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.
Cite
@article{arxiv.2109.08779,
title = {Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study},
author = {Deepthi Sen},
journal= {arXiv preprint arXiv:2109.08779},
year = {2021}
}
Comments
for CRM module, see https://github.com/deepthisen/CapacitanceResistanceModel