Estimating uncertainties on net erosion from well‐log porosity data

Abstract

Estimating the amount of erosion experienced by a sedimentary basin during its geological 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 allows 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 effectively mapped into a probabilistic solution for net erosion; (b) posterior uncertainties 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 applicability 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 porosity data. Although appealing and successful with synthetic data, this strategy provides results which are strongly dependent on the calibration data and the geological history of the sediments sampled by the target well. Highlights• A probabilistic approach to estimate net erosion from well-log compaction data is presented. • Uncertainties in the compaction model are effectively propagated in the solution for net erosion. • The novel methodology is tested against synthetic data and real data from a well in the Barents Sea.

A Bayesian Approach for Thermal History Reconstruction in Basin Modeling

“…Indeed this is not possible from a single well. However, our estimates of Δ E are in agreement with previous studies that reported between 1.8 and 2.5 km of net erosion for the same area (Ktenas et al., 2018; Licciardi et al., 2019). Moreover, including porosity data in the analysis contributes to reduce the uncertainties associated with Δ E (see Figure 10a).…”

“…This specific location was chosen based on the good quality of available data and in particular of porosity data. In addition, estimates of net erosion in the area are available from previous studies (e.g., Ktenas et al, 2018;Licciardi et al, 2019) and will be compared with our results. The TI data consist of four AFT (each with a range of compositional groups, based on Cl content) and ten VR samples.…”

“…The compaction parameters for sandstone and siltstone are poorly known in this area, so we chose broad Gaussian prior distributions with mean values equal to those reported in (Sclater & Christie, 1980). For shale, we use the values obtained by Licciardi et al (2019) by fitting porosity data from a well in the Barents Sea considered to have not undergone erosion. Values of thermal conductivity (Table S2) were loosely based on previous studies (Gac et al, 2018;Klitzke et al, 2016) and to reflect their dependence on quartz content.…”

“…For shale, we use the values obtained by Licciardi et al. (2019) by fitting porosity data from a well in the Barents Sea considered to have not undergone erosion. Values of thermal conductivity (Table S2) were loosely based on previous studies (Gac et al., 2018; Klitzke et al., 2016) and to reflect their dependence on quartz content.…”

A Bayesian approach for thermal history reconstruction in basin modeling

“…Indeed this is not possible from a single well. However, our estimates of ΔE are in agreement with previous studies that reported between 1.8 and 2.5 km of net erosion for the same area (Ktenas et al, 2018;Licciardi et al, 2019). Moreover, including porosity data in the analysis contributes to reduce the uncertainties associated with ΔE (see Figure 10a).…”