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.