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

Seil Ki, Ilsik Jang, Booho Cha, Jeonggyu Seo and Oukwang Kwon
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Seil Ki: E&P Technical Center, Korean National Oil Corporation, Ulsan 44538, Korea
Ilsik Jang: Department of Energy and Resources Engineering, Chosun University, Gwangju 61452, Korea
Booho Cha: E&P Domestic Business Unit, Korean National Oil Corporation, Ulsan 44538, Korea
Jeonggyu Seo: E&P Technical Center, Korean National Oil Corporation, Ulsan 44538, Korea
Oukwang Kwon: E&P Technical Center, Korean National Oil Corporation, Ulsan 44538, Korea

Energies, 2020, vol. 13, issue 18, 1-19

Abstract: 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 abnormal or missing values as a result of gauge malfunctions, noise, outliers, and operational instability. RNNs employing LSTM cells to prevent long-term memory loss have been widely used to predict time series data. In this study, an RNN with the LSTM method was used to restore abnormal or missing wellhead and bottom-hole pressures in three intervals within a production sequence of more than eight years in duration. The pressure restoration was performed using various input features for RNNs with LSTM models based on the characteristics of the available data. It was carried out through three sequential processes and the results were acceptable with a mean absolute percentage error no more than 5.18%. The reliability of the proposed method was verified through a comparison with the results of a physical model.

Keywords: RNN; LSTM; recurrent neural network; long short-term memory; missing pressure data; restoration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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