Beyond Detection: The Next Frontier in Flare Management
SCADA driven Root Cause Analysis
Flaring events are among the most visible and closely scrutinized operational signals in the oil and gas sector. Yet while detection technologies have evolved dramatically, most producers are still asking a deeper question: why did this event happen?
In our last post, Signals in the Noise, we explored how clustering algorithms can fingerprint recurring flaring behaviors hidden inside massive SCADA datasets. That work helps identify when and how often events occur. The next frontier — and arguably the most transformative — is understanding why they happen.
Three Layers of Root Cause Analysis
Accurate methane and flaring mitigation depends on thorough root cause analysis (RCA). Through our work with operators, we’ve found three key levels of insight:
- Event Category – What Type of Event Was It?
Categorization provides clarity: is the event a routine flare, a non-routine release, or an unlit flare? This first layer builds the foundation for actionable analysis. - Equipment Level – What Triggered It?
Which sensor or piece of equipment caused the flare? Compressor shutdowns, pilot failures, or control valve malfunctions often leave recognizable “signatures” in SCADA data. Linking these signals to their operational triggers transforms detection into understanding. - Context Level – Why Did It Happen?
This is where AI meets operational storytelling. Using correlations across SCADA tags, our algorithms can generate a structured narrative — a short, human-readable explanation of why the event occurred. It’s the bridge between machine logic and operator intuition.
Why Root Cause Precision Matters
Precise RCA isn’t just a technical upgrade — it’s an operational advantage:
- Regulatory confidence: Builds trust in reported data and supports frameworks like OGMP 2.0, Subpart W, and Canada’s methane regulations.
- Operational efficiency: Cuts repetitive investigations and reduces false alarms.
- Economic optimization: Avoids over-reporting while prioritizing the highest-impact abatement opportunities.
When systems can say, “This flare was triggered by a compressor shutdown following low suction pressure.” it redefines transparency and control.
The Human-in-the-Loop Advantage
Even the best AI models improve when paired with field expertise. That’s why modern emissions intelligence tools must let operators validate, override, or confirm the AI’s assessment of root cause.
Every user action — confirming or correcting an event — becomes a training signal that continuously refines model performance. This keeps automation grounded in reality while ensuring ongoing accuracy as operations evolve.
From Detection to Understanding
Flaring and venting management is shifting from detection toward continuous intelligence. By integrating high-frequency SCADA data, automated root cause analysis, and human feedback, operators can transition from reactive reporting to proactive prevention.
In Summary
At Arolytics, we’re building a next generation of emissions intelligence — solutions that help operators turn complex signals into clarity, and insights into measurable reductions.
“Root cause analysis isn’t about replacing human expertise — it’s about amplifying it. The better we understand why emissions occur, the faster we can eliminate them.” - Stephan Becker, Chief Product Officer at Arolytics
Stay tuned next week for an exciting product announcement, relating to this work.
Please contact info@arolytics.com if you have any questions.
—-