Frank Male is an assistant research professor in Energy and Mineral Engineering at Penn State. He has a Ph.D. in physics from UT Austin and bachelor's degrees in physics and political science from Kansas State University. He has worked to bridge disciplines, collaborating closely with geoscientists, other reservoir engineers, and data scientists. His Ph.D. focused on accurately predicting production for each US shale gas well individually. Much of his work uses machine learning to capture the interplay between reservoir engineering and geological concerns.
Male conducts research in these areas:
- Machine learning-enabled petrophysics
- Shale gas and tight oil reservoir analysis
- Natural Language Processing of technical documents
- Pipeline risk modeling
- CO2-enhanced oil recovery and geothermal well connectivity analysis
Publications
Selected Technical Papers and Presentations
- Properties of high-performing horizontal wells in the Midland Basin
F Male, R Dommisse, LJ Sivila, S Hamlin, ED Goodman
Interpretation 11 (4), T697-T706 - Bluebonnet: Scaling solutions for production analysis from unconventional oil and gas wells
F Male, MP Marder, LM Ruiz-Maraggi, LW Lake
Journal of Open Source Software 8 (88), 5255 - Three common statistical missteps we make in reservoir characterization
F Male, JL Jensen
AAPG Bulletin 106 (11), 2149-2161 - Economic analysis of CCUS: Accelerated development for CO2 EOR and storage in residual oil zones under the context of 45Q tax credit
B Ren, F Male, IJ Duncan
Applied Energy 321, 119393 - The paradox of increasing initial oil production but faster decline rates in fracking the Bakken Shale: Implications for long-term productivity of tight oil plays
F Male, IJ Duncan
Journal of Petroleum Science and Engineering 208, 109406 - Comparison of permeability predictions on cemented sandstones with physics-based and machine-learning approaches
F Male, JL Jensen, LW Lake
Journal of Natural Gas Science and Engineering 77, 103244 - Using a segregated flow model to forecast production of oil, gas, and water in shale oil plays
F Male
Journal of Petroleum Science and Engineering 180, 48-61 - Assessing impact of uncertainties in decline curve analysis through hindcasting
F Male
Journal of Petroleum Science and Engineering 172, 340-348 - Using data analytics to assess the impact of technology change on production forecasting
F Male, C Aiken, IJ Duncan
SPE Annual Technical Conference and Exhibition, D011S008R006 - Managing the increasing water footprint of hydraulic fracturing in the Bakken Play, United States
BR Scanlon, RC Reedy, F Male, M Hove
Environmental science & technology 50 (18), 10273-10281 - The impact of pressure and fluid property variation on well performance of liquid-rich Eagle Ford shale
SA Gherabati, J Browning, F Male, SA Ikonnikova, G McDaid
Journal of Natural Gas Science and Engineering 33, 1056-1068 - Gas production in the Barnett Shale obeys a simple scaling theory
TW Patzek, F Male, M Marder
Proceedings of the National Academy of Sciences 110 (49), 19731-19736
Invited Presentations
- “Three Common Statistical Missteps We Make in Reservoir Characterization,” Pennsylvania State University Energy and Mineral Engineering Department, November 2022.
- “Three Common Statistical Missteps We Make in Reservoir Characterization,” Society of Petroleum Engineers Gulf Coast Section Data Science Convention, July 2021.
- “Big data on a budget,” University of Texas Petroleum and Geosystems Engineering Department, Claude R. Hocott Lectureship, November 2020.
- “Data-driven and Physics-based analysis for tight oil production,” Center for Nonlinear Dynamics - University of Texas at Austin, 12 February 2020.
- “Technology progress and physical constraints on tight oil production,” University of Texas Energy Symposium, 24 October 2019.
- “Unconventional Permian Basin: Well Declines and Resource in Place Estimates,” Kansas State University Department of Geology, 15 March 2018.
- “Resource assessments and the future of data analytics in the oil field,” Lloyd’s Register IP/IC User Consortium, 5 October 2017.
Scientific Software
Male has written several software tools to improve reservoir engineering and formation evaluation workflows. Each code repository is available under a permissive open-source license. These include:
- Pywaterflood: https://github.com/frank1010111/pywaterflood
Capacitance resistance modeling for waterflood, CO2-enhanced oil recovery, and geothermal connectivity estimation between injecting and producing wells. Capacitance resistance model outputs have been used for flood management and intervention planning. - Bluebonnet: https://github.com/frank1010111/bluebonnet
Bluebonnet is a set of rate transient analysis tools. These physical scaling solutions can match and predict tight oil and shale gas production, investigate novel flow mechanisms, and develop probabilistic forecast scenarios. - Petrelpy: https://github.com/frank1010111/petrelpy
Petrelpy includes tools for getting data and interpretations in and out of Schlumberger's Petrel software. - Statistical Missteps: https://github.com/frank1010111/statistical_missteps
This supplement to the Three Common Statistical Missteps paper has Python notebooks for Monte Carlo experiments showing common pitfalls in applying statistics and machine learning to reservoir characterization data.
Awards
- University of Texas Pre-commencement speaker, 2015
- Cozzarelli Prize for Engineering and Applied Sciences–PNAS, 2013
- Phi Beta Kappa Honor Society