基于深度AUC最大化算法的井漏风险预测

井漏是石油天然气钻井过程中经常面临的一项重要挑战,它的发生会极大的降低钻井效率,增加钻井成本。运用人工智能技术实现井漏风险的精准预测具有重要意义。文章将钻井液泄露分类问题转化为不平衡分类问题。类别间的非均衡性和钻探特征间缺乏高度相关性,对传统的深度学习模型提出了挑战。在这种情况下,传统的准确率度量很难正确评估模型性能。此外,研究引入了一种称为FAUC-S的深度AUC最大化(DAM)算法实现井漏风险预测,该算法是通过关注困难样本的AUC损失来训练组合深度学习模型。实验中还应用了一些经典的深度学习模型实现井漏风险的分类。实验结果表明,与其他3个模型相比,FAUC-S获得了最高的精确度、召回率和F 1分数,实验验证了FAUC-S模型优越的分类性能。因此,该深度模型的成功应用可以有效地帮助钻井队解决钻井问题。

Is the Renminbi a Global Currency?An Evaluation Based on Offshore Foreign Exchange Market Trading Patterns

This article provides a new framework to evaluate the status of Renminbi internationalization.It proposes that the trading patterns of a currency in the global foreign exchange market embody the currency’s position in the international monetary system.Based on foreign exchange trading data provided by CLS Group,the article constructs a ranking of major international currencies including the Renminbi.It finds that the Renminbi shares more similarities in foreign exchange trading patterns with the established global currencies like the US dollar and the Euro than with those regional currencies.The article also explores the policy implications that the new evaluation approach provides.