Experimental investigation and validation of flow control device (FCD) performance and design in thermal oil production

Oil recovery processes use flow control devices (FCDs) to ensure uniforms flow of fluids with minimized potential for well failure. These devices operate by restricting the flow through nozzles causing its velocity and pressure to significantly change. For the flow to keep its momentum, its pressure has to drop which unfortunately increases the likelihood of local well failure to occur. In this research, the performance of various nozzle types will be tested to investigate the effect of geometry on the pressure drop.

Integration of Machine Learning with Distributed Temperature and Acoustic Sensing to Build Data-Driven Dynamic Reservoir Model

This project will develop practical workflows, algorithms and programming codes for inferring unknown reservoir properties from distributed temperature and acoustic sensing data. In-situ pressure and flow conditions can be interpreted from downhole fiber signals gathered in real time, which are used to estimate unknown heterogeneous reservoir parameters continuously. Machine learning methods will be incorporated to facilitate the handling of large amount of measured data and computations more efficiently.

Experimental Evaluation of Surface and Deep Filtration Sand Control Solution

Sand production during extraction of bitumen in oil sand industry is the most significant challenge which results in many operational problems such as erosion of downhole and surface equipment, collapse of the formation, and subsequently a dramatic increase in capital and operating costs of the production plant. Mesh weaves are currently used to reject sand during production and mitigate these effects. The proposed research project will investigate the flow, and solid retention capacity of different mesh weaves in various test cells.

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.

Study of unplugging sand control devices using shock waves

This research aims at better understanding the performance of Wireline Applied Stimulation Pulse (WASP) technique in formation damage reduction in oil and gas wells. Hydrocarbon production rate decreases as a result of plugging the sand control devices located in the wellbore region. Shock waves generated by the WASP technique help breaking the sources of formation damage into smaller pieces; As a result, small particles can be carried to the surface.