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A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification

Oughton, Rachel H.; Wooff, David A.; Hobbs, Richard W.; Swarbrick, Richard E.; O'Connor, Stephen A.

A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification Thumbnail


David A. Wooff

Richard W. Hobbs

Richard E. Swarbrick

Stephen A. O'Connor


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 the multivariate nature of the system. We propose the pore pressure sequential dynamic Bayesian network (PP SDBN) as an appropriate solution to both these issues. The PP SDBN models the relationships between quantities in the pore pressure system, such as pressures, porosity, lithology and wireline log data, using conditional probability distributions based on geophysical relationships to capture our uncertainty about these variables and the relationships between them. When wireline log data is given to the PP SDBN, the probability distributions are updated, providing an estimate of pore pressure along with a probabilistic measure of uncertainty that reflects the data acquired and our understanding of the system. This is the advantage of a Bayesian approach. Our model provides a coherent statistical framework for modelling the pore pressure system. The specific geophysical relationships used can be changed to better suit a particular setting, or reflect geoscientists’ knowledge. We demonstrate the PP SDBN on an offshore well from West Africa. We also perform a sensitivity analysis, demonstrating how this can be used to better understand the working of the model and which parameters are the most influential. The dynamic nature of the model makes it suitable for real time estimation during logging while drilling. The PP SDBN models shale pore pressure in shale rich formations with mechanical compaction as the overriding source of overpressure. The PP SDBN improves on existing methods since it produces a probabilistic estimate that reflects the many sources of uncertainty present.


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.

Journal Article Type Article
Acceptance Date Oct 31, 2017
Online Publication Date Nov 2, 2017
Publication Date 2018-03
Deposit Date Aug 19, 2015
Publicly Available Date Nov 10, 2017
Journal Geophysics
Print ISSN 0016-8033
Electronic ISSN 1942-2156
Publisher Society of Exploration Geophysicists
Peer Reviewed Peer Reviewed
Volume 83
Issue 2
Pages D27-D39


Accepted Journal Article (3.7 Mb)

Copyright Statement
© 2018 Society of Exploration Geophysicists. All rights reserved

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