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Looking up the AI maturity curve in E&P opportunities, challenges and the impact on geoscience work
2019
Zenodo
Machine Learning (ML) has been capable for three decades, to infer lithology, sedimentary facies, porosity, and fluid saturation as functions of wireline logs. Now, ML is moving from R&D projects and into the tool box of the generalist, transforming the subsurface workflow. In addition to being fueled by algorithmic development, data, and high-performance compute; this transformation is enabled by the emergence of data analytics platforms, that facilitate; i) practical use of ML methods by the
doi:10.5281/zenodo.2578736
fatcat:ubnjy7t27ffmfkcpdumrx5na2e