Computer Vision for the Subsurface

A very large part of the energy that you use today comes from the geology beneath your feet. This includes conventional oil, gas and coal, but also cleaner sources of energy such as geothermal energy, carbon capture and storage for conventional reservoirs, geological hydrogen, and even for aeolians, a characterization of the subsurface is necessary for engineering. The subsurface is also a vital reservoir of drinkable water, and of brines rich in critical minerals. Hence, a proper characterization of geology in the subsurface is essential to human activites.

Automatic core description is a big research theme in our lab, as previous studies have shown that humans are inconsistent in descriptive tasks. This is important, because the facies defined during core descriptions are used to interpret environmental changes, and also petrophysical classes for subsurface energy. Our work to date includes supervised classification and object detection, semi-supervised learning for core deformation, and rethinking core description as a regression task rather than a classification. We are now working towards foundation models for generalizable downstream applications.

Generative AI is more often associated with LLMs and vision-language models, less with subsurface geology. However, in a series of progressively more complex projects, we show that generative AI can help geologists interpret the subsurface more accurately than if they rely solely on log data. We first explore how formation microscanner (FMS) data can serve as a prior to a generative model (Pix2PixHD) and demonstrated that geologists were more accurate when describing Dunham texture on synthetic cores than on raw FMS data. More recently, we continued this work by integrating conventional logs and FMS data in a new architecture for the generator model of Pix2PixHD, achieving even more realistic results.

Seismic data is a key source of information for subsurface geology, but its processing and interpretation can be time and resource intensive, as well as subjective. We are working on novel mathematical loss functions for neural network to improve seismic processing such as missing trace reconstruction or inversions. These AI techniques are orders of magnitude faster than conventional inversion or reconstruction techniques. For automatic interpretation of seismic stratigraphy, we overcome the issue of lack of labelled training data by generating realistic synthetic data. We have experimented with several techniques to achieve this, including generating simple seismic termination and full geological cross sections with forward stratigraphic modelling. We then use forward modelling of a seismic impendance response of the synthetic geology to obtain our training data. We demonstrate on actual data that the trained models perform well.