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Outputs (20)

Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks (2015)
Presentation / Conference Contribution
Oughton, R., Wooff, D., Swarbrick, R., & Hobbs, R. (2015). Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks. . https://doi.org/10.3997/2214-4609.201413296

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.

Joint Inversion. (2007)
Presentation / Conference Contribution
Hobbs, R., Jegen, M., Chen, J., & Heincke, B. (2007). Joint Inversion.

The Metropolis Algorithm (2006)
Presentation / Conference Contribution
Hobbs, R., Flecha, I., Rasmussen, T., Carbonell, R., & Danielsen, M. (2006). The Metropolis Algorithm.