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QoS-aware real-time pub/sub middleware for drilling data management in petroleum industry

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Abstract

The availability of real-time drilling data communication and management is highly critical to the exploration and production business of any petroleum company. Such critical mission requires oil companies to use the cutting edge ICT technologies, which will increase profitability of the company by increasing the efficiency and reducing drilling costs. Moreover, short delay in data transfer could results in severe damages, injury, environmental pollution, or even loss of lives. Data Distribution Service (DDS) is the state of the art in middleware technologies, standardized by Object Management Group (OMG) since 2003; and it is designed for distributed low latency and high reliability systems with rich QoS policies. In this paper, I reviewed the current drilling data communication and management techniques in a selected Oil and Gas Company, and propose an optimization solution based on DDS technology. Furthermore, I evaluated the performance of DDS middleware with different data types, data loads, and different networks including limited bandwidth networks. A prototype for my proposed solution is implemented using NI-LABVIEW and RTI-DDS then evaluated in terms of latency and throughput. The results show that DDS is a promising technology that can improve the performance of petroleum industry while maintaining the correct safety measures.

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Acknowledgments

I would like to acknowledge Real Time Innovation Company for their support during this research and also I would like to thank the Real-Time Distributed Systems Lab team in Computer Engineering Department at King Fahd University of Petroleum and Minerals, Dhahran-Saudi Arabia.

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  1. Distributed Real-Time Systems Laboratory, Computer Engineering Department, King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia

    Basem Almadani

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  1. Basem Almadani
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Correspondence to Basem Almadani.

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Almadani, B. QoS-aware real-time pub/sub middleware for drilling data management in petroleum industry. J Ambient Intell Human Comput 7, 287–299 (2016). https://doi.org/10.1007/s12652-015-0332-5

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