Paper SPE-204063-MS, presented at the SPE/IADC International Drilling Conference and Exhibition, Virtual, March 2021.

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.