Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

David A. Wood has more than forty years of international gas, oil, and broader energy experience since gaining his Ph.D. in geosciences from Imperial College London in the 1970s. His expertise covers multiple fields including subsurface geoscience and engineering relating to oil and gas exploration and production, energy supply chain technologies, and efficiencies. For the past two decades, David has worked as an independent international consultant, researcher, training provider, and expert witness. He has published an extensive body of work on geoscience, engineering, energy, and machine learning topics. He currently consults and conducts research on a variety of technical and commercial aspects of energy and environmental issues through his consultancy, DWA Energy Limited. He has extensive editorial experience as a founding editor of Elsevier’s Journal of Natural Gas Science & Engineering in 2008/9 then serving as Editor-in-Chief from 2013 to 2016. He is currently Co-Editor-in-Chief of Advances in Geo-Energy Research.

1. Regression models to estimate total organic carbon (TOC) from well-log data
2. Predicting brittleness indexes in tight formation sequences
3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences
4. Permeability and water saturation distributions in complex reservoirs
5. Trapping mechanisms in potential sub-surface carbon storage reservoirs
6. The accurate picking of formation tops in field development wells
7. Assessing formation loss of circulation risks with mud-log datasets
8. Delineating fracture densities and apertures using well-log image data
9. Determining reservoir microfacies using photomicrograph and computed tomography image data
10. Characterizing coal-bed methane reservoirs with well-log datasets