Restoration of Missing Pressures in a Gas Well Using Recurrent Neural Networks with Long Short-Term Memory Cells

Restoration of Missing Pressures in a Gas Well Using Recurrent Neural Networks with Long Short-Term Memory Cells

This study proposes a data-driven method based on recurrent neural networks (RNNs) with long short-term memory (LSTM) cells for restoring missing pressure data from a gas production well. Pressure data recorded by gauges installed at the bottom hole and wellhead of a production well often contain ab...

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Bibliographic Details
Main Authors: Seil Ki, Ilsik Jang, Booho Cha, Jeonggyu Seo, Oukwang Kwon
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Energies
Subjects:
RNN
LSTM
recurrent neural network
long short-term memory
missing pressure data
restoration
Online Access:https://www.mdpi.com/1996-1073/13/18/4696

Internet

https://www.mdpi.com/1996-1073/13/18/4696

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