Emil D. Attanasi, Ph.D.
Emil Attanasi is a Supervisory Research Economist (Scientist Emeritus) with the USGS Geology, Energy & Minerals (GEM) Science Center in Reston, VA.
Emil Attanasi has been an economist with the U.S. Geological Survey since 1972. His work focuses on the valuation of hydrologic data, development of resource assessment methods for undiscovered oil and gas, assessment of CO2-EOR potential, and the application of economics to oil, gas, and minerals resource assessments.
Professional Experience
United States Geological Survey since 1972
Education and Certifications
Ph.D. University of Missouri, 1972, Economics
M.S. George Mason University, 2003, Statistical Science
B.A. Evangel College, 1969, Mathematics
Affiliations and Memberships*
American Economic Association, 1972 – present
Science and Products
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources - data release
In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO2-EOR) technologies were applied in amenable oil reservoirs underlying the onshore and State waters area of the conterminous United States. The assessment also includes estimates of the magnitude of CO2 s
Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters
As more hydrocarbon production from hydraulic fracturing and other methods produce large volumes of water, innovative methods must be explored for treatment and reuse of these waters. However, understanding the general water chemistry of these fluids is essential to providing the best treatment options optimized for each producing area. Machine learning algorithms can often be applied to datasets
Filter Total Items: 150
Machine learning approaches to identify lithium concentration in petroleum produced waters
Prices for battery-grade lithium have increased substantially since 2020, which is propelling the search for additional sources of this important element. Battery-grade lithium is predominately recovered from continental brines. Most crude oil and natural gas wells recover briny formation water, which may represent an additional source. Chemical analysis of these waters has been shown to indicate
Authors
E. D. Attanasi, Timothy Coburn, Philip A. Freeman
Reconnaissance survey for potential energy storage and carbon dioxide storage resources of petroleum reservoirs in western Europe
Energy producers and utilities use oil and gas reservoirs for gas storage to meet peak seasonal demand or to supplement intermittent energy production. These reservoirs are also suitable for the long-term storage of carbon dioxide (CO2), a greenhouse gas. This study reports on a reconnaissance analysis of the potential magnitude of storage resources in 9424 known oil and gas reservoirs from 24 cou
Authors
Emil D. Attanasi, Philip A. Freeman
Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada
Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to con
Authors
Emil D. Attanasi, Philip A. Freeman
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results
In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO2-EOR) technologies were applied to amenable oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO2 that
Authors
Peter D. Warwick, Emil D. Attanasi, Madalyn S. Blondes, Sean T. Brennan, Marc L. Buursink, Steven M. Cahan, Colin A. Doolan, Philip A. Freeman, C. Özgen Karacan, Celeste D. Lohr, Matthew D. Merrill, Ricardo A. Olea, Jenna L. Shelton, Ernie R. Slucher, Brian A. Varela
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary
IntroductionIn 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that might be produced by using current carbon dioxide enhanced oil recovery (CO2-EOR) technologies in amenable conventional oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes est
Authors
Peter D. Warwick, Emil D. Attanasi, Madalyn S. Blondes, Sean T. Brennan, Marc L. Buursink, Steven M. Cahan, Colin A. Doolan, Philip A. Freeman, C. Özgen Karacan, Celeste D. Lohr, Matthew D. Merrill, Ricardo A. Olea, Jenna L. Shelton, Ernie R. Slucher, Brian A. Varela
Decision analysis and CO2–Enhanced oil recovery development strategies
This paper analyzes the relationship between actual reservoir conditions and predicted measures of performance of carbon dioxide enhanced oil recovery (CO2–EOR) programs. It then shows how CO2–EOR operators might maximize the value of their projects by approaching implementation using a “flexible selective” pattern development strategy, where the CO2–EOR program patterns are selectively developed
Authors
E. D. Attanasi, Philip A. Freeman
Random forest
This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting mode
Authors
Emil D. Attanasi, Timothy Coburn
Machine learning can assign geologic basin to produced water samples using major ion geochemistry
Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a
Authors
Jenna L. Shelton, Aaron M. Jubb, Samuel Saxe, Emil D. Attanasi, Alexei Milkov, Mark A Engle, Philip A. Freeman, Christopher Shaffer, Madalyn S. Blondes
Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated or smoothed can impact analytical and predictive modeling results. This paper discusses findings regarding such impacts on estimating change points in production flow regimes of horizontal hydraulically fractured shale oil wells producing from the middle member of the Bakken Formation. Change points that signal transitions in flow regimes are important becaus
Authors
T. C. Coburn, Emil D. Attanasi
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity
Geologists and petroleum engineers have struggled to identify the mechanisms that drive productivity in horizontal hydraulically fractured oil wells. The machine learning algorithms of Random Forest (RF), gradient boosting trees (GBT) and extreme gradient boosting (XGBoost) were applied to a dataset containing 7311 horizontal hydraulically fractured wells drilled into the middle member of the Bakk
Authors
Emil D. Attanasi, Philip A. Freeman, Timothy Coburn
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity
In recent years, geologists and petroleum engineers have struggled to clearly identify the mechanisms that drive productivity in horizontal, hydraulically-fractured oil wells producing from the middle member of the Bakken formation. This paper fills a gap in the literature by showing how this play’s heterogeneity affects factors that drive well productivity. It is important because understanding t
Authors
Emil D. Attanasi, Philip A. Freeman, Tim Coburn
Implications of aggregating daily production data on estimates of ultimate recovery from horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated can impact analytical and predictive modeling results. In this short paper we discuss some of our findings regarding the impacts of data aggregation on estimating change points in the production profiles of horizontal hydraulically fractured Bakken oil wells. Change points occur when production transitions from one flow regime to another. Change point determi
Authors
T. C. Coburn, Emil D. Attanasi
Science and Products
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources - data release
In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO2-EOR) technologies were applied in amenable oil reservoirs underlying the onshore and State waters area of the conterminous United States. The assessment also includes estimates of the magnitude of CO2 s
Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters
As more hydrocarbon production from hydraulic fracturing and other methods produce large volumes of water, innovative methods must be explored for treatment and reuse of these waters. However, understanding the general water chemistry of these fluids is essential to providing the best treatment options optimized for each producing area. Machine learning algorithms can often be applied to datasets
Filter Total Items: 150
Machine learning approaches to identify lithium concentration in petroleum produced waters
Prices for battery-grade lithium have increased substantially since 2020, which is propelling the search for additional sources of this important element. Battery-grade lithium is predominately recovered from continental brines. Most crude oil and natural gas wells recover briny formation water, which may represent an additional source. Chemical analysis of these waters has been shown to indicate
Authors
E. D. Attanasi, Timothy Coburn, Philip A. Freeman
Reconnaissance survey for potential energy storage and carbon dioxide storage resources of petroleum reservoirs in western Europe
Energy producers and utilities use oil and gas reservoirs for gas storage to meet peak seasonal demand or to supplement intermittent energy production. These reservoirs are also suitable for the long-term storage of carbon dioxide (CO2), a greenhouse gas. This study reports on a reconnaissance analysis of the potential magnitude of storage resources in 9424 known oil and gas reservoirs from 24 cou
Authors
Emil D. Attanasi, Philip A. Freeman
Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada
Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to con
Authors
Emil D. Attanasi, Philip A. Freeman
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results
In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO2-EOR) technologies were applied to amenable oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO2 that
Authors
Peter D. Warwick, Emil D. Attanasi, Madalyn S. Blondes, Sean T. Brennan, Marc L. Buursink, Steven M. Cahan, Colin A. Doolan, Philip A. Freeman, C. Özgen Karacan, Celeste D. Lohr, Matthew D. Merrill, Ricardo A. Olea, Jenna L. Shelton, Ernie R. Slucher, Brian A. Varela
National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary
IntroductionIn 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that might be produced by using current carbon dioxide enhanced oil recovery (CO2-EOR) technologies in amenable conventional oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes est
Authors
Peter D. Warwick, Emil D. Attanasi, Madalyn S. Blondes, Sean T. Brennan, Marc L. Buursink, Steven M. Cahan, Colin A. Doolan, Philip A. Freeman, C. Özgen Karacan, Celeste D. Lohr, Matthew D. Merrill, Ricardo A. Olea, Jenna L. Shelton, Ernie R. Slucher, Brian A. Varela
Decision analysis and CO2–Enhanced oil recovery development strategies
This paper analyzes the relationship between actual reservoir conditions and predicted measures of performance of carbon dioxide enhanced oil recovery (CO2–EOR) programs. It then shows how CO2–EOR operators might maximize the value of their projects by approaching implementation using a “flexible selective” pattern development strategy, where the CO2–EOR program patterns are selectively developed
Authors
E. D. Attanasi, Philip A. Freeman
Random forest
This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting mode
Authors
Emil D. Attanasi, Timothy Coburn
Machine learning can assign geologic basin to produced water samples using major ion geochemistry
Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a
Authors
Jenna L. Shelton, Aaron M. Jubb, Samuel Saxe, Emil D. Attanasi, Alexei Milkov, Mark A Engle, Philip A. Freeman, Christopher Shaffer, Madalyn S. Blondes
Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated or smoothed can impact analytical and predictive modeling results. This paper discusses findings regarding such impacts on estimating change points in production flow regimes of horizontal hydraulically fractured shale oil wells producing from the middle member of the Bakken Formation. Change points that signal transitions in flow regimes are important becaus
Authors
T. C. Coburn, Emil D. Attanasi
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity
Geologists and petroleum engineers have struggled to identify the mechanisms that drive productivity in horizontal hydraulically fractured oil wells. The machine learning algorithms of Random Forest (RF), gradient boosting trees (GBT) and extreme gradient boosting (XGBoost) were applied to a dataset containing 7311 horizontal hydraulically fractured wells drilled into the middle member of the Bakk
Authors
Emil D. Attanasi, Philip A. Freeman, Timothy Coburn
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity
In recent years, geologists and petroleum engineers have struggled to clearly identify the mechanisms that drive productivity in horizontal, hydraulically-fractured oil wells producing from the middle member of the Bakken formation. This paper fills a gap in the literature by showing how this play’s heterogeneity affects factors that drive well productivity. It is important because understanding t
Authors
Emil D. Attanasi, Philip A. Freeman, Tim Coburn
Implications of aggregating daily production data on estimates of ultimate recovery from horizontal hydraulically fractured Bakken oil wells
The level to which data are aggregated can impact analytical and predictive modeling results. In this short paper we discuss some of our findings regarding the impacts of data aggregation on estimating change points in the production profiles of horizontal hydraulically fractured Bakken oil wells. Change points occur when production transitions from one flow regime to another. Change point determi
Authors
T. C. Coburn, Emil D. Attanasi
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government