Machine Learning for Unconventional Resources Workshop 2019 (MLUR)


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.

Talks were held on a wide variety of topics ranging from “Integration of ML & Natural Language Processing for Semi-Autonomous Subsurface Property Predictions” to “Applications of Machine Learning for Improving Shale Geophysics Exploration and Development” to “Combining Science-based prediction and machine learning for real-time forecasting of pressure management strategies in the Marcellus Shale”. In addition, keynote speakers Jim Hollis, GM Upstream at Descartes Laboratories gave a Luncheon Talk on “Optimizing Unconventional Development through Geospatial AI” and Vikas Vats, Chief Analytics Officer at Verisk shared his thoughts on “Trends and applications of ML: An interdisciplinary view” from his experience in the application of ML-AI-DA for a wide range of industries.


Panels discussions were held by noted experts in the industry on the following topics: “Data: Storage, Access and Security”, “Advances in Hydraulic Fracturing Using Machine Learning”, “Capturing Corporate Memory for Oilfield Decision Systems” and “Service Companies View of Machine Learning for Unconventional Resources” and selected posters were also presented by service companies and universities on topics such as “Digital Twins for Automated Drilling Fluids Rheology Adjustment at Rigsite” and “Automated Microseismic Event Detection in DAS Data with Convolution Neural Networks. More details on call for papers workshop and the agenda can be found at and We also want to acknowledge the sponsoring companies:

  1. Wood Mackenzie (A Verisk Business)

  2. Descartes Labs

  3. Emerson

  4. Fracgeo


Overall, the workshop’s goal to facilitate initiation, discussion and interaction of novel ideas, approaches and methods around theoretical and applied Machine Learning, Artificial Intelligence and Data Analytic topics amongst the industry, government research labs and academia was successfully achieved. The consensus amongst the delegates was for the MLUR Workshop to be held on a bi-annual basis in the future, covering a broader scope of industry advances. Details on the next workshop will be announced shortly.




















Main 2.png