Ocean Economy and Fault Diagnosis of Electric Submersible Pump applied in Floating platform

Zhang, Panlong; Chen, Tingkai; Wang, Guochao; Peng, Changzheng

doi:10.1016/j.enavi.2017.05.005

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…The mapping relation between faults and changing trends of parameter features is shown in Table 1. According to the theory of probability and statistics by Zhang, Chen, and Wang (2017) in the current study, the expectation reflects the average of the random variable size. The maximum value and the minimum value reflect the deviation of the data.…”

Section: Feature Extractionmentioning

confidence: 97%

Condition monitoring and fault diagnosis of electric submersible pump based on wellhead electrical parameters and production parameters

Hua

2018

Systems Science & Control Engineering

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Section: Feature Extractionmentioning

confidence: 97%

Condition monitoring and fault diagnosis of electric submersible pump based on wellhead electrical parameters and production parameters

Hua

2018

Systems Science & Control Engineering

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“…ere are two common threshold diagnosis methods: direct threshold method, which sets different thresholds for production parameters [9]; the indirect threshold method extracts the characteristic values of production parameters and sets different thresholds for them, such as expected value, average value, variance, and other characteristics [10][11].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

Yang

Wang

Zheng³

et al. 2022

Mathematical Problems in Engineering

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Aiming at the popularization and application of a real-time monitoring parameter acquisition system of the electric submersible pump (ESP) well, this paper proposes a fault diagnosis method of ESP well operation based on the SPC rule and prior knowledge fusion. Based on the study of parameter variation rules of ESP well, the SPC expansion rule model is established; by analyzing the variation of some typical characteristic parameters of ESP well, combined with SPC expansion rules and expert experience, a priori knowledge of fault diagnosis of ESP well is formed, that is, multiparameter fault analysis table and weight factor; the SPC extended rule model and prior knowledge are fused to establish the fault probability model of ESP well, form the fault diagnosis method of ESP well, develop the online fault diagnosis software of ESP well, and deploy it in 425 ESP wells in a block. Taking five types of tubing leakage, pump wear, shaft breakage, gas influence, and pump plugging as examples, the application process of fault diagnosis method is analyzed. The research and application show that compared with other fault diagnosis methods, this method needs a smaller time window and higher diagnosis accuracy. By setting multiple time windows, this diagnosis method is applied to calculate the fault probability of ESP well in real time, solve the real time and accurate identification of 14 sudden faults and gradual faults, and significantly improve the intelligent diagnosis level of production faults of ESP well.

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“…The previous literature includes applications that only deal with statistical analysis in a descriptive way, − , while the rest are diagnostic analyses. The diagnostic analysis literature can be divided into two groups.…”

Section: Introductionmentioning

confidence: 99%

“…The diagnostic analysis literature can be divided into two groups. The first group encompasses applications that rely on ammeter charts, which is an old technology in ESP troubleshooting. ,, The second group includes applications that depend on pump-deployed sensors. − …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs)

et al. 2022

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Electrical submersible pumps (ESPs) are considered the second-most widely used artificial lift method in the petroleum industry. As with any pumping artificial lift method, ESPs exhibit failures. The maintenance of ESPs expends a lot of resources, and manpower and is usually triggered and accompanied by the reactive process monitoring of multivariate sensor data. This paper presents a methodology to deploy the principal component analysis and extreme gradient boosting trees (XGBoosting) in predictive maintenance in order to analyze real-time sensor data to predict failures in ESPs. The system contributes to an efficiency increase by reducing the time required to dismantle the pumping system, inspect it, and perform failure analysis. This objective is achieved by applying the principal component analysis as an unsupervised technique; then, its output is pipelined with an XGBoosting model for further prediction of the system status. In comparison to traditional approaches that have been utilized for the diagnosis of ESPs, the proposed model is able to identify deeper functional relationships and longer-term trends inferred from historical data. The novel workflow with the predictive model can provide signals 7 days before the actual failure event, with an F1-score more than 0.71 on the test set. Increasing production efficiencies through the proactive identification of failure events and the avoidance of deferment losses can be accomplished by means of the real-time alarming system presented in this work.

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Ocean Economy and Fault Diagnosis of Electric Submersible Pump applied in Floating platform

Cited by 9 publications

References 11 publications

Condition monitoring and fault diagnosis of electric submersible pump based on wellhead electrical parameters and production parameters

Condition monitoring and fault diagnosis of electric submersible pump based on wellhead electrical parameters and production parameters

Fault Diagnosis Method and Application of ESP Well Based on SPC Rules and Real-Time Data Fusion

Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs)

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