eDrilling:在 SPE 国际钻井会议上发表卡管事故预测论文

通过利用大量数据并将其与人工智能的力量相结合,我们构建了智能、直观的模型来预测卡管事件的发生。这些模型与统计模型一起工作,以 24/7 方式监控操作并发现人眼错过的错误。通过能够及早发现事故迹象,我们可以避免事故发生。

钻井过程更加高效、安全地完成,从而减少能源消耗。它还可以减少事故发生时可能发生的污染。

技术演示将于 3 月 7 日星期三在德克萨斯州加尔维斯顿岛会议中心举行。在这里注册

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eDrilling: to Present a Paper on Prediction of Stuck Pipe Incidents at the SPE International Drilling Conference

By harnessing large amounts of data and combining it with the power of artificial intelligence, we build smart, intuitive models that predict the occurrence of stuck pipe incidents. These models work alongside statistical models to monitor operations 24/7 and notice errors that the human eye miss. By being able to detect signs of mishap early enough, we avoid it.

The drilling process is completed more efficiently and safely, thereby reducing the energy spent. It also reduces pollution that otherwise would occur if an incident did take place.

Technical presentation is to be held on Wednesday, March 7th, at the Galveston Island Convention Center, Texas. Register here.

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