  In this report, two commonly used data-driven models for predicting well
production under a waterflood setting: the capacitance resistance model (CRM)
and recurrent neural networks (RNN) are compared. Both models are completely
data-driven and are intended to learn the reservoir behavior during a water
flood from historical data. This report serves as a technical guide to the
python-based implementation of the CRM model available from the associated
GitHub repository.
