We build ML-supported digital twins for upstream, midstream, and refining assets. Forecast the life-limiting physical parameters of pumps, compressors, turbines, and pipelines, instead of guessing at Remaining Useful Life from biased shutdown history.
Most upstream and midstream operations run on a mix of run-to-failure for low-criticality assets and hour-meter preventive servicing for everything else. The work that pays back fastest is on critical rotating equipment: forecast the parameter that ages the asset, advise on the operating point that extends life.
Run to failure. ESP fails downhole, gas turbine trips, pipeline anomaly only caught at flow meters. Repair logistics dominate the cost.
Calendar overhauls and hour-meter servicing. Better than reactive, but you over-service some assets and miss the ones drifting fastest in real conditions.
Forecast a critical physical parameter with a known engineering limit. Use the parameter's own history, your operating setpoints, and leading indicators from upstream sensors.
Remaining Useful Life is calculated from when assets actually stop. But pumps and turbines stop for many reasons that aren't wear-related: scheduled overhauls, demand swings, undetected fabrication defects, business decisions. Training a model on biased shutdown history teaches it the bias, not the failure.
Sensors fail, drift, and live through years of vibration, heat, sand, and corrosive service. A model relying on small deviations between sensors fires alerts when the measurement hardware ages, not when the equipment does. Operators stop trusting it within six months.
"The model says pull this ESP in 17 days" doesn't survive contact with a production engineer who has run that field for a decade. Without a physical reason tied to a measurable parameter, predictions get ignored.
Many predictive maintenance platforms assume always-on connectivity. Offshore platforms, remote wellsites, and pipelines often don't have it. Models that can't run locally, with sync windows when connectivity returns, don't reach production.
The model only reflects what we give it. When the data carries the real behavior of the system, the predictions make sense. When the data is messy or misunderstood, the output becomes noise. This is where predictive maintenance projects are won or lost. Not in the algorithm but in the understanding.
A digital twin, in our usage, is a model of one or two physical parameters that limit the asset's life, with limits the engineering team already understands. ESP motor temperature has a thermal envelope. Gas turbine hot-section TIT/EGT has a blade limit. Compressor polytropic efficiency has a degradation threshold. Forecast those, and you forecast life directly.
The forecast uses three input families. Each one carries different signal.
Production engineers see the forecast against today's actual reading. Trust builds in weeks, not quarters.
Each prediction ties to specific operating setpoints and upstream signals. "Why did it predict this" has an answer.
Vary an operating setpoint within tolerance, see the forecast shift. The system advises the next operating move, not just an alarm.
Each one anchored to a parameter with a known engineering limit, scoped tightly enough to deliver a working PoC in 6 to 8 weeks.
ESP pull jobs are expensive, especially offshore. We forecast motor temperature and vibration trends from frequency, intake gas content, and downhole conditions, then advise on operating points that extend run-life and on early intervention windows that beat unplanned failure.
Gas turbines on platforms and at compressor stations are limited by hot-section wear, which tracks directly to TIT/EGT. We forecast that parameter from load patterns and operating setpoints, and identify the load-mix combinations that extend blade life without giving up throughput.
Compressor degradation creeps in through fouled internals and tip clearance growth. We forecast efficiency drop from discharge conditions and load patterns, then prescribe load shifts and anti-surge tuning that protect throughput while extending overhaul intervals.
Pipelines and heater tubes age with thermal and stress cycles. Anomaly detection catches deviations from normal operating envelopes; soft sensors infer tube skin temperatures where direct measurement isn't available. Predictions integrate with the CMMS so flagged anomalies become work orders with named owners.
Once you forecast a physical parameter, you can vary the operating setpoints within their safe envelope and watch the forecast move. That turns the model from an alarm system into an operating advisor: change the pump frequency, the turbine load, the anti-surge margin, and see the projected life of the asset shift in real time.
Real-time setpoint modification, all in inference mode. Useful in the field, where every "what if" matters.
Implement operating envelopes that keep parameters inside their boundaries and preserve the relationships between them.
Surface the top one or two control adjustments that extend run life without compromising throughput or safety.
Visualization style based on Adam's published model output. The aviation example translates directly to industrial gas turbines: lower fan speed demand extends the hot section life; pushing past the envelope accelerates blade wear.
A forecast that nobody acts on doesn't change anything. The pattern that works in the field isn't another grid of widgets to check every morning, it's an intelligent notification layer that surfaces what changed, where to look, and what the reasonable next step is. Especially valuable for remote operations where attention is scarce.
The system pushes when something matters. No daily ritual of scanning SCADA mosaics or hunting through emailed reports for the one well, pump, or compressor that's drifting.
Each alert points to the specific asset and parameter that moved. The recipient doesn't have to reverse-engineer what triggered the notification across hundreds of wells or tens of compressor stations.
Not "this is anomalous." Instead: "ESP motor temperature is drifting toward thermal envelope; consider reducing pump frequency by 3% or scheduling a workover within the operating window."
It's not a dashboard for the sake of a dashboard. It's an intelligent notification-based system that shows you where to look, and what the reasonable next step is. You don't check every dashboard every morning. You get a notification when something needs your attention.
Workshop with operations, production engineering, and reliability teams. Identify the one failure mode and one critical parameter that costs you most.
Walk the data and the equipment. Sensor accuracy, drift, baselines. Time-series storage selection. Connectivity assessment for edge or hybrid deployment.
Build the parameter forecast on real field data. Start simple. Add complexity only where it earns its place. Validate with production engineers.
Edge or hybrid deployment, CMMS and SCADA integration, operator UI on existing screens. AI Advisor patterns for the top one or two operating setpoints.
A digital twin doesn't have to be a perfect replica of the asset. It has to be a useful one. Build the model around the parameter that limits life, validate it against historical operations, and you have something production engineers will actually trust.
For a global chemical manufacturer, we built predictive maintenance across two factories generating tens of billions of IoT records, combining supervised models for known failure modes with anomaly detection for the long tail. The same pattern applies to upstream and midstream operations: forecast the parameter that ages the asset, advise on the operating point that extends life.
Read the case study →Three weeks. Your real field data. Honest assessment of whether predictive maintenance fits your situation, including the cases where it doesn't. The technical analysis is yours to keep regardless of what you decide.
ul. Mostowa 38
61-854 Poznań
Poland
C.11 310 Col. Santa Gertrudis Copo
Merida, 97305
Mexico
10 York Rd
London SE1 7ND
United Kingdom
c/o Rödl RAe, Taunus Tower, Mergenthalerallee 73-75
65760 Eschborn
Germany
City Place 1401 Lake Plaza Dr, Ste. 200
Spring, TX 77389
United States
