Conceptual Big Data Architecture for the Oil and Gas Industry

Alıguliyev, Ramiz M.; İmamverdiyev, Yadigar

doi:10.25045/jpit.v08.i1.01

Cited by 7 publications

(8 citation statements)

References 19 publications

(20 reference statements)

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“…The papers [41,43] deal with the construction of an architecture, strategy and decisionmaking of oil and gas industry using Big Data technologies. Big Data technologies provide key approaches and tools for generating data management systems for oil and gas industry.…”

Section: The Methods Used For the Intellectualization Of Oil And Gas mentioning

confidence: 99%

Prospects of Intelligent Oil and Gas Fields

Shikhaliyev¹

2018

JPIT

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The article deals with the application of intelligent technologies in oil and gas fields for solving various problems. These include the intellectualization of analysis of large amounts of data collected from oil and gas fields, the intellectualization of the drilling process, the forecast of reserves and the optimization of oil and gas production activity, the optimization of the location and management of oil and gas fields, etc. At the same time, intellectualization of oil and gas fields is impossible without the use of information technology. The intellectualization of oil and gas fields leads to an increase in the effectiveness of remote monitoring and management of oil and gas production, the accuracy of geophysical research, safety, and the reduction of structural costs and the continued competitiveness of oil companies.

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Section: The Methods Used For the Intellectualization Of Oil And Gas mentioning

confidence: 99%

Prospects of Intelligent Oil and Gas Fields

Shikhaliyev¹

2018

JPIT

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“…In the midstream sector alone, every 150,000 miles of pipeline produces up to 10 terabytes of data [1]. While several efforts have been made to gain insights into these data in the upstream and downstream sectors [27], [28] through big data analytics, the midstream sector is left almost unexploited. Efficient data and service management in the midstream sector can reduce the annual downtime by 70% and the associated cost by 22% [29] through timely failure detection and predictive maintenance.…”

Section: Data and Service Managementmentioning

confidence: 99%

Resilient IoT-based Monitoring System for Crude Oil Pipelines

Ahmed¹,

Mouël²,

Stouls³

2020

2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

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Pipeline networks dominate the oil and gas midstream sector, and although the safest means of transportation for oil and gas products, they are susceptible to failures. These failures are due to manufacturing defects, environmental effects, material degradation, or third party interference through sabotage and vandalism. Internet of Things (IoT)-based solutions are promising to address these by monitoring and predicting failures. However, some challenges remain in the deployment of industrial IoT-based solutions, as the reliability, the robustness, the maintainability, the scalability, the energy consumption, etc. This paper is therefore aimed at highlighting potential solutions for detection and mitigation of pipeline failures while addressing the robustness, the cost and scalability issues of such approach efficiently across the network infrastructure, data and service layers.

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“…The collected data is captured by using tens of thousands of sensors in surface facilities and subsurface wells. These data-collecting sensors provide real-time and continuous monitoring of operational assets and environmental conditions [11]. Hence, solutions for handling massive data for oil & gas industries with unique architectures have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [11] propose a conceptual big data architecture for oil & gas industry for storing and analyzing acquired data in real-time. This architecture uses a service bus to coordinate data flows, reduce transfer costs, enable data storage, and provide information about the status of transferred data.…”

Section: Related Workmentioning

confidence: 99%

Towards an Architecture for Handling Big Data in Oil and Gas Industries: Service-Oriented Approach

Azzedin¹,

Ghaleb²

2019

ijacsa

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Existing architectures to handle big data in Oil & gas industry are based on industry-specific platforms and hence limited to specific tools and technologies. With these architectures, we are confined to big data single-provider solutions. The idea of multi-provider big data solutions is essential. When building up big data solutions, organizations should embrace the best-inclass technologies and tools that different providers offer. In this article, we hypothesize that the limitations of the proposed bigdata architectures for oil and gas industries can be addressed by a Service Oriented Architecture approach. In this article, we are proposing the idea of breaking complex systems to simple separate yet reliable distributed services. It should be noted that loose coupling exists between the interacting services. Thus, our proposed architecture enables petroleum industries to select the necessary services from the SOA-based ecosystem and create viable big data solutions.

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Conceptual Big Data Architecture for the Oil and Gas Industry

Cited by 7 publications

References 19 publications

Prospects of Intelligent Oil and Gas Fields

Prospects of Intelligent Oil and Gas Fields

Resilient IoT-based Monitoring System for Crude Oil Pipelines

Towards an Architecture for Handling Big Data in Oil and Gas Industries: Service-Oriented Approach

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