Uncertainty Quantification of Real-Time Pore Pressure Models Caused by Normal Compaction Trendline Setting

Abstract Pore pressure related wellbore instability is a recognized drilling challenge requiring attention as early as possible to mitigate and remediate severe drilling hazards such as kicks. Methods exist to quantify the effects of certain input parameters on pore pressure models, but the effect of the different, partly manual stages in pore pressure modeling on the uncertainty has rarely been addressed in the past. This paper highlights uncertainty sources associated with the basic stages in pore pressure modeling: shale discrimination using the gamma ray log and definition of the normal compaction trend line, either manually or using a regression analysis. Furthermore, filtering of the data prior to trend line definition may also add to some amount of uncertainty. These uncertainties in pore pressure prediction introduced by the modeling stages is demonstrated on an example data set from an offshore well. In particular, we show that applying different shale discrimination approaches can introduce pore pressure variations. Furthermore, a variation in depth intervals over which the trend line is automatically determined by linear regression can add more uncertainty to the pore pressure model. An approach is presented to quantify pore pressure uncertainty by automatically analyzing the variation in normal compaction trend lines. The application of the proposed method aims to constrain and quantify the error associated in modeling pore pressure in real-time, based on surface and subsurface (MWD and LWD) data. Modeling both pore pressure and its uncertainty creates additional value for safer drilling, because rig and remote monitoring personnel are made aware of a dynamic updated pore pressure range in addition to the possible pressure regimes interpreted during pre-drill modeling.