Philip A. Freeman
Philip Freeman is an Operations Research Analyst with the USGS Geology, Energy & Minerals (GEM) Science Center in Reston, VA.
Philip obtained his B.S. in Engineering at Cornell University in 1986. He is an Operations Research Analyst at the Eastern Energy Resources Science Center in Reston, Virginia. His experience in resource assessment work spans oil and gas, coal, reserve growth, and carbon dioxide storage resources. He has worked on teams tasked to develop probabilistic resource assessment methodologies. His contributions to economic analysis includes developing business development models, constructing geographic analysis and estimating annual production functions. He has experience combining large datasets of well, reservoir and field data for physical characterization of rock and/or fluid properties and using GIS for analysis or presentation.
Professional Experience
1999 to present: Operations Research Analyst, U.S. Geological Survey, Geology, Energy & Minerals Science Center, Reston, VA
1997 to 1999: Cartographic Technician, U.S. Geological Survey, Eastern Energy Resources Team, Reston, VA
Education and Certifications
B.S. Engineering. Operations Research and Industrial Engineering, Cornell University, 1986
Science and Products
Economics of Energy Transitions
Economics, Energy Resources, and Future Energy Supply
Economics of Global Marginal Hydrocarbon and Non-traditional Resources
Economics of U.S. Oil and Gas Resources
Lithium observations, machine-learning predictions, and mass estimates from the Smackover Formation brines in southern Arkansas
Carbon Dioxide Storage Resources-Anadarko and Southern Oklahoma Basins: Chapter R. Spatial Data
Carbon Dioxide Storage Resources - Appalachian Basin, Black Warrior Basin, Illinois Basin, and Michigan Basin: Chapter P, Spatial Data
Carbon Dioxide Storage Resources-Wind River Basin: Chapter O, Spatial Data
Compilation of Geospatial Data (GIS) for the Mineral Industries of Select Countries in the Indo-Pacific
U.S. Geological Survey National Produced Waters Geochemical Database (ver. 3.0, December 2023)
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of the People's Republic of China
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Select Countries in Southwest Asia
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources - data release
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa
Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters
Federal Lands Greenhouse Gas Emissions and Sequestration in the United States: Estimates 2005-14 - Data Release
Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning
Machine learning approaches to identify lithium concentration in petroleum produced waters
A residual oil zone (ROZ) assessment methodology with application to the central basin platform (Permian Basin, USA) for enhanced oil recovery (EOR) and long-term geologic CO2 storage
Reconnaissance survey for potential energy storage and carbon dioxide storage resources of petroleum reservoirs in western Europe
Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada
Geologic energy storage
Introduction As the United States transitions away from fossil fuels, its economy will rely on more renewable energy. Because current renewable energy sources sometimes produce variable power supplies, it is important to store energy for use when power supply drops below power demand. Battery storage is one method to store power. However, geologic (underground) energy storage may be able to retain
Analysis of the United States documented unplugged orphaned oil and gas well dataset
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary
Decision analysis and CO2–Enhanced oil recovery development strategies
Machine learning can assign geologic basin to produced water samples using major ion geochemistry
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity
Science and Products
Economics of Energy Transitions
Economics, Energy Resources, and Future Energy Supply
Economics of Global Marginal Hydrocarbon and Non-traditional Resources
Economics of U.S. Oil and Gas Resources
Lithium observations, machine-learning predictions, and mass estimates from the Smackover Formation brines in southern Arkansas
Carbon Dioxide Storage Resources-Anadarko and Southern Oklahoma Basins: Chapter R. Spatial Data
Carbon Dioxide Storage Resources - Appalachian Basin, Black Warrior Basin, Illinois Basin, and Michigan Basin: Chapter P, Spatial Data
Carbon Dioxide Storage Resources-Wind River Basin: Chapter O, Spatial Data
Compilation of Geospatial Data (GIS) for the Mineral Industries of Select Countries in the Indo-Pacific
U.S. Geological Survey National Produced Waters Geochemical Database (ver. 3.0, December 2023)
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of the People's Republic of China
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Select Countries in Southwest Asia
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources - data release
Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa
Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters
Federal Lands Greenhouse Gas Emissions and Sequestration in the United States: Estimates 2005-14 - Data Release
Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning
Machine learning approaches to identify lithium concentration in petroleum produced waters
A residual oil zone (ROZ) assessment methodology with application to the central basin platform (Permian Basin, USA) for enhanced oil recovery (EOR) and long-term geologic CO2 storage
Reconnaissance survey for potential energy storage and carbon dioxide storage resources of petroleum reservoirs in western Europe
Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada
Geologic energy storage
Introduction As the United States transitions away from fossil fuels, its economy will rely on more renewable energy. Because current renewable energy sources sometimes produce variable power supplies, it is important to store energy for use when power supply drops below power demand. Battery storage is one method to store power. However, geologic (underground) energy storage may be able to retain