AWS for Industries

Interpreting 3D seismic data automatically using Amazon SageMaker

Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. Amazon SageMaker and Apache MXNet on AWS can automate horizon picking using deep learning techniques.

In this post, I use these services to build and train a custom deep-learning model for the interpretation of geological features on 3D seismic data. The purpose of this post is to show oil and gas data scientists how they can quickly and easily create customized semantic-segmentation models.

Amazon SageMaker is a fully managed service that enables data scientists to build, train, tune, and deploy machine learning models at any scale. This service provides a powerful and scalable compute environment that is also easy to use.


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Don Bulmer

Don Bulmer

Don Bulmer leads AWS industry marketing for Energy – Oil and Gas. Before joining AWS, Don was a VP and Executive Partner at Gartner responsible for building the company's advisory business for Chief Marketing Executives supporting clients across oil & gas, utilities, financial services, healthcare, retail, CPG, media, higher education, and manufacturing industries. Don also held executive marketing, communication, and brand strategy roles at Shell, SAP, and various Internet and entertainment start-up companies.