  We investigate an applicability of Bayesian-optimization (BO) to optimize
hyperparameters associated with support-vector-machine (SVM) in order to
classify facies using elastic properties derived from well data in the East
Central Graben, UKCS. The cross-plot products of the field dataset appear to be
successfully classified with non-linear boundaries. Although there are a few
factors to be predetermined in the BO scheme such as an iteration number to
deal with a trade-off between the prediction accuracy and the computational
cost, this approach effectively reduces possible human subjectivity connected
to the architecture of the SVM. Our proposed workflow might be beneficial in
resource-exploration and development in terms of subsurface objective technical
evaluations.
