Data-Centric, Interactive Deep Learning for Complex Geological Features: A Groningen Case Study
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.
A new, data-centric, and interactive deep learning methodology using InteractivAI, leverages neural networks to accurately yet quickly predict separate deformed facies in the Groningen study area.
The results presented in this case study 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 potential impacts on prospective petroleum systems or planned well paths.
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