首页|Findings from Wadia Institute of Himalayan Geology Update Understanding of Machi ne Learning (Missing Log Prediction Using Machine Learning Perspectives: a Case Study From Upper Assam Basin)
Findings from Wadia Institute of Himalayan Geology Update Understanding of Machi ne Learning (Missing Log Prediction Using Machine Learning Perspectives: a Case Study From Upper Assam Basin)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Uttrakhand, India, by Ne wsRx journalists, research stated, "In the field of reservoir characterization a nd management, the completeness and accuracy of geophysical logs are pivotal. Of ten, these logs are marred by missing segments or distortions due to logistical and environmental challenges in boreholes." Financial support for this research came from Science Engineering Research Board (SERB), India. The news correspondents obtained a quote from the research from the Wadia Instit ute of Himalayan Geology, "To address this issue, our study introduces synergist ic method combining log data preconditioning with advanced machine learning (ML) techniques-including k-nearest neighbors, support vector machine, decision tree , random forest, extreme gradient boosting, gaussian process regression, and art ificial neural networks. We focus on uncovering the complex, nonlinear relations hips inherent in geophysical logs through a robust analysis involving a correlat ion matrix and F-test for predictor significance and ranking. We applied this ap proach to the wireline logs of the Lakadong-Therria Formation in the Bhogpara oi l field, India, to demonstrate the effectiveness of ML in reliably predicting mi ssing logs. Notably, our ML models adeptly forecast bulk density logs using data from gamma-ray, deep resistivity, neutron porosity, and photoelectric factor lo gs. The high R-squared correlation coefficients achieved (R2 score: over 0.89 in training and 0.85 in testing phases) attest to the accuracy of our predictions. "
UttrakhandIndiaAsiaCyborgsEmergi ng TechnologiesMachine LearningWadia Institute of Himalayan Geology
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