Energy · AI Delivery
Replacing decades-old forecasting with a custom ML application.
Annual savings~$850k/yr
Context
A mid-sized upstream oil & gas operator had forecast reservoir performance the same way for decades. The geophysical models demanded heavy manual data preparation and long computation cycles, so forecasts arrived only once a month — and weren't always accurate. Because production forecasting was both rare and imprecise, logistics never stayed in sync with actual output, driving avoidable storage and transport costs straight into the company's P&L.
The problem
- 01Unexpected oil storage and transportation costs caused by poor production planning.
- 02Inaccurate well-performance forecasting.
- 03Excessive engineering effort spent on manual data preparation.
What we did
- 01Replaced the conventional forecasting approach with machine-learning models built in Python, R, and C++.
- 02Automated data ingestion and preparation with Apache NiFi and PostgreSQL.
- 03Built a custom UI in Node.js and Angular.js so engineers could analyze model behavior, tune parameters, and keep optimizing.
Results
- 01~$850,000 saved per year — logistics now tracks production, eliminating additional and unexpected costs.
- 02Well-performance forecasting accuracy improved by 34%.
- 0334,000+ engineering hours freed per year by automating the data flow.