0940 Grimsgaard Deep Learning Neural Network Solution Applied to Seismic Horizon 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/

Jens Grimsgaard, Equinor
Deep Learning is the fastest growing field within Artificial Intelligence. It has enabled self-driving cars,
provided automated assistants, empowered fraud detection, modernized supply chains and much more.
We are on the brink of a revolution which will have impact on our daily lives - both privately and
professionally.
Deep Learning is a field of Machine Learning which makes use of Big Data and has proven its worth in
image recognition and segmentation. In the field of Exploration and Production a natural application for
Deep Learning is in the image recognition and segmentation task many of us do every day by
interpreting seismic. We can use many of the seismic interpretation projects carried throughout our
company's lifespan as a basis for learning. Each point interpreted on seismic is embedded with an
interpreter's competence, knowledge and reasoning.
In Equinor R&T, we initiated a project (DELI - Deep Learning applied to seismic Interpretation) at the
beginning of 2017, making use of Deep Learning's pattern recognition to detect key horizons in seismic
images in the same manner our experienced interpreters do. The research project has been in tight
collaboration with Norsk Regnesentral (Norwegian Computing Center). Our first challenge was to find
suitable and representative data to train the networks. Secondly, constructing and testing out many
different network models and configurations was a necessary but time-consuming task.
The solution (HorizonNet) which has proved best results, is based on a U-Net, a fully convolutional
neural network. Carefully selected surveys with qualified key seismic horizon interpretations were used
to pre-train the network. Representative lines in the target survey have their key horizons interpreted.
The interpretation was used to fine tune the pre-trained network. The network was then used to generate
predictions for the whole target cube. After the predictions were generated, the results were postprocessed
to remove spikes and clearly wrong stratigraphic ordering.
The resulting predictions were compared with our mainly used autotracker, which were given the same
input lines. We used the F1-score to compare the results. Conclusions based on tests performed so far
are:
• Autotracker and HorizonNet give similar results on easy horizons. HorizonNet performs
marginally better since the predictions follow minor faults
• With subtle and discontinuous horizons and horizons with polarity shift, the HorizonNet
achieves a far better F1-score than the autotracker. In one survey the HorizonNet's F1-score
was between 80 and 90%, while the autotracker fails in large areas giving a F1-score
between 30 and 50%. The HorizonNet is also showing good results with detecting pinchout
zones.