Quantification of uncertainty in a multistage/multiparameter modeling workflow: Pore pressure from geophysical well logs

Abstract

Calculating subsurface pressures and predicting overpressured zones, in particular for safe drilling operations, is an integrated approach based on data and assumptions from various sources. Uncertainties arise from the input data and assumptions, but also from the pore-pressure modeling workflow including shale discrimination and the definition of a normal compaction trend line. These stages are usually performed manually by an expert and are prone to subjective, human interpretation. The quantification of pore-pressure uncertainty associated with the manual modeling stages is, therefore, challenging. Algorithms were developed to account for and quantify the resulting uncertainty and enable automated user support of at least parts of the workflow, thus introducing more objectivity into the modeling steps. The first algorithm performs a statistical analysis on gamma ray logs to discriminate between shale and nonshale formations. The second algorithm calculates a series of normal compaction trend lines from porosity-indicating logs from which average pore-pressure models and uncertainty envelopes can be determined. Furthermore, the behavior of trend-line envelopes from the series of trend lines was quantified by a parameter [Formula: see text], which turned out to become constant in the overpressure region. The algorithms were applied to 23 data sets from different regions worldwide. Pore-pressure uncertainty was identified to be in the range of up to 8% for shale discrimination and less than 20% for normal compaction trend-line setting. In addition, pore-pressure uncertainty in the overpressure zone correlated with the [Formula: see text]-factor, which can be used to estimate pore-pressure uncertainty at greater depth from while-drilling measurements in the normal compaction zone. The results also exemplify regional uncertainty variations, which imply that modeling parameters need to be adjusted for specific regions. Moreover, the examples demonstrate that automated algorithms are beneficial methods to add objectivity and reproducibility to the modeling procedure.