Dr Rachel Oughton's Outputs (2)

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.

Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks (2015)
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.