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Martin-Daniel Lacasse
Can drilling data provide more than drilling optimization?
Abstract
Safe and successful subsurface operations rely on reasonable estimates of geomechanical characteristics of rock formations, including rock mechanical properties and in-situ stresses. Teverra has been focusing on the development of novel methods for extracting the required information from drilling data, the development of geomoechanical models aimed at optimizing hydraulic fracturing or maintaining wellbore integrity and stability, and consulting and training in those areas.
The emergence of machine learning methods enabled the identification of statistical correlations between the abundant drilling records, well logs, and rock mechanical properties, opening the door to novel approaches for obtaining the geomechanical characteristics of rock formations.
In this talk, we will present a few field tests where these new technologies were applied and discuss how these technologies can be deployed and improved for large-scale operations.
Biography
Dr. Martin-Daniel Lacasse currently serves as the Technology and Innovation Leader for Teverra. This includes helping build the intellectual property portfolio and driving the Teverra technology strategy forward. He brings close to 30 years of experience in research, most of it spent at the Corporate Strategic Research Laboratory of ExxonMobil. Martin is recognized for spearheading the development and deployment of novel seismic inversion methods in ExxonMobil, and during his career led multiple research teams on topics ranging from seismic imaging to induced seismicity. Before retiring as a Distinguished Research Associate, he was managing all leveraging activities related to geological carbon sequestration (GCS) in XOM-funded Energy Centers in top universities and national labs, managing an internal research group on GCS, and was an advisor on all research programs in the physical and mathematical sciences in the fundamental research lab of ExxonMobil.
Lacasse received a Ph.D. in Physics from McGill University, after which he was at Princeton University for a joint industrial post-doctoral research fellowship. He served on the Board of Governors of the Institute of Mathematics and its Applications at the University of Minnesota, and is currently the associate director of the INMAS program at the Applied Mathematics and Statistic department at the Johns Hopkins University.
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