Energy & Oil · Pipeline anomaly detection
A leak that SCADA misses is a spill you own
Banao builds pipeline anomaly-detection models that run continuously on your historian and SCADA tags — pressure deviations, flow-balance breaks, and corrosion signatures — and alert before a small signal becomes a reportable event.
The models wire into your existing infrastructure: no new field instruments, no rip-and-replace. Indian Oil runs AI on downstream operations data through the same integration pattern.
Indian Oil— AI integrated with downstream operations data and existing plant systems.
The first call is free · 45 minutes · no obligation
What we build
What a Banao pipeline anomaly deployment includes
Pipeline anomaly detection is not a single model — it is pressure analysis, flow accounting, corrosion trending, and alert logic working together. We own the full stack.
Pressure-deviation scoring
Models that learn the normal pressure envelope for each segment and score every deviation in real time — distinguishing a valve actuation from the early signature of a leak or blockage.
Flow-balance analysis across gathering lines
Volume-in versus volume-out accounting that surfaces unexplained losses in gathering networks before they appear in a monthly reconciliation report.
Corrosion-signature trending
Historical pressure and inspection data combined to model corrosion rate by segment, so integrity engineers know which sections are approaching tolerance before the next pig run.
Multi-sensor alert correlation
Single-sensor alerts are noisy. The model confirms an anomaly across pressure, flow, and temperature simultaneously before paging a controller — reducing false alarms that breed alert fatigue.
Historian and SCADA integration — legacy included
Models connect to PI-style historians, legacy DCS, and existing SCADA via read-only taps. No new field instrumentation required where tag density is sufficient.
Incident-severity triage and control-room dashboard
Alerts arrive ranked by estimated severity and segment, not as a raw score. The control-room interface shows what matters first, with operator override and feedback built in.
Receipts
Where this pattern is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Anomaly models on downstream pipeline and operations data
India's largest downstream operator carries vast volumes of pipeline and process telemetry. Banao applies pressure and flow models to that historian data — integrated with existing plant systems via read-only taps — flagging deviations before they escalate to reportable events.
Dogfooding
We operate on the AI we build
Banao runs a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our engineering hires. Vikaas drives our own demand-generation pipeline.
A pipeline anomaly model that has to work inside a company that depends on it gets hardened in a way a vendor demo never does. That discipline is what reaches your assets.
Screens Banao's own engineering hires — the same model we sell to clients.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When pipeline anomaly detection isn't the right investment
Not every pipeline segment justifies a model. We will tell you before you spend:
- Sparse instrumentation: if a gathering segment has one pressure sensor every 40 km, the model cannot triangulate an anomaly location. The honest answer is instrument first.
- High baseline noise: some process units run with naturally high flow variance. If the signal-to-noise ratio is poor, a model produces alerts faster than operators can act on them.
- Short pipeline tenure: a model needs enough historical cycles to learn normal. A recently commissioned line may need six to twelve months of stable data before anomaly detection adds signal.
How we start
How we start — fixed-price before any commitment
We scope a pipeline anomaly project against your actual tag density and historian quality, not a demo dataset.
- 01
AI Discovery Sprint
2 weeks · fixed price
We connect to a read-only export of your historian, audit tag coverage by segment, test a baseline pressure-deviation model, and hand back a feasibility report and ROI estimate — yours regardless of next steps. The Sprint fee is credited against the build if you proceed.
- 02
Build
Data engineering first: historian cleaning, tag mapping, and gap-filling are deliverables, not preconditions. Models ship with SCADA integration and a control-room interface.
- 03
Production & continuous improvement
Live deployment with operator override, alert-feedback logging, and a model update cycle. Every operator correction tightens the alert threshold for that segment.
FAQ
Frequently asked questions
Our pipeline historian is incomplete — can we still build a model?
Yes, with caveats. We start from what exists and use the Discovery Sprint to map the gaps. Historian cleaning and tag mapping are part of the build deliverable. Where a segment is truly dark, we tell you instrumentation comes first.
How does the model distinguish a valve actuation from a leak?
Valve events follow a recognisable pressure signature — fast and bilateral. A leak develops more slowly and asymmetrically. The model is trained on your actual event history from day one, so it learns your valve patterns before calling anomalies on them.
Will it generate too many alerts for the control room to act on?
Alert fatigue is the most common reason pipeline-monitoring AI gets ignored. Our models require corroboration across multiple sensors before paging a controller, and alert severity is ranked — not a raw score. The control room sees what matters most, not everything at once.
How do we make the business case before committing budget?
The Discovery Sprint produces a per-segment opportunity map and ROI estimate — fixed price, yours to keep regardless of next steps. Worst case you have an independent assessment of where anomaly detection pays and where it doesn't. That is the document for the investment committee.
Can the model locate a leak, or only detect that one exists?
Detection comes before localisation. The model will tell you which segment is anomalous and the estimated severity. Precise metre-level location requires additional instrumentation density, which we scope honestly in the Sprint if your operator needs it.
Get started
Bring your hardest pipeline segment
Tell us about your worst-instrumented line or the segment that has given you the most trouble. In 45 minutes we will tell you whether anomaly detection is feasible — and what it would take.
Book a Discovery Sprint