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The relative permeabilities of CO2 and brine are important parameters that account for two-phase flow behavior, CO2 saturation distribution, and injectivity. CO2/brine relative permeability curves from the literature show low endpoint CO2 permeability values and high residual brine saturation values. These are the most distinguishing aspects of the CO2/brine relative permeability from oil/water and gas/oil. In this study, this aspect is investigated experimentally by employing a wide range of CO2 injection flow rates. As a result, all the measurements align with previous studies, having low endpoint relative permeability and high residual brine saturation values. They have obvious relationships with the changes in CO2 flow rates. As the CO2 flow rate increases, the endpoint relative permeability increases, the residual brine saturation decreases, and they converge to specific values. These imply that a high CO2 injection flow rate results in high displacement efficiency, but the improvement in efficiency decreases as the flow rate increases. The reasons are identified with the concept of the viscous and capillary forces, and their significance in the CO2 injection into a reservoir is analyzed.

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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.

No abstract