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Data-Centric, Interactive Deep Learning for Complex Geological Features: a Groningen Case Study
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Third EAGE Digitalization Conference and Exhibition, Mar 2023, Volume 2023, p.1 - 5
Abstract
Summary
Detailed interpretation of complex facies intervals within high-resolution 3D seismic data is a tedious and time-consuming process, even with the assistance of traditional deep learning methods. Traditional, windowed waveform classification algorithms can have a non-unique solution and are impacted heavily by interpreter bias and laterally varying data quality. This is the case in deformed facies intervals, such as post-depositional deformation of complex geological sequences, where tectonic reactivation and/or salt tectonism have re-worked sequences of post-salt siliciclastics into complicated packages that are difficult to interpret. These heavily reworked zones are prolific throughout the North Sea and can play an important role in fluid migration and containment. With manual interpretation methods, it is extraordinarily difficult to map these re-worked sediments. Their complexity usually means such sequences are under-interpreted, which introduces pre-drill uncertainties about the well path or target itself. Therefore, we propose a new, data-centric, and interactive deep learning methodology that leverages neural networks to accurately predict separate deformed facies in the Groningen Area. The results were obtained in a fraction of the time compared to traditional interpretation workflows and allow geoscientists to better characterize complex geologic units while also determining its impacts on prospective petroleum systems or planned well paths.
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