Machine Learning-Based Well Design to Enhance Unconventional Energy Production

Default ARPA-E Project Image

Program:
DIFFERENTIATE
Award:
$897,577
Location:
Los Alamos, New Mexico
Status:
ALUMNI
Project Term:
09/07/2020 - 09/30/2022
Website:

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:

Security:

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

Environment:

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.

Economy:

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

Contact

ARPA-E Program Director:
Dr. Daniel Cunningham
Project Contact:
Daniel O'Malley
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
omalled@lanl.gov

Partners

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

Related Projects

• Carnegie Mellon University (CMU) - Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
• Carnegie Mellon University (CMU) - High-fidelity Accelerated Design of High-performance Electrochemical Systems
• General Electric (GE) Global Research - Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
• General Electric (GE) Global Research - IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
• IBM T.J. Watson Research Center - Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design
• Iowa State University (ISU) - Context-Aware Learning for Inverse Design in Photovoltaics
• Julia Computing - Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture
• Lawrence Berkeley National Laboratory (LBNL) - Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials
• Los Alamos National Laboratory (LANL) - Machine Learning-Based Well Design to Enhance Unconventional Energy Production
• Massachusetts Institute of Technology (MIT) - Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces
• Massachusetts Institute of Technology (MIT) - Global Optimization of Multicomponent Oxide Catalysts for OER/ORR
• National Renewable Energy Laboratory (NREL) - End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
• National Renewable Energy Laboratory (NREL) - INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
• Northwestern University - Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials
• Pacific Northwest National Laboratory (PNNL) - Machine Learning for Natural Gas to Electric Power System Design
• Princeton University - MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics
• Stanford University - Energy Efficient Integrated Photonic Systems based on Inverse Design
• United Technologies Research Center (UTRC) - MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)
• United Technologies Research Center (UTRC) - Learning Enabled Network Synthesis (LENS)
• University of Maryland (UMD) - Invertible Design Manifolds for Heat Transfer Surfaces (INVERT)
• University of Michigan, Dearborn - ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters
• University of Missouri - Deep Learning Prediction of Protein Complex Structures
• University of Texas at Austin (UT Austin) - Learning Optimal Aerodynamic Designs

Release Date:
04/05/2019