This paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at the drill bit. The study aims to develop a robust regression model that can generalize across domains by training on 60 second labeled sequences of 1 Hz surface drilling data to predict the SSI. The model is tested in wells that are different from those used during training. To fine-tune the model architecture, a grid search approach is employed to optimize key hyperparameters. A comparative analysis of the Adversarial Domain Generalization (ADG), Invariant Risk Minimization (IRM) and baseline models is presented, along with an evaluation of the effectiveness of transfer learning (TL) in improving model performance. The ADG and IRM models achieve performance improvements of 10% and 8%, respectively, over the baseline model. Most importantly, severe events are detected 60% of the time, against 20% for the baseline model. Overall, the results indicate that both ADG and IRM models surpass the baseline, with the ADG model exhibiting a slight advantage over the IRM model. Additionally, applying TL to a pre-trained model further improves performance. Our findings demonstrate the potential of domain generalization approaches in drilling applications, with ADG emerging as the most effective approach.

翻译：本文对钻井背景下时间序列数据的领域泛化技术进行了全面比较，重点关注连续粘滑指数的预测——这是评估钻头处井下扭转振动的关键指标。该研究旨在通过训练60秒标记的1Hz地面钻井数据序列来预测SSI，从而开发一个能够跨领域泛化的鲁棒回归模型。模型在与训练所用井不同的测试井中进行评估。为优化模型架构，采用网格搜索方法对关键超参数进行调优。研究对比分析了对抗性领域泛化模型、不变风险最小化模型与基线模型的性能，并评估了迁移学习在提升模型表现方面的有效性。ADG和IRM模型相较于基线模型分别实现了10%和8%的性能提升。最重要的是，严重事件检测率达到60%，而基线模型仅为20%。总体而言，结果表明ADG和IRM模型均优于基线模型，其中ADG模型较IRM模型略有优势。此外，在预训练模型上应用迁移学习能进一步提升性能。我们的研究结果证明了领域泛化方法在钻井应用中的潜力，其中ADG展现出最佳效果。