0920 Stinson Applications  in geoscience at Total  Deep Neural Networks in seismic interpretation
Channel: Force Seminars
Date: 2019-10-04

2019 Force Symposium machine learning and subsurface data
Presentations https://www.npd.no/en/force/archive/hackathon-2019/

P. Stinson1, F. Joncour2, S. Guillon2 , P. Goutorbe2, L. Castanie2
1 Total E&P Norge AS, Stavanger
2 Total E&P Research and Technology USA Inc., Sunnydale CA
The promise of Al for seismic interpretation is to reduce long, manual interpretation tasks to a few hours
of machine learning based processing, hence secure more time for geoscientists to focus on high
added value tasks and better understand the subsurface. Total's GAIA project is a collaboration with
Google Cloud Platform to provide tools for geoscientists. During the first year the focus has been on
seismic interpretation and document classification and interpretation.
Supervised learning assumes that the training data and the target production data have a similar
distribution. Many applications of convolutional Neural Networks (CNN) published in the context of
seismic interpretation address this by making predictions on subsets of the same dataset as the one used
for training. While this is a good approach to prove the relevance of these architectures to segment
seismic images, it is of limited use to interpreters until the model's performance generalizes to new
datasets, not seen at training time.
Focusing on fault detection, data diversity is then related both to the seismic (amplitude, dip, frequency
spectrum .. ) and to the faults (dip, azimuth, size, throw, density, ... ), and getting a large and uniform
distribution of these parameters is the challenge. It is possible to significantly expand the data
distribution and address the data representativeness issue using synthetics seismic data and data
augmentation techniques, both processing and geometry-based.
Using post-processing techniques, the CNN's output can be converted to individual fault surfaces.
We plan to deploy the 2D model in into our internal interpretation package in 2019. Ongoing work is
focused on extraction of individual fault surfaces, and development of the 3D fault model.