Case Study: Schlumberger accelerates well data extraction with Snorkel AI

How Schlumberger uses Snorkel Flow to enhance proactive well management

Schlumberger, the global energy technology and services company, needed a faster way to extract critical geological and field data from highly unstructured daily reports, PDFs, and logs. Its teams faced inconsistent document formats, manual labeling that took 1–3 hours per document, and difficulty identifying 18 industry-specific entities; Snorkel AI’s Snorkel Flow was used to help solve this information-extraction challenge.

Using Snorkel Flow, Snorkel AI helped Schlumberger build a data-centric AI application in just three days to automatically extract key entities from diverse document types. The team reached an 85% F1 score quickly and improved it to 91.4%, while reducing report-processing time from 1–3 hours to just a few seconds and successfully generalizing across 15 document structures.

Schlumberger

Monisha Manoharan

Senior Machine Learning Engineer