Time-Series Data Augmentation Techniques for Improving Automated Drilling Dysfunction Classifiers
Bibliographic record
Abstract
Abstract Detecting drilling dysfunctions from surface data is not always easy as downhole vibrations tend to get damped before they reach surface sensors. Building machine learning models to recognize patterns in the surface data requires vibration signals captured by downhole sensors for training purposes. Such datasets are not widely available and therefore a methodology to expand these datasets is highly desirable. This work explores ways to utilize data augmentation to artificially diversify and increase datasets to build better models. Stick-slip (including full-stick), bit bounce, whirl, and bit balling are the primary dysfunctions considered in this work. Bayesian networks are used as classifiers to keep the model intuitive, and address situations where some input data is missing or unavailable. Once the dysfunction events in the downhole dataset were labeled, data augmentation techniques were used to generate synthetic data for scenarios where data was sparse. The dataset used in the project consisted of nine wells (with 19 bit runs). Most of the bit runs had a downhole vibration sensor at the bit, while some had sensors along the string as well. Of these 19 bit runs, 15 were used for training and four were used to test the models. Various data augmentations techniques were applied, and validated manually as appropriate synthetic data. In the case of full-stick event detection, the saw tooth pattern in the surface torque signal was captured and provided as an input to the classifier. The classifiers thus trained were able to detect the dysfunctions using data from surface sensors to a high level of accuracy and with low false alarm rates. This paper presents models to predict downhole dysfunctions from surface data alone. This paper also provides guidance on data augmentation techniques that use sparse downhole datasets to improve machine learning drilling advisory models. For identifying drilling dysfunction from surface data, the tortuosity of the well is also taken into account.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".