AI Applications in Oil & Gas

Seismic Interpretation

Deep learning auto-detects faults, classifies facies, and identifies direct hydrocarbon indicators. Reduces interpretation time from weeks to hours.

Use case: A CNN trained on 500 interpreted seismic lines auto-picks fault surfaces with 92% accuracy, saving 3 weeks of geoscientist time.

AI-Assisted History Matching

ML-based proxy models run thousands of parameter combinations to calibrate reservoir models in days instead of months.

Use case: A surrogate model trained on 5,000 simulation runs predicts recovery factor for new well placements in seconds, enabling rapid screening of 200 infill locations.

ROP Optimisation

ML models analyse drilling parameters in real time to recommend optimal WOB, RPM, and flow rate to maximise rate of penetration while staying within safe limits.

Use case: An ROP model recommends increasing RPM from 120 to 145 based on formation hardness, improving ROP by 22% through a carbonate section.

Stuck Pipe & Kick Detection

Anomaly detection models identify early signatures of stuck pipe, lost circulation, or kicks from surface and downhole sensor data - providing minutes to hours of early warning.

Production Forecasting

ML-based decline curve analysis accounts for non-standard decline behaviour, multiphase flow effects, and operational events that traditional DCA methods struggle with.

Predictive Maintenance

LSTM and autoencoder models detect anomalies in ESP, compressor, and pump sensor data, predicting failures days to weeks in advance.

Use case: An LSTM predicts compressor bearing failure 10 days before it occurs, preventing a $1.2M emergency repair and 8 days of deferred production.