Robotics & Machine Learning Daily News2024,Issue(MAY.1) :76-77.

Studies from China University of Petroleum Yield New Data on Machine Learning (A pplication of the Dynamic Transformer Model With Well Logging Data for Formation Porosity Prediction)

Robotics & Machine Learning Daily News2024,Issue(MAY.1) :76-77.

Studies from China University of Petroleum Yield New Data on Machine Learning (A pplication of the Dynamic Transformer Model With Well Logging Data for Formation Porosity Prediction)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. Accordingto news reporting from Qingdao, People’ s Republic of China, by NewsRx journalists, research stated, “Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evalu ating thepermeability and fluid migration performance of underground rocks. In order to overcome the limitationsof traditional logging porosity interpretation methods in the face of geological complexity and nonlinearrelationships, the D ynamic Transformer model in machine learning was introduced in this study, aimin gto improve the accuracy and generalization ability of logging porosity predict ion.”

Key words

Qingdao/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/China University of Petrole um

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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