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-based methods such as the Eaton ratio method, requiring an idealised normal compaction trend and using a single wireline log as a proxy for porosity. Such methods do not account for the complex and multivariate nature of the system, or for the many sources of uncertainty. We propose a Bayesian network approach for modelling pore pressure, using conditional probability distributions to capture the joint behaviour of the quantities in the system (such as pressures, porosity, lithology, wireline logs). These distributions allow the inclusion of expert scientific information, for example a compaction model relating porosity to vertical effective stress and lithology is central to the model. The probability distribution for each quantity is updated in light of data, producing a prediction with uncertainty that takes into account the whole system, knowledge and data. Our method can be applied to a setting where an offset well is used to learn about the compaction behaviour of the planned well, and we demonstrate this with two wells from the Magnolia field.


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