Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study
This is an incremental comparison for reservoir engineers, offering a technical guide without new breakthroughs.
The study compared two data-driven models, the capacitance resistance model (CRM) and recurrent neural networks (RNN), for predicting well production in waterflood settings, finding that both learn reservoir behavior from historical data but no concrete performance numbers were provided.
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.