CN111817880A - Oil and gas field production equipment health management system and implementation method - Google Patents
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Abstract
The invention belongs to the field of fault processing of oil and gas field production equipment, and discloses a health management system of the oil and gas field production equipment and an implementation method thereof, wherein the system comprises a data acquisition layer, a system supporting layer, an intelligent analysis layer and an intelligent application layer, the intelligent analysis layer constructs a health state model for evaluating the performance of the equipment, predicts the health state of the performance of input equipment, and establishes a pretreatment scheme corresponding to the processing according to the health state of the performance of the input equipment; the intelligent application layer displays the evaluation result of the intelligent analysis layer aiming at the health state of the equipment, and simultaneously displays the corresponding preprocessing scheme according to the health state of the input equipment performance; the management system realizes effective utilization of mass data, realizes quick pre-judgment of equipment faults and avoids failure in replacing fault parts in time; through the evaluation of the health state of the equipment, the regular equipment maintenance times are effectively reduced, a large amount of maintenance funds are saved, and the equipment operation time is increased, so that the crude oil yield is improved.
Description
Technical Field
The invention belongs to the field of fault treatment of oil and gas field production equipment, and particularly relates to a health management system of the oil and gas field production equipment and an implementation method of the health management system.
Background
The oil and gas field production equipment is specially used for productive exploitation, gathering and transportation, storage and transportation, treatment, monitoring, measurement and the like of oil, gas and the like. Oil and gas field production equipment management faces the challenges of oil and gas coexistence, severe environment, space limitation, offshore distance, multiple types, complex technology, high cost, untimely maintenance and other risks. Oil and gas field production equipment, such as: common faults of compressors, water pumps, boilers, motors, power generation equipment and the like include unbalance, misalignment, mechanical looseness, electromagnetic vibration, oil film oscillation, shaft bending, bearing abrasion, foundation looseness, impeller mechanism faults, shaft channeling of the pump, coupling defects and the like. The management and maintenance of these facilities requires a great deal of manpower and material resources.
At present, the maintenance of oil and gas field production equipment in the system is mainly carried out in two modes, namely equipment failure, passive shutdown, quick arrangement of personnel for maintenance, finding of a certain part problem, replacement of a failed part, and immediate purchase if no spare part exists, so that the influence on the production system is very large. And secondly, a regular maintenance plan is arranged, for example, maintenance months are set every year, and the production is specially stopped for large-scale maintenance, so that the failure rate of equipment can be reduced, but the problem is also caused, namely, no failure occurs and the production time is wasted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an oil and gas field production equipment health management system and an implementation method, and solves the problems in the background art.
The purpose of the invention can be realized by the following technical scheme:
an oil-gas field production equipment health management system comprises a data acquisition layer, a system supporting layer, an intelligent analysis layer and an intelligent application layer,
the data acquisition layer acquires the health state data of the performance of the input equipment and transmits the acquired data to the system supporting layer;
the system supporting layer is used for processing and storing the health state data of the acquired input equipment performance;
the intelligent analysis layer constructs a health state model for evaluating the performance of the equipment according to the equipment working state data stored in the system supporting layer and predicts the health state of the performance of the input equipment; the method comprises the steps of dividing the health state of the performance of the input equipment into normal prediction, fault prediction or emergent prediction, and establishing a preprocessing scheme for correspondingly processing the predicted fault or the emergent prediction when the health state of the performance of the input equipment is evaluated as the predicted fault or the emergent prediction;
the intelligent application layer is used for displaying the evaluation result of the intelligent analysis layer aiming at the health state of the equipment, and displaying a corresponding preprocessing scheme according to the predicted fault or the predicted emergency fault when the health state of the input equipment performance is evaluated as the predicted fault or the predicted emergency fault.
Furthermore, the algorithm for predicting the health state of the performance of the input equipment in the intelligent analysis layer is based on a support vector machine algorithm, a fault diagnosis and early warning algorithm of a vibration signal, a neural network current card identification model algorithm and a deep reliability network algorithm.
Further, the input device performance is divided into device component performance and device lifetime.
Further, the intelligent application layer performs timely notification in a pop-up message, short message or intelligent voice telephone mode when the health status of the input device performance is evaluated as a predicted emergency fault.
Further, the data acquisition layer acquires the device data in a manner that includes: acquiring performance data of equipment in real time through a sensor and detection equipment;
performance data of the equipment is obtained through database extraction, conversion and loading;
and the performance data of the equipment is actively input through manual work.
An implementation method of a health management system of oil and gas field production equipment comprises the following steps:
s1: collecting basic data and working data of oil-gas field production equipment, and setting a threshold value required to be initially set;
s2: setting the maintenance algorithm parameters through a threshold value, and simultaneously optimizing the maintenance parameters;
s3: taking planned maintenance time and remaining time of the oil and gas field production equipment as a reference model, and taking a diagnosis library, a maintenance library, a state library and an expert library of the oil and gas field production equipment as supports to predict maintenance time and parts;
s4: diagnosing and reasoning by predicting the maintenance time of the oil-gas field production equipment and parts thereof, and summarizing the maintenance cost;
s5: and comprehensively scoring the predicted maintenance scheme through an intelligent algorithm, performing simulation training through a simulation system, predicting again if the predicted maintenance scheme is not the optimal maintenance strategy, determining the optimal maintenance strategy if the predicted maintenance scheme is the optimal maintenance strategy, and storing the optimal maintenance strategy and enriching the information of the maintenance library.
The invention has the beneficial effects that:
according to the invention, the effective utilization of mass data is realized through the management system, the fault of the oil-gas field production equipment is quickly judged in advance, the failure of timely replacing fault parts is avoided, and meanwhile, the prediction accuracy rate reaches over 80%; through the evaluation of the health state of the equipment, the regular equipment maintenance times are effectively reduced, a large amount of maintenance funds are saved, and the equipment operation time is increased, so that the crude oil yield is improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of an oil and gas field production facility health management system framework of an embodiment of the present invention;
FIG. 2 is a flow chart of an implementation method of the health management system of the oil and gas field production equipment in the embodiment of the invention.
Detailed Description
The following will clearly and completely describe the technical solutions in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the health management system for oil and gas field production equipment comprises a data acquisition layer, a system support layer, an intelligent analysis layer and an intelligent application layer.
The data acquisition layer is used for acquiring the health state data of the input equipment and transmitting the acquired data to the system supporting layer;
in this embodiment, the health status data of the input device is collected by the following methods:
1) the performance data of oil-gas field production equipment is collected in real time through various sensors and detection equipment;
2) acquiring performance data of oil-gas field production equipment through extraction, conversion and loading by a database of the existing information system;
3) acquiring performance data of oil-gas field production equipment through a webservice interface provided by the existing information system;
4) the performance data of the oil-gas field production equipment is actively input manually.
And the system supporting layer is used for processing and storing the acquired working state data of the oil and gas field production equipment so as to facilitate the standardization of the acquired working state data of the oil and gas field production equipment, the formulation and implementation of rules of the working state data and the monitoring of flow data of the working state data.
In this embodiment, the data support layer uses both a relational database Oracle and a Hadoop-based distributed system infrastructure.
In practical use, the data acquisition layer and the system supporting layer are physically isolated, and particularly, data exchange between the two mutually isolated acquisition layers and the system supporting layer is realized through a safety isolation gateway between the data acquisition layer and the system supporting layer.
The end user can access the system application after passing through the identity authentication and the firewall; the system is connected with other systems through an interface system; and carrying out disaster recovery backup on the server application and the database.
The intelligent analysis layer is used for constructing a health state model of the evaluation equipment by means of health state data analysis and a machine learning algorithm of oil-gas field production equipment in the system support layer, and predicting the health state of the input equipment, wherein the health state of the performance of the input equipment is divided into normal prediction, fault prediction or emergent fault prediction;
the intelligent analysis layer is established with a preprocessing scheme for correspondingly processing the predicted fault or the predicted emergency fault when the health state of the input equipment performance is evaluated as the predicted fault or the predicted emergency fault;
when the health state of the input equipment is evaluated to be a predicted fault or a predicted fault, fault preprocessing is correspondingly performed according to the health state of the input equipment, so that systematic health management of the equipment is facilitated, and therefore health state monitoring, fault diagnosis, fault prediction, autonomous fault preprocessing and the like of the equipment are achieved.
In this embodiment, in the actual use process, the intelligent analysis layer uses the working state data (such as the data collected by the data collection layer and the related historical statistical data) of the massive oil and gas field production equipment in the system support layer, and through various artificial intelligence algorithms, for example:
health degree evaluation and prediction based on a support vector machine: based on an SVM (Support vector machine), firstly, extracting the characteristics of the running state of equipment (voltage, temperature, current, vibration signals, acceleration, flow, lift and the like), and then, judging whether the data is linearly separable or not by data preprocessing, wherein a linear SVM predictor is adopted for linearly separable; the linear inseparable non-linear SVM predictor is adopted, if the equipment is healthy, data are stored in an equipment characteristic database, the health characteristic database changes through continuous learning, some basic data are set according to experience initially, and machine learning is conducted through big data continuously in the later stage, so that the accuracy is improved. The system displays the health degree of the equipment in the forms of curves and the like according to the data calculated by the model;
fault diagnosis and early warning based on vibration signals: fault diagnosis and early warning are carried out according to the 'non' characteristic of the signal, wherein the fault diagnosis and early warning comprise non-stability, non-regularity, non-continuity, non-periodicity, asymmetry, nonlinearity, non-uniformity, non-structure, non-certainty and the like; for example, time-frequency distribution and wavelet analysis are performed on non-stationary signals; carrying out high price statistic analysis on the non-Gaussian signals; fractal and correlation dimension analysis is carried out on the nonlinear signals to extract fault characteristics; the time domain waveform has obvious characteristics, and signals such as periodic signals, harmonic signals, short pulses and the like can be directly observed, so that the initial judgment on the running state of the equipment can be made; the frequency spectrum analysis is to study a complex time domain waveform by Fourier transformation into a plurality of single harmonic components, so that the amplitude distribution and the energy distribution of each harmonic component can be clearly known, and then the running state of the equipment is judged;
fault diagnosis based on neural network current card identification model: the BP neural network diagnosis of the equipment is mainly based on a template of a current card, and a training set of equipment diagnosis is formed by extracting various characteristic parameters of a current curve of the current card reflecting various working conditions, so that the neural network of the equipment is established, and the real-time monitoring and diagnosis of the production process of the equipment is realized.
At present, most oil wells can acquire real-time current of equipment, and a system simultaneously supports direct analysis of the real-time current so as to realize fault diagnosis;
fault diagnosis based on deep level credibility network: the belief network is also called belief network and Bayes network, is an extension of Bayes method, and is a directed graph simulating causal relationship in human reasoning process, each node of the directed graph is a variable with multiple values to represent a certain hypothesis or observation result, and the relationship among the nodes is represented by connecting lines between the nodes. For example, the probability of various faults can be calculated by using a Bayesian network algorithm according to the big data of parameters such as current, voltage, inlet pressure, inlet flow, temperature and the like;
the method comprises the steps that cloud computing is carried out based on a distributed server, direct influence factors and indirect influence factors of all indexes are researched through a machine learning means, reasonable operation limits of all fault standards are determined, prediction results are obtained, for example, fault information is obtained according to data characteristics, an intelligent analysis layer analyzes performance degradation degree and degradation trend of oil and gas field production equipment components according to historical statistical data and a data fusion method, and the process of developing to functional faults is predicted; or predicting the service life of the equipment according to the historical information of the equipment and predicting the service life of the equipment according to the fault prediction model.
The intelligent application layer is used for displaying the evaluation result of the intelligent analysis layer aiming at the health state of the equipment, and displaying a corresponding preprocessing scheme according to the predicted fault or the predicted emergency fault when the health state of the input equipment performance is evaluated as the predicted fault or the predicted emergency fault; and when the health state of the equipment is evaluated to predict the emergency fault, timely informing the equipment in a pop-up information, short message or intelligent voice telephone mode.
Specifically, the intelligent application layer correspondingly displays various monitoring, predicting and fault diagnosis results of the health state of the equipment by using various existing front-end technologies, such as JS, HTML5, GIS, LBS and the like, wherein the display of the diagnosis results comprises modules of state monitoring, fault diagnosis, fault prediction, maintenance guarantee, equipment management, routing inspection management, vehicle scheduling management and the like, and the display mode comprises but is not limited to display in the form of graphs, curves, tables and the like;
the state monitoring comprises equipment state overview, equipment detailed information and equipment alarm information, wherein the equipment state overview displays the key state information of the key equipment in the forms of graphs, curves, tables and the like; the detailed equipment information shows the detailed operation information of a specific piece of equipment in the form of graphs, curves, tables and the like; and the equipment alarm information shows the detailed information of the equipment alarm and transmits the detailed information to a related responsible person through a short message and an intelligent voice call.
The working principle is as follows: the method is based on massive real-time monitoring data of oil-gas field production equipment, utilizes a big data system, and realizes fault alarm and fault prediction of the equipment through artificial intelligence algorithms such as a neural network and a deep reliability network, and carries out full life cycle management on the equipment;
the large data system is introduced through oil-gas field production equipment, so that the data operation speed is increased; the method comprises the following steps of introducing an artificial intelligence algorithm of a support vector machine into health degree evaluation and prediction of oil-gas field production equipment; analyzing the equipment vibration signal for fault diagnosis and prediction of oil-gas field production equipment; the neural network algorithm is used for equipment current card identification, so that rapid fault identification is realized; wherein, the deep level credibility network intelligent algorithm is used for judging the equipment failure.
According to the invention, the effective utilization of mass data is realized through the management system, the rapid pre-judgment of equipment faults is realized, the failure of timely replacing fault parts is avoided, and meanwhile, the prediction accuracy rate reaches more than 80%; through the evaluation of the health state of the equipment, the regular equipment maintenance times are effectively reduced, a large amount of maintenance funds are saved, and the equipment operation time is increased, so that the crude oil yield is improved.
As shown in fig. 2, an implementation method of a health management system for oil and gas field production equipment includes:
s1: collecting basic data and working data of oil-gas field production equipment, and setting a threshold value required to be initially set;
the data of oil and gas field production equipment is collected in real time through a data collection layer, characteristic data is extracted and stored in a system supporting layer; and the system supporting layer automatically carries out initialization setting on the related early warning data through the characteristic data to finish the auxiliary condition of the predicted maintenance.
S2: setting the maintenance algorithm parameters through a threshold value, and simultaneously optimizing the maintenance parameters; so as to be convenient for the standardization of the obtained working state data of the oil-gas field production equipment, the formulation and the realization of the rules of the working state data and the flow data monitoring of the working state data.
S3: taking planned maintenance time and remaining time of the oil and gas field production equipment as a reference model, and taking a diagnosis library, a maintenance library, a state library and an expert library of the oil and gas field production equipment as supports to predict maintenance time and parts; the prediction method comprises a time sequence model prediction method, a gray model prediction method, a neural network prediction method and the like;
the diagnostic library is used for storing fault data of the oil and gas field production equipment, and is convenient for matching according to the collected fault data of the oil and gas field production equipment at the later stage;
the maintenance library is used for recording and storing fault maintenance data of the oil and gas field production equipment and simultaneously recording the storage capacity of parts for maintaining the oil and gas field production equipment in real time;
the state library is used for recording the performance parameters of the real-time monitored and stored oil-gas field production equipment;
the expert database is used for integrally storing the expert research or the opinion in the corresponding field of the oil and gas field production equipment, and when the fault of the oil and gas field production equipment is detected, the expert research or the opinion in the corresponding field is displayed corresponding to the oil and gas field production equipment;
s4: diagnosing and reasoning by predicting the maintenance time of the oil-gas field production equipment and parts thereof, and summarizing the maintenance cost;
s5: and comprehensively scoring the predicted maintenance scheme through an intelligent algorithm, performing simulation training through a simulation system, predicting again if the predicted maintenance scheme is not the optimal maintenance strategy, determining the optimal maintenance strategy if the predicted maintenance scheme is the optimal maintenance strategy, and storing the optimal maintenance strategy and enriching the information of the maintenance library.
The intelligent prediction is comprehensively predicted through a physical model, a knowledge model and a statistical model to form a hybrid maintenance prediction model.
The invention relies on the real-time data of oil and gas field production equipment to establish a digital mirror image model, combines a mechanism model with an intelligent data analysis tool and establishes a virtual and entity mutual mapping analysis model, namely the application of a digital twin technology; the production equipment is diagnosed by various diagnostic means, including time-frequency diagnostic method, statistical diagnostic method, neural network diagnostic method, etc.
The method comprises the steps of evaluating the current state of the equipment and predicting the future state according to the operation information of the oil-gas field production equipment, wherein the prediction method comprises a time sequence model prediction method, a grey model prediction method, a neural network prediction method and the like; the intelligent prediction is comprehensively predicted through a physical model, a knowledge model and a statistical model to form a hybrid maintenance prediction model.
The invention fully utilizes the operational research optimization theory and continuously optimizes the operation, maintenance, scheduling, diagnosis and safety guarantee; maintenance feasibility analysis is carried out according to results of state monitoring, intelligent diagnosis and intelligent prediction from the aspects of personnel, resources, time, cost and the like, and a maintenance decision is determined by an intelligent maintenance decision method.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (6)
1. A health management system for oil and gas field production equipment comprises a data acquisition layer, a system supporting layer, an intelligent analysis layer and an intelligent application layer,
the data acquisition layer acquires the health state data of the performance of the input equipment and transmits the acquired data to the system supporting layer;
the system supporting layer is used for processing and storing the health state data of the acquired input equipment performance;
the intelligent analysis layer constructs a health state model for evaluating the performance of the equipment according to the equipment working state data stored in the system supporting layer and predicts the health state of the performance of the input equipment; the method comprises the steps of dividing the health state of the performance of the input equipment into normal prediction, fault prediction or emergent prediction, and establishing a preprocessing scheme for correspondingly processing the predicted fault or the emergent prediction when the health state of the performance of the input equipment is evaluated as the predicted fault or the emergent prediction;
the intelligent application layer is used for displaying the evaluation result of the intelligent analysis layer aiming at the health state of the equipment, and displaying a corresponding preprocessing scheme according to the predicted fault or the predicted emergency fault when the health state of the input equipment performance is evaluated as the predicted fault or the predicted emergency fault.
2. The oil and gas field production equipment health management system of claim 1, wherein the algorithm of the intelligent analysis layer that predicts the health status of the input equipment performance is based on a fault diagnosis and early warning algorithm including a support vector machine algorithm, a vibration signal, a neural network current card recognition model algorithm, and a deep reliability network algorithm.
3. The oil and gas field production equipment health management system of claim 1, wherein the input equipment performance is divided into equipment component performance and equipment life.
4. The system of claim 1, wherein the intelligent application layer notifies the user in time by pop-up messages, sms, or smart voice calls when the health status of the input device performance is assessed as predictive of an emergency failure.
5. The oil and gas field production equipment health management system of claim 1, wherein the data collection layer collects equipment data in a manner comprising: acquiring performance data of equipment in real time through a sensor and detection equipment;
performance data of the equipment is obtained through database extraction, conversion and loading;
and the performance data of the equipment is actively input through manual work.
6. An implementation method of a health management system of oil and gas field production equipment is characterized by comprising the following steps:
s1: collecting basic data and working data of oil-gas field production equipment, and setting a threshold value required to be initially set;
s2: setting the maintenance algorithm parameters through a threshold value, and simultaneously optimizing the maintenance parameters;
s3: taking planned maintenance time and remaining time of the oil and gas field production equipment as a reference model, and taking a diagnosis library, a maintenance library, a state library and an expert library of the oil and gas field production equipment as supports to predict maintenance time and parts;
s4: diagnosing and reasoning by predicting the maintenance time of the oil-gas field production equipment and parts thereof, and summarizing the maintenance cost;
s5: and comprehensively scoring the predicted maintenance scheme through an intelligent algorithm, performing simulation training through a simulation system, predicting again if the predicted maintenance scheme is not the optimal maintenance strategy, determining the optimal maintenance strategy if the predicted maintenance scheme is the optimal maintenance strategy, and storing the optimal maintenance strategy and enriching the information of the maintenance library.
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| CN112329953A (en) * | 2020-10-29 | 2021-02-05 | 中冶南方（武汉）热工有限公司 | Full-process intelligent diagnosis and optimized operation system for industrial furnace |
| CN112785091A (en) * | 2021-03-04 | 2021-05-11 | 湖北工业大学 | Method for performing fault prediction and health management on oil field electric submersible pump |
| CN112884325A (en) * | 2021-02-22 | 2021-06-01 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Method and system for application analysis and health condition evaluation of customer station equipment |
| CN113379196A (en) * | 2021-05-17 | 2021-09-10 | 国网浙江省电力有限公司宁波供电公司 | Transformer equipment management evaluation system based on digital twin technology |
| CN113408969A (en) * | 2021-08-19 | 2021-09-17 | 北京航空航天大学 | Maintenance scheme generation method and system for distributed system |
| CN113469383A (en) * | 2021-07-06 | 2021-10-01 | 山西大数据产业发展有限公司 | Equipment remote predictive maintenance system and method based on Internet mode |
| CN113570231A (en) * | 2021-07-22 | 2021-10-29 | 浙江幸福航天科技有限公司 | Power station health management system |
| CN116957361A (en) * | 2023-07-27 | 2023-10-27 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
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Application publication date: 20201023 |