Biography:
Emmanuel Obasi is a Graduate of Petroleum engineering from the University of wyoming. His research interests span physics-informed machine learning, production forecasting, and unconventional reservoir analytics. He holds expertise in decline curve analysis, deep learning architectures, and their application to petroleum engineering challenges. His current work focuses on developing hybrid frameworks that integrate classical reservoir engineering principles with modern neural network architectures for improved production prediction in tight-gas formations.
Title : From empirical decline to intelligent forecasting: A hybrid deep learning framework embedding arps physics for unconventional tight-gas reservoir production prediction