Building Data-Driven Permanent Real-Time Full Wellbore Flow Monitoring Using Hybrid Distributed Fibre-Optic Simultaneous Vibration and Temperature Sensing Technology

In this project, we develop a framework to use the data from fiber sensing technologies to smart monitoring of Oil and Gas Reservoirs. The project involves extensive lab experiments simulating different monitoring conditions. Different configurations for installation of sensing equipment will be examined. The optimum location of tubing will be also determined. Signal processing methods will be used to extract useful information from the raw fiber-sensed data. Through experiments, we will record and analyze the relationship of fiber-sensed signals and the flow conditions. Machine learning algorithms will aid us to better understand such relationship and to predict the conditions at an actual field reservoir.

Faculty Supervisor:

Petr Musilek

Student:

Mohammad Mohammadtabar

Partner:

RGL Reservoir Management Inc.

Discipline:

Engineering - mechanical

Sector:

Oil and gas

University:

Program: