电容电阻模型与循环神经网络在井连通性评价中的对比研究
Capacitance Resistance Model and Recurrent Neural Network for Well Connectivity Estimation : A Comparison Study
摘要：
在这份报告中，两个常用的数据驱动模型预测油井产量注水设置：电容电阻模型（CRM）和递归神经网络（RNN）进行了比较。这两个模型都是完全数据驱动的，旨在从历史数据中了解注水过程中的储层动态。此报告作为CRM模型的Python实现的技术指南，可从相关的GitHub存储库中获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
