138: The power and pitfalls of modern geostatistical models

Danilo Jotta Ariza Ferreira discusses modern geostatistic methodologies from the December 2021 special section on Latin America in The Leading Edge. Danilo shares how to avoid two common pitfalls in geostatistical modeling, why models are always wrong (but you should still use them), and highlights a few innovations in seismic-based geophysical solutions. This conversation will help geophysicists working in Latin America, those using modern modeling techniques, and those looking to connect modern research to their work, no matter the location. Visit https://seg.org/podcast to read the full show notes and find the full archive for Seismic Soundoff. RELATED LINKS * Antonio J. Velásquez, Wagner Moreira Lupinacci, and Carlos Eduardo Molinares, (2021), "Introduction to this special section: Latin America," The Leading Edge 40: 874–874. (https://doi.org/10.1190/tle40120874.1) * Danilo Jotta Ariza Ferreira, Gabriella Martins Baptista de Oliveira, Thais Mallet Castro, Raquel Macedo Dias, and Wagner Moreira Lupinacci, (2021), "Geostatistics assisted by machine learning for reservoir property modeling: A case study in presalt carbonates of Buzios Field, Brazil," The Leading Edge 40: 876–885. (https://doi.org/10.1190/tle40120876.1) * Read the special section: Latin America (https://library.seg.org/toc/leedff/40/12) CREDITS SEG produces Seismic Soundoff to benefit its members, the scientific community, and inform the public on the value of geophysics. To show your support for the show, please leave a 5-star rating on Apple Podcasts and Spotify. It takes less than five seconds to leave a 5-star rating and is the number one action you can take to show your appreciation for this free resource. You can follow the podcast to hear the latest episodes on Apple Podcasts, Google Podcasts, and Spotify. Original music created by Zach Bridges. This episode was hosted, edited, and produced by Andrew Geary at 51 features, LLC. Thank you to the SEG podcast team: Jennifer Cobb, Kathy Gamble, and Ally McGinnis.