CN114386647A - Method and system for predicting energy consumption of oil and gas field industry - Google Patents
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Abstract
The invention discloses a method and a system for predicting energy consumption of an oil and gas field industry, wherein the method comprises the following steps: collecting crude oil yield, natural gas yield and energy consumption, and generating a historical production energy consumption data set; classifying the two attribute data of crude oil yield and natural gas yield by adopting a k-means clustering algorithm to obtain a classification result and marking each class; respectively carrying out normalization processing on the labeled data sets corresponding to each category, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, and respectively establishing and training a neural network energy consumption prediction model corresponding to each category; and acquiring the planned yield of crude oil and the planned yield of natural gas, determining the category of the planned yield on the basis of the classification result, carrying out normalization processing according to a normalization rule corresponding to the category, and inputting the normalized data into an energy consumption prediction model corresponding to the category to obtain an energy consumption prediction result.
Description
Technical Field
The invention relates to the technical field of oil and gas field production, in particular to a method and a system for predicting energy consumption of an oil and gas field industry.
Background
Energy is the basis of economic development, and with the rapid development of social economy in the world, the consumption of energy is also increased rapidly, and the energy problem becomes more and more prominent in the world. Meanwhile, oil and gas field enterprises also face the problems of increased production energy consumption, relatively low yield and the like in the daily production process.
In order to respond to the requirements of energy conservation and emission reduction in the petrochemical industry, technical energy conservation and management energy conservation become important concerns of oil and gas field enterprises. The energy consumption prediction finally realizes the prediction of the future energy consumption of the enterprise by analyzing historical energy consumption data and relying on an artificial intelligence algorithm, and the energy consumption prediction can be used as an effective energy-saving measure to help the enterprise realize energy conservation and consumption reduction, help the oil and gas field enterprise reasonably arrange production, reduce energy emission in the production process and improve the energy management efficiency of the oil and gas field enterprise.
In the prior art, the energy consumption prediction problem is only taken as a regression problem, namely, the influence factors of the energy consumption are determined and input into a selected regression model as characteristic vectors, and then the energy consumption corresponding to the characteristic vectors is taken as output to train the model until the loss function of the model is converged. In the process, the internal spatial distribution characteristics of the data set are not considered, and in the whole data set, the spatial distribution of the data set can form clusters with different distributions, each cluster is a small subset, and the interior of the cluster has a strong association relationship. In the prediction process, because the characteristics of each cluster are not fully considered, the accuracy of the prediction result is low, and the reference value is poor.
In view of the above, a technical solution that can overcome the above problems, fully consider the characteristics of the data set, and improve the accuracy of the prediction result is needed.
Disclosure of Invention
In order to overcome the problems, the invention provides a method and a system for predicting energy consumption in the oil and gas field industry, which can solve the problem of predicting energy consumption in the production process of oil and gas field enterprises, help the oil and gas field enterprises to reasonably arrange production, reduce energy dissipation in the production process and improve the energy management efficiency of the oil and gas field enterprises.
In a first aspect of an embodiment of the present invention, a method for predicting energy consumption in an oil and gas field industry is provided, where the method includes:
collecting crude oil yield, natural gas yield and energy consumption, and generating a historical production energy consumption data set;
selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class;
respectively normalizing the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category;
and acquiring planned crude oil yield and planned natural gas yield, determining the categories of the planned crude oil yield and the planned natural gas yield on the basis of the classification results, carrying out normalization processing according to a normalization rule corresponding to the categories, and inputting the normalized data into an energy consumption prediction model corresponding to the categories to obtain an energy consumption prediction result.
In a second aspect of the embodiments of the present invention, a system for predicting energy consumption in oil and gas field industry is provided, the system including:
the data acquisition module is used for acquiring the crude oil yield, the natural gas yield and the energy consumption and generating a historical production energy consumption data set;
the classification module is used for selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result and marking each class;
the data processing module is used for respectively carrying out normalization processing on the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category;
and the prediction module is used for acquiring the planned yield of crude oil and the planned yield of natural gas, determining the categories of the planned yield of crude oil and the planned yield of natural gas on the basis of the classification result, carrying out normalization processing according to a normalization rule corresponding to the categories, and inputting the normalized data into an energy consumption prediction model corresponding to the category to obtain an energy consumption prediction result.
In a third aspect of the embodiments of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the computer device implements a method for predicting energy consumption of an oil and gas field industry.
In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is presented, which stores a computer program, which when executed by a processor, implements a method of predicting energy consumption in an oil and gas field industry.
According to the method and the system for predicting the energy consumption of the oil and gas field industry, a historical production energy consumption data set is generated by collecting the yield of crude oil, the yield of natural gas and the energy consumption; selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class; respectively normalizing the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category; the method comprises the steps of obtaining planned crude oil yield and planned natural gas yield, determining the categories of the planned crude oil yield and the planned natural gas yield on the basis of the classification results, carrying out normalization processing according to a normalization rule corresponding to the categories, inputting normalized data into an energy consumption prediction model corresponding to the categories to obtain an energy consumption prediction result, effectively solving the problem of energy consumption prediction in the production process of oil and gas field enterprises, reducing energy dissipation in the production process of the oil and gas field enterprises, and improving the energy management efficiency and the intelligent management level of the oil and gas field enterprises.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting energy consumption of an oil and gas field industry according to an embodiment of the invention.
FIG. 2 is a schematic flow chart of collecting historical production energy consumption data according to an embodiment of the present invention.
FIG. 3 is a flow chart illustrating data classification according to an embodiment of the invention.
FIG. 4 is a flow chart illustrating data processing according to an embodiment of the invention.
Fig. 5 is a schematic flow chart of energy consumption prediction of an oil and gas field according to an embodiment of the invention.
Fig. 6 is a schematic diagram of a system architecture for predicting energy consumption in the oil and gas field industry according to an embodiment of the invention.
Fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
According to the embodiment of the invention, a method and a system for predicting energy consumption of an oil and gas field industry are provided.
The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the invention.
Fig. 1 is a schematic flow chart of a method for predicting energy consumption of an oil and gas field industry according to an embodiment of the invention. As shown in fig. 1, the method includes:
step S101, collecting crude oil yield, natural gas yield and energy consumption, and generating a historical production energy consumption data set;
s102, selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class;
step S103, respectively carrying out normalization processing on the labeled data sets corresponding to each category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording a normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category;
and step S104, acquiring the planned yield of crude oil and the planned yield of natural gas, determining the categories of the planned yield of crude oil and the planned yield of natural gas on the basis of the classification results, carrying out normalization processing according to a normalization rule corresponding to the categories, inputting the normalized data into an energy consumption prediction model corresponding to the category to obtain an energy consumption prediction result.
In one embodiment, step S101 further includes:
whether missing data exists or not is checked in the historical production energy consumption data set;
if yes, filling missing data by using the arithmetic mean value of adjacent data;
after filling, the rationality is manually confirmed, and if the data is not reasonably corrected, a historical production energy consumption data set with complete and reasonable data is obtained.
In step S101, at least 60 pieces of historical data are collected, where each piece of historical data corresponds to the crude oil production, the natural gas production, and the energy consumption of one month. I.e. historical production data and energy consumption data are collected for at least 5 consecutive years.
In step S102, when the historical production energy consumption data set is classified according to attribute data by adopting a k-means clustering algorithm, the number of classification categories is 2-4; the number of the historical data and the number of the classification categories can be adjusted according to actual conditions, so that the data items contained in each category can not be less than 20.
In step S103, the specific process further includes:
the method comprises the steps of taking the crude oil yield and the natural gas yield of each month in a historical production energy consumption data set as input, taking the energy consumption of each month as output, respectively establishing and training three-layer neural network energy consumption prediction models comprising 2 input layer nodes and 1 output layer node, and obtaining an energy consumption prediction model corresponding to each category after training.
The number of neurons in the middle layer of the three-layer neural network energy consumption prediction model can be between 2 and 4, and the specific situation can be determined according to the size of the data set in each category.
In step S104, the specific process further includes:
and acquiring the planned yield of the crude oil and the planned yield of the natural gas, and determining the categories of the planned yield of the crude oil and the planned yield of the natural gas by adopting a KNN algorithm on the basis of the classification result.
For a clearer explanation of the method for predicting the energy consumption of the oil and gas field industry, each step is described below with reference to a specific example, however, it should be noted that the example is only for better illustration of the present invention and is not to be construed as an undue limitation on the present invention.
Step S101:
referring to fig. 2, a flow chart of collecting historical production energy consumption data sets according to an embodiment of the invention is shown. As shown in fig. 2, the specific process is as follows:
step S11, according to triple data format D (X)1,X2Y) collecting historical monthly natural gas yield, crude oil yield and comprehensive energy consumption data of the oil and gas field enterprise in a period of time according to the time sequence, wherein X1Representing the natural monthly yield of the enterprise, X2The monthly crude oil yield of the enterprise is represented, and the monthly comprehensive energy consumption of the enterprise is represented by Y.
When historical data is collected, it is required to ensure that the collected triple data amount is not less than 60, namely monthly historical production data and energy consumption data of the enterprise within at least 5 years.
Step S12, arranging all the ternary group data according to the time sequence, and checking whether missing values exist; if there is a missing value, go to step S13; if there is no missing value, go to step S14.
Step S13, the arithmetic mean of the neighboring data is used to replace the missing value, and the service expert is consulted to ensure the validity and accuracy of the newly calculated missing value.
Since the filled missing value is an arithmetic mean value of adjacent data, there may be unreasonable situations, and thus confirmation may be performed manually to ensure validity and accuracy of the missing value.
Step S14, using the data set as historical production energy consumption data set, marked as Di(X1,X2,Y)。
Step S102:
fig. 3 is a schematic flow chart of data classification according to an embodiment of the invention. As shown in fig. 3, the specific process is as follows:
step S21, adopting a K-means clustering algorithm to the data set D of the step S101i(X1,X2Y) according to attribute (X)1,X2) It is classified. In the classification process, the number M of the classes is ensured to be between 2 and 4, and the number of data items contained in each class is ensured to be not less than 20.
Step S22, obtaining classification results, marking each class as Ci(i＝1,2,3….)。
Step S103:
fig. 4 is a schematic flow chart of data processing according to an embodiment of the invention. As shown in fig. 4, the specific process is as follows:
step S31, for each category CiData set D in (1)i(X1,X2Y), a compound of (X)1,X2) And (4) carrying out normalization processing on the attributes, mapping values of the two attributes to be between 0 and 1 respectively, and recording the normalization rules of the values.
Step S32, classifying the category CiIn (1), the normalized data is labeled as di(x1,x2Y); in each category, a three-layer neural network energy consumption prediction model is constructed, an input node comprises two neurons, a middle layer comprises 3 neurons (the number of specific neurons can be determined according to the scale of a data set)The output layer contains 1 neuron. In the training process, (x)1,x2) Attributes serve as inputs to the network and Y serves as an output from the network. After training is finished, the root mean square error RMSE is adopted as an evaluation index of the network, and each class C is subjected to evaluationi(i＝1，2，…,M)The corresponding neural network is marked as Ni(i＝1，2，…,M)。
Step S104:
referring to fig. 5, a schematic flow chart of energy consumption prediction of an oil and gas field according to an embodiment of the present invention is shown. As shown in fig. 5, the specific process is as follows:
step S41, collecting the crude oil yield and natural gas yield of the enterprise in some months in the futurei(Z1,Z2) Tag, Attribute (Z)1,Z2) Representing natural gas production and crude oil production.
Step S42, grouping the binary group into a data set TiJudging the specific class C to which the KNN belongs by adopting a KNN algorithmi(as described in step S102).
Step S43, according to the normalization rule corresponding to the category, the data set is normalized, and the normalized data set is marked as ti(z1,z2)；
Step S44, the data set (z)1,z2) Input of attributes into such categories CiCorresponding neural network NiAnd marking the output of the network as R' to obtain a final energy consumption prediction result.
It should be noted that although the operations of the method of the present invention have been described in the above embodiments and the accompanying drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the operations shown must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Having described the method of the exemplary embodiment of the present invention, next, a system for predicting energy consumption of an oil and gas field industry according to an exemplary embodiment of the present invention will be described with reference to fig. 6.
The implementation of the prediction system of the energy consumption in the oil and gas field industry can be referred to the implementation of the method, and repeated details are omitted. The term "module" or "unit" used hereinafter may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Based on the same inventive concept, the invention also provides a system for predicting energy consumption in the oil and gas field industry, as shown in fig. 6, the system comprises:
the data acquisition module 610 is used for acquiring the crude oil yield, the natural gas yield and the energy consumption and generating a historical production energy consumption data set;
the classification module 620 is used for selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class;
the data processing module 630 is configured to perform normalization processing on the labeled data sets corresponding to each category, map two attributes, namely natural gas yield and crude oil yield, between 0 and 1, record a normalization rule of each category, use the crude oil yield and the natural gas yield as inputs, use energy consumption as outputs, and respectively establish and train a neural network energy consumption prediction model corresponding to each category to obtain an energy consumption prediction model corresponding to each category;
and the prediction module 640 is configured to obtain a planned yield of crude oil and a planned yield of natural gas, determine categories to which the planned yields of crude oil and natural gas belong based on the classification result, perform normalization processing according to a normalization rule corresponding to the categories, and input normalized data into an energy consumption prediction model corresponding to the category to which the data belongs, so as to obtain an energy consumption prediction result.
In one embodiment, the data acquisition module 610 is further configured to:
whether missing data exists or not is checked in the historical production energy consumption data set;
if yes, filling missing data by using the arithmetic mean value of adjacent data;
after filling, the rationality is manually confirmed, and if the data is not reasonably corrected, a historical production energy consumption data set with complete and reasonable data is obtained.
In one embodiment, the data collection module 610 collects at least 60 pieces of historical data, each piece of historical data corresponds to the crude oil production, the natural gas production and the energy consumption of one month.
In an embodiment, when the classification module 620 classifies the historical production energy consumption data set according to the attribute data by using a k-means clustering algorithm, the number of classification categories is 2 to 4.
In an embodiment, the data processing module 630 is specifically configured to:
the method comprises the steps of taking the crude oil yield and the natural gas yield of each month in a historical production energy consumption data set as input, taking the energy consumption of each month as output, respectively establishing and training three-layer neural network energy consumption prediction models comprising 2 input layer nodes and 1 output layer node, and obtaining an energy consumption prediction model corresponding to each category after training.
In one embodiment, the prediction module 640 is specifically configured to:
and acquiring the planned yield of the crude oil and the planned yield of the natural gas, and determining the categories of the planned yield of the crude oil and the planned yield of the natural gas by adopting a KNN algorithm on the basis of the classification result.
It should be noted that although several modules of the prediction system of energy consumption of the oil and gas field industry are mentioned in the above detailed description, such partitioning is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module according to embodiments of the invention. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
Based on the aforementioned inventive concept, as shown in fig. 7, the present invention further provides a computer device 700, which includes a memory 710, a processor 720, and a computer program 730 stored in the memory 710 and executable on the processor 720, wherein the processor 720 executes the computer program 730 to implement the aforementioned method for predicting energy consumption in the oil and gas field industry.
Based on the above inventive concept, the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the aforementioned method for predicting energy consumption in the oil and gas field industry.
According to the method and the system for predicting the energy consumption of the oil and gas field industry, a historical production energy consumption data set is generated by collecting the yield of crude oil, the yield of natural gas and the energy consumption; selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class; respectively normalizing the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category; the method comprises the steps of obtaining planned crude oil yield and planned natural gas yield, determining the categories of the planned crude oil yield and the planned natural gas yield on the basis of the classification results, carrying out normalization processing according to a normalization rule corresponding to the categories, inputting normalized data into an energy consumption prediction model corresponding to the categories to obtain an energy consumption prediction result, effectively solving the problem of energy consumption prediction in the production process of oil and gas field enterprises, reducing energy dissipation in the production process of the oil and gas field enterprises, and improving the energy management efficiency and the intelligent management level of the oil and gas field enterprises.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (14)
1. A prediction method for energy consumption in oil and gas field industry is characterized by comprising the following steps:
collecting crude oil yield, natural gas yield and energy consumption, and generating a historical production energy consumption data set;
selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result, and marking each class;
respectively normalizing the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category;
and acquiring planned crude oil yield and planned natural gas yield, determining the categories of the planned crude oil yield and the planned natural gas yield on the basis of the classification results, carrying out normalization processing according to a normalization rule corresponding to the categories, and inputting the normalized data into an energy consumption prediction model corresponding to the categories to obtain an energy consumption prediction result.
2. The method for predicting energy consumption of the oil and gas field industry according to claim 1, wherein crude oil yield, natural gas yield and energy consumption are collected to generate a historical production energy consumption data set, and the method further comprises the following steps:
whether missing data exists or not is checked in the historical production energy consumption data set;
if yes, filling missing data by using the arithmetic mean value of adjacent data;
after filling, the rationality is manually confirmed, and if the data is not reasonably corrected, a historical production energy consumption data set with complete and reasonable data is obtained.
3. The method for predicting energy consumption of oil and gas field industry according to claim 2, wherein the method further comprises:
at least 60 pieces of historical data are collected, and each piece of historical data corresponds to the crude oil yield, the natural gas yield and the energy consumption of one month.
4. The oil and gas field industry energy consumption prediction method as claimed in claim 3, wherein when the historical production energy consumption data set is classified according to the attribute data by adopting a k-means clustering algorithm, the number of classification categories is 2-4.
5. The method for predicting the energy consumption of the oil and gas field industry according to claim 1, wherein the step of respectively establishing and training the neural network energy consumption prediction model corresponding to each category by taking the crude oil yield and the natural gas yield as input and the energy consumption as output to obtain the energy consumption prediction model corresponding to each category comprises the following steps:
the method comprises the steps of taking the crude oil yield and the natural gas yield of each month in a historical production energy consumption data set as input, taking the energy consumption of each month as output, respectively establishing and training three-layer neural network energy consumption prediction models comprising 2 input layer nodes and 1 output layer node, and obtaining an energy consumption prediction model corresponding to each category after training.
6. The method for predicting energy consumption of the oil and gas field industry according to claim 1, wherein the steps of obtaining the planned yield of crude oil and the planned yield of natural gas, and determining the categories of the planned yield of crude oil and the planned yield of natural gas based on the classification result comprise:
and acquiring the planned yield of the crude oil and the planned yield of the natural gas, and determining the categories of the planned yield of the crude oil and the planned yield of the natural gas by adopting a KNN algorithm on the basis of the classification result.
7. A system for predicting energy consumption of an oil and gas field industry, the system comprising:
the data acquisition module is used for acquiring the crude oil yield, the natural gas yield and the energy consumption and generating a historical production energy consumption data set;
the classification module is used for selecting the crude oil yield and the natural gas yield as attribute data, classifying the historical production energy consumption data set by adopting a k-means clustering algorithm according to the attribute data to obtain a classification result and marking each class;
the data processing module is used for respectively carrying out normalization processing on the data sets corresponding to each marked category, mapping two attributes of natural gas yield and crude oil yield between 0 and 1, recording the normalization rule of each category, taking the crude oil yield and the natural gas yield as input and the energy consumption as output, respectively establishing and training a neural network energy consumption prediction model corresponding to each category, and obtaining an energy consumption prediction model corresponding to each category;
and the prediction module is used for acquiring the planned yield of crude oil and the planned yield of natural gas, determining the categories of the planned yield of crude oil and the planned yield of natural gas on the basis of the classification result, carrying out normalization processing according to a normalization rule corresponding to the categories, and inputting the normalized data into an energy consumption prediction model corresponding to the category to obtain an energy consumption prediction result.
8. The oil and gas field industry energy consumption prediction system of claim 7, wherein the data collection module is further configured to:
whether missing data exists or not is checked in the historical production energy consumption data set;
if yes, filling missing data by using the arithmetic mean value of adjacent data;
after filling, the rationality is manually confirmed, and if the data is not reasonably corrected, a historical production energy consumption data set with complete and reasonable data is obtained.
9. The oil and gas field industry energy consumption prediction system of claim 8, wherein the data collection module is further configured to:
at least 60 pieces of historical data are collected, and each piece of historical data corresponds to the crude oil yield, the natural gas yield and the energy consumption of one month.
10. The oil and gas field industry energy consumption prediction system of claim 9, wherein when the classification module classifies the historical production energy consumption data set according to attribute data by using a k-means clustering algorithm, the number of classification categories is 2-4.
11. The oil and gas field industry energy consumption prediction system of claim 7, wherein the data processing module is specifically configured to:
the method comprises the steps of taking the crude oil yield and the natural gas yield of each month in a historical production energy consumption data set as input, taking the energy consumption of each month as output, respectively establishing and training three-layer neural network energy consumption prediction models comprising 2 input layer nodes and 1 output layer node, and obtaining an energy consumption prediction model corresponding to each category after training.
12. The oil and gas field industry energy consumption prediction system of claim 7, wherein the prediction module is specifically configured to:
and acquiring the planned yield of the crude oil and the planned yield of the natural gas, and determining the categories of the planned yield of the crude oil and the planned yield of the natural gas by adopting a KNN algorithm on the basis of the classification result.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when executing the computer program.
14. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523181A (en) * | 2023-05-22 | 2023-08-01 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
| CN118297245A (en) * | 2024-06-06 | 2024-07-05 | 宁德时代新能源科技股份有限公司 | Energy consumption prediction method, device, computer equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523181A (en) * | 2023-05-22 | 2023-08-01 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
| CN116523181B (en) * | 2023-05-22 | 2024-01-26 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
| CN118297245A (en) * | 2024-06-06 | 2024-07-05 | 宁德时代新能源科技股份有限公司 | Energy consumption prediction method, device, computer equipment and storage medium |
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