Overview:
This talk provides a brief overview of applications of artificial intelligence (AI), machine learning and data analysis (DA) methods to various exploration and production problems. Various topics such as big data, soft computing, natural language processing, human-machine interface and deep learning will be highlighted.

Selected case studies from each of the key phases of the E&P process, namely exploration, drilling/development and production/EOR using AI-DA, will be highlighted. The lecture will conclude with the introduction of a new program at UH that focuses on Artificial Intelligence Machine Learning and Data Analytics for Energy Exploration and Production or AIM-DEEP.

We will demonstrate AIM-DEEP's value proposition based on its multidisciplinary nature and its focus on the academic-industry alliance, complemented by government funding.

Speaker:
Dr. Fred Aminzadeh
Professor of Petroleum Engineering, University of Houston

Dr. Fred Aminzadeh is Professor of Petroleum Engineering at the University of Houston and director of the Artificial Intelligence Machine Learning and Data Analytics for Energy Exploration and Production (AIM-DEEP) program. He serves on the advisory board of DOE/NETL's SMART Initiative. He is also chairman of FACT (FACT-Corp.com)

Dr. Aminzadeh has more than 30 years of experience in industry and academia. His technical expertise includes: Machine Learning, AI, Pattern Recognition, Signal Processing, Big Data, 3D/4D Seismics, Reservoir Monitoring, CO2 Storage, Reserve Evaluation and Microseismics. He served as President of the Society of Exploration Geophysicists (2007-2008) and represented SEG on the Unconventional Resources Technology Advisory Committee (URTAC) and the SPE Reserves Evaluation Committee (SPPE). He is a Fellow of IEEE, a member of the Russian Academy of Natural Sciences and an honorary member of the Azerbaijan Oil Academy. He received honorary membership of the Society of Exploration Geophysicists (SEG) in 2018 and the Society of Petroleum Engineers (SPE) Western Region Reservoir Characterization and Formation Evaluation Awards in 2014 and 2015.

He previously worked in technical and management positions, including Professor of Petroleum and Electrical Engineering at the University of Southern California (USC) and Executive Director of the Global Energy Network (gen.usc.edu) and Reservoir Monitoring Consortium. He was a geophysical technology manager at Unocal (now Chevron). He also served as president and CEO of dGB-USA (dgbes.com) and on the technical staff at Bell Laboratories. He has consulted with several national laboratories, including Lawrence Berkeley (LBNL), Lawrence Livermore (LLNL), Los Alamos (LANL), Oak Ridge (ORNL), and National Energy (NETL). He holds four U.S. patents, with another five pending. He has written 15 books and more than 400 publications covering major areas.

For more information, visit: https://uh.edu/uh-energy/energy-events/ai-data-analytics

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