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.
Highlights
Production rates and flowing pressures in oil/gas production wells are essential information for planning field development and reservoir management to maximize recovery
Multiphase flow meters can enable production rates to be measured in real time [1]; in reality, owing to the high cost of flow meters, the common practice is to apply a back-allocation methodology based on the field total production measured through the high-pressure separator that connects all the production wells [2]
The tubing head pressure (THP) and bottom-hole pressure (BHP) are measured in real time using gauges installed at the wellhead and bottom-hole sites, respectively
Summary
Production rates and flowing pressures in oil/gas production wells are essential information for planning field development and reservoir management to maximize recovery. Multiphase flow meters can enable production rates to be measured in real time [1]; in reality, owing to the high cost of flow meters, the common practice is to apply a back-allocation methodology based on the field total production measured through the high-pressure separator that connects all the production wells [2]. This back-allocation method is commonly carried out based on the periodic measurements of the test separator that measures the production rate of each well. Data quality control can be performed using advanced techniques such as reservoir simulations or well modeling
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