Assessment of Reservoir Uncertainty for Reservoir Production Using a Deep-Learning Technique > Petroleum & Marine Research > R&D Activities > KOREA INSTITUTE OF GEOSCIENCE AND MINERAL RESOURCES
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KOREA INSTITUTE OF GEOSCIENCE AND MINERAL RESOURCES
THE WORLD'S LEADING RESEARCH
INSTITUTE OF GEOSCIENCE

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ASSESSMENT OF RESERVOIR UNCERTAINTY FOR RESERVOIR PRODUCTION USING A DEEP-LEARNING TECHNIQUE

Traditional reservoir uncertainty assessments require long simulation times and considerable amounts of static and dynamic data. Recently, machine-learning algorithms have been utilized in the petroleum industry for efficient predictions of future production levels. These methods help to reduce the number of reservoir simulations and manual processing by experts significantly. Currently, we are focusing on the following research items: feature extraction algorithms such as autoencoders and time-series data analysis of, for instance, recurrent neural networks (Fig. 1).


Fig. 1. Example of an efficient uncertainty estimation using a deep-learning algorithm. Fig. 1. Example of an efficient uncertainty estimation using a deep-learning algorithm.

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