踢球和失循环事件是非生产时间的主要原因。在没有良好的流入和流出传感器的情况下，坑体积趋势为流入和损失检测提供了最佳可能性，但由于没有监测或感测到坑的外部泥浆添加和移除，因此会发生错误。在整篇论文中，作者介绍了一种方法，该方法使用贝叶斯网络将时间序列数据中检测到的趋势与司钻备忘录的自然语言处理所识别的事件进行聚合，从而显着提高井涌和井漏检测的准确性和鲁棒性。
该项目的目标是开发一个模型，利用现成的实时地面信号来检测和量化井涌和失循环事件。该模型使用钻井备忘录来补充实时地面信号。
Kick and lost-circulation events are major contributors to nonproductive time. In the absence of good flow-in and flow-out sensors, pit-volume trends offer the best possibility for influx and loss detection, but errors occur because external mud addition and removal to the pits is not monitored or sensed. In the complete paper, the authors introduce a method that uses a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing of driller memos, thereby improving significantly the accuracy and robustness of kick and lost-circulation detection.
The objective of this project was to develop a model to detect and quantify kick and lost-circulation events using readily available real-time surface signals. This model used drilling memos to supplement real-time surface signals.
