Applications of Machine Learning for Rapid Analysis of DAS
Wednesday, May 12, 2021
11:00 am - 12:00 pm
About the Event
Distributed acoustic sensing (DAS) data provide important insights into hydraulic fracturing programs, and applications include characterization of developing fracture systems as well as operational issues related to casing integrity and plug performance. Application of machine learning techniques include huge data volumes, with results depending on analysis of features with broad ranges of scales in space and time. In this talk, we will discuss several examples of scientific machine learning (SciML) techniques to address these problems. Given the rapid delivery of large data volumes, automatic detection of microseismic events is essential.
About the Speaker
Rick GIbson is Cheif Geophysicist for NanoSeis, focusing on microseismic data analysis, modeling, inversion and exploring how AI technologies could be incorporated into standard workflows. Prior to joining NanoSeis, Gibson had a 22- year long and distinguished career at Texas A&M University where he was a professor of geophysics and associate director of the Berg-Hughes Center for Petroleum and Sedimentary Systems.