Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pr...Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.展开更多
背景心血管疾病是全球人类死亡的重要原因,其发病隐匿、病情复杂多变,预后不佳。早期识别并积极干预潜在危重患者对改善患者预后至关重要。目的对国内外心血管疾病风险早期预警评估工具研究进行范围综述,总结分析其评估内容及应用情况,...背景心血管疾病是全球人类死亡的重要原因,其发病隐匿、病情复杂多变,预后不佳。早期识别并积极干预潜在危重患者对改善患者预后至关重要。目的对国内外心血管疾病风险早期预警评估工具研究进行范围综述,总结分析其评估内容及应用情况,为我国心血管疾病患者早期预警评估工具的选择提供参考。方法以范围综述方法学框架为指导,系统检索中国知网、万方数据知识服务平台、维普网、中国生物医学文献数据库、PubMed、Web of Science、Cochrane Library、Embase、CINAHL、Scopus数据库。检索时限为建库至2023年5月。由2名研究者独立筛选文献和提取资料,从评估内容、研究对象、验证方法、信效度以及预测效能等方面进行分析。结果共纳入16篇文献,其中7篇关于评估工具的开发验证,9篇关于评估工具的本土化应用,涉及20个心血管疾病风险早期预警评估工具。分析结果表明,各评估工具均包含3~17个评估内容,其中出现频率较高的是年龄、收缩压、呼吸频率、血氧饱和度、心率、合并症、意识水平以及性别。2篇文献的信效度检验结果表明信度、效度良好,其他研究均缺少信效度评价。10篇文献报告了评估工具的曲线下面积(AUC),AUC为0.550~0.9269。结论心血管疾病风险早期预警评估工具种类多样,但质量仍有不足,缺少特异性评估工具。未来仍需要进一步验证现有工具的信效度,并结合疾病特征开发本土化且具备良好信效度的心血管专科早期预警评估工具。展开更多
基金The project is supported by CNPC Key Core Technology Research Projects(2022ZG06)received by Qing Wangproject funded by China Postdoctoral Science Foundation(2021M693508)received by Qing Wang.Basic Research and Strategic Reserve Technology Research Fund Project of Institutes directly under CNPC received by Qing Wang.
文摘Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.
文摘背景心血管疾病是全球人类死亡的重要原因,其发病隐匿、病情复杂多变,预后不佳。早期识别并积极干预潜在危重患者对改善患者预后至关重要。目的对国内外心血管疾病风险早期预警评估工具研究进行范围综述,总结分析其评估内容及应用情况,为我国心血管疾病患者早期预警评估工具的选择提供参考。方法以范围综述方法学框架为指导,系统检索中国知网、万方数据知识服务平台、维普网、中国生物医学文献数据库、PubMed、Web of Science、Cochrane Library、Embase、CINAHL、Scopus数据库。检索时限为建库至2023年5月。由2名研究者独立筛选文献和提取资料,从评估内容、研究对象、验证方法、信效度以及预测效能等方面进行分析。结果共纳入16篇文献,其中7篇关于评估工具的开发验证,9篇关于评估工具的本土化应用,涉及20个心血管疾病风险早期预警评估工具。分析结果表明,各评估工具均包含3~17个评估内容,其中出现频率较高的是年龄、收缩压、呼吸频率、血氧饱和度、心率、合并症、意识水平以及性别。2篇文献的信效度检验结果表明信度、效度良好,其他研究均缺少信效度评价。10篇文献报告了评估工具的曲线下面积(AUC),AUC为0.550~0.9269。结论心血管疾病风险早期预警评估工具种类多样,但质量仍有不足,缺少特异性评估工具。未来仍需要进一步验证现有工具的信效度,并结合疾病特征开发本土化且具备良好信效度的心血管专科早期预警评估工具。
