For several years, Oil & Gas industry, just like many others, is trying its hardest to implement machine learning into its workflows. If done right, deep learning can enhance accuracy and simultaneously speed up the entire pipeline of oil production, from the very early stages of exploration to drilling.

But, needless to say, researchers must carefully inspect hundreds of architectures and training approaches before the deployment of neural networks into the daily work of seismic specialists. This calls for quick model prototyping, which in turn needs the ability to rapidly generate data.

Unfortunately, core formats for storing volumetric seismic information…

Sergey Tsimfer

Sergey Tsimfer is a Data Analyst at Gazprom-Neft who works on seismic tasks, including reservoir modelling and horizon detection via deep learning approaches.

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