Choke Performance Prediction
ML Engineer (Research)
About the Project
A postgraduate research project benchmarking machine learning against classical empirical correlation for predicting oil production rate through surface chokes in mature Niger Delta wells. Built on 1,000+ daily observations from the Opukishi field spanning 7 wells (A–G) across 4 flow stations, the Random Forest regressor predicts oil rate in bbl/day from choke size, wellhead and tubing-head pressure, separator pressure, water cut, gas-oil ratio, temperature, viscosity, and oil specific gravity — then is scored head-to-head against the Gilbert correlation that petroleum engineers have used for decades.
Key Highlights
- Benchmarked Random Forest regression directly against the classical Gilbert empirical correlation on the same held-out data
- Built the dataset from 1,000+ daily observations across 7 wells and 4 flow stations, standardizing units and column names across sources
- Cleaned for real field conditions — sensor errors, shut-in periods, workovers and maintenance windows were flagged and removed rather than silently averaged in
- Tuned with GridSearchCV and analysed feature importance to identify which production variables actually drive the prediction
- Ran residual error analysis across six sequential notebooks, from data cleaning through to multi-well comparison
Technical Challenges
Convincing domain experts that ML can outperform an established petroleum engineering formula required rigorous benchmarking rather than a headline accuracy number. The residual analysis was what did it: Gilbert's correlation showed systematic error in specific operating regimes that the Random Forest captured. The harder data problem was upstream — a mature field spends a lot of time not producing, so shut-ins, workovers and sensor faults had to be excluded deliberately, with every preprocessing decision documented. Left in, they teach the model that zero flow is normal.