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Statistical Modelling for Online Pore Pressure Prediction from Well LogsNormal access

Authors: J. Paglia, J. Eidsvik, A. Grøver and A.E. Lothe
Event name: 80th EAGE Conference and Exhibition 2018
Session: Poster: Geopressure A
Publication date: 11 June 2018
DOI: 10.3997/2214-4609.201800915
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 543.6Kb )
Price: € 20

Summary:
The challenge of pore pressure prediction near a well is studied. Pre-drill understanding of the pore pressure distribution is available from a 3D geological model for pressure build-up and release using a basin modelling approach, and this is updated when well logs are gathered while drilling. Sequential Bayesian methods are used to conduct online pore pressure prediction, with associated uncertainty quantification. Spatial modeling of pore pressure variables means that the data at one well depth location will also be informative of the pore pressure variables at other depths and lateral locations. A workflow is exemplified using real data. The prior model is based on a Gaussian process fitted from geological modeling of this field, while the likelihood model of well log data is assessed by an exploration well in the same area. Results are presented by re-playing a drilling situation in this context.


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