论文标题
通过机器学习预测井钻的异常事件
Forecasting the abnormal events at well drilling with machine learning
论文作者
论文摘要
我们提出了一种数据驱动和物理知识的算法，用于钻探事故预测。核心机器学习算法使用代表时间序列的钻井遥测中的数据。我们已经开发了时间序列的功能袋表示，使该算法能够实时预测六种类型的钻探事故的概率。机器学习模型经过了100种不同俄罗斯石油和天然气井的125次钻探事故的培训。验证表明，该模型可以预测假阳性速率为40％的70％的钻井事故。该模型介绍了井结构中部分预防钻井事故。
We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning algorithm uses the data from the drilling telemetry representing the time-series. We have developed a Bag-of-features representation of the time series that enables the algorithm to predict the probabilities of six types of drilling accidents in real-time. The machine-learning model is trained on the 125 past drilling accidents from 100 different Russian oil and gas wells. Validation shows that the model can forecast 70% of drilling accidents with a false positive rate equals to 40%. The model addresses partial prevention of the drilling accidents at the well construction.
