Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH–SC method

In the mid-19th century, the first oil well was drilled, which marked the beginning of the world oil economy. This era embarked on the phase of evolution and development of society in every aspect, with huge dependence on petroleum products, which subsequently led to tremendous growth in terms of technological advancements in the exploration and production activities over time. With the existing petroleum fields approaching toward depletion, the global need for energy resources is increasing simultaneously. This continuously rising demand has led us to move to complex environments for oil and gas exploration and production activities. Moreover, the real-time monitoring of reservoirs and well operations produces enormous data, which require efficient processing and analysis tools for decisionmaking regarding field development and asset management. [1,2] Subsequently, the need for optimum facilities design, [3] transportation [4] and refining [5] activities at reduced cost has also grown. Several attempts [6-9] are also being made to develop unconventional fields commercially based on technoeconomic viability. The implementation of computer-based techniques has proven to be promising toward many challenging problems for various oilfield operations. Several researchers have shown that the machine learning (ML) and data analytics (DA) approaches hold promising solutions toward the operational challenges being encountered at present in the industry and can efficiently help in resolving the issues related to interpretation and analysis of large datasets. [10-15] Nikravesh and Aminzadeh [16] have shown the use of neural networks (NNs) and fuzzy logic for mining petroleum data. Li and Li [17] combined a NN and cluster analysis to put forth a predictive model for identifying complex lithology. Fath et al. [18] proposed a novel approach for bubble point pressure prediction using an NN model with reservoir temperature, solution gas oil ratio (GOR), oil gravity, and gas specific gravity as input attributes. Ahmadi and Bahadori [19] showed the use of a supervised learning algorithm to determine the well placement and conning occurrence in horizontal wells. Maucec et al. [20] performed data mining and ML on well stimulation data for enhancing the prediction capabilities. Gupta et al. [21] applied DA for safeguarding real-time electrical submersible pumping operations. Cadei et al. [22] forecasted operational upsets using advanced analytics in an upstream production system. Dongxiao et al. [23] presented in their research that long short-term memory (LSTM), cascaded LSTM, and a fully connected NN can be utilized for generating synthetic well logs by supplementing the missing logging input data. Ghorbani et al. [24] proposed an artificial Intelligence (AI)based approach to predict the flow rate of oil from an orifice

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“…A new model should to be designed to reduce the operation time of the algorithm for fault diagnosis of downhole sucker rod pumps. [ 64 ]…”

Section: Suggestions and Discussionmentioning

confidence: 99%

“…Li et al [ 64 ] demonstrated an unsupervised fast black hole–spectral clustering (FBH‐SC) learning paradigm for fault diagnostics in sucker rod pumping wells with the application of the CritC function to select the clustering number and the most suitable scale parameter.…”

Section: And Da Applications In Upstream Petroleum Industrymentioning

confidence: 99%

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

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“…It not only reflects the downhole working conditions of the beam pumping unit [2] but also calculates the balance of the pumping unit, reducer output shaft torque, and motor power [3,4]. At the same time, the actual polished rod dynamometer card can be matched with the fault cards, which are often used to diagnose the fault of the oil well [5][6][7][8][9][10][11]. us, it is very meaningful to study the polished rod load.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Polished Rod Dynamometer Card in Sucker Rod Pumping System with Wear

Wang

Liu

2018

Shock and Vibration

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In the oil production process, the wear of friction pairs in sucker rod pumping installations will increase over time, which leads to the failure of the sucker rod pumping system. In order to study the effect of wear on the sucker rod pumping system, a wear model between the plunger and pump barrel was established. By analyzing the wear law, the wear volume and wear time of the pump barrel were calculated under abrasive wear. The forces of the sucker rod microunit were analyzed, and the wave equation of the sucker rod was established. Based on the given boundary and initial conditions, a mixed difference method was used to solve the equation. Taking the no. L2111 well of an oilfield as an example, the change curves of the wear volume and wear time with abrasive particle diameter were plotted, and the polished rod dynamometer card considering wear was predicted. The results showed that the increased clearance caused by wear will reduce the polished rod load on upstroke of the sucker rod pumping system, which could provide a theoretical basis for the next fault diagnosis.

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“…Third, most of the working condition recognition methods require a large number of labelled training samples. In real projects, the labelled working condition samples are difficult to obtain and the cost is very high, while the recognition methods based on the unlabelled training samples are usually not ideal in recognition accuracy (Li, Gao, Zhou, & Han, 2015;Liang, 2015). Fourth, affected by damping coefficient and 'division by zero', the working condition recognition methods with pump dynamometer cards or electric power cards often produce errors when calculating the features (Liang, 2015;Sun, 2011).…”

Section: Introductionmentioning

confidence: 99%

Hessian-regularized weighted multi-view canonical correlation analysis for working condition recognition of sucker-rod pumping wells

Zhou

Wang

Liu

et al. 2018

Systems Science & Control Engineering

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In order to more accurately recognize and understand the working condition of sucker-rod pumping wells so as to maximally reduce the cost and increase the profit, a large amount of data has been collected during oil production with sucker-rod pumping wells. In view of the sucker-rod pumping production system in big data and IOT (Internet of things) of oil-gas production, to solve the limitations in the existing working condition recognition research and further improve the recognition accuracy and practicality with fewer labelled working condition samples by utilizing the measured parameters from multiple information sources effectively, in this paper, a novel working condition recognition method based on Hessian-regularized weighted multi-view canonical correlation analysis is proposed. Firstly, the features of the measured ground dynamometer cards, electrical power, wellhead temperature and wellhead pressure data are extracted as four different feature views based on the prior information, empirical knowledge and mechanism analysis. Then a model based on Hessian-regularized weighted multi-view canonical correlation analysis and cosine nearest neighbour multi-classification algorithm is established. The proposed method is applied to the recognition of eleven kinds of working conditions from sixty sucker-rod pumping wells in a certain block in Shengli Oilfield, China. In the case where there are small number of labelled training samples, based on cosine nearest neighbour classification method, the recognition rates are increased by 3.44% and 1.5% compared with traditional recognition methods based on measured ground dynamometer cards and electrical power data, respectively. In contrast to methods based on traditional multisources of feature connection, multi-view canonical correlation analysis as well as the unweighted Hessian-regularized multi-view canonical correlation analysis, the recognition rates are increased by 4.46%, 2.21% and 1.62%, respectively.

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Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH–SC method

Cited by 46 publications

References 28 publications

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

Prediction and Analysis of Polished Rod Dynamometer Card in Sucker Rod Pumping System with Wear

Hessian-regularized weighted multi-view canonical correlation analysis for working condition recognition of sucker-rod pumping wells

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