IoT-based wireless seismic quality control

Author:

Jamali-Rad Hadi1,Campman Xander1,MacKay Ian2,Walk Wim1,Beker Mark3,van den Brand Jo3,Bulten Henk Jan4,van Beveren Vincent4

Affiliation:

1. Shell Global Solutions International B.V.

2. Shell UK Exploration and Production Ltd.

3. Innoseis B.V.

4. National Institute for Subatomic Physics.

Abstract

At the present, sensors are everywhere across different sectors of the oil and gas industry. Seismic acquisition in upstream, pipeline monitoring in midstream, and asset tracking in downstream are examples of applications in which we need more and more sensors to satisfy a pressing need for accuracy. Sensor data in many cases should be quickly aggregated and coordinated, sometimes from harsh environments where crew intervention and maintenance must be minimized for safety and cost reasons. This mandates data collection/transmission strategies that are power efficient and demand minimal maintenance to operate autonomously. To address this issue, a unified wireless sensing framework is required that consists of the following three components: low-power, long-range wireless sensors with inherent compatibility with the “Internet of Things” (IoT); advanced scalable wireless networking protocols; and data storage/analytics on the cloud for analysis and decision making. These three components combined create a flexible, plug-and-play, scalable network that provides worldwide accessibility to the data and is cost efficient because you pay as you grow for storage and computation. Aiming at materializing such a ubiquitous wireless sensing paradigm, we have studied the feasibility of using a new family of IoT-based wireless technologies: so-called low-power wide-area networks (LPWANs). We have conducted a proof-of-concept field test in which we have employed LoRa, a predominant member of the LPWAN family, for real-time seismic quality control/monitoring. Our field test results corroborate that cheap (less than US$10) subscription-free LoRa wireless modules can be embedded into our seismic recording systems allowing us to transmit more than 6 MB of data per node per day, while the data can be transmitted over distances of a few kilometers with less than a milliwatt of average power consumption. The transmitted data can be monitored in real time on the cloud for further analysis and decision making.

Publisher

Society of Exploration Geophysicists

Subject

Geology,Geophysics

Reference17 articles.

1. Barakat, S., 2008, Systems and methods for seismic data acquisition employing asynchronous, decoupled data sampling and transmission: U.S. Patent 0219094 A1.

2. Ericsson Research, 2016, Cellular networks for massive IoT: Ericsson White Paper.

3. Fischer, J., F. Kuhnlenz, and K. Ahrens, 2009, Model-based development of self-organizing earthquake early warning systems: Proceedings of International Conference on Mathematical Modelling, 1–19.

4. The Self-organizing Seismic Early Warning Information Network (SOSEWIN)

5. Hollis, J., J. Iseli, M. Williams, and S. Hoenmans, 2005, The future of land seismic: E&P, November, 33–37.

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