Developing a Smart Tool for Enhanced Oil Recovery Screening Based on Artificial Intelligence

Oil production constitutes a significant portion of the world’s demand for sources of energy and raw materials for production of numerous daily-needed items. However, most of the currently producing oilfields are in their production decline phases with much of their oil left unproduced due to technical barriers. Sustained production of these underground resources depends on methods such as Enhanced Oil Recovery (EOR), which involves injection of specific material or energy in oil reservoirs to enhance oil displacement towards producing wells. The first step in making EOR implementation decisions is screening the available EOR technologies and methods. Despite several screening methods proposed over the past five decades, there is no advanced screening method suitable for universal use. The aim of this project is developing Artificial Intelligence (AI)-based EOR screening tools to identify the critical screening parameters as well as to assess and rank the EOR options for any oil reservoir.

Faculty Supervisor:

Sohrab Zendehboudi


Seyyed Masoud Seyyedattar Shoushtar


Springboard Atlantic




Professional, scientific and technical services


Memorial University of Newfoundland



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