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 porosityindicating 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 Q, 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 trendline setting. In addition, pore-pressure uncertainty in the overpressure zone correlated with the Q-factor, which can be used to estimate pore-pressure uncertainty at greater depth from whiledrilling 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.

Predrill prediction of subsalt pore pressure from seismic impedance

“…The magnitude of the above estimated error seems to be on the low end of 5% to 20% reported by Wessling et al (2013) for predictions based on sonic-and resistivity-log data using Eaton's method. The well-log-based method might not have uncertainties in estimating velocity or resistivity, but uncertainties are involved in estimating effective stress, overburden stress, and the normal compaction trend of sediments.…”

A multi-proxy approach to detect the pore pressure and the origin of overpressure in sedimentary basins: An example from the Gulf of Suez rift basin

“…Frontiers in Earth Science frontiersin.org 03 Wessling et al, 2013;Radwan et al, 2019b) and basin modeling (e.g., Nordgård Bolås et al, 2004;Radwan et al, 2020a;Li et al, 2022) are used to predict and estimate pore pressure in frontier sedimentary basins. Drilling parameters and well logs can be used for pressure estimation while drilling (e.g., Mouchet and Mitchele, 1989;Zhang, 2011;Peters et al, 2017;Bourgoyne et al, 1991;Alam et al, 2019).…”

Modelling of Formation Pore Pressure Inversion during Tight Reservoir Drilling

“…Currently, formation pore pressure models are commonly based on seismic and logging data, and the uncertainty within these models is influenced primarily by widely used model coefficients and measurement accuracy [1][2][3][4]. Inaccurate prediction and underestimation of formation pore pressure typically lead to pressure underbalance in an open hole section of the well, causing fluid to enter the wellbore.…”