首页|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)
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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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.”
QingdaoPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningChina University of Petrole um
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