CN113898334A - Intelligent parameter analysis method and system for multifunctional comprehensive tester of pumping well - Google Patents
Intelligent parameter analysis method and system for multifunctional comprehensive tester of pumping well Download PDFInfo
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- 238000004458 analytical method Methods 0.000 title claims abstract description 113
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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, obtaining a first test parameter set of the pumping well, and constructing a first parameter butt joint model; 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 on the first parameter docking model through docking a first test parameter set to obtain a first docking parameter set; denoising the first butt joint parameter set to obtain a second butt joint parameter set; and inputting the first target characteristic data into the 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, time for searching abnormal parameters is long, and the accuracy of measuring the parameters is easy to be interfered by noise is low, so that the intelligent parameter analysis efficiency is reduced are solved.
Description
Technical Field
The invention relates to the technical field of pumping well testing, in particular to an intelligent parameter analysis method and system for a multifunctional comprehensive tester of a pumping well.
Background
The pumping well comprehensive tester is mainly used for testing parameters such as oil well liquid level depth, indicator diagram, casing pressure and the like, provides reliable data support for immediately knowing the working condition of a pumping unit and the oil well submergence degree, and is ideal testing equipment in oil field production. The comprehensive tester for the pumping well has the characteristics that a pulse sound wave generator is used as a liquid level test sound source, so that acoustic bombs and a high-pressure nitrogen cylinder are avoided; the indicator diagram sensor has suspension type, movable type, hydraulic type and bayonet type, and can meet different use occasions; the wellhead connector is provided with a hand-clapping type and a pull wire type; the indicator diagram and the liquid level can be tested simultaneously, so that the dual-purpose function of one machine is realized, and the working efficiency is effectively improved.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned 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 the parameters is easy to be interfered by noise, so that the intelligent parameter analysis efficiency is reduced.
Disclosure of Invention
The embodiment of the application provides the intelligent analysis method and the system for the parameters of the multifunctional comprehensive tester of the pumping well, and solves the technical problems that in the prior art, when the pumping well is abnormal, time consumption for searching abnormal parameters is long, and the accuracy of measuring the parameters is easy to be interfered by noise, so that the intelligent parameter analysis efficiency is reduced. The method achieves the technical effects of rapidly docking related parameters through the analysis of the related parameters, improving the accuracy of the docking parameter measurement result through accurate noise reduction, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis.
In view of the above problems, the embodiment of the present application provides an intelligent analysis method and system for parameters of a multifunctional comprehensive tester for a rod-pumped well.
In a first aspect, an embodiment of the present application provides an intelligent analysis method for parameters of a multifunctional comprehensive tester for an oil pumping well, where the method includes: obtaining a first test parameter set of the pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; inputting a first preset analysis target 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; performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; performing feature extraction 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 a multifunctional comprehensive tester parameter intelligent analysis system for an oil pumping well, wherein the system comprises: a first obtaining unit, 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; the first construction unit is used for constructing a first parameter docking model according to the attribute knowledge model of the pumping well; the second obtaining unit is used for obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; a first execution unit, 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, 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 noise reduction processing; a fourth obtaining unit, configured to perform feature extraction according to the second docking parameter set to obtain first target feature 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; a sixth obtaining unit, configured to obtain 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 comprehensive tester for an oil pumping well, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the computer program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining a first test parameter set of the pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; inputting a first preset analysis target 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; performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; performing feature extraction 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 of obtaining the first test analysis report according to the first output information, the embodiment of the application provides the intelligent analysis method and the system for the parameters of the multifunctional comprehensive tester of the pumping well, so that the technical effects of analyzing the relevant parameters through the relevant parameters, quickly butting the relevant parameters, accurately reducing noise, improving the accuracy of the butt joint parameter measurement result, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis are achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for intelligently analyzing parameters of a multifunctional integrated tester for a rod-pumped well according to an embodiment of the present application;
FIG. 2 is a schematic flow chart showing the analysis parameter representation of a method for intelligently analyzing parameters of a multifunctional integrated tester for a rod-pumped well according to an embodiment of the present invention;
FIG. 3 is a schematic view of a process for performing feature coincidence analysis according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a process for obtaining a second docking parameter set according to an intelligent analysis method for parameters of a multifunctional integrated tester for a rod-pumped well according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart illustrating the noise identification and reduction process performed by the intelligent analysis method for the parameters of the multifunctional integrated tester for the rod-pumped well according to the embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating adjustment of multiple batches of maturity cycle nodes according to an embodiment of the present disclosure;
FIG. 7 is a schematic flow chart illustrating the generation of constraints with noise parameter determination according to the intelligent analysis method for parameters of the multifunctional integrated tester for a rod-pumped well in the embodiment of the present application;
FIG. 8 is a schematic structural diagram of an intelligent analysis system for parameters of a multifunctional integrated tester for a rod-pumped 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.
Description of reference numerals: 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 the intelligent analysis method and the system for the parameters of the multifunctional comprehensive tester of the pumping well, and solves the technical problems that in the prior art, when the pumping well is abnormal, time consumption for searching abnormal parameters is long, and the accuracy of measuring the parameters is easy to be interfered by noise, so that the intelligent parameter analysis efficiency is reduced. The method achieves the technical effects of rapidly docking related parameters through the analysis of the related parameters, improving the accuracy of the docking parameter measurement result through accurate noise reduction, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis.
Summary of the application
The pumping well comprehensive tester is mainly used for testing parameters such as oil well liquid level depth, indicator diagram, casing pressure and the like, provides reliable data support for immediately knowing the working condition of a pumping unit and the oil well submergence degree, and is ideal testing equipment in oil field production. The comprehensive tester for the pumping well has the characteristics that a pulse sound wave generator is used as a liquid level test sound source, so that acoustic bombs and a high-pressure nitrogen cylinder are avoided; the indicator diagram sensor has suspension type, movable type, hydraulic type and bayonet type, and can meet different use occasions; the wellhead connector is provided with a hand-clapping type and a pull wire type; the indicator diagram and the liquid level can be tested simultaneously, so that the dual-purpose function of one machine 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 the parameters is easy to be interfered by noise, so that the technical problem of reducing the intelligent parameter analysis efficiency is solved.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
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 pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; inputting a first preset analysis target 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; performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; performing feature extraction 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 thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a method for intelligently analyzing parameters of a multifunctional comprehensive tester for a rod-pumped well, where the method includes:
s100: obtaining a first test parameter set of the 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 butt joint model according to the attribute knowledge model of the pumping well;
specifically, the first comprehensive tester is a multifunctional tester for any pumping well, and the first comprehensive tester can be used for measuring a plurality of parameters of the pumping well to obtain the first test parameter set including parameters such as liquid level position and bottom hole pressure. And analyzing the attributes of the pumping well, and constructing an attribute knowledge model of the pumping well based on the historical test data of the pumping well. Obtaining parameters needing deep analysis according to actual problems needing to be solved in production, and constructing the first parameter docking model according to the parameters and the attribute knowledge model of the oil pumping well. According to the relevant parameters of the problem to be solved, the intelligent parameter analysis efficiency can be improved.
S300: obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target;
s400: inputting a first preset analysis target 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: performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing;
specifically, when the comprehensive tester detects an abnormal condition or a worker finds an abnormal condition, the first preset analysis target, that is, 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, where the first docking set is from the first test parameter set. Due to environmental factors or self factors of a test instrument, noise exists in the measurement result of some parameters, and the analysis and calculation of the parameters are interfered, so that the first butt-joint parameter set is subjected to noise reduction processing to obtain a second butt-joint parameter set after the noise is removed. Noise monitoring of the condition of downhole equipment is performed in conjunction with a sound detector for noise pattern recognition, and anomalies that can be detected by the detector include valve function, pipe leaks, rod bends, and the like. Noise interference can be removed through methods such as noise detection and noise reduction processing, and therefore accuracy of a butt joint parameter measurement result is improved.
S600: performing feature extraction 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, the measurement results of each parameter in the second docking parameter set are analyzed and extracted to obtain abnormal feature data, and the abnormal feature data of the parameters is most likely to cause a safety problem. The risk evaluation model can evaluate a safety risk coefficient caused by parameter abnormity, the first target characteristic data is input into the risk evaluation model for analysis to obtain a corresponding risk coefficient, the corresponding risk coefficient is the first output information, and the first test analysis report is obtained after calculation is carried out through a conventional formula in the field of oil pumping wells according to a risk evaluation result. And risk analysis is carried out on the first target characteristic data, so that the safety of operation can be improved, and the scientificity, pertinence and reliability of abnormal risk analysis are improved.
Further, as shown in fig. 2, the embodiment of the present application further includes:
s810: judging whether the parameter representation form is a curve representation form or not by analyzing the parameter representation form of the first docking parameter set, wherein the parameter representation form comprises a data representation form and a curve representation form;
s820: if the parameter representation form is the curve representation form, denoising and data processing are carried out according to a first parameter processing mode;
s830: and if the parameter representation form is the data representation form, performing noise reduction and data processing according to a second parameter processing mode.
Specifically, the multifunctional comprehensive tester for the pumping well in the market at present has an advanced data management system, can automatically generate a curve through measuring parameters, perform 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, is not in the curve expression form, and presents the data in a single numerical value form, namely the data expression form. And if the parameter representation form is a curve representation form, performing noise reduction processing and data processing on curve data by adopting the first parameter processing method. And if the parameter representation form is a data representation form, performing noise reduction processing and data processing by adopting the second parameter processing mode. The efficiency of parameter noise reduction and depth analysis can be improved, and therefore the analysis coping capability of abnormal conditions is improved.
Further, as shown in fig. 3, the performing feature extraction according to the second docking parameter set to obtain first target feature data, where the step S600 further includes:
s610: obtaining all category information of the second docking parameters by performing multi-parameter category division on the second docking parameter set;
s620: obtaining parameter characteristic information corresponding to the second docking parameter by performing characteristic extraction on the parameter of each category in all the category information;
s630: performing characteristic coincidence analysis on the parameter characteristic information to obtain a first coincidence characteristic;
s640: and taking the first coincidence feature as the first target feature data.
Specifically, the second docking parameter set is a parameter after noise reduction, and multiple classes of the second docking parameter set are divided, and different types of parameters exceeding a normal range may cause different types of abnormal problems. And respectively extracting features in each category, and extracting and dividing a plurality of representative parameter feature information to obtain the parameter feature information corresponding to the second docking parameters. Further, the parameter characteristics under each category and the current abnormal condition are subjected to characteristic coincidence analysis, a plurality of parameter characteristics which cause the same abnormality are screened out, the parameter characteristics are the first coincidence characteristics, the first coincidence characteristics are used as the first target characteristic data, subsequent parameter analysis is carried out, and the pertinence and the analysis efficiency of the parameter analysis can be improved.
Further, as shown in fig. 4, the step S500 further includes performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set:
s510: performing label identification according to each parameter category of the second docking parameter set to generate a first input sample label;
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 judging the noisy parameter and carrying out 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 labeled by 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 then noisy parameters and non-noisy parameters are distinguished. And performing parameter sample liniment 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 the first similarity coefficient as a noisy parameter when the first similarity coefficient reaches a certain similarity, and further performing noise reduction processing on the noisy parameter to obtain a second butt joint parameter set.
Further, as shown in fig. 5, the step S550 of performing noise parameter judgment and noise reduction processing according to the first similarity coefficient to obtain the second docking parameter set includes:
s551: carrying out noise identification on the first butt-joint 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 noise parameter set, and the second identification parameter set is a non-noise 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, so that 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 carrying out noise reduction processing on the first identification parameter, improving the accuracy of the noise reduction processing, and generating a third identification parameter set, wherein the third set parameter set and the second identification parameter set are both non-noisy data to form a second butt-joint parameter, so that a parameter measurement result is closer to real data, and the effectiveness of the parameter 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, obtaining a first matching instruction;
s563: matching the first input sample parameter and the first noisy sample parameter for multiple times 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 a constraint condition for judging the noisy parameter.
Specifically, a similarity threshold is preset, and when the first similarity coefficient meets the preset similarity threshold, the first matching instruction is obtained, where the first matching instruction can perform multiple matching on the first input sample parameter and the first noisy sample parameter, where multiple matching may be performed by performing multiple similarity calculation at multiple frequency points to obtain the first matching result, where the first matching result is the number of successful matching in the multiple matching process. And taking the first matching result and the first similarity coefficient as constraint conditions for judging the noisy parameter, so that the accuracy of judging the noisy parameter can be improved through multiple times of matching.
Further, as shown in fig. 7, after the constructing the first similarity model, step S520 further includes:
s521: obtaining a first identification performance index by carrying out similarity identification calculation on the first similarity model;
s522: if the first identification performance index is smaller than a preset identification performance index, performing 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 noisy sample according to the N confidence intervals;
s524: and optimizing the first similarity model according to the first newly-added noisy 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. 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 index is poor and is lower than the preset recognition performance index, performing confidence interval analysis on an error of the first recognition performance index in the preset performance index. The confidence interval is an estimated interval of the overall parameter constructed from the sample statistics, and exhibits a degree to which the true value of the parameter falls around the measurement result with a certain probability, which gives the degree of confidence of the measured value of the measured parameter. And screening N confidence intervals which are larger than a preset requirement from the first noisy sample parameter, and selecting the N confidence intervals according to certain fault tolerance. And newly-added samples are contained in the N confidence intervals, and the obtained first newly-added noisy sample is input into the first similarity model for model optimization, so that the technical effects of optimizing the model performance and improving the model accuracy can be achieved.
In summary, the method and the system for intelligently analyzing the parameters of the multifunctional comprehensive tester for the rod-pumped well provided by the embodiment of the application have the following technical effects:
1. the method comprises the steps of obtaining a first test parameter set of the pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; inputting a first preset analysis target 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; performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; performing feature extraction 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 of obtaining the first test analysis report according to the first output information, the embodiment of the application provides the intelligent analysis method and the system for the parameters of the multifunctional comprehensive tester of the pumping well, so that the technical effects of analyzing the relevant parameters through the relevant parameters, quickly butting the relevant parameters, accurately reducing noise, improving the accuracy of the butt joint parameter measurement result, improving the operation safety and improving the scientificity, pertinence and reliability of abnormal risk analysis are achieved.
2. Due to the adoption of the method for selecting the N confidence intervals, the fault tolerance of parameter selection can be improved, and the data sample amount can be expanded, so that a newly added sample is input into a corresponding model for model optimization, and the technical effects of optimizing the model performance and improving the model accuracy can be achieved.
Example two
Based on the same inventive concept as the intelligent analysis method for the parameters of the multifunctional comprehensive tester for the rod-pumped well in the previous embodiment, as shown in fig. 8, the embodiment of the present application provides an intelligent analysis system for the parameters of the multifunctional comprehensive tester for the rod-pumped well, wherein the system comprises:
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 an attribute knowledge model 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 abnormality detection target;
a first executing unit 14, where the first executing unit 14 is configured to input a first preset analysis target 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;
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 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 comprises:
a second execution unit, configured to determine whether a parameter representation form of the first docking parameter set is a curve representation form by analyzing the parameter representation form, where the parameter representation form includes a data representation form and a curve representation form;
a third execution unit, configured to perform noise reduction and data processing according to the first parameter processing manner if the parameter representation form is the curve representation form;
and the fourth execution unit is used for performing noise reduction and data processing according to a second parameter processing mode if the parameter representation form is the data representation form.
Further, the system comprises:
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 all the category information;
a ninth obtaining unit, configured to obtain a first coincidence feature by performing feature coincidence analysis on the parameter feature information;
a fifth execution unit configured to take the first coincidence feature as the first target feature data.
Further, the system comprises:
a first generating unit, configured to perform label identification according to each parameter category of the second docking parameter set, and generate a first input sample label;
a second construction unit for constructing a first similarity model;
a tenth obtaining unit, configured to obtain a first noisy sample label by performing parameter sample acquisition on the first comprehensive tester;
an eleventh obtaining unit, configured to input the first input sample label and the first noisy sample label into the first similarity model for similarity analysis, so as to obtain a first similarity coefficient;
a twelfth obtaining unit, configured to perform noise parameter judgment and noise reduction processing according to the first similarity coefficient, and obtain the second docking parameter set.
Further, the system comprises:
a thirteenth obtaining unit, configured to perform noisy identification on the first pairing 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;
a second generation unit, configured to generate a third identification parameter set by performing 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 comprises:
a sixth execution unit, configured to determine whether the first similarity coefficient is greater than a preset similarity threshold;
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, and obtain a first matching result, where the first matching result is a number of successful matches;
a seventh execution unit, configured to use the first matching result and the first similarity coefficient as constraint conditions for the noisy parameter determination.
Further, the system comprises:
a seventeenth obtaining unit, configured to obtain a first recognition performance index by performing similarity recognition calculation on the first similarity model;
an eighth execution unit, configured to perform confidence interval analysis on the first noisy sample parameter if the first recognition performance index is smaller than a preset recognition performance index, and screen out 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 noisy sample according to the N confidence intervals;
a ninth execution unit to optimize the first similarity model according to the first newly added noisy sample.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 9,
based on the same inventive concept as the intelligent analysis method for the parameters of the multifunctional comprehensive tester of the rod-pumped well in the previous embodiment, the embodiment of the application also provides an intelligent analysis system for the parameters of the multifunctional comprehensive tester of the rod-pumped well, which comprises the following steps: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, 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 connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
The communication interface 303 is a system using any transceiver or the like, and is used for communicating with other devices or communication networks, such as ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact-disc-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium 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 a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer-executable instructions stored in the memory 301, so as to implement the intelligent analysis method for parameters of the multifunctional integrated tester for a rod-pumped well provided by the above-mentioned embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically 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 pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters; constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well; obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target; inputting a first preset analysis target 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; performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing; performing feature extraction 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 understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized 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, cause the processes or functions described in accordance with the embodiments of the application to occur, 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 a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by general purpose processors, digital signal processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic systems, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the 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 the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells 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. For example, a storage medium may be coupled to the processor such 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 be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the 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 present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.
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 pumping well according to a first comprehensive tester, wherein the first test parameter set comprises a plurality of test parameters;
constructing a first parameter butt joint model according to the attribute knowledge model of the pumping well;
obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target;
inputting a first preset analysis target 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;
performing noise reduction processing on the first docking parameter set to obtain a second docking parameter set, wherein the second docking parameter set is a parameter after the noise reduction processing;
performing feature extraction 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 representation form is a curve representation form or not by analyzing the parameter representation form of the first docking parameter set, wherein the parameter representation form comprises a data representation form and a curve representation form;
if the parameter representation form is the curve representation form, denoising and data processing are carried out according to a first parameter processing mode;
and if the parameter representation form is the data representation form, performing 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 docking parameter set to obtain first target feature data, the method further comprising:
obtaining all category information of the second docking parameters by performing multi-parameter category division on the second docking parameter set;
obtaining parameter characteristic information corresponding to the second docking parameter by performing characteristic extraction on the parameter of each category in all the category information;
performing characteristic coincidence analysis on the parameter characteristic information to obtain a first coincidence characteristic;
and taking the first coincidence feature as the first target feature data.
4. The method of claim 1, wherein the second set of docking parameters is obtained by denoising the first set of docking parameters, the method further comprising:
performing label identification according to each parameter category of the second docking parameter set to generate a first input sample label;
constructing a first similarity model;
acquiring a first noisy sample label by acquiring a parameter sample of the first comprehensive tester;
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;
and judging the noisy parameter and carrying out 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 second docking parameter set is obtained by performing noise parameter determination and noise reduction processing according to the first similarity coefficient, and the method further comprises:
carrying out noise identification on the first butt-joint 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 noise parameter set, and the second identification parameter set is a non-noise 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, obtaining a first matching instruction;
matching the first input sample parameter and the first noisy sample parameter for multiple times 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 a constraint condition for judging the noisy parameter.
7. The method of claim 4, wherein after the constructing the first similarity model, the method further comprises:
obtaining a first identification performance index by carrying out similarity identification calculation on the first similarity model;
if the first identification performance index is smaller than a preset identification performance index, performing 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 noisy sample according to the N confidence intervals;
and optimizing the first similarity model according to the first newly-added noisy sample.
8. An intelligent parameter analysis system for a multifunctional comprehensive tester of an oil pumping well, wherein the system comprises:
a first obtaining unit, 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;
the first construction unit is used for constructing a first parameter docking model according to the attribute knowledge model of the pumping well;
the second obtaining unit is used for obtaining a first preset analysis target, wherein the first preset analysis target is an abnormal detection target;
a first execution unit, 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, 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 noise reduction processing;
a fourth obtaining unit, configured to perform feature extraction according to the second docking parameter set to obtain first target feature 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;
a sixth obtaining unit, configured to obtain a first test analysis report according to the first output information.
9. An intelligent parameter analysis system for a multifunctional comprehensive tester of an oil pumping well comprises: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1-7.
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