Next-Generation Proactive Flare Monitoring

A titan in the European oil and gas industry confronted a global environmental and operational challenge: the significant annual flaring of natural gas. With a staggering 140 billion cubic meters of natural gas flared globally each year, the need for advanced, precise monitoring tools became an operational and environmental imperative. This industry giant collaborated with Sigmoidal to develop a next-generation flare monitoring system, empowered by artificial intelligence, to measure and manage these emissions effectively.

What was the business objective?

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:

• Advanced Monitoring for Decision-Making: To innovate an AI-integrated monitoring system that could guide decision-making within the burning system management realm, thereby enhancing operational efficiency and environmental stewardship.
• Streamlined Equipment Utilization: To minimize the need for multiple monitoring devices across facilities by implementing wide-spectrum imaging technology, thereby reducing equipment redundancy and associated costs.
• Enhanced Measurement Precision: To achieve unprecedented levels of accuracy and precision in flare monitoring, ensuring every emission is accounted for and measured with the highest precision.
• Cost-Effectiveness and Environmental Consideration: To deliver a cost-effective solution that meets the industry’s economic needs and aligns with its commitment to reducing environmental impact.

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How did we accomplish it?

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.

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The Results

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:

• The precision of emission measurements saw a remarkable increase, achieving 98% greater accuracy in thermal radiation quantification.
• The sigmoidal tool enabled quick identification of flaring sources and then generating potential solutions. By comparing this real-time data to the AI models, the system can predict when a facility will exceed its flaring targets so that remedial action can be taken in advance.
• The firm's operational efficiency was substantially boosted, with AI providing deep insights that informed better management of their burning systems.
• The AI-powered solution brought about a marked enhancement in detecting and analyzing flare emissions. The system's predictive analytics capabilities substantially reduced greenhouse gas emissions, aligning with global sustainability targets and reinforcing the company's commitment to environmental stewardship.
  

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.