Ai helps speed up geological fault and horizon interpretation
An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
Artificial IntelligenceMachine Learning
Summary
Geologists often study underground layers and faults using seismic data, but interpreting this data can be slow and sometimes biased. The authors show how artificial intelligence can improve this process by cleaning up noisy data and automatically identifying important layers and faults with very little human input. They demonstrate their approach on real 3D seismic data from the Netherlands. This work shows that AI tools have now become reliable enough to be part of everyday geological modeling and decision-making.
What this means in practice
- •For geological survey teams: Rapidly interpret seismic horizons and faults for subsurface mapping to improve efficiency and reduce bias in geological models.
- •For oil and gas exploration teams: Automate seismic data interpretation workflows to accelerate reservoir characterization before drilling decisions.
Tested on one dataset.
Authors
Stefan Carpentier, Jan Diederik van Wees, Eva de Boever, Jan Niederau, Camille Chapeland, Suzanne Atkins, Boris Boullenger, Jens Wollenweber
Abstract
Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. For optimal operation, these techniques require many well-constrained input data. In the framework of the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects and to accelerate Implicit modeling, Machine Learning (ML) methods have been tested and implemented in a toolkit for the interpretation of (onshore) seismic data from the shallow to deep range (+- 300 - 3500 m). The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data. The first step is to improve the signal by applying AI techniques like self-supervised and semi-supervised contrastive learning CNN's for noise reduction and interpolation. Next, horizons and faults are interpreted with minimal use of human-generated training data by using (semi-) self-supervised methods. The resulting developed toolkit supports the application of the implemented algorithms in an efficient workflow. As a first demonstration, the top of the Dutch Maassluis Formation has been interpreted in the Leeuwarden and Waalwijk 3D seismic cubes. Overall, this study demonstrates that AI-assisted interpretation workflows have reached a level of maturity that allows their integration into applied geological modeling and decision-making.