06FORCE Eidsvik Sequential Bayesian methods for spatial on line pore pressure prediction from well l
Channel: Force Seminars
Date: 02/10/2018

Title: "Sequential Bayesian methods for spatial on-line pore-pressure prediction from well log data" 
Abstract:
We study the challenge of pore pressure prediction based on i) pre-drill understanding of the pore pressure distribution which is available from a 3D geological model for pressure build, and ii) well logs 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. For our case, the prior model is based on a Gaussian process fit from geological modeling of this field, while the likelihood model of well log data is assessed from data in an exploration well in the same area. Results are presented by re-playing a drilling situation in this context.
 Joint work with Jacopo Paglia, Arnt Grøver and Ane Lothe.