Outputs (6)

Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology (2018)
Journal Article
Li, Y., Zhang, S., Hobbs, R., Caiado, C., Sproson, A., Selby, D., & Rooney, A. (2019). Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology. Science Bulletin, 64(3), 189-197. https://doi.org/10.1016/j.scib.2018.12.019

Geochronology is essential for understanding Earth’s history. The availability of precise and accurate isotopic data is increasing; hence it is crucial to develop transparent and accessible data reduction techniques and tools to transform raw mass sp... Read More about Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology.

A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification (2017)
Journal Article
Oughton, R. H., Wooff, D. A., Hobbs, R. W., Swarbrick, R. E., & O'Connor, S. A. (2018). A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification. Geophysics, 83(2), D27-D39. https://doi.org/10.1190/geo2016-0566.1

Pore-pressure estimation is an important part of oil-well drilling, since drilling into unexpected highly pressured fluids can be costly and dangerous. However, standard estimation methods rarely account for the many sources of uncertainty, or for th... Read More about A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification.

Markov Chain Monte Carlo inversion of temperature and salinity structure of an internal solitary wave packet from marine seismic data (2016)
Journal Article
Tang, Q., Hobbs, R., Zheng, C., Biescas, B., & Caiado, C. (2016). Markov Chain Monte Carlo inversion of temperature and salinity structure of an internal solitary wave packet from marine seismic data. Journal of Geophysical Research: Oceans, 121(6), 3692-3709. https://doi.org/10.1002/2016jc011810

Marine seismic reflection technique is used to observe the strong ocean dynamic process of nonlinear internal solitary waves (ISWs or solitons) in the near-surface water. Analysis of ISWs is problematical because of their transient nature and limitat... Read More about Markov Chain Monte Carlo inversion of temperature and salinity structure of an internal solitary wave packet from marine seismic data.

Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well (2015)
Report
Oughton, R., Wooff, D., Hobbs, R., O'Connor, S., & Swarbrick, R. (2015). Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well. [No known commissioning body]

Pore pressure estimation is a crucial yet difficult problem in the oil industry. If unexpected overpressure is encountered while drilling it can result in costly challenges and leaked hydrocarbons. Prediction methods often use empirical porosity-base... Read More about Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well.

Bayesian Strategies to Assess Uncertainty in Velocity Models (2012)
Journal Article
Caiado, C. C., Goldstein, M., & Hobbs, R. W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis, 7(1), 211-234. https://doi.org/10.1214/12-ba707

Quantifying uncertainty in models derived from observed seismic data is a major issue. In this research we examine the geological structure of the sub-surface using controlled source seismology which gives the data in time and the distance between th... Read More about Bayesian Strategies to Assess Uncertainty in Velocity Models.

Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks
Presentation / Conference Contribution
Oughton, R., Wooff, D., Swarbrick, R., & Hobbs, R. (2015, June). Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks. Presented at 77th EAGE Conference & Exhibition 2015 : Earth Science for Energy and Environment., Madrid, Spain

Pore pressure prediction is vital when drilling a well, as unexpected overpressure can cause drilling challenges and uncontrolled hydrocarbon leakage. Predictions often use porosity-based techniques, relying on an idealised compaction trend and using... Read More about Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks.