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

TL;DR

This study evaluates domain generalization techniques like ADG and IRM for predicting the Stick-Slip Index from time series drilling data, demonstrating improved accuracy and event detection over baseline models, with transfer learning further enhancing results.

Contribution

The paper compares domain generalization methods for time series drilling data and shows ADG's superiority in predicting torsional vibrations across different wells.

Findings

ADG improves performance by 10% over baseline

Severe event detection increased to 60% with ADG

Transfer learning enhances model accuracy

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.