دانلود رایگان مقاله انگلیسی شبکه های بی سیم مبتنی بر اینترنت اشیا برای کاربرد های لرزه ای - وایلی 2018

Abstract

There is growing pressure from regulators on operators to adhere to increasingly stricter regulations related to the environment and safety. Hence operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their license to operate. A deeper understanding of production optimization and production-related risk requires strengthened knowledge of reservoir behavior and overburden dynamics. To accomplish this, sufficient temporal and spatial resolution is required as well as an integration of various sources of measurements. At the same time, tremendous developments are taking place in sensors, networks, and data analysis technologies. Sensors and accompanying channels are getting smaller and cheaper and yet they offer high fidelity. New ecosystems of ubiquitous wireless communications including Internet of Things (IoT) nowadays allow anyone to affordably connect to the Internet at any time and anywhere. Recent advances in cloud storage and computing combined with data analytics allow fast and efficient solutions to handle considerable amounts of data. This paper is an effort to pave the way for exploiting these three fundamental advances to create IoT-based wireless networks of seismic sensors. To this aim, we propose to employ a recently developed IoT-based wireless technology, so called lowpower wide-area networks (LPWANs), to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely-operated minimum-maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs we show that neither the low data-rate nor the low duty-cycle of LPWANs impose fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay-tolerant. In order to confirm this claim, we cast our ideas into a practical large-scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small-scale field test in which we have employed our IoT-based wireless nodes for real-time seismic quality control (QC) over clouds.

VIII. CONCLUDING REMARKS

Wireless seismic technologies are being picked up by the market with an unprecedented rate in the past few years. This is because compared to the traditional cable seismic they offer a more costefficient solution with less environmental impact without the complications of transporting, maintaining and retrieving cable-based systems; they are less demanding in terms of maintenance and provide the possibility of real-time data acquisition.

We have observed the advent of a new generation of wireless technologies (so-called LPWANs) with inherent Internet of things (IoT) compatibilities and we have set them at the core of our networking design. Our proposed design combines affordable low-power long-range wireless technologies, advanced and scalable networking protocols, and Internet of sensors with cloud computing for storage and processing. The result is a plug and play network where anyone can define/add new sensors; it can operate in a (near) real-time fashion to address a wide variety of demands, and it will be scalable to thousands of sensors with worldwide accessibility to the acquired data. We have proposed two IoT-based wireless networking architectures based on different categories of LPWANs and have matched them with our seismic scenarios of interest. We then have presented a practical study on Groningen field where we have incorporated our design architectures and have corroborated our detailed networking quantitative estimates. Finally, we have put an step further in materializing our vision of IoT-based wireless seismic by conducting a proof-of-concept field test with LoRa and have presented promising first results for seismic QC.