摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据Ne wsRx记者从印度Uttrakhand发回的新闻报道，研究表明：“在储层特征描述和管理领域，地球物理测井的完整性和准确性至关重要。在10份测井中，由于钻井中的后勤和环境挑战，这些测井被缺失的部分或扭曲破坏。”这项研究的财政支持来自印度科学工程研究委员会(SERB)。为了解决这个问题，我们将测井数据预处理与先进的机器学习(ML)技术相结合，包括k近邻、支持向量机、决策树、随机森林、极梯度提升、高斯过程回归和人工神经网络，引入了协同方法，重点揭示了复杂的、复杂通过相关矩阵和F-检验预测显著性和等级的稳健分析，我们将该方法应用于印度Bhogpara Oil油田Lakadong-Therria组的电缆测井，证明了ML方法在可靠预测测井中的有效性，值得注意的是，我们的ML模型能够很好地利用伽玛射线、深部电阻率和测井资料预测容重测井。获得的高r平方相关系数（在训练阶段超过0.89，在测试阶段超过0.85）证明了我们预测的准确性。
Abstract
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. "
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