CN113898334B - Intelligent analysis method and system for parameters of multifunctional comprehensive tester of oil pumping well - Google Patents
Intelligent analysis method and system for parameters of multifunctional comprehensive tester of oil pumping well Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
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- E21B47/00—Survey of boreholes or wells
- E21B47/008—Monitoring of down-hole pump systems, e.g. for the detection of "pumped-off" conditions
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
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Abstract
The invention provides an intelligent analysis method and system for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the method comprises the following steps: according to the first comprehensive tester, a first test parameter set of the oil pumping well is obtained, and a first parameter docking model is constructed; obtaining a first preset analysis target, inputting the first preset analysis target into a first parameter docking model as a target condition, and performing parameter docking by the first parameter docking model through docking a first test parameter set to obtain a first docking parameter set; noise reduction processing is carried out on the first docking parameter set, and a second docking parameter set is obtained; and inputting the first target characteristic data into a risk assessment model to obtain first output information, and further obtaining a first test analysis report. The technical problems that in the prior art, when an oil pumping well is abnormal, the time for searching abnormal parameters is long, and the accuracy of measuring parameters is low due to noise interference are solved, so that the intelligent parameter analysis efficiency is reduced.
Description
Technical Field
The application relates to the technical field of oil pumping well testing, in particular to an intelligent analysis method and system for parameters of a multifunctional comprehensive tester of an oil pumping well.
Background
The comprehensive tester for the pumping well is mainly used for testing parameters such as the liquid level depth of the pumping well, an indicator diagram, the casing pressure and the like, provides reliable data support for knowing the working condition of the pumping unit and the sinking degree of the pumping well in real time, and is ideal testing equipment in oilfield production. The oil pumping well comprehensive tester has the following characteristics that a pulse sound wave generator is used as a liquid level testing sound source, so that a sound bomb and a high-pressure nitrogen bottle are avoided; the indicator diagram sensor is provided with a suspension type, a movable type, a hydraulic type and a bayonet type, so that different use occasions can be met; the wellhead connector is provided with a hand-beating type and a pull-wire type; the work diagram and the liquid level can be tested simultaneously, so that the function of one machine with two purposes is realized, and the working efficiency is effectively improved.
However, in the process of implementing the technical scheme of the embodiment of the application, the inventor discovers that the above technology has at least the following technical problems:
when the oil pumping well is abnormal, the time for searching abnormal parameters is long, and the accuracy of measuring parameters is low due to noise interference, so that the intelligent parameter analysis efficiency is reduced.
Disclosure of Invention
The embodiment of the application provides an intelligent analysis method and an intelligent analysis system for parameters of a multifunctional comprehensive tester for an oil pumping well, which solve the technical problems that in the prior art, when the oil pumping well is abnormal, the time for searching abnormal parameters is long, and the accuracy of measuring parameters is low due to noise interference, so that the intelligent parameter analysis efficiency is reduced. The method achieves the technical effects of quickly butting related parameters through related parameter analysis, improving accuracy of a butting parameter measurement result through accurate noise reduction, improving operation safety, and improving scientificity, pertinence and reliability of abnormal risk analysis.
In view of the above problems, the embodiment of the application provides an intelligent analysis method and system for parameters of a multifunctional comprehensive tester of an oil pumping well.
In a first aspect, an embodiment of the present application provides a method for intelligently analyzing parameters of a multifunctional comprehensive tester for an oil pumping well, where the method includes: obtaining a first test parameter set of the oil pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing; extracting features according to the second docking parameter set to obtain first target feature data; inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target; and obtaining a first test analysis report according to the first output information.
On the other hand, the embodiment of the application provides an intelligent analysis system for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the system comprises: the first obtaining unit is used for obtaining a first test parameter set of the oil pumping well according to the first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; the first construction unit is used for constructing a first parameter butt joint model according to the attribute knowledge model of the oil pumping well; the second obtaining unit is used for obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; the first execution unit is used for inputting a first preset analysis target serving as a target condition into the first parameter docking model, and the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; the third obtaining unit is used for obtaining a second docking parameter set through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; the fourth obtaining unit is used for extracting the characteristics according to the second docking parameter set to obtain first target characteristic data; a fifth obtaining unit, configured to input the first target feature data into a risk assessment model for assessment, and obtain first output information according to the risk assessment model, where the first output information is a risk coefficient corresponding to the first preset analysis target; and the sixth obtaining unit is used for obtaining a first test analysis report according to the first output information.
In a third aspect, an embodiment of the present application provides an intelligent analysis system for parameters of a multifunctional integrated tester for an oil pumping well, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspects when executing the program.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
due to the adoption of the first comprehensive tester, a first test parameter set of the oil pumping well is obtained, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing; extracting features according to the second docking parameter set to obtain first target feature data; inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target; according to the technical scheme that the first test analysis report is obtained according to the first output information, the embodiment of the application achieves the technical effects of quickly butting related parameters through associated parameter analysis, improving the accuracy of a butting parameter measurement result through accurate noise reduction, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis by providing the intelligent analysis method and the intelligent analysis system for the parameters of the multifunctional comprehensive tester of the oil pumping well.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic flow chart of an intelligent analysis method for parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the application;
FIG. 2 is a schematic flow chart of a method for intelligently analyzing parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the application;
FIG. 3 is a schematic flow chart of a method for performing feature coincidence analysis for intelligent analysis of parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the application;
FIG. 4 is a schematic flow chart of a method for intelligently analyzing parameters of a multifunctional comprehensive tester for an oil pumping well to obtain a second docking parameter set according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a process flow of carrying out noise identification and noise reduction treatment for an intelligent analysis method for parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the application;
FIG. 6 is a schematic flow chart of an intelligent analysis method for parameters of a multifunctional comprehensive tester for an oil pumping well for adjusting a plurality of mature period nodes in batches according to an embodiment of the application;
FIG. 7 is a schematic flow chart of a constraint condition for generating noisy parameter judgment of an intelligent analysis method for parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the application;
FIG. 8 is a schematic diagram of a system for intelligent analysis of parameters of a multifunctional comprehensive tester for an oil pumping well according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a first constructing unit 12, a second obtaining unit 13, a first executing unit 14, a third obtaining unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, a sixth obtaining unit 18, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.
Detailed Description
The embodiment of the application provides an intelligent analysis method and an intelligent analysis system for parameters of a multifunctional comprehensive tester for an oil pumping well, which solve the technical problems that in the prior art, when the oil pumping well is abnormal, the time for searching abnormal parameters is long, and the accuracy of measuring parameters is low due to noise interference, so that the intelligent parameter analysis efficiency is reduced. The method achieves the technical effects of quickly butting related parameters through related parameter analysis, improving accuracy of a butting parameter measurement result through accurate noise reduction, improving operation safety, and improving scientificity, pertinence and reliability of abnormal risk analysis.
Summary of the application
The comprehensive tester for the pumping well is mainly used for testing parameters such as the liquid level depth of the pumping well, an indicator diagram, the casing pressure and the like, provides reliable data support for knowing the working condition of the pumping unit and the sinking degree of the pumping well in real time, and is ideal testing equipment in oilfield production. The oil pumping well comprehensive tester has the following characteristics that a pulse sound wave generator is used as a liquid level testing sound source, so that a sound bomb and a high-pressure nitrogen bottle are avoided; the indicator diagram sensor is provided with a suspension type, a movable type, a hydraulic type and a bayonet type, so that different use occasions can be met; the wellhead connector is provided with a hand-beating type and a pull-wire type; the work diagram and the liquid level can be tested simultaneously, so that the function of one machine with two purposes is realized, and the working efficiency is effectively improved. In the prior art, when an oil pumping well is abnormal, the time for searching abnormal parameters is long, and the accuracy of measuring parameters is low due to noise interference, so that the technical problem of intelligent parameter analysis efficiency is reduced.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the embodiment of the application provides an intelligent analysis method for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the method comprises the following steps: obtaining a first test parameter set of the oil pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing; extracting features according to the second docking parameter set to obtain first target feature data; inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target; and obtaining a first test analysis report according to the first output information.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the embodiment of the application provides an intelligent analysis method for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the method comprises the following steps:
s100: obtaining a first test parameter set of the oil pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters;
s200: constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well;
specifically, the first comprehensive tester is any pumping well multifunctional tester, and can be used for measuring a plurality of parameters of the pumping well to obtain the first test parameter set, wherein the first test parameter set comprises parameters such as a liquid level position, a bottom hole pressure and the like. And analyzing the attribute of the oil pumping well, and constructing an attribute knowledge model of the oil pumping well based on historical test data of the oil pumping well. Obtaining parameters needing deep analysis according to actual problems to be solved in production, and constructing the first parameter butt joint model according to the parameters and the attribute knowledge model of the oil pumping well. According to relevant parameters of the butt joint of the problems to be solved, the intelligent analysis efficiency of the parameters can be improved.
S300: obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target;
s400: inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set;
s500: a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing;
specifically, when the comprehensive tester detects an abnormal condition or a worker finds the abnormal condition, the first preset analysis target, namely the abnormal detection target, is obtained for abnormal parameter detection, and further, parameter docking is performed according to the first preset analysis target to obtain the first docking set, wherein the first docking set is from the first test parameter set. Because of the environmental factors or the factors of the testing instrument, the measurement results of certain parameters have noise, and the analysis and operation of interference parameters are performed, the noise reduction treatment is performed on the first docking parameter set, and a second docking parameter set with the noise removed is obtained. Noise monitoring of the condition of the downhole equipment is performed in combination with a sound detector for noise pattern recognition, and anomalies that the detector can detect include valve function, pipe leakage, rod bending, etc. Noise interference can be removed by noise detection, noise reduction treatment and other methods, so that accuracy of a butt joint parameter measurement result is improved.
S600: extracting features according to the second docking parameter set to obtain first target feature data;
s700: inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target;
s800: and obtaining a first test analysis report according to the first output information.
Specifically, since the analysis and extraction of the feature values are performed on the measurement results of the parameters in the second docking parameter set, abnormal feature data of the parameters are obtained, and the abnormal feature data of the parameters are very likely to be the cause of the security problem. The risk assessment model can assess the safety risk coefficient caused by parameter abnormality, the first target feature data is input into the risk assessment model for analysis, the corresponding risk coefficient is obtained, and the first test analysis report is obtained after calculation according to a conventional formula in the field of the pumping unit well and the risk assessment result as the first output information. And carrying out risk analysis on the first target characteristic data can improve the safety of operation, thereby improving the scientificity, pertinence and reliability of abnormal risk analysis.
Further, as shown in fig. 2, the embodiment of the present application further includes:
s810: judging whether the parameter expression form is a curve expression form or not by analyzing the parameter expression form of the first butt joint parameter set, wherein the parameter expression form comprises a data expression form and a curve expression form;
s820: if the parameter expression form is the curve expression form, noise reduction and data processing are carried out according to a first parameter processing mode;
s830: and if the parameter expression form is the data expression form, carrying out noise reduction and data processing according to a second parameter processing mode.
Specifically, the multifunctional comprehensive tester for the pumping unit well on the market at present has an advanced data management system, can automatically generate a curve through measuring parameters, carry out statistical analysis on the measurement result of the first butt joint parameter set, judge whether the data is in a curve expression form or not, and represent the data in a single numerical value form, namely the data expression form. And if the parameter expression form is a curve expression form, adopting the first parameter processing method to perform noise reduction processing and data processing on curve data. And if the parameter expression form is a data expression form, adopting the second parameter processing mode to perform noise reduction processing and data processing. The efficiency of parameter noise reduction and depth analysis can be improved, so that the analysis coping ability of abnormal conditions is improved.
Further, as shown in fig. 3, the step S600 further includes:
s610: obtaining all category information of the second docking parameters by carrying out multi-parameter category division on the second docking parameter set;
s620: extracting the characteristics of the parameters of each category in the information of all categories to obtain parameter characteristic information corresponding to the second docking parameters;
s630: obtaining a first coincidence characteristic by carrying out characteristic coincidence analysis on the parameter characteristic information;
s640: and taking the first coincident feature as the first target feature data.
Specifically, the second docking parameter set is a parameter after noise reduction processing, the second docking parameter set is divided into multiple categories, and different types of abnormal problems can be caused when the parameters of different categories exceed the normal range. And respectively extracting the characteristics from each category, wherein the extracted and divided representative parameter characteristic information is the parameter characteristic information corresponding to the second docking parameter. Further, feature coincidence analysis is performed on the parameter features in each category and the existing abnormal conditions, a plurality of parameter features causing the same abnormality are screened out, the plurality of parameter features are the first coincidence features, the first coincidence features are used as the first target feature data, and subsequent parameter analysis is performed, so that the pertinence of the parameter analysis and the analysis efficiency can be improved.
Further, as shown in fig. 4, the step S500 further includes:
s510: performing tag identification according to each parameter category of the second docking parameter set to generate a first input sample tag;
s520: constructing a first similarity model;
s530: acquiring a first noisy sample label by acquiring a parameter sample of the first comprehensive tester;
s540: inputting the first input sample label and the first noisy sample label into the first similarity model for similarity analysis to obtain a first similarity coefficient;
s550: and carrying out noisy parameter judgment and noise reduction processing according to the first similarity coefficient to obtain the second butt joint parameter set.
Specifically, each parameter category of the second docking parameter set is marked through a label, the first input sample label is generated, the first similarity model is constructed, the first similarity model is a noise identification model, parameters influenced by noise can be identified, and further noisy parameters and non-noisy parameters are distinguished. And performing parameter sample wiping through the first comprehensive tester to obtain the marked first noisy sample label. And inputting the first input sample label and the first noisy sample label into the first similarity model for similarity analysis to obtain a first similarity coefficient, judging that the first similarity coefficient is a noisy parameter when the first similarity coefficient reaches a certain similarity, further performing noise reduction treatment on the noisy parameter to obtain a second butting parameter set, improving the accuracy of noise identification, improving the noise reduction effect, greatly reducing unnecessary noise reduction treatment and ensuring the reliability of the butting parameter.
Further, as shown in fig. 5, the step S550 includes:
s551: carrying out noisy identification on the first docking parameter set according to the first similarity coefficient to obtain a first identification parameter set and a second identification parameter set, wherein the first identification parameter set is a noisy parameter set, and the second identification parameter set is a non-noisy parameter set;
s552: generating a third identification parameter set by carrying out noise reduction processing on the noisy parameters in the first identification parameter set;
s553: and obtaining the second docking parameter set according to the third identification parameter set and the second identification parameter set.
Specifically, the first docking parameter set is subjected to noisy identification according to the first similarity coefficient, noisy parameters and non-noisy parameters can be identified, all noisy parameters form the first identification parameter, and all non-noisy parameters form the second identification parameter. And then, noise reduction processing is carried out on the first identification parameters, the accuracy of the noise reduction processing is improved, the third identification parameter set is generated, the third set parameter set and the second identification parameter set are non-noisy data, and the second butt joint parameters are formed, so that the parameter measurement result is closer to real data, and the effectiveness of the parameters is improved.
Further, as shown in fig. 6, the embodiment of the present application further includes:
s561: judging whether the first similarity coefficient is larger than a preset similarity threshold value or not;
s562: when the first similarity coefficient is larger than the preset similarity threshold value, a first matching instruction is obtained;
s563: carrying out multiple matching on the first input sample parameter and the first noisy sample parameter according to the first matching instruction to obtain a first matching result, wherein the first matching result is the number of successful matching;
s564: and taking the first matching result and the first similarity coefficient as constraint conditions for judging the noisy parameters.
Specifically, a similarity threshold is preset, when the first similarity coefficient meets the preset similarity threshold, the first matching instruction is obtained, the first matching instruction can match the first input sample parameter and the first noisy sample parameter for multiple times, multiple times of matching can be multiple times of similarity calculation under multiple frequency points, the first matching result is obtained, and the first matching result is the number of successful matching in multiple times of matching. And taking the first matching result and the first similarity coefficient as constraint conditions for judging the noisy parameters, and improving the accuracy of judging the noisy parameters by multiple times of matching.
Further, as shown in fig. 7, after the first similarity model is constructed, step S520 further includes:
s521: obtaining a first recognition performance index by carrying out similarity recognition calculation on the first similarity model;
s522: if the first recognition performance index is smaller than a preset recognition performance index, carrying out confidence interval analysis on the first noisy sample parameter, and screening N confidence intervals larger than a preset requirement from the first noisy sample parameter;
s523: obtaining a first newly added noise sample according to the N confidence intervals;
s524: and optimizing the first similarity model according to the first newly added noise sample.
Specifically, similarity recognition calculation is performed through the first similarity model, and a first recognition performance index is obtained, wherein the recognition performance index can measure the recognition accuracy. And when the first recognition performance index is smaller than a preset recognition performance index, performing confidence interval analysis on the first noisy sample parameter, in other words, when the first recognition performance is poorer and lower than the preset recognition performance, performing confidence interval analysis on the error of the first recognition performance index in the preset performance index. The confidence interval refers to an estimated interval of the overall parameter constructed from the sample statistic, and the confidence interval shows the degree to which the true value of this parameter falls around the measurement result with a certain probability, which gives the degree of confidence of the measured value of the measured parameter. N confidence intervals larger than a preset requirement are screened out from the first noisy sample parameters, and the N confidence intervals are selected according to certain fault tolerance. And in the N confidence intervals, new added samples are contained, and the obtained first new added noisy samples are input into the first similarity model to perform model optimization, so that the technical effects of optimizing the model performance and improving the model accuracy can be achieved.
In summary, the intelligent analysis method and the intelligent analysis system for parameters of the multifunctional comprehensive tester for the oil pumping well provided by the embodiment of the application have the following technical effects:
1. due to the adoption of the first comprehensive tester, a first test parameter set of the oil pumping well is obtained, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing; extracting features according to the second docking parameter set to obtain first target feature data; inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target; according to the technical scheme that the first test analysis report is obtained according to the first output information, the embodiment of the application achieves the technical effects of quickly butting related parameters through associated parameter analysis, improving the accuracy of a butting parameter measurement result through accurate noise reduction, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis by providing the intelligent analysis method and the intelligent analysis system for the parameters of the multifunctional comprehensive tester of the oil pumping well.
2. The method for selecting the N confidence intervals is adopted, so that the fault tolerance of parameter selection can be improved, the data sample size can be expanded, the newly added samples are input into the corresponding model, model optimization is performed, the model performance can be optimized, and the technical effect of improving the model accuracy can be achieved.
Example two
Based on the same inventive concept as the intelligent analysis method of the parameters of the multifunctional comprehensive tester of the pumping unit well in the foregoing embodiment, as shown in fig. 8, an embodiment of the present application provides an intelligent analysis system of parameters of the multifunctional comprehensive tester of the pumping unit well, where the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain a first test parameter set of the rod-pumped well according to a first comprehensive tester, where the first test parameter set includes a plurality of test parameters;
a first construction unit 12, where the first construction unit 12 is configured to construct a first parameter docking model according to the knowledge model of the properties of the rod-pumped well;
a second obtaining unit 13, where the second obtaining unit 13 is configured to obtain a first preset analysis target, where the first preset analysis target is an anomaly detection target;
The first execution unit 14 is configured to input a first preset analysis target as a target condition into the first parameter docking model, where the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set;
a third obtaining unit 15, where the third obtaining unit 15 is configured to obtain a second docking parameter set by performing noise reduction processing on the first docking parameter set, where the second docking parameter set is a parameter after the noise reduction processing;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to perform feature extraction according to the second docking parameter set to obtain first target feature data;
a fifth obtaining unit 17, where the fifth obtaining unit 17 is configured to input the first target feature data into a risk assessment model for assessment, and obtain first output information according to the risk assessment model, where the first output information is a risk coefficient corresponding to the first preset analysis target;
a sixth obtaining unit 18, where the sixth obtaining unit 18 is configured to obtain a first test analysis report according to the first output information.
Further, the system includes:
the second execution unit is used for judging whether the parameter expression form is a curve expression form or not through analyzing the parameter expression form of the first butt joint parameter set, wherein the parameter expression form comprises a data expression form and a curve expression form;
the third execution unit is used for carrying out noise reduction and data processing according to the first parameter processing mode if the parameter expression form is the curve expression form;
and the fourth execution unit is used for carrying out noise reduction and data processing according to the second parameter processing mode if the parameter expression form is the data expression form.
Further, the system includes:
a seventh obtaining unit, configured to obtain all category information of the second docking parameter by performing multi-parameter category division on the second docking parameter set;
an eighth obtaining unit, configured to obtain parameter feature information corresponding to the second docking parameter by performing feature extraction on a parameter of each category in the all-category information;
A ninth obtaining unit, configured to obtain a first coincidence feature by performing feature coincidence analysis on the parameter feature information;
and the fifth execution unit is used for taking the first coincident characteristic as the first target characteristic data.
Further, the system includes:
the first generation unit is used for carrying out tag identification according to each parameter category of the second docking parameter set to generate a first input sample tag;
the second construction unit is used for constructing the first similarity model;
a tenth obtaining unit, configured to obtain a first noisy sample tag by performing parameter sample collection on the first comprehensive tester;
an eleventh obtaining unit, configured to input the first input sample tag and the first noisy sample tag into the first similarity model for similarity analysis, to obtain a first similarity coefficient;
and the twelfth obtaining unit is used for carrying out noise carrying parameter judgment and noise reduction processing according to the first similarity coefficient to obtain the second butting parameter set.
Further, the system includes:
a thirteenth obtaining unit, configured to perform noisy identification on the first docking parameter set according to the first similarity coefficient, to obtain a first identification parameter set and a second identification parameter set, where the first identification parameter set is a noisy parameter set, and the second identification parameter set is a non-noisy parameter set;
the second generation unit is used for generating a third identification parameter set by carrying out noise reduction processing on the noisy parameters in the first identification parameter set;
a fourteenth obtaining unit, configured to obtain the second docking parameter set according to the third identification parameter set and the second identification parameter set.
Further, the system includes:
the sixth execution unit is used for judging whether the first similarity coefficient is larger than a preset similarity threshold value or not;
a fifteenth obtaining unit, configured to obtain a first matching instruction when the first similarity coefficient is greater than the preset similarity threshold;
a sixteenth obtaining unit, configured to perform multiple matching on the first input sample parameter and the first noisy sample parameter according to the first matching instruction, to obtain a first matching result, where the first matching result is a number of successful matching;
And the seventh execution unit is used for taking the first matching result and the first similarity coefficient as constraint conditions for judging the noisy parameters.
Further, the system includes:
a seventeenth obtaining unit configured to obtain a first recognition performance index by performing a similarity recognition calculation on the first similarity model;
the eighth execution unit is used for analyzing the confidence interval of the first noisy sample parameter if the first recognition performance index is smaller than a preset recognition performance index, and screening N confidence intervals larger than a preset requirement from the first noisy sample parameter;
an eighteenth obtaining unit, configured to obtain a first newly added noise sample according to the N confidence intervals;
and the ninth execution unit is used for optimizing the first similarity model according to the first newly added noise sample.
Exemplary electronic device
An electronic device of an embodiment of the application is described below with reference to figure 9,
based on the same inventive concept as the intelligent analysis method of the parameters of the multifunctional comprehensive tester of the pumping well in the foregoing embodiment, the embodiment of the application further provides an intelligent analysis system of the parameters of the multifunctional comprehensive tester of the pumping well, which comprises: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method of any of the first aspects.
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 9, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303 uses any transceiver-like system for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local areanetworks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or may be an electrically erasable programmable read-only memory (EEPROM), compact-only-memory (CD-ROM) or other optical disk storage, optical disk storage (including compact, laser, optical, digital versatile, blu-ray, etc.), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the inventive arrangements, and is controlled by the processor 302 for execution. The processor 302 is configured to execute computer-implemented instructions stored in the memory 301, so as to implement the intelligent analysis method for parameters of the multifunctional comprehensive tester for the rod-pumped well provided by the embodiment of the application.
Alternatively, the computer-executable instructions in the embodiments of the present application may be referred to as application program codes, which are not particularly limited in the embodiments of the present application.
The embodiment of the application provides an intelligent analysis method for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the method comprises the following steps: obtaining a first test parameter set of the oil pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter docking model according to the attribute knowledge model of the oil pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target; inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set; a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing; extracting features according to the second docking parameter set to obtain first target feature data; inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target; and obtaining a first test analysis report according to the first output information.
Those of ordinary skill in the art will appreciate that: the first, second, etc. numbers referred to in the present application are merely for convenience of description and are not intended to limit the scope of the embodiments of the present application, nor represent the sequence. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any one," or the like, refers to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b, or c (species ) may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the available medium. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The various illustrative logical blocks and circuits described in connection with the embodiments of the present application may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic system, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the general purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software unit executed by a processor, or in a combination of the two. The software elements may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In an example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may reside in a terminal. In the alternative, the processor and the storage medium may reside in different components in a terminal. 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.
Although the application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.
Claims (9)
1. An intelligent analysis method for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the method comprises the following steps:
obtaining a first test parameter set of the oil pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters;
analyzing the attribute of the oil pumping well according to the historical test data of the oil pumping well, and constructing an attribute knowledge model of the oil pumping well;
Constructing a first parameter docking model based on the plurality of test parameters and the attribute knowledge model of the oil pumping well;
obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target;
inputting a first preset analysis target serving as a target condition into the first parameter docking model, wherein the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set;
a second docking parameter set is obtained through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter subjected to noise reduction processing;
extracting features according to the second docking parameter set to obtain first target feature data;
inputting the first target characteristic data into a risk assessment model for assessment, and obtaining first output information according to the risk assessment model, wherein the first output information is a risk coefficient corresponding to the first preset analysis target;
and obtaining a first test analysis report according to the first output information.
2. The method of claim 1, wherein the method further comprises:
Judging whether the parameter expression form is a curve expression form or not by analyzing the parameter expression form of the first butt joint parameter set, wherein the parameter expression form comprises a data expression form and a curve expression form;
if the parameter expression form is the curve expression form, noise reduction and data processing are carried out according to a first parameter processing mode;
and if the parameter expression form is the data expression form, carrying out noise reduction and data processing according to a second parameter processing mode.
3. The method of claim 1, wherein the feature extraction is performed according to the second set of docking parameters to obtain first target feature data, the method further comprising:
obtaining all category information of the second docking parameters by carrying out multi-parameter category division on the second docking parameter set;
extracting the characteristics of the parameters of each category in the information of all categories to obtain parameter characteristic information corresponding to the second docking parameters;
obtaining a first coincidence characteristic by carrying out characteristic coincidence analysis on the parameter characteristic information;
and taking the first coincident feature as the first target feature data.
4. The method of claim 1, wherein the obtaining a second set of docking parameters by denoising the first set of docking parameters, the method further comprises:
performing tag identification according to each parameter category of the second docking parameter set to generate a first input sample parameter;
constructing a first similarity model;
acquiring a first noisy sample parameter by acquiring a parameter sample of the first comprehensive tester;
inputting the first input sample parameters and the first noisy sample parameters into the first similarity model for similarity analysis to obtain a first similarity coefficient;
and carrying out noisy parameter judgment and noise reduction processing according to the first similarity coefficient to obtain the second butt joint parameter set.
5. The method of claim 4, wherein the noisy parameter determination and the noise reduction process are performed according to the first similarity coefficient to obtain the second set of docking parameters, the method further comprising:
carrying out noisy identification on the first docking parameter set according to the first similarity coefficient to obtain a first identification parameter set and a second identification parameter set, wherein the first identification parameter set is a noisy parameter set, and the second identification parameter set is a non-noisy parameter set;
Generating a third identification parameter set by carrying out noise reduction processing on the noisy parameters in the first identification parameter set;
and obtaining the second docking parameter set according to the third identification parameter set and the second identification parameter set.
6. The method of claim 4, wherein the method further comprises:
judging whether the first similarity coefficient is larger than a preset similarity threshold value or not;
when the first similarity coefficient is larger than the preset similarity threshold value, a first matching instruction is obtained;
carrying out multiple matching on the first input sample parameter and the first noisy sample parameter according to the first matching instruction to obtain a first matching result, wherein the first matching result is the number of successful matching;
and taking the first matching result and the first similarity coefficient as constraint conditions for judging the noisy parameters.
7. The method of claim 4, wherein after the constructing the first similarity model, the method further comprises:
obtaining a first recognition performance index by carrying out similarity recognition calculation on the first similarity model;
if the first recognition performance index is smaller than a preset recognition performance index, carrying out confidence interval analysis on the first noisy sample parameter, and screening N confidence intervals larger than a preset requirement from the first noisy sample parameter;
Obtaining a first newly added noise sample according to the N confidence intervals;
and optimizing the first similarity model according to the first newly added noise sample.
8. An intelligent analysis system for parameters of a multifunctional comprehensive tester of an oil pumping well, wherein the system comprises:
the first obtaining unit is used for obtaining a first test parameter set of the oil pumping well according to the first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters;
the first construction unit is used for analyzing the attribute of the oil pumping well according to the historical test data of the oil pumping well and constructing an attribute knowledge model of the oil pumping well;
the third construction unit is used for constructing a first parameter butt joint model based on the plurality of test parameters and the attribute knowledge model of the oil pumping well;
the second obtaining unit is used for obtaining a first preset analysis target, wherein the first preset analysis target is an abnormality detection target;
the first execution unit is used for inputting a first preset analysis target serving as a target condition into the first parameter docking model, and the first parameter docking model performs parameter docking by docking the first test parameter set to obtain a first docking parameter set;
The third obtaining unit is used for obtaining a second docking parameter set through noise reduction processing on the first docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing;
the fourth obtaining unit is used for extracting the characteristics according to the second docking parameter set to obtain first target characteristic data;
a fifth obtaining unit, configured to input the first target feature data into a risk assessment model for assessment, and obtain first output information according to the risk assessment model, where the first output information is a risk coefficient corresponding to the first preset analysis target;
and the sixth obtaining unit is used for obtaining a first test analysis report according to the first output information.
9. An intelligent analysis system for parameters of a multifunctional comprehensive tester of an oil pumping well, comprising: a processor coupled to a memory for storing a program which, when executed by the processor, causes the system to perform the method of any one of claims 1 to 7.
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