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\begin{document}

\title{Machine Learning Assisted Exploration and Production of Subsurface
Energy and Carbon Geo-Sequestration: A Review}



\author[1,1]{Rui Liu}%
\author[2,2]{Siddharth Misra}%
\affil[1]{Texas  A\&M University}%
\affil[2]{Texas A\&M University}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Machine learning has led to improvements in the efficiency and efficacy
of subsurface engineering and characterization efforts that benefits the
hydrocarbon and geothermal exploration and production as well as in
carbon geo-sequestration. There have been rapid increases in sensor
deployment, data acquisition, data storage, and data processing for
purposes of geothermal/fossil energy development and exploration along
with carbon geo-sequestration. This has promoted large-scale development
of data-driven methods, machine learning and data analytics workflows to
find and extract energy and material resources from the subsurface
earth. Subsurface data ranges from nano-scale to kilometer-scale passive
as well as active measurements in the form of physical fluid/solid
samples, images, 3D scans, time-series data, waveforms, and depth-based
multi-modal signals representing various physical phenomena, ranging
from transport, chemical, mechanical, electrical, and thermal
properties, to name a few. Integration of such varied multimodal,
multipoint, time-varying data sources being acquired at varying scales,
rates, resolutions, and volumes mandates robust machine learning methods
to better characterize and engineer the subsurface earth. This review
paper lays out popular machine learning applications in exploration,
extraction, and recovery of subsurface energy resources, primarily in
hydrocarbon exploration and production industry with potential
applications in geothermal energy production and carbon
geo-sequestration.%
\end{abstract}%



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\textbf{Hosted file}

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