Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study

2021/09/17 by Deepthi Sen, Sen, Deepthi
Computer Science · Engineering · Mathematics · #Enhanced Oil Recovery Techniques #Hydrocarbon exploration and reservoir analysis #Reservoir Engineering and Simulation Methods #cs.LG #stat.AP

paper · pdf · doi:10.48550/arxiv.2109.08779

for CRM module, see https://github.com/deepthisen/CapacitanceResistanceModel

arxiv created 2021/09/17 · arxiv updated 2021/09/21

Abstract

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.

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