Correlation path in stratigraphic well correlation: uncertainties and data quality

@inproceedings{caumon_correlation_RM2024,
 abstract = {Stratigraphic correlation between several one-dimensional borehole sections is generally achieved by expert-based interpretation and sedimentological reasoning to produce a best-case scenario. To gain productivity and make the process reproducible, automation methods defined since the 1980’s have mainly translated the correlation (or alignment) problem as an optimization task. Therefore, stratigraphic layering, which determines rock unit volumes, connectivity and ways to measure distances in geomodels, is most often deterministic. However, the stratigraphic correlation problem is highly non-unique, because of sparse sampling, lateral variability, complex interactions between tectonic and sedimentary processes and because the types of data and the stratigraphic principles are many. In this work, we discuss some of the computational strategies to sample this uncertainty. N-best solutions are a possible option for pairwise correlations but become computationally intractable when the number of stratigraphic sections increases. Moreover, they explore the space of solutions only locally around the optimum. In multi-well correlation, finding the global optimum itself is highly challenging when the number of sections and samples is large. We use a hierarchical and incremental correlation which guarantees the consistency of the solution and naturally prevents crossings. However, the correlation path has an impact on the solution, so we propose strategies to randomize this path based on well distances. This is achieved either using geographic distances between boreholes or distances coming from local pairwise correlations. We test these strategies on a synthetic benchmark data set generated from a forward stratigraphic model, and we evaluate the effect of the randomization strategies on the produced correlation outcomes. The proposed approach has implications to help find the optimal correlation, to generate a diverse but acceptable set of scenarios, and to analyze the spatial variability of the solution along boreholes, which may be a local indication of interpretation quality.},
 author = {Caumon, Guillaume and Antoine, Christophe},
 booktitle = {Proc. 2024 RING Meeting},
 language = {en},
 pages = {10},
 publisher = {ASGA},
 title = {Correlation path in stratigraphic well correlation: uncertainties and data quality},
 year = {2024}
}