Artificial general intelligence (AGI), or strong AI, aims to replicate human-like cognitive abilities across diverse tasks and domains, demonstrating adaptability and learning like human intelligence. In contrast, weak AI refers to systems designed for specific tasks, lacking the broad cognitive flexibility of AGI. This paper introduces a novel approach to optimize oil production by integrating fundamental principles of AGI with geophysical data inversion and continuous monitoring techniques. Specifically, the study explored how AGI-inspired algorithms, combined with established reinforcement learning (RL) techniques, can enhance borehole electric/electromagnetic monitoring and reservoir fluid mapping technology. This integration aims to mitigate the risk of unwanted water invasion in production wells while optimizing oil extraction. The proposed methodology leverages real-time geophysical data analysis and automated regulation of oil production. The paper begins by outlining the key features of AGI and RL, and then discusses their application in electric/electromagnetic monitoring to define optimal production policies. The effectiveness of this approach was verified through synthetic tests, showing significant improvements in production efficiency, resource recovery, and environmental impact reduction.
Citation: Paolo Dell'Aversana. Reservoir geophysical monitoring supported by artificial general intelligence and Q-Learning for oil production optimization[J]. AIMS Geosciences, 2024, 10(3): 641-661. doi: 10.3934/geosci.2024033
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| [9] |
Linglong Du .
Long time behavior for the visco-elastic damped wave equation in |
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Artificial general intelligence (AGI), or strong AI, aims to replicate human-like cognitive abilities across diverse tasks and domains, demonstrating adaptability and learning like human intelligence. In contrast, weak AI refers to systems designed for specific tasks, lacking the broad cognitive flexibility of AGI. This paper introduces a novel approach to optimize oil production by integrating fundamental principles of AGI with geophysical data inversion and continuous monitoring techniques. Specifically, the study explored how AGI-inspired algorithms, combined with established reinforcement learning (RL) techniques, can enhance borehole electric/electromagnetic monitoring and reservoir fluid mapping technology. This integration aims to mitigate the risk of unwanted water invasion in production wells while optimizing oil extraction. The proposed methodology leverages real-time geophysical data analysis and automated regulation of oil production. The paper begins by outlining the key features of AGI and RL, and then discusses their application in electric/electromagnetic monitoring to define optimal production policies. The effectiveness of this approach was verified through synthetic tests, showing significant improvements in production efficiency, resource recovery, and environmental impact reduction.
The journal retracts the paper entitled "Decay estimates for the wave equation with partial boundary memory damping" [1].
After the article was published, the authors decided to withdraw the article.
This retraction was approved by the Editor in Chief of the journal Networks and Heterogeneous Media.
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Paolo Dell'Aversana. Reservoir geophysical monitoring supported by artificial general intelligence and Q-Learning for oil production optimization[J]. AIMS Geosciences, 2024, 10(3): 641-661. doi: 10.3934/geosci.2024033
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