Highlighting automated well-correlation variability by computing correlation graph distance

Computer-assisted multi-well correlation aims at computing a large set of possible stratigraphic correlation scenarios from the same input data. WeCo (an automated multi-well correlation software developed by the RING Team) uses an adapted version of the Dynamic Time Warping to classify the simulated realizations from the most likely (lowest correlation cost) to the less likely (highest correlation cost). This correlation cost is given by a cost function corresponding to a principle of correlation (e.g., lithostratigraphy, chronostratigraphy, etc.). The aim of this work is to define a consistent metric to compare well correlations generated by WeCo, and to classify them according to other criteria than the correlation cost. Since a correlation can be represented by an directed acyclic graph, whose nodes represent marker associations and edges represent transitions between marker associations, this work aims at applying the Hausdorff distance to compare two graphs structures. In this work, the use of the Hausdorff distance to compare multiple well correlations helps us to identify clusters of well correlations which have different costs, and to differentiate two well correlations having the same correlation cost.