A growing machine learning approach to optimize use of prestack and poststack seismic data
Kamal Hami‐Eddine, Bruno de Ribet, Patrick Durand, Patxi Gascue · 2017
Machine Learning and Neural Networks have been used in the oil and gas industry for several years. The main focus of these technologies has been to predict facies distribution from seismic data, or to cluster log data into electro-facies. Some tentative methods for expanding their applicability have been tested, but to date, these have failed to become part of the main interpretation workow stream. The question is how machine learning technology can be used to perform fault interpretation, AVO analysis and geobody detection more quickly and easily. We propose a new type of machine learning approach to accelerate daily interpretation tasks. Presentation Date: Wednesday, September 27, 2017 Start Time: 3:55 PM Location: 350D Presentation Type: ORAL