Written by Matt Morris, Director of Geoscience and Deep Learning
There’s a global race underway to bring machine learning (ML) techniques to knowledge workers across every industry that extracts insights from data. Oil and gas is no exception. Many exploration and production (E&P) operators and service companies are exploring the technology to help illuminate patterns in seismic data where fuzzy pattern recognition is at the heart of many tasks. Fundamental innovations in ML during the past decade have led to an explosion of solutions to problems that have historically been too “fuzzy” to solve with traditional sequential code. ML could be a great fit for many of these challenging geophysical problems.
For example, many features like reefs, levees, and sand injectites aren’t amenable to traditional mapping technologies like auto-tracking or opacity carving, but their distinctive character in the seismic fabric makes them highly detectable with ML. In cases like this, it makes sense to bring deep learning into your workflow. The technology offers the potential to make you a faster, more productive interpreter and delivers a better quality solution than what you could do manually.
Until recently, deep learning-based workflows have been out of reach for the individual geoscientist. They required significant GPU compute resources and the aid of specialized data scientists and data engineers. Bluware, an AWS partner, and AWS have worked together to remove all these barriers of entry, democratizing access to deep learning for every geoscientist in E&P.