Rachel Oughton, PhD Durham University
Assistant Professor in Statistics in the Department of Mathematical Sciences
Department of Mathematical Sciences
- Applied statistics
- Bayesian Statistics
- Oughton, R.H., Wooff, D.A., Swarbrick, R.E. & Hobbs, R.W. (2015), Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks. Proceedings of 77th EAGE Conference & Exhibition 2015: Earth Science for Energy and Environment, 77th EAGE Conference & Exhibition 2015: Earth Science for Energy and Environment. Madrid, Spain, European Association of Geoscientists and Engineers (EAGE), Th N114 01.
- Oughton, Rachel H., Wooff, David A., Hobbs, Richard W., Swarbrick, Richard E. & O'Connor, Stephen A. (2018). A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification. Geophysics 83(2): D27-D39.
- Oughton, Rachel H. & Craig, Peter S. (2016). Hierarchical Emulation: a method for modeling and comparing nested simulators. SIAM/ASA Journal on Uncertainty Quantification 4(1): 495-519.
- Oughton, Rachel H., Wooff, David A. & O'Connor, Stephen A. (2014). A Bayesian shifting method for uncertainty in the open-hole gamma-ray log around casing points. Petroleum Geoscience 20(4): 375-391.
- Oughton, R.H., Wooff, D.A., Hobbs, R.W., O'Connor, S.A. & Swarbrick, R.E. (2015). Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well. Petroleum Geostatistics 2015. Biarritz, France, European Association of Geoscientists and Engineers (EAGE).