  Geoscience domain experts traditionally correlate formation tops in the
subsurface using geophysical well logs (known as well-log correlation) by-hand.
Based on individual well log interpretation and well-to-well comparisons, these
correlations are done in the context of depositional models within a
stratigraphic framework. Recently, many researchers have focused on automatic
well-log correlation using a variety of warping algorithms that measure well
similarity, and both unsupervised and supervised machine learning methods that
assign categorical labels based on known tops in many other wells. These
methods require a standardized suite of digital well logs (i.e. gamma ray logs
for every well) along with the depth to the top of the formations, which might
not be available in many cases. Herein, we propose a method that does not use
geophysical well logs for correlation, but rather uses already picked tops in
multiple wells to recommend the depth to the remaining unpicked tops in the
wells. This recommender system calculates the depth to all formation tops in
all the wells for two different datasets in two different basins. The Teapot
Dome dataset is composed of lithostratigraphic formation tops, and the
Mannville Group dataset is composed of sequence-stratigraphic (representing
multiple lithologic groups within a stratigraphic unit) formation tops. For the
demonstration, mean absolute error and root mean squared error of four-fold
cross-validation compares the recommender system predictions to the ground
truth human interpretations. The recommender system is competitive and often
outperforms state of the art spline interpolation methods. Lastly, increasing
the size of the training dataset decreases the prediction error, and that
variance in error decreases with increasing formation tops picked in each
formation and well for the lithostratigraphic top picks.
