Internet of things and big data analytics for smart oil field malfunction diagnosis

Xu, Birong; Wang, Weijiang; Wu, Yue; Shi, Yueting; Lü, Chang

doi:10.1109/icbda.2017.8078802

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…This framework is intended for distributed storage and distributed processing of massive amounts of data on computer clusters built from commodity hardware, which is a much cheaper and more effective solution than running a dedicated data center. The ecosystem of Hadoop includes different frameworks and tools to handle various types of storing, data aggregation, processing resource management, searching, and data analysis for different types of heterogeneous data (structured, semi-structured, and unstructured) [37]. The main core of Apache Hadoop consists of two sub-projects, which are HDFS for storage, and MapReduce for processing.…”

Section: • Data Protectionmentioning

confidence: 99%

“…Also, it supports an algorithm library about big data machine-learning algorithms like clustering, classification, and regression, deep learning such as MLib (Machine Learning Library) [49]. Spark is mainly used to increase the processing speed of the application and it can be used with Hadoop data sources as a programming model for processing [37].…”

Section: • Data Protectionmentioning

confidence: 99%

“…Hive is a fast, accessible way of responding to complex queries through SQL-like queries (e.g. HiveQL) and to analyse big data [37].…”

Section: • Data Protectionmentioning

confidence: 99%

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Big data and IoT-based applications in smart environments: A systematic review

Hajjaji

Boulila

Farah

et al. 2021

Computer Science Review

272

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Section: • Data Protectionmentioning

confidence: 99%

Section: • Data Protectionmentioning

confidence: 99%

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Big data and IoT-based applications in smart environments: A systematic review

Hajjaji

Boulila

Farah

et al. 2021

Computer Science Review

272

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“…A new algorithm based on extreme learning machine model was proposed to determine the marine oil spill regions in SAR images [23]. A large number of historical data of oil and water wells were applied to monitor changes of some important parameters of the well site, which were used in the trend prediction and the early warning system [24].…”

Section: Introductionmentioning

confidence: 99%

An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System

et al. 2019

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In recent years, data mining technology has been widely used in different fields. For petroleum industry, the application of data mining method not only can analyze the data faster and more accurately but also can reduce the defects such as misjudgment and missed judgment caused by relying on artificial monitoring. Fracturing is one of the key techniques for increasing petroleum production. Due to the contingency of the abnormal variation of the fracturing construction curve, there is also a large lag and contingency by using the construction curve for sand block warning. Frequent false alarms and delayed alarms often occur. Therefore, the paper proposes an early-warning method of the sand plug of fracturing based on data mining. First, an early warning model of double logarithmic curve sand plug of fracturing is established, and the time series analysis algorithm is used to predict the oil pressure and casing pressure in the double logarithmic curve sand plug of fracturing risk warning model, so the early warning accuracy is improved. And then the general regression neural network (GRNN) algorithm is designed to optimize the prediction results of the time domain analysis. The improved affinity propagation (AP) clustering algorithm is used to cluster the monitoring data to help to improve the accuracy of the subsequent slope calculation, so as to improve the coincidence rate of the fracturing and sand plug risk. Finally, the risk warning model is applied and analyzed on site, and the validity and accuracy of the model are verified. The model is embedded in the remote monitoring system to realize online remote intelligent monitoring of risks by urban office workers. INDEX TERMS AP clustering, data mining, early warning, sand plug of fracturing, GRNN algorithm.

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