Combining the Power of IoT and Big Data to Unleash the Potential of Digital Oil Field

AlBar, Abdullah; Asfoor, Hasan; Goz, Ahmad; Ansari, Najmul

doi:10.2523/19045-ms

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Indeed, the plethora of information bombarding decision-makers has been documented as counter-productive, having the effect of grey-noise that must be washed out of systems in order for the limited meaningful data to be extracted. [11,12] Then there is the problem of cyber security. In the US, 75% of companies operating in the energy sector report having experienced cyber-related attacks.…”

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confidence: 99%

Has the Internet of Things made a real difference to the management of projects?

Martek¹,

Hosseini²,

Edwards³

et al. 2023

Research Handbook on Complex Project Organizing

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The 'internet of things' (IoT) is a wonderful development. That is the consensus. For engineers who love technical things, what could be more desirable than a technology that links together every other technology. The rationale, of course, is that the IoT is useful; and will in time undoubtedly become essential. Here is how Parise et. al., explain what we have all come to accept."In ordinary conditions and especially in critical conditions, a prompt availability of all the needed data is essential to decision making. Therefore, IoT is able to assist the solution of problems in real-time, offers opportunities to integrate the physical world into a computerbased systems, producing efficient enhancements, welfares, and assisting human works." [1]

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mentioning

confidence: 99%

Has the Internet of Things made a real difference to the management of projects?

Martek¹,

Hosseini²,

Edwards³

et al. 2023

Research Handbook on Complex Project Organizing

View full text Add to dashboard Cite

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The role of data management in the Industrial Internet of Things

AlSuwaidan

2020

Concurrency and Computation

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The rapid development of Internet technology, networking, and emergence technologies has deeply impacted manufacturing. Moreover, the massive data being generated have increased tremendously. All of these factors have supported the transformation of the traditional manufacturing paradigm toward smart manufacturing. At present, the Industrial Internet of Things (IIoT) is an underlying platform for various smart manufacturers seeking an effective and powerful structure. Oil and gas is an industry affected by this transformation. This article discusses the role of data management in supporting the IIoT and smart manufacturing. It also proposes a taxonomy that classifies and categorizes the pillars of data management in the IIoT. It includes the basis of important parameters such as data sources, machine learning, performance management, business intelligent, and big data. In order to measure the role of data management in the IIoT, we consider Saudi Arabia as a case study for the IIoT, and review and explore the current challenges and potential future solutions. In addition, we propose an Internet of Things (IoT)-based reliable architecture for monitoring the flow of operations in the oil and gas industry.

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Diagnosis of Operating Conditions of the Electrical Submersible Pump via Machine Learning

Brasil

Maitelli

Nascimento

et al. 2022

Sensors

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In wells that operate by electrical submersible pump (ESP), the use of automation tools becomes essential in the interpretation of data. However, the fact that the wells work with automated systems does not guarantee the early diagnosis of operating conditions. The analysis of amperimetric charts is one of the ways to identify fail conditions. Generally, the analysis of these histographics is performed by operators who are often overloaded, generating a decrease in the efficiency of observing the well operating conditions. Currently, technologies based on machine learning (ML) algorithms create solutions to early diagnose abnormalities in the well’s operation. Thus, this work aims to provide a proposal for detecting the operating conditions of the ESP pump from electrical current data from 24 wells in the city of Mossoró, Rio Grande do Norte state, Brazil. The algorithms used were Decision Tree, Support Vector Machine, K-Nearest Neighbor and Neural Network. The algorithms were tested without and with hyperparameter tuning based on a training dataset. The results confirm that the application of the ML algorithm is feasible for classifying the operating conditions of the ESP pump, as all had an accuracy greater than 87%, with the best result being the application of the SVM model, which reached an accuracy of 93%.

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Combining the Power of IoT and Big Data to Unleash the Potential of Digital Oil Field

Cited by 3 publications

References 4 publications

Has the Internet of Things made a real difference to the management of projects?

Has the Internet of Things made a real difference to the management of projects?

The role of data management in the Industrial Internet of Things

Diagnosis of Operating Conditions of the Electrical Submersible Pump via Machine Learning

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