Next-Generation Proactive Flare Monitoring
Nov 20, 2023
Nov 20, 2023
These objectives were not independent silos but interconnected facets of a holistic strategy aimed at redefining flare monitoring with a future-forward approach that balanced operational demands with environmental responsibilities:
The journey toward this innovative system began with a comprehensive analysis of the company's existing flare-monitoring processes and the integration of wide-spectrum imaging technologies. These advanced cameras could capture detailed images across both the visible and infrared spectrums, significantly enhancing the data available for analysis while reducing the number of devices needed at the facilities.
The integration of AI began with training machine learning models on the data captured by thermal radiation transducers, utilizing advanced algorithms to recognize the presence of flares and quantify the emissions with a high degree of accuracy. Our ML models were designed to build upon the existing knowledge of gas compositions and the geometric layout of the flare systems, transforming raw data into insightful information for operational staff.
The AI system's ability to process and learn from the data in real-time enabled a significant leap in the precision of flare monitoring. This learning process was iterative, refining the model's predictive capabilities with each new data point. As a result, the monitoring system evolved into a tool that provided real-time analytics and became more intelligent and efficient over time.
Through the strategic application of Sigmoidal's AI consulting expertise, the outcome of the collaboration with the European oil and gas leader was a resounding success, marking a significant milestone in the industry's efforts to modernize and optimize flare monitoring systems. The integration of AI into their operations led to a cascade of improvements:
Moreover, the AI-driven system became a complementary tool for further research and development within the company. The feedback loop created by the AI models ensured that the monitoring system became more intelligent and reliable over time, offering an ever-improving basis for decision-making.
Predictive Analytics: Used to forecast flare system behaviors, enabling proactive adjustments.
AI-Driven Wide-Spectrum Imaging: Encompassing both the visible and infrared spectrum.
Data Synthesis & Interpretation: Analyzed the influx of data to provide actionable insights.
ML: Optimized global parameters, creating a cycle of continuous improvement and learning.
Increase in monitoring accuracy, translating to substantial cost savings in gas management.
Savings annually with improved emission measurement and management system.
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