Quantifiable outcomes from legacy subsurface migrations and high-integrity ML deployments.
A global energy major held 40 years of subsurface data across three incompatible legacy databases. We refactored the metadata architecture to allow for real-time ML-driven reservoir modeling.
Transitioning from reactive maintenance to predictive ML models required a total overhaul of edge-to-cloud governance. We implemented a high-integrity pipeline that validated data at the source.