One of the key data sources that supports those assessments is IHS Markit’s Well Data and Production Data datasets (https://ihsmarkit.com/index.html). The IHS Markit’s US Well Data and US Production Data databases contain close to 500 million records describing many facets of oil and gas exploration and development such as fields, reservoirs, well logs, formation tops etc. This massive data resource has the potential to provide much greater value for the USGS if approached with emerging data analytical approaches. Research is needed into data science and machine learning techniques that can help us to identify untapped potential in these and related databases that could impact the geologic and quantitative aspects of USGS resource studies and assessments.
We seek applicants for this Mendenhall Research Opportunity who are interested in developing analytical approaches for intelligently sifting through large geologic datasets to identify key parameters and relationships that will provide an improved understanding of the processes and patterns that underpin USGS resource studies. Possible research topics include:
Applicants are expected to have a strong background in data science and machine learning as well as some knowledge of petroleum geology. Interested applicants are strongly encouraged to contact the advisors below early in the application process to discuss project ideas.
Additional Reading on Energy Resource Assessments
Overview: National Academies of Sciences, Engineering, and Medicine. 2018. Future Directions for the U.S. Geological Survey’s Energy Resources Program. Washington, DC: The National Academies Press. (see beginning of Chapter 3) https://doi.org/10.17226/25141.
Methods: Charpentier, R.R., and Cook, T.A., 2010, Improved USGS methodology for assessing continuous petroleum resources, version 2.0: U.S. Geological Survey Data Series 547, 22 p. and program. Revised November 2012.
Schmoker, J. W., & Klett, T. R., 2007. US Geological Survey assessment concepts for conventional petroleum accumulations: Chapter 24 in Petroleum systems and geologic assessment of oil and gas in the San Joaquin Basin Province, California (No. 1713-24). US Geological Survey.
Example: Whidden, K.J., Pitman, J.K., Pearson, O.N., Paxton, S.T., Kinney, S.A., Gianoutsos, N.J., Schenk, C.J., Leathers-Miller, H.M., Birdwell, J.E., Brownfield, M.E., Burke, L.A., Dubiel, R.F., French, K.L., Gaswirth, S.B., Haines, S.S., Le, P.A., Marra, K.R., Mercier, T.J., Tennyson, M.E., and Woodall, C.A., 2018, Assessment of undiscovered oil and gas resources in the Eagle Ford Group and associated Cenomanian–Turonian strata, U.S. Gulf Coast, Texas, 2018: U.S. Geological Survey Fact Sheet 2018–3033, 4 p., https://pubs.er.usgs.gov/publication/fs20183033.
Proposed Duty Station: Denver, CO
Areas of PhD: Data science, machine learning, computer science, geology, geophysics, petroleum geology, or related fields (candidates holding a PhD in other disciplines that also have extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications: Applications must meet one of the following qualifications: Research Geologist, Research Geophysicist, Research Computer Scientist, Research Statistician, Operations Research Analyst. (This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
Research Advisors: Kate Whidden, (303) 236-7788, firstname.lastname@example.org; Greg Gunther, (303) 236-5884, email@example.com; Seth Haines, (303) 236-5709, firstname.lastname@example.org; Karen Jenni, (303) 236-5766, email@example.com; Leslie Hsu, (303) 202-4080, firstname.lastname@example.org.
Human Resources Office Contact: Elissa Gregory, email@example.com, 303-236-9579
|Summary of Opportunities|