Forecasting & Prediction of supply for Oil/Gas Companies

Production forecasts for oil and gas wells relied on manual decline-curve analysis performed periodically by reservoir engineers, leaving planning teams working from stale estimates between updates. Equipment failures on pumps, compressors, and separators were largely reactive, discovered only after sensor anomalies triggered alarms or output visibly dropped, resulting in unplanned downtime and expensive emergency repairs. Field engineers spent significant time manually compiling data from systems, maintenance logs, and inspection reports into reports for operations leadership, with no standardized way to flag which wells or equipment needed urgent attention versus routine monitoring.

Built an AI-driven predictive maintenance and production forecasting pipeline using gradient-boosted trees and LSTM models trained on historical production, pressure, temperature, and vibration data to predict well decline and equipment failures 7–30 days in advance. Streamed SCADA/IoT sensor data into a feature store powering both forecasting and real-time anomaly detection. Integrated Claude as a reasoning layer to convert forecasts, anomaly scores, maintenance history, and equipment manuals into grounded, plain-language maintenance recommendations with explainable risk factors. Delivered confidence intervals and risk scores through operational dashboards, automatically triggering maintenance workflows for high-risk assets, with continuous model recalibration based on actual production and failure outcomes.

Unplanned equipment downtime was reduced as failure risk was identified an average of 12 days before it would previously have surfaced through reactive monitoring, with a real-time reflex layer closing the remaining gap by triggering immediate alerts or interlocks the moment a sensor reading crosses a validated safety threshold — catching sudden failures the day-scale forecasting models aren't designed to see. Production forecast accuracy improved significantly compared to periodic manual decline-curve estimates, giving planning teams a continuously updated view instead of stale point-in-time forecasts. Report compilation time for field engineers dropped sharply since Claude auto-generated operator briefings from live model outputs instead of manual data pulls. Maintenance teams were able to shift a meaningful share of work from reactive/emergency repairs to scheduled preventive maintenance, and the reflex layer's deterministic, sub-second response to critical thresholds further reduced safety exposure during acute equipment events, independent of model or API availability.