Oil and gas facilities record large volumes of sensor data every hour, yet many critical operating decisions still rest on static spreadsheets and individual judgment. The historian holds years of process behavior that rarely reaches the control room in a form operators can act on.
The constraints those decisions have to manage are familiar: margin leakage, unplanned downtime, safety incidents, volatile energy costs and tightening emissions targets. Models built for a specific plant can learn from existing historian data, show where value is being lost, and write setpoints back to the distributed control system (DCS), so the data changes how the unit runs.
Why production optimization matters
Energy waste and off-spec product are among the most persistent losses in oil and gas operations, and flaring and excess energy use are hard to eliminate with conventional control. A unit run away from its best operating point costs margin and adds emissions on every barrel. Closing that gap also supports plant reliability and helps develop engineers who are at ease working with AI.
The five applications below each target a specific constraint in oil and gas operations.
1. Keep compressors and fired heaters at their efficient point
Compressors and fired heaters are chronic sources of energy loss and downtime. Compression is often the largest power consumer in a gas facility, and furnaces set much of a refinery's fuel burn.
Closed-loop control reads temperature, vibration and flow signals continuously and moves load, suction pressure and fuel-air ratio toward the most efficient operating point as feed and conditions change. That reduces energy use and emissions without waiting for an engineer to retune the unit. For more on compression, see recovering centrifugal compressor efficiency.
On fired heaters, models that track furnace draft and bridgewall temperature together with firing and feed conditions can flag the conditions that lead to coking and tube failure before they force an unplanned shutdown. The result is steadier throughput, lower fuel and power use, and equipment that stays online.
2. Reduce energy use without cutting throughput
Energy is often the largest controllable cost in a plant's operating budget, yet conventional control rarely coordinates the many units competing for the same steam, power and fuel. Models trained on full-plant data close that gap. They adjust process flows, feed blends and firing rates across distillation columns, compressors and heaters to get more useful work from existing equipment while production targets hold.
Demand forecasting adds a second lever. Models can predict site-wide steam loads, electric submersible pump duty cycles or chilled-water requirements hours ahead, then reschedule setpoints so high-draw operations land in off-peak windows.
Energy savings and steady barrels can coexist when the whole facility is coordinated. That supports lower emissions without giving up refinery profitability.
3. Improve product quality with predictive models
Predictive models move quality control from hindsight to foresight. Trained on years of temperature, pressure and flow data, they forecast critical specifications such as sulfur, Reid vapor pressure (RVP), API gravity, octane and heating value before samples come back from the lab. When a deviation starts to develop, the system alerts the control room and suggests setpoint adjustments, so off-spec product is corrected before it leaves the unit.
Many refineries rely on soft sensors, models that infer product properties continuously from process measurements. They fill the gap where direct measurement is slow or impractical, give engineers a running view of qualities that once waited on lab testing, and help isolate the root cause of an excursion faster.
Off-spec product leads to reprocessing, price discounts and shipping delays, so catching deviations early protects margin and keeps on-spec product moving. The same early warning applied to equipment faults can lower maintenance spend and extend equipment life.
4. Increase throughput through equipment coordination
Isolated control loops keep individual pumps, compressors and heaters on target, but they miss the chain reaction that starts when a separator pressure swing forces downstream units to throttle back. Plant-wide coordination closes that gap. A model that reads live data from every critical asset anticipates gas-liquid mismatches, adjusts recycle rates and balances flare constraints before they slow production.
Single-variable PID loops act on one measurement at a time. A multivariable controller trained with reinforcement learning weighs many constraints at once, including capacity, energy and emissions, and sends coordinated setpoints to the existing APC or DCS minute by minute.
More stable throughput from existing equipment can defer new wells or expansion projects and lower energy per barrel at the same time.
5. Help operators act faster in complex environments
A typical control room handles thousands of tags, alarms and KPIs, often with leaner crews than before. A model that scans process variables, maintenance logs and environmental readings can bring the few things that matter to the front.
Anomaly detection flags pressure spikes or flare risk early. Recommended moves are presented for operators to act on, and in closed loop the controller writes setpoints within limits the operations team sets, with operators able to take over at any time.
Recommendations arrive as prioritized moves the operator can review, so the model works as a teaching tool that supports more informed decisions. Teams recover from upsets faster, keep hard-won tribal knowledge in the plant, and run more safely and consistently.
How Imubit applies this
In refining and gas processing, Map traces lost margin, excess energy and off-spec product to the recurring decisions behind them, such as heater firing, compressor loading and cut-point targets. Model learns each unit's nonlinear behavior from its own history with Deep Learning Process Control® (DLPC®), guided by first principles. Control takes those decisions to closed loop, inside limits set by plant engineers, writing setpoints to the APC and DCS already in place. In refining, Imubit's published result is a $0.25/bbl margin improvement.
