Fuzzy Analysis for Progressing Cavity Pump

Correa, José R.; Carvalho, Paulo César de Faccio; Filardi, Vitor; Leizer, Schnitman; Lepkison, Herman

doi:10.2523/90110-ms

Cited by 4 publications

(1 citation statement)

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“…Jose F. Correa et al Proposed a new working condition analysis method of screw pump well, which combines rough set theory with expert system. This method can not only judge the working condition of screw pump well, but also give the optimization scheme of fault well, which is convenient for managers to adjust the fault well [6].…”

Section: Introductionmentioning

confidence: 99%

Research on Working Condition Diagnosis of Submersible Screw Pump

Feng

Liu

et al. 2022

E3S Web Conf.

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As the development of each oil field enters the middle and late stage, sand production, wax deposition and the continuous increase of well depth, electric submersible screw pump wells frequently break down, and hidden troubles of equipment can not be found in time, which makes oil wells produce with faults. It increases the wear and tear of equipment, seriously affects the production of oil wells, and causes unnecessary economic losses. Therefore, improving the accuracy of the working condition diagnosis of the electric submersible screw pump production system plays a vital role in improving the overall management level of the electric submersible screw pump well. This can not only enhance the managers’ correct understanding of this kind of oil well system, but also comprehensively monitor the working state of the well. It can find the fault as soon as possible, analyze the cause of the fault, and avoid the accident by adjusting the production parameters, so that the downhole screw pump can work safely, reliably and effectively, and maximize the maintenance free period and economic benefits of the electric submersible screw pump well.

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Section: Introductionmentioning

confidence: 99%

Research on Working Condition Diagnosis of Submersible Screw Pump

Feng

Liu

et al. 2022

E3S Web Conf.

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Dynamometer Card Classification Using Case-Based Reasoning for Rod Pump Failure Identification

Wibawa¹,

Handjoyo²,

Prasetyo³

et al. 2020

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Sucker Rod Pumps (SRP) have been extensively utilized in the Duri field Heavy Oil Operations Unit (HOOU) for more than 6000 production wells. Approximately 2000 of these wells are equipped with dynamometer online that generates a daily dynamometer card (DC). Historically, the pump cards evaluation has led to the identification of several mechanical pump issues such as a traveling valve and standing valve leak that directly impact production. One step of the traditional process to identification of rod pump failure is based on a manual pump card shape analysis performed for individual wells by different engineers throughout production history. To improve efficiency and reliability of shape analysis, Artificial Intelligence-based data analysis has been recently integrated in the oil and gas industry. This article proposes an approach to pump card classification, developed by the Integrated Optimization Decision Support Center, using a modified Case-Based Reasoning or computer reasoning by analogy approach where new problems are solved by comparison to analogous problems solved in the past. The proposed methodology begins with definition of a reference DC for every known type of mechanical failure. The reference cards define the analogy set. Actual pump cards are then normalized and compared for similarity against each reference card or analogy using Euclidean distance measure between the actual and reference cards. For each actual pump card, the output of this approach is a set of similarity scores which indicate the pump failure type corresponding to references card shape, if any. The analysis is enhanced through the addition of rules based on pump operational parameters that result in specific pump failure signals. The methodology has been verified against DC evaluations from Subject Matter Experts (SME) and is demonstrated to provide robust pump failure signals more efficiently than by manual interpretation of DC for a series of individual wells.

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A Deep Learning Model to Intelligently Identify the Working Status of Screw Pumps for Oil Well Lifting

Zhen

Dong

Zheng

et al. 2021

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Screw pumps have been widely used in many oilfields to lift the oil from wellbore to ground. The pump failure and delayed repair means well shut and production loss. A deep learning model is constructed to quickly identify the working status and accurately diagnose the failure types of the screw pumps, which can help the workers always get the information and give a fast repair. Firstly, running parameters of the screw pump, such as electric current, voltage, and instantaneous rate of flow, are obtained through the Real-time Data Acquisition System. Then the correlations between values or trends of those parameters and working status of the screw pump are calculated or analyzed. Results show that there is a good correlation between the current characteristics and various working status of screw pump. Current data at different times are expressed in polar coordinates, with the polar diameter representing the current value and the polar angle representing the time. The current-time curves of massive oil wells are then plotted in images with fixed resolution and divided into nine different groups to correspond to nine frequent working status of screw pump. A convolutional neural network (CNN) model is initialized, with the current-time curve as its input and the number codes representing working status as its output. Images mentioned above are used to train the CNN model, and the model parameters, such as the number of convolution layers, the size of convolution kernels and the activation function are optimized to minimize the training losses, which are the differences between the output codes and the right codes corresponding to the images. Finally, a robust CNN model is established, which can quickly and accurately judge the working state of the screw pump through electric current data. Based on this model, a software system connected with the oilfield database is developed, which can obtain the running parameters of the screw pumps in real time, identify their working states, judge the fault types of the abnormal situations, give alarms, and put forward solution suggestions. The system has now been widely used in Shengli Oilfield, which can help staff know the working conditions and fault types of abnormal wells in real time, speed up the maintenance progress, shorten the pump shutdown time and improve the production.

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Fuzzy Analysis for Progressing Cavity Pump

Cited by 4 publications

References 0 publications

Research on Working Condition Diagnosis of Submersible Screw Pump

Research on Working Condition Diagnosis of Submersible Screw Pump

Dynamometer Card Classification Using Case-Based Reasoning for Rod Pump Failure Identification

A Deep Learning Model to Intelligently Identify the Working Status of Screw Pumps for Oil Well Lifting

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