Sand monitoring in pipelines using Distributed Data Fusion algorithm

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.