Philip A. Freeman (Former Employee)
Science and Products
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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 — 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...
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. Ozgen 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 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...
Authors
Emil Attanasi, Philip A. Freeman
Machine learning can assign geologic basin to produced water samples using major ion geochemistry 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...
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
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity 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...
Authors
Emil D. Attanasi, Philip A. Freeman, Timothy Coburn
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity 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...
Authors
Emil D. Attanasi, Philip A. Freeman, Tim Coburn
Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale
Residual oil zones (ROZs) are increasingly gaining interest as potential reservoirs for carbon dioxide (CO2) storage. Here, we present a national- and regional-scale methodology for estimating prospective CO2 storage resources in residual oil zones. This methodology uses a volumetric equation that accounts for CO2 storage as a free phase in pore space and as a dissolved phase in oil and...
Authors
Sean Sanguinito, Harpreet Singh, Evgeniy M. Myshakin, Angela L. Goodman, Robert M. Dilmore, Timothy C. Grant, David Morgan, Grant Bromhal, Peter D. Warwick, Sean T. Brennan, Philip A. Freeman, C. Ozgen Karacan, Charles Gorecki, Wesley Peck, Matthew Burton-Kelly, Neil Dotzenrod, Scott Frailey, Rajesh Pawar
Science and Products
Filter Total Items: 14
No Result Found
Filter Total Items: 78
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 — 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...
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. Ozgen 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 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...
Authors
Emil Attanasi, Philip A. Freeman
Machine learning can assign geologic basin to produced water samples using major ion geochemistry 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...
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
Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity 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...
Authors
Emil D. Attanasi, Philip A. Freeman, Timothy Coburn
Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity 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...
Authors
Emil D. Attanasi, Philip A. Freeman, Tim Coburn
Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale
Residual oil zones (ROZs) are increasingly gaining interest as potential reservoirs for carbon dioxide (CO2) storage. Here, we present a national- and regional-scale methodology for estimating prospective CO2 storage resources in residual oil zones. This methodology uses a volumetric equation that accounts for CO2 storage as a free phase in pore space and as a dissolved phase in oil and...
Authors
Sean Sanguinito, Harpreet Singh, Evgeniy M. Myshakin, Angela L. Goodman, Robert M. Dilmore, Timothy C. Grant, David Morgan, Grant Bromhal, Peter D. Warwick, Sean T. Brennan, Philip A. Freeman, C. Ozgen Karacan, Charles Gorecki, Wesley Peck, Matthew Burton-Kelly, Neil Dotzenrod, Scott Frailey, Rajesh Pawar