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• W3201156649 abstract "Natural gas plays a crucial role in sustaining the economic development. The production behavior of natural gas reservoir is significantly affected by the connecting aquifer. To investigate the aquifer and reservoir properties, the pressure buildup well test is conducted and the pressure transient data need to be analyzed. However, the pressure transient data analysis usually involves a procedure to build an analytical or numerical model to predict the pressure transient dynamics. With the existence of many historical records on the well test data and advancement of machine learning method, a data-driven method is proposed here to automate the well test data analysis of water-drive gas reservoir. The dataset generated from the previous well test records is used as the training set for the machine learning method. To identify the water invasion mode, the random forest classification method is introduced, and the discrete linear segment slopes are extracted from the pressure derivative curve as the feature. To predict the pressure transient dynamics, the random forest regression method is proposed to construct a projection between the aquifer/reservoir properties and the pressure transient curves. Once the water invasion mode is determined and the data-driven model to predict the pressure transient dynamic is constructed, the ensemble Kalman filter is used to estimate the aquifer/reservoir properties from the well testing data. The results show that the random forest classification method can accurately identify the water invasion mode and the random forest regression based ensemble Kalman filter method can estimate the aquifer/reservoir properties accurately with the reduced uncertainty." @default.
• W3201156649 created "2021-09-27" @default.
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• W3201156649 date "2022-01-01" @default.
• W3201156649 modified "2023-10-16" @default.
• W3201156649 title "An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method" @default.
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• W3201156649 doi "https://doi.org/10.1016/j.petrol.2021.109492" @default.
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