Deep learning in energy

Monitoring and detecting natural resources.

Data from the subsurface of the earth such as borehole imagery and seismic data is especially important in oil and gas exploration. Automated interpretation of this complex data has great potential to be useful for tasks like monitoring and detecting natural resources.

The innovations in Energy will focus on robust and reliable methods for automatic analysis of complex imagery from the digital subsurface for more efficient and detailed oil and gas exploration. Equinor has a major goal of becoming more data-driven and maximizing the value of the vast amount of data available; especially on the Norwegian Continental Shelf. A part of this includes challenges related to analysis of the digital subsurface, e.g. in the form of seismic and borehole imagery, which is important for oil and gas exploration. Automated analysis of these data can lead to large savings in time and resources and more efficient and precise exploration.

The amount and quality of labelled training data is a challenge in this field. Existing interpretations are not made for machine learning purposes. Hence, the annotation quality for the task is unknown and interpretations are generally incomplete. Generating realistic simulated data is difficult as simulated data tends to be too simple. For many of these problems context and dependencies, through exploitation of prior knowledge of the geology, dependencies in space and time or results derived from existing solutions could improve predictions.

Visual Intelligence are advancing deep learning to overcome these challenges.

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