Claire Birnie

Research Scientists

Research Scientist



Bldg#1, 3203-CU15

Research Interests

Claire is a machine learning specialist focusing on the application of data science techniques to subsurface related problems. During her time in industry she developed several ML procedures to aid operational planning, production forecasting, detection of anomalous flow in wells, and micro seismic event detection. Her contributions spanned from proof-of-concept research through to implementation of production-ready technologies that required distributed training of large neural networks on cloud computing. She is particularly passionate about demystifying 'Artificial Intelligence' and its potential use within the Energy sector.

Selected Publications

  • The potential of self-supervised networks for random noise suppression in seismic data, Birnie, C.E., Ravasi, M., Liu, S., and Alkhalifah, T., Artificial Intelligence in Geosciences 2, 47-59, (2021)
  • Generating Custom Word Embedding for Geoscientific Corpi, Birnie, C.E., and Ravasi, M., First Break 38 (7), 61-67, (2020)
  • On the importance of benchmarking algorithms under realistic noise conditions, Birnie, C.E., Chambers, K., Angus, D., and Stork, A.L., Geophysical Journal International 221 (1), 504-520, (2020)
  • Improving the quality and efficiency of operational planning with risk management with ML and NLP, Birnie, C.E., Sampson, J., Sjaastad, E., Johansen, B., Obrestad, L., Larsen, and R., Khamassi, A., SPE Offshore Europe Conference and Exhibition, (2019)
  • Is CO2 injection at Aquistore aseismic­ A combined seismological and geomechanical study of early injection operations, Stork, A.L., Nixon, C.G., Hawkes, C.D., Birnie, C., White, D.J., Schmitt, D.R. and Roberts, B., International Journal of Greenhouse Gas Control 75, 107-124, (2018)
  • Seismic arrival enhancement through the use of noise whitening. Birnie, C., Chambers, K., and Angus, D., Physics of the Earth and Planetary Interiors 262, 80-89, (2017)
  • Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site. Birnie, C., Chambers, K., Angus, D., and Stork, A., Geophysical Journal International 206 (2), 1246-1260, (2016)


  • Ph.D., Computational Geophysics, University of Leeds, UK, 2018
  • Microsoft Professional Program in Data Science, 2017
  • B.Sc., Geophysics and Meteorology, University of Edinburgh, UK, 2014 

Professional Profile

  • 2018-2021: Data Scientist, Digital Center of Excellence, Equinor (née Statoil), Norway
  • 2017: R&D Intern, Nanometrics, Canada (Remote) 
  • 2016-2017: Visiting Researcher, University of Western Australia, Perth, Australia

Scientific and Professional Membership

  • European Association of Geoscientists and Engineers (EAGE)
  • Society of Exploration Geophysicists (SEG)
  • Associate Editor of SEG Geophysics Journal
  • Committee member of EAGE AI special community


  • Sep 2019 - Finalist for best application of AI, The DataSci & AI Awards
  • Jun 2017 - Subsurface Machine Learning Hackathon, Best Presentation Award.
  • Dec 2016 - Codess and Microsoft Scholarship for Professional Program in Data Science.
  • Aug 2016 - Australian Bicentennial Scholarship Award.

KAUST Affiliations

  • Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
  • Physical Science and Engineering Division (PSE)

Research Interests Keywords

Signal processing Geophysics Deep learning