Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction

Hana YAHIA, Bruno Figliuzzi, Florent Di Meglio, Gerbaud, Stephane Menand, Mohamed MAHJOUB

Abstract: 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.

Submission Length: Long submission (more than 12 pages of main content)

Changes Since Last Submission: Small changes in the conclusion.

Assigned Action Editor: ~Haoliang_Li2

Submission Number: 3790