摘要
由一名新闻记者-机器人与机器学习日报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道的主题。据新闻报道NewsRx记者在中国人民共和国成都的报道中说，“这很困难。”为解决油气井水泥护套被酸性气体腐蚀的问题，以及水泥护套中酸性气体的变化水泥环腐蚀后抗压强度(CS)是影响密封性能的关键水泥护套。在本研究中,我们使用了四种传统的机器学习(ML)算法-人工神经网络算法神经网络(ANN)、支持向量机回归(SVR)、极限学习机(ELM)和随机森林（RF）-建立水泥石CS预测模型。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting from Chengdu, People’s Republ ic of China, by NewsRx journalists, research stated, “It is difficultto solve t he problem that the cement sheath of oil and gas wells is corroded by acid gas, and the change incompressive strength (CS) of the cement sheath after corrosion is the key to affecting the sealing capacityof the cement sheath. In this stud y, we used four traditional machine learning (ML) algorithms-artificialneural n etwork (ANN), support vector machine regression (SVR), extreme learning machine (ELM), andrandom forest (RF)-to establish a model for predicting the CS of corr oded cement stone.”
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