Sand monitoring in pipelines using Distributed Data Fusion algorithm

A. Abdelgawad, M. Bayoumi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Installation of a system to monitor and measure sand production from an oil well would be valuable to assist in optimizing well productivity and to detect sand as early as possible. In this paper we present a framework for sand monitoring using Wireless Sensor Network (WSN). The framework combines two modules: a Sand Rate Calculation (SRC) module and a Distributed Data Fusion (DDF) module. The framework is designed to collect data from oil pipeline using acoustic sensors (SENACO AS100) in real time. A test bed was established from ten acoustic sensors mounted on a closed loop pipeline. Each acoustic sensor is attached to WSN node. Each node calculates its local sand rate using SRC module. Every node sends its sand rate to the neighbors. The DDF module at each node is using its own local sand rate and the neighbors' sand rate to calculate the global sand rate. The DDF is implemented using a Distributed Kalman Filter (DKF). The proposed framework was successfully evaluated throughout experimental tests.

Original languageEnglish
Title of host publicationSAS 2011 - IEEE Sensors Applications Symposium, Proceedings
Pages217-220
Number of pages4
DOIs
StatePublished - 2011
Event6th IEEE Sensors Applications Symposium, SAS 2011 - San Antonio, TX, United States
Duration: Feb 22 2011Feb 24 2011

Publication series

NameSAS 2011 - IEEE Sensors Applications Symposium, Proceedings

Conference

Conference6th IEEE Sensors Applications Symposium, SAS 2011
Country/TerritoryUnited States
CitySan Antonio, TX
Period02/22/1102/24/11

Keywords

  • Distributed Data Fusion
  • Distributed Kalman Filter
  • Wireless Sensor Network

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