Real-time Hydrocarbon Blending Optimization Using Virtual Analyzers

Misha Askerka, Director of Analytics at Validere, leading the charge in real-time hydrocarbon blending optimization using virtual analyzers. With a background in physical chemistry and machine learning, he’s shared actionable insights at the O & G Digital Twin Conference 2024. Explore his strategies in this blog.

Misha Askerka had shared a vision where technology played a crucial role in a large factory that dealt with crude oil. Back then, the factory faced uncertainty about the quality of the oil they handled, which could lead to problems. They had to be cautious, which sometimes meant missing out on profits, or take risky chances. However, a smart solution emerged: digital twin-type tools. These tools used advanced technology to estimate the oil’s quality and helped the factory improve it in real-time. They also measured how certain they were about their estimates, ensuring safety. Thanks to these tools, the factory could be smarter about managing the oil, increasing profits without taking unnecessary risks, and ensuring everyone’s safety and happiness. It was like having a knowledgeable assistant in the factory, making things better.

Midstream crude oil facilities often lack information on their product quality, which leads to either overly conservative/unoptimized or risky blending programs
One way companies can address this is with digital twin type solutions that can accurately predict unknown product quality properties and automatically optimize operations in real-time. Using probabilistic approach to optimization allows companies to:
Quantify the measurement uncertainty and its sources
Optimize safety margins
Provide a justifiable measurement program
Once midstream companies implement these digital twin controls, they can increase blending terminal profits by blending closer to the targets while reducing the shut-in risk by carefully accounting for measurement uncertainty.

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