Estimating uncertainties on net erosion from well‐log porosity data

• Published in: Basin Research
• Main Authors: Licciardi, Andrea, Gallagher, Kerry, Clark, S.
• Other Authors: Géosciences Rennes (GR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Equinor Research
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; Barents Sea
• Online Access: https://insu.hal.science/insu-02476846; https://doi.org/10.1111/bre.12366

International audience Estimating the amount of erosion experienced by a sedimentary basin during its geo-logical history plays a key role in basin modelling. In this paper, we present a novel probabilistic approach to estimate net erosion from porosity–depth data from a single well. Our approach uses a Markov chain Monte Carlo algorithm which readily al-lows us to deal with imprecise knowledge of the lithology‐dependent compaction parameters in a joint inversion scheme using multiple lithologies. The results using synthetic data highlight the advantages of our approach over conventional techniques for net erosion estimation: (a) uncertainties on compaction parameters can be effec-tively mapped into a probabilistic solution for net erosion; (b) posterior uncertain-ties are easy to quantify; (c) the joint inversion scheme can automatically reconcile porosity data from different lithologies. Our results also underscore the critical role of prior assumptions on controlling the retrieved estimates for net erosion. Using real data from a well in the Barents Sea, we simulate three possible scenarios of variable prior assumptions on compaction parameters to demonstrate the general applicabil-ity of our approach. Strong prior assumptions on the compaction parameters led to unrealistic estimates of net erosion for the target well, indicating the assumptions are probably inappropriate. Our preferred strategy for this dataset is to include additional data to constrain the normal compaction trend of the sediments. This provides a net erosion estimate for the target well of about 2300 m with a standard deviation of 140 m which is in line with previous studies. Finally, we discuss potential guidelines to deal with real applications in which data from normally compacted sediments are not available. One is to use our algorithm as a hypothesis‐testing tool to evaluate the results under a large set of assumed compaction parameters. A second is to infer compaction parameters and net erosion simultaneously from the target well ...