  The main task in oil and gas exploration is to gain an understanding of the
distribution and nature of rocks and fluids in the subsurface. Well logs are
records of petro-physical data acquired along a borehole, providing direct
information about what is in the subsurface. The data collected by logging
wells can have significant economic consequences, due to the costs inherent to
drilling wells, and the potential return of oil deposits. In this paper, we
describe preliminary work aimed at building a general framework for well log
prediction.
  First, we perform a descriptive and exploratory analysis of the gaps in the
neutron porosity logs of more than a thousand wells in the North Sea. Then, we
generate artificial gaps in the neutron logs that reflect the statistics
collected before. Finally, we compare Artificial Neural Networks, Random
Forests, and three algorithms of Linear Regression in the prediction of missing
gaps on a well-by-well basis.
