Machine Learning-Based Well Design to Enhance Unconventional Energy Production

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Los Alamos, New Mexico
Project Term:
09/07/2020 - 09/30/2022

Technology Description:

Los Alamos National Laboratory (LANL) seeks to increase the efficiency with which oil and gas are extracted from unconventional reservoirs while reducing the environmental impact of such processes. Current hydrofracturing-enabled extraction efficiencies are only 5 to 10%. LANL seeks to improve upon these levels by developing physics-informed machine learning (ML) based models from field data to discover effective well design characteristics. LANL will use its ML framework, which is based on recent advances in ML, differentiable programming, and cloud computing, to extract actionable information to enhance energy production while mitigating its environmental impact. Current baseline well design testing takes about 4 years and costs more than $6M per well. LANL predicts that if completely deployed, it is expected that the proposed framework will reduce the time and cost of well design by 40% and energy extraction can be tripled.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:


Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.


Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.


Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.


ARPA-E Program Director:
Dr. Daniel Cunningham
Project Contact:
Daniel O'Malley
Press and General Inquiries Email:
Project Contact Email:


University of New Mexico
Stanford University
Julia Computing, Inc.
University of Texas, Austin
Massachusetts Institute of Technology
Chevron Energy Technology Company

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