresponding to the oil well operation group, wherein the weight parameter represents the power demand load of the oil well operation group; Step S5: Allocate electric energy to each of the oil well operation groups according to the weight parameters corresponding to each of the oil well operation groups and the expected power consumption of each of the oil well production units.

[0005] Optionally, in one embodiment of the present invention, the step in step S2, determining the electric energy impact parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, comprises the following steps: Step S21, respectively normalizing and updating the current power consumption and the current operating power to obtain updated current power consumption and the current operating power; determining the expected operation start time and the expected operation end time corresponding to the minimum expected operation time according to the minimum expected operation time; Step S22, calculate the sum of the current power consumption of all the oil well production units to obtain a first total power consumption; determine the power impact parameter corresponding to the oil well production unit based on the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption.

[0006] Optionally, in one embodiment of the present invention, the step in step S22, according to the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption, determines the power energy impact parameter corresponding to the oil well production unit, including the following steps: Step S221: Substitute the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption into the electric energy characteristic calculation formula for calculation, so as to obtain the electric energy influence degree parameter corresponding to the oil well production unit, wherein the electric energy characteristic calculation formula is as follows: ; is the electric energy impact parameter corresponding to the oil well production unit, is the minimum expected runtime, is the expected operation start time, is the expected end time of the operation, is the energy consumption coefficient of the oil well production unit, , is the current power consumption of the oil well production unit, is the first total power consumption, is the current operating power of the oil well production unit.

[0007] Optionally, in one embodiment of the present invention, the step in step S2, performing expected correction on the current power consumption according to the power impact parameter to obtain expected power consumption, comprises the following steps: Step S23, determine whether the power impact parameter corresponding to the oil well production unit is less than or equal to the preset power impact balance coefficient. If so, take the current power consumption as the expected power consumption. Otherwise, calculate the expected power consumption by the power consumption correction formula, wherein the power consumption correction formula is as follows: ; is the expected power consumption of the oil well production unit, is the current power consumption of the oil well production unit, is the electric energy impact parameter corresponding to the oil well production unit, is the preset electric energy impact balance coefficient, is the power factor of the oil well production unit, .

[0008] Optionally, in one embodiment of the present invention, the step in step S4, weighting the oil well operation group according to the electric energy influence parameter and the current operating power of all the oil well production units in the oil well operation group, thereby obtaining the weight parameter corresponding to the oil well operation group, includes the following steps: Step S41, calculating the average operating power of the oil well operation group according to the current operating powers of all the oil well production units in the oil well operation group; Step S42: for each of the oil well production units in the oil well operation group, determine a weight component corresponding to the oil well production unit according to a relative relationship between the current operating power and the average operating power of the oil well production unit; Step S43: Obtain the weight parameter corresponding to the oil well operation group according to the weight components corresponding to all the oil well production units.

[0009] Optionally, in one embodiment of the present invention, step S5 includes the following steps: Step S51: for each of the oil well operation groups, obtain the sum of the expected power consumption of all the oil well production units in the oil well operation group to obtain a first total power consumption; Step S52, calculating the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group to obtain a standard allocated power; Step S53: Allocate electric energy not less than the standard allocated electric energy to the oil well operation group.

[0010] Optionally, in one embodiment of the present invention, the step in step S1, inputting each group of the historical operation data as training data into a back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network, includes the following steps: Step S11, inputting any group of the historical operation data as training data into a back propagation neural network built based on the Hongmeng system, taking the historical power consumption and the historical operation power as input data, and taking the historical effective operation time as preset output data, training the back propagation neural network once; Step S12, executing step S11 multiple times until the number of training times of the back propagation neural network reaches the number of groups of the historical operation data.

[0011] In the second aspect, an embodiment of the present invention provides an oil and gas field electric energy metering control system based on the localization of the Hongmeng system, including: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in the first aspect is implemented.

[0012] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in the first aspect.

[0013] The oil and gas field electricity metering control method based on the localization of the Hongmeng system proposed in the present invention realizes oil and gas field electricity metering and distribution through an electricity metering control system constructed based on the Hongmeng system, which has greater flexibility and scalability. Specifically, by traversing the operating conditions of the oil well production unit in a preset historical period to train a back propagation neural network, and then based on the pre-trained back propagation neural network, the minimum expected operating time of the oil well production unit under the current situation is obtained. The minimum expected operating time reflects the expected operating conditions of the oil well production unit. Based on this, the electricity influence parameters corresponding to the oil well production unit and the expected operating conditions can be further determined. The expected power consumption required can improve the accuracy of electricity metering in oil and gas fields, and then multiple oil well operation groups are obtained by dividing the correlation between all oil well production units. Since the expected operating conditions of the oil well production units in each oil well operation group match, the corresponding weight parameters are determined by assigning weights to the oil well operation groups. The weight parameters characterize the power demand load of the oil well operation groups. Finally, according to the weight parameters corresponding to the oil well operation groups and the expected power consumption of each oil well production unit, accurate power allocation for the oil well operation groups can be achieved to ensure that the oil and gas fields are in good operating condition, which is beneficial to improving the overall production efficiency of the oil and gas fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of an oil and gas field electric energy metering control method based on the localization of the Hongmeng system provided by an embodiment of the present invention; Figure 2 yes Figure 1 A partial flowchart of the step S1 in “inputting each group of historical operation data as training data into a back propagation neural network built based on the Hongmeng system for training, thereby obtaining a pre-trained back propagation neural network”; Figure 3 yes Figure 1 A partial flow chart of the step S2 in step "determining the electric energy impact parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power"; Figure 4 yes Figure 1 A partial flow chart of the step S2 in step "making an expected correction to the current power consumption according to the power impact parameter to obtain the expected power consumption"; Figure 5 yes Figure 1 A partial flow chart of the step S4 in which "weights are assigned to the oil well operation group according to the power influence parameters and current operating powers of all oil well production units in the oil well operation group, thereby obtaining weight parameters corresponding to the oil well operation group"; Figure 6 yes Figure 1Flow chart of step S5 in FIG. Figure 7 It is a structural schematic diagram of an oil and gas field electric energy metering control system based on the localization of the Hongmeng system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, an embodiment of the present invention provides an oil and gas field power metering control method based on the localization of the Hongmeng system. The method can be but is not limited to being applied to a power metering control system built based on the Hongmeng system. The oil and gas field is equipped with multiple oil well production units, and the power metering control system is respectively connected to each oil well production unit. Specifically, it can include but is not limited to steps S1 to S5; wherein, the power metering control system in this embodiment can be but is not limited to being built based on the localization of the open source Hongmeng system. For example, the hardware architecture and software architecture associated with the power metering control system can be built based on the microkernel of the open source Hongmeng system, thereby realizing a localization based on the Hongmeng system. It can be understood that through the efficient computing environment and stable data logic framework provided by the open source Hongmeng system, the electricity of the oil and gas fields can be metered and distributed, thereby improving the accuracy of electricity metering in the oil and gas fields and ensuring the overall production efficiency of the oil and gas fields. Since the Hongmeng system is a distributed operating system well known to those skilled in the art, in order to avoid redundancy, the domestically produced Hongmeng system will not be described here. The specific architecture and parameters of the electricity metering control system built based on the Hongmeng system can be set as needed. There is no restriction here. It only needs to ensure that it can be applied to, but is not limited to, the oil and gas field electricity metering control method provided in this embodiment.

[0016] It should be noted that in different application scenarios, the number and type of oil well production units configured in the oil and gas field may be different, and specific settings need to be made according to the actual scenario. For example, the oil well production unit can be but is not limited to an oil production unit (such as an oil pump and its related supporting systems), a water injection unit, an oil transportation unit, and a pollution treatment unit, etc. However, no matter what the scenario is, it can be applied to the oil and gas field electricity metering control method provided in this embodiment. The only difference is that the associated parameters involved in each embodiment are not the same in different application scenarios, but this is not the only limitation.

[0017] Step S1, traversing the operation status of the oil well production unit in a preset historical period, thereby obtaining multiple groups of historical operation data, wherein each group of historical operation data includes the historical power consumption, historical operation power and historical effective operation time of one of the oil well production units in the preset historical period; inputting each group of historical operation data as training data into the back propagation neural network built based on the Hongmeng system for training, thereby obtaining a pre-trained back propagation neural network; It should be noted that the preset historical period can be selected according to actual conditions. It is usually recommended to select a historical period close to the current scene for research, so that the error of the historical operation data obtained may be relatively small, but this is not the only limitation. The operation status of the oil well production unit in the preset historical period can be determined by consulting the recorded work log, historical monitoring data, etc., among which the historical power consumption can be but not limited to representing the total power consumption of the oil well production unit in the preset historical period, the historical operating power can be but not limited to representing the average operating power of the oil well production unit in the preset historical period, and the historical effective operation time can be but not limited to representing the actual operation time of the oil well production unit in the preset historical period. That is to say, for example, the time occupied by the oil well production unit stopping or suspending work due to specific working conditions in the preset historical period is not included in the historical effective operation time. In other words, the historical effective operation time reflects the actual operation status of the oil well production unit in the preset historical period; Step S2: for each oil well production unit, obtain the current power consumption and current operating power of the oil well production unit, use the current power consumption and current operating power as input data, input into the pre-trained back propagation neural network, so as to obtain the minimum expected operating time of the oil well production unit; determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, and make an expected correction to the current power consumption according to the power influence degree parameter to obtain the expected power consumption; Step S3, according to the matching relationship between each power impact parameter and a plurality of pre-configured power impact characteristic intervals, all oil well production units are divided according to their correlation, thereby obtaining a plurality of oil well operation groups, wherein each oil well operation group includes a plurality of oil well production units; Step S4: for each oil well operation group, weight the oil well operation group according to the power influence parameters and current operating power of all oil well production units in the oil well operation group, so as to obtain a weight parameter corresponding to the oil well operation group, and the weight parameter represents the power demand load of the oil well operation group; Step S5: Allocate electric energy to each oil well operation group according to the weight parameters corresponding to each oil well operation group and the expected power consumption of each oil well production unit.

[0018] In this step, the operating conditions of the oil well production units in a preset historical period are traversed to train a back propagation neural network, and then the minimum expected operating time of the oil well production unit under the current situation is obtained based on the pre-trained back propagation neural network. The minimum expected operating time reflects the expected operating conditions of the oil well production unit. Based on this, the electric energy impact parameter corresponding to the oil well production unit and the expected power consumption required under the expected operating conditions can be further determined, which can improve the accuracy of electric energy metering in the oil and gas field. Then, multiple oil well operation groups are obtained by dividing all the oil well production units by correlation. Since the expected operating conditions of the oil well production units in each oil well operation group match, the corresponding weight parameters are determined by assigning weights to the oil well operation groups. The weight parameters characterize the electric energy demand load of the oil well operation group. Finally, according to the weight parameters corresponding to the oil well operation group and the expected power consumption of each oil well production unit, accurate electric energy distribution for the oil well operation group can be achieved to ensure that the oil and gas field is in a good operating state, which is conducive to improving the overall production efficiency of the oil and gas field.

[0019] In one embodiment, the multiple power impact characteristic intervals in step S3 can be configured accordingly according to the actual application scenario, for example, by setting multiple power impact threshold parameters for distinction. Specifically, if a first power impact threshold parameter and a second power impact threshold parameter are set, and the first power impact threshold parameter is less than the second power impact threshold parameter, then each power impact parameter can be compared with the first power impact threshold parameter and the second power impact threshold parameter one by one, and all power impact parameters less than the first power impact threshold parameter are classified into one category, and all power impact parameters greater than the second power impact threshold parameter are classified into another category, and all power impact parameters greater than or equal to the first power impact threshold parameter and less than or equal to the second power impact threshold parameter are classified into another category. In this way, three different power impact characteristic intervals can be determined, and the oil well production units corresponding to all the power impact parameters in each power impact characteristic interval constitute an oil well operation group. Finally, multiple oil well operation groups can be obtained, and all oil well production units in each oil well operation group can be determined accordingly.

[0020] like Figure 2 As shown, in one embodiment of the present invention, the steps in step S1, input each group of historical operation data as training data into a back propagation neural network built based on the Hongmeng system for training, thereby obtaining a pre-trained back propagation neural network, which may specifically include but is not limited to the following steps: Step S11: input any set of historical operation data as training data into the back propagation neural network built based on the Hongmeng system, use the historical power consumption and historical operation power as input data, and use the historical effective operation time as the preset output data to train the back propagation neural network once; Step S12, executing step S11 multiple times until the number of training times of the back propagation neural network reaches the number of groups of historical operation data.

[0021] In this step, by inputting any set of historical operation data as training data into the back propagation neural network built based on the Hongmeng system, using historical power consumption and historical operating power as input data, and using historical effective operating time as preset output data, the back propagation neural network is trained once. This can enhance the training effect of using historical effective operating time as output data, so as to improve the input recognition performance of the back propagation neural network. In particular, by performing single training of the back propagation neural network with multiple sets of historical operation data in multiple times, the training effect of the historical operation data can be further enhanced, which is beneficial to optimizing the training accuracy of the back propagation neural network, thereby improving the robustness of the back propagation neural network. It should be noted that the back propagation neural network is a network model well known to those skilled in the art, and will not be described in detail here to avoid redundancy.

[0022] like Figure 3 As shown, in one embodiment of the present invention, the step in step S2, determining the power impact parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, includes the following steps: Step S21, respectively normalize and update the current power consumption and the current operating power to obtain updated current power consumption and current operating power; determine the expected operation start time and the expected operation end time corresponding to the minimum expected operation duration according to the minimum expected operation duration; Step S22, calculate the sum of the current power consumption of all oil well production units to obtain a first total power consumption; determine the power impact parameter corresponding to the oil well production unit based on the updated current power consumption, current operating power, expected operation start time, expected operation end time and the first total power consumption.

[0023] In this step, the current power consumption and the current operating power are normalized and updated to obtain standardized and unified current power consumption and current operating power, so as to facilitate subsequent effective and accurate calculations, and the corresponding expected operating start time and expected operating end time are determined according to the minimum expected operating time, so that the specific operating period of the minimum expected operating time can be known; on this basis, the sum of the current power consumption of all oil well production units is considered to obtain the first total power consumption, so that the electric energy impact parameters corresponding to the oil well production units are effectively and reliably determined according to the updated current power consumption, current operating power, expected operating start time, expected operating end time and the first total power consumption.

[0024] In one embodiment, the step in step S22, according to the updated current power consumption, current operating power, expected operation start time, expected operation end time and the first total power consumption, determines the power impact parameter corresponding to the oil well production unit, including the following steps: Step S221: Substitute the updated current power consumption, current operating power, expected operation start time, expected operation end time and the first total power consumption into the power characteristic calculation formula for calculation, so as to obtain the power influence degree parameter corresponding to the oil well production unit, wherein the power characteristic calculation formula is as follows: ; is the electric energy impact parameter corresponding to the oil well production unit, is the minimum expected runtime, is the expected start time of the operation, is the expected end time of the operation, is the energy consumption coefficient of the oil well production unit, , is the current power consumption of the oil well production unit, is the first total power consumption, is the current operating power of the oil well production unit, The size of represents the energy conversion efficiency of the oil well production unit, which can be determined by the inherent performance of the oil well production unit. For example, while traversing the operation status of the oil well production unit in a preset historical period, the energy consumption coefficient of the oil well production unit in this period can be determined according to its historical operation status, that is, .

[0025] In one embodiment, the normalization update processing may be, but is not limited to: performing data cleaning on the current power consumption and the current operating power, and converting the current power consumption and the current operating power into their corresponding normalized values ​​according to a preset normalization ratio, thereby obtaining the updated current power consumption and the current operating power; the expected operation start time and the expected operation end time corresponding to the minimum expected operation duration may be, but are not limited to, segmented, that is, there may be corresponding pause times and restart times between the expected operation start time and the expected operation end time. For example, if the expected operation start time is 09:00 and the expected operation end time is 18:00, and the whole is continuous, then That is 09:00, That is 18:00; if the expected start time is 09:00, it ends at 12:00, then restarts at 14:00, and finally ends at 18:00, then in the first run, That is 09:00, That is 12:00. Similarly, during the second run, That is 14:00, That is, at 18:00, the two operation processes are calculated separately and finally summarized. It can be seen that even if the operation processes in different stages are distinguished, the electric energy characteristic calculation formula provided in the above embodiment can still be applied.

[0026] like Figure 4 As shown, in one embodiment of the present invention, the steps in step S2, performing expected correction on the current power consumption according to the power impact parameter to obtain the expected power consumption, may include but is not limited to the following steps: Step S23, determine whether the power impact parameter corresponding to the oil well production unit is less than or equal to the preset power impact balance coefficient. If so, take the current power consumption as the expected power consumption. Otherwise, calculate the expected power consumption by the power consumption correction formula, where the power consumption correction formula is as follows: ; is the expected power consumption of the oil well production unit, is the current power consumption of the oil well production unit, is the electric energy impact parameter corresponding to the oil well production unit, To preset the power influence balance coefficient, is the power factor of the oil well production unit, , It is the ratio of active power to apparent power in the oil well production unit, and is numerically equal to the cosine value of the phase difference between the voltage and current of the oil well production unit.

[0027] In this step, the preset power influence balance coefficient is not the only limitation and needs to be determined accordingly according to the actual application scenario; if it is judged that the power influence parameter corresponding to the oil well production unit is less than or equal to the preset power influence balance coefficient, it means that the power influence parameter corresponding to the oil well production unit is within the normal threshold range and there is no need to provide additional power for it. Therefore, the current power consumption is used as the expected power consumption. On the contrary, if it is judged that the power influence parameter corresponding to the oil well production unit is greater than the preset power influence balance coefficient, it means that the power influence parameter corresponding to the oil well production unit is within a range exceeding the threshold, and this part of the additional power influence parameter needs to be compensated separately. Therefore, by calculating the difference between the preset power influence balance coefficient and the power influence parameter, the power consumption required to be compensated is calculated in combination with the current power consumption and power factor of the oil well production unit, and then the power consumption required to be compensated is added to the current power consumption of the oil well production unit to obtain the expected power consumption. The expected power consumption calculated in this way can reflect the actual power consumption demand of the oil well production unit and more accurately estimate the effective power consumption of the oil well production unit.

[0028] like Figure 5 As shown, in one embodiment of the present invention, the steps in step S4, weighting the oil well operation group according to the power influence parameters and current operating power of all oil well production units in the oil well operation group, thereby obtaining the weight parameter corresponding to the oil well operation group, may include but is not limited to the following steps: Step S41, calculating the average operating power of the oil well operation group according to the current operating power of all oil well production units in the oil well operation group; Step S42: for each oil well production unit in the oil well operation group, determine the weight component corresponding to the oil well production unit according to the relative relationship between the current operation power and the average operation power of the oil well production unit; Step S43: Obtain the weight parameters corresponding to the oil well operation group according to the weight components corresponding to all oil well production units.

[0029] In this step, the weight component corresponding to the oil well production unit is determined by judging the relative relationship between the current operating power and the average operating power of the oil well production unit, so that the weight parameter corresponding to the oil well operation group can be obtained by summarizing and calculating the weight components corresponding to all the oil well production units. Specifically, according to the relative relationship between the current operating power and the average operating power of the oil well production unit, there are many ways to determine the weight component corresponding to the oil well production unit, for example, using the weight configuration formula shown below to allocate the corresponding weight component to each oil well production unit: ; in, The weight component assigned to each oil well production unit, is the electric energy impact parameter corresponding to the oil well production unit, For the The power impact parameters corresponding to each oil well production unit, Assign parameters to preset weights, is the current operating power of the oil well production unit, is the average operating power of the oil well operation group, is the total number of all oil well production units in the oil well operation group, .

[0030] In one embodiment, after the weight components corresponding to all oil well production units are determined, the sum of the weight components corresponding to all oil well production units is calculated to obtain the weight parameter corresponding to the oil well operation group.

[0031] like Figure 6 As shown, in one embodiment of the present invention, step S5 may include but is not limited to the following steps: Step S51: for each oil well operation group, obtain the sum of the expected power consumption of all oil well production units in the oil well operation group to obtain a first total power consumption; Step S52, calculating the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group to obtain the standard allocated power; Step S53: Allocate electric energy not less than the standard allocated electric energy to the oil well operation group.

[0032] In this step, the sum of the expected power consumption of all oil well production units in the oil well operation group is obtained to obtain the total power consumption required for the operation of the oil well operation group. Since the weight parameter represents the power demand load of the oil well operation group, the influence of the weight parameter is taken into consideration. The power consumption required for the actual operation of the oil well operation group needs to match the weight parameter. Therefore, by calculating the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group, the standard allocated power required for the actual operation of the oil well operation group is obtained. Therefore, power not less than the standard allocated power is allocated to the oil well operation group to ensure that the oil well operation group can maintain normal operating conditions, which is beneficial to improving the overall production efficiency of the oil and gas field.

[0033] Figure 7 This is a structural diagram of an oil and gas field power metering control system 1000 based on the localization of the Hongmeng system provided by an embodiment of the present invention. Figure 7 As shown, the oil and gas field electric energy metering control system 1000 based on the localization of the Hongmeng system includes a memory 1100 and a processor 1200. The number of the memory 1100 and the processor 1200 can be one or more. Figure 7In the embodiment, a memory 1100 and a processor 1200 are taken as an example; the memory 1100 and the processor 1200 in the device may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0034] The memory 1100, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the oil and gas field power metering control method based on the localization of the Hongmeng system provided in any embodiment of the present invention. The processor 1200 implements the above-mentioned oil and gas field power metering control method based on the localization of the Hongmeng system by running the software programs, instructions and modules stored in the memory 1100.

[0035] The memory 1100 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function. In addition, the memory 1100 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 1100 may further include a memory remotely arranged relative to the processor 1200, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions, which are used to execute the oil and gas field electricity metering control method based on the localization of the Hongmeng system as provided in any embodiment of the present invention.

[0037] An embodiment of the present invention also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the localized oil and gas field electricity metering control method based on the Hongmeng system as provided in any embodiment of the present invention.

[0038] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0039] In hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0040] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process or an execution thread, and a component may be located on a computer or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may communicate, for example, through local or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, or a network, such as the Internet interacting with other systems through signals).

Claims

1. A method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system, characterized in that: Applied to an electric energy metering control system built based on the Hongmeng system, the oil and gas field is equipped with multiple oil well production units, and the electric energy metering control system is respectively connected to each of the oil well production units; the method comprises the following steps: Step S1: Traverse the operating conditions of the oil well production unit within a preset historical period to obtain multiple sets of historical operating data, wherein each set of the historical operating data includes the historical power consumption, historical operating power, and historical effective operating time of one of the oil well production units within the preset historical period; input each set of the historical operating data as training data into a back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network; Step S2: For each of the oil well production units, obtain the current power consumption and current operating power of the oil well production unit, use the current power consumption and the current operating power as input data, and input them into the pre-trained back propagation neural network, thereby obtaining the minimum expected operating time of the oil well production unit; determine the power impact parameter corresponding to the oil well production unit based on the minimum expected operating time, the current power consumption, and the current operating power, and perform an expected correction on the current power consumption based on the power impact parameter to obtain the expected power consumption; Step S3: dividing all the oil well production units according to the matching relationship between each of the power impact parameters and the pre-configured power impact characteristic intervals, thereby obtaining a plurality of oil well operation groups, wherein each of the oil well operation groups includes a plurality of the oil well production units; Step S4: For each oil well operation group, weight the oil well operation group according to the power impact parameter and the current operating power of all the oil well production units in the oil well operation group, thereby obtaining a weight parameter corresponding to the oil well operation group, wherein the weight parameter represents the power demand load of the oil well operation group; Step S5: Allocate electric energy to each of the oil well operation groups according to the weight parameters corresponding to each of the oil well operation groups and the expected power consumption of each of the oil well production units.

2. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 1 is characterized in that: The step in step S2, determining the power impact parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption, and the current operating power, includes the following steps: Step S21: performing normalization and updating processing on the current power consumption and the current operating power respectively, thereby obtaining updated current power consumption and the current operating power; and determining an expected operation start time and an expected operation end time corresponding to the minimum expected operation time according to the minimum expected operation time; Step S22: Calculate the sum of the current power consumption of all the oil well production units to obtain a first total power consumption; determine the power impact parameter corresponding to the oil well production unit based on the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption.

3. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 2 is characterized in that: The step S22 includes determining the power impact parameter corresponding to the oil well production unit based on the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time, and the first total power consumption, and includes the following steps: Step S221: Substitute the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time, and the first total power consumption into the power characteristic calculation formula to calculate, thereby obtaining the power influence parameter corresponding to the oil well production unit, wherein the power characteristic calculation formula is as follows: ； is the electric energy impact parameter corresponding to the oil well production unit, is the minimum expected runtime, is the expected operation start time, is the expected end time of the operation, is the energy consumption coefficient of the oil well production unit, , is the current power consumption of the oil well production unit, is the first total power consumption, is the current operating power of the oil well production unit.

4. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 2 is characterized in that: The step S2, performing expected correction on the current power consumption according to the power impact parameter to obtain expected power consumption, includes the following steps: Step S23: determine whether the power impact parameter corresponding to the oil well production unit is less than or equal to the preset power impact balance coefficient. If so, use the current power consumption as the expected power consumption. Otherwise, calculate the expected power consumption using the power consumption correction formula, where the power consumption correction formula is as follows: ； is the expected power consumption of the oil well production unit, is the current power consumption of the oil well production unit, is the electric energy impact parameter corresponding to the oil well production unit, is the preset electric energy impact balance coefficient, is the power factor of the oil well production unit, .

5. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 1 is characterized in that: The step S4, wherein weights are assigned to the oil well operation group according to the power influence parameters and the current operating powers of all the oil well production units in the oil well operation group, thereby obtaining weight parameters corresponding to the oil well operation group, includes the following steps: Step S41: Calculate the average operating power of the oil well operation group based on the current operating power of all the oil well production units in the oil well operation group; Step S42: for each of the oil well production units in the oil well operation group, determine a weight component corresponding to the oil well production unit according to a relative relationship between the current operating power and the average operating power of the oil well production unit; Step S43: Obtain the weight parameter corresponding to the oil well operation group according to the weight components corresponding to all the oil well production units.

6. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 1 is characterized in that: The step S5 comprises the following steps: Step S51: For each oil well operation group, obtain the sum of the expected power consumption of all the oil well production units in the oil well operation group to obtain a first total power consumption; Step S52: Calculate the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group to obtain a standard allocated power; Step S53: Allocate electric energy not less than the standard allocated electric energy to the oil well operation group.

7. The oil and gas field electric energy metering control method based on the localization of the Hongmeng system according to claim 1 is characterized in that: The step in step S1, inputting each group of the historical operation data as training data into the back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network, includes the following steps: Step S11: Input any set of the historical operating data as training data into a back-propagation neural network built based on the Hongmeng system, use the historical power consumption and the historical operating power as input data, and use the historical effective operating time as preset output data to train the back-propagation neural network once; Step S12: Execute step S11 multiple times until the number of training times of the back propagation neural network reaches the number of groups of the historical operation data.

8. An oil and gas field electric energy metering and control system based on the localization of Hongmeng system, characterized in that: include: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that A processor-executable program is stored therein, and when the processor-executable program is executed by the processor, it is used to implement the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in any one of claims 1 to 7.

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