  Drilling boreholes for gas and oil extraction is an expensive process and
profitability strongly depends on characteristics of the subsurface. As
profitability is a key success factor, companies in the industry utilise well
logs to explore the subsurface beforehand. These well logs contain various
characteristics of the rock around the borehole, which allow petrophysicists to
determine the expected amount of contained hydrocarbon. However, these logs are
often incomplete and, as a consequence, the subsequent analyses cannot exploit
the full potential of the well logs.
  In this paper we demonstrate that Machine Learning can be applied to
\emph{fill in the gaps} and estimate missing values. We investigate how the
amount of training data influences the accuracy of prediction and how to best
design regression models (Gradient Boosting and neural network) to obtain
optimal results. We then explore the models' predictions both quantitatively,
tracking the prediction error, and qualitatively, capturing the evolution of
the measured and predicted values for a given property with depth. Combining
the findings has enabled us to develop a predictive model that completes the
well logs, increasing their quality and potential commercial value.
