Ricardo A. Olea, Ph.D.
Ricardo Olea is a Research Mathematical Statistician with the USGS Geology, Energy & Minerals (GEM) Science Center in Reston, VA.
Ricardo has extensive experience in quantitative modeling in the earth sciences and public health, primarily in the areas of petroleum geology and engineering, coal resource assessment, geostatistics, classical statistics, compositional data modeling, economic evaluation, well log analysis, marine geology, medical geography, geophysics, and geohydrology.
Professional Experience
2006 to Present: U.S. Geological Survey. Statistical Support: Coal Resource Assessment Methodology Implementation; Mathematical Model Enhancement; Exploitation of subsurface geologic resources in the land loss in coastal Louisiana; Advance the application of statistics to the earth sciences world-wide; and Reducing the risk of explosions at underground coal mines
Department of Petroleum Engineering, Stanford University
Marine Geology Section, Baltic Research Institute, University of Rostock, Warnemünde, Germany
Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina Chapel Hill
Research Scientist, Kansas Geological Survey, Lawrence, Kansas
Exploration Seismologist, Geostatistician, Log Analyst, Economic Analyst, and Reservoir Engineer, National Oil Company of Chile (ENAP)
Education and Certifications
Ph.D. Engineering, Chemical and Petroleum Engineering, University of Kansas
Mining Engineer Degree, University of Chile
Affiliations and Memberships*
Compositional Data Association, Member
Society of Petroleum Engineers, Member
American Association of Petroleum Geologists, Member
International Association for Mathematical Geosciences, Member
Sigma Xi and serves as an Associate Editor of the Springer journal Stochastic Environmental Research and Risk Assessment
International Association for Mathematical Geology (IAMG), Secretary General (1992–96) and President (1996–2000)
Honors and Awards
IAMG Krumbein Medal - Science and Professional Contributions, 2004
Science and Products
Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data
Carbon dioxide enhanced oil recovery performance according to the literature
A database and probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention in the United States
Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms
Calorific value and compositional ultimate analysis with a case study of a Texas lignite
Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite
Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification
Cokriging of compositional balances including a dimension reduction and retrieval of original units
Robust and resistant semivariogram modelling using a generalized bootstrap
Modeling uncertainty in coal resource assessments, with an application to a central area of the Gillette coal field, Wyoming
Reply to: Turner, R.E., 2014. Discussion of: Olea, R.A. and Coleman, J.L., Jr., 2014. A synoptic examination of causes of land loss in southern Louisiana as related to the exploitation of subsurface geologic resources, Journal of Coastal Research, 30(5),
Characterization of the Marcellus Shale based on computer-assisted correlation of wireline logs in Virginia and West Virginia
Non-USGS Publications**
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data
Carbon dioxide enhanced oil recovery performance according to the literature
A database and probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention in the United States
Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms
Calorific value and compositional ultimate analysis with a case study of a Texas lignite
Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite
Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification
Cokriging of compositional balances including a dimension reduction and retrieval of original units
Robust and resistant semivariogram modelling using a generalized bootstrap
Modeling uncertainty in coal resource assessments, with an application to a central area of the Gillette coal field, Wyoming
Reply to: Turner, R.E., 2014. Discussion of: Olea, R.A. and Coleman, J.L., Jr., 2014. A synoptic examination of causes of land loss in southern Louisiana as related to the exploitation of subsurface geologic resources, Journal of Coastal Research, 30(5),
Characterization of the Marcellus Shale based on computer-assisted correlation of wireline logs in Virginia and West Virginia
Non-USGS Publications**
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
*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