公开(公告)号：US08752648B2

公开(公告)日：2014-06-17

申请号：US13883822

申请日：2012-10-26

申请人： Thomas Goebel ,  Roberto Valerio Molina ,  Ricardo Vilalta ,  Kinjal Dhar Gupta

发明人： Thomas Goebel ,  Roberto Valerio Molina ,  Ricardo Vilalta ,  Kinjal Dhar Gupta

IPC分类号： E21B45/00 ,  E21B44/00 ,  G06N3/08 ,  E21B41/00 ,  G06N99/00

CPC分类号： E21B41/0092 ,  E21B44/00 ,  G06K9/6292 ,  G06N3/08 ,  G06N99/005

摘要： Predicting a drill string stuck pipe event. At least some of the illustrative embodiments are methods including: receiving a plurality of drilling parameters from a drilling operation; applying the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.

摘要翻译： 预测钻柱卡住管道事件。 至少一些说明性实施例是一种方法，包括：从钻井操作接收多个钻孔参数; 将所述多个钻孔参数应用于包括并行操作的至少三个机器学习算法的整体预测模型，每个机器学习算法基于所述多个钻孔参数中的至少一个来预测未来卡箍管事件的发生概率 综合预测模型基于每个机器学习算法的未来卡通管道事件的发生概率创建组合概率; 并且向钻井操作者提供未来卡住管道事件的可能性的指示，基于组合概率的指示。

公开(公告)号：US20140110167A1

公开(公告)日：2014-04-24

申请号：US13883822

申请日：2012-10-26

申请人： Thomas Goebel ,  Roberto Valerio Molina ,  Ricardo Vilalta ,  Kinjal Dhar Gupta

发明人： Thomas Goebel ,  Roberto Valerio Molina ,  Ricardo Vilalta ,  Kinjal Dhar Gupta

IPC分类号： E21B41/00 ,  G06N99/00 ,  G06N3/08 ,  E21B44/00

CPC分类号： E21B41/0092 ,  E21B44/00 ,  G06K9/6292 ,  G06N3/08 ,  G06N99/005

摘要： Predicting a drill string stuck pipe event. At least some of the illustrative embodiments are methods including: receiving a plurality of drilling parameters from a drilling operation; applying the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.

摘要翻译： 预测钻柱卡住管道事件。 至少一些说明性实施例是一种方法，包括：从钻井操作接收多个钻孔参数; 将所述多个钻孔参数应用于包括并行操作的至少三个机器学习算法的整体预测模型，每个机器学习算法基于所述多个钻孔参数中的至少一个来预测未来卡箍管事件的发生概率 综合预测模型基于每个机器学习算法的未来卡通管道事件的发生概率创建组合概率; 并且向钻井操作者提供未来卡住管道事件的可能性的指示，基于组合概率的指示。