In this podcast (click here) the current advances in Artificial Intelligence (AI), and Data Analytic (DA) with a focus on future trends of AI-DA in the energy industry were discussed. Several AI-DA applications in petroleum engineering and geosciences that are expected to be transformative were highlighted and a parallel between advances made in exploitation of shale resources over the last 10-20 years and what AI-DA promises to deliver over the next 10-20 years was drawn.
MLUR 2020 was organized to be held in Houston, May 7-9 per the above link. Unfortunately this workshop is postponed due to COVID19. We will monitor the situation and make an updated announcement at least three months before the new date.
Fred Aminzadeh was named to SMART Advisory Board, December 2019
Department of Energy (DOE)'s National Energy Technology Laboratory (NETL) has recently launched SMART Initiative: Science-Informed, Machine Learning, Accelerating, Real Time Decisions for Subsurface Applications. SMART is a multi-year multi-National Laboratory, Research Organization and Academic effort to address real time monitoring and visualization challenges for both Carbon Storage and Oil and Gas application. Its four person Advisory Board includes Dr. Grant Bromhal of NETL, George Guthrie of LANL, Dr. Srikanta Mishra of Battelle and Dr. Fred Aminzadeh of FACT
FACT, in association with the University of Houston, successfully held a workshop on Machine Learning for Unconventional Resources at the University of Houston Technology Bridge from November 18th to November 19th, 2019. Over 80 delegates from various E&P companies, oilfield service companies, National Labs and Universities attended the workshop to discuss new advances as well as challenges faced in adopting Machine Learning, Artificial Intelligence and Data Analytics (ML-AI-DA) techniques in unconventional resources.
Hydraulic fracturing has been a source of both achievement and controversy for years, and it continues to be a hot-button issue all over the world. It has made the United States an energy-exporting country once again and kept the price of gasoline low, for consumers and companies. This collection of papers is the first in the series, Sustainable Energy Engineering, tackling this very complex process of hydraulic fracturing and its environmental and economic ramifications. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry.
FACT Inc. and University of Houston are organizing a joint workshop with the primary goal of bringing together a wide variety of participants to facilitate initiation, discussion and interaction of novel ideas, approaches and methods around theoretical and applied Machine learning, Artificial Intelligence and Data Analytic (ML-AI-DA) topics, in a broad spectrum of disciplines in the energy industry. The organizing committee is soliciting papers to identify key topics and speakers for the MLUR workshop as well as brainstorming sessions for the future direction of research and development on Big Data and Deep Learning for more efficient exploration and production of unconventional resources with reduced cost and increased efficiency.