B. Marquardt, MarqMetrix, Seattle, Washington (U.S.)
Chemometrics, the application of statistics
to the field of chemical analysis, is often used in the oil and gas industry to
make product quality predictions. The data—which is typically derived from process
analytical tools such as Raman, near infrared (NIR), gas chromatography (GC), high-performance
liquid chromatography (HPLC) and others—is critical for oil and gas companies to unlock
valuable information about chemical composition, physical properties and other
parameters of oil, gas and blended fuels.
Chemometrics allows oil and gas companies (FIG. 1) to turn data into
dollars and is becoming the de facto method for processing analyzer instrument
output. The information can then be used to fast track the delivery of products
to the market, reduce processing costs and maximize value.
Particularly useful when deployed in conjunction with Raman
spectroscopy, chemometrics translates the information-rich spectra to accurately
measure the composition of refined fuel properties of gasoline, jet and diesel
Raman spectroscopy can be used to predict the different chemical
compounds that make up the sample. It can be used to analyze the API number, research
octane number (RON) or motor octane number (MON), Reid vapor pressure (RVP), or
to predict the concentrations of hydrocarbons (C1 to C6+),
carbon dioxide (CO2), and nitrogen (N2).
The challenge is that companies have typically created predictive
models using other types of instrumentation, which are validated and approved
by their performance committees and that satisfy American Society for Testing
and Materials (ASTM) standards. As a result, many labs may be reluctant to adopt
new instruments due to the prospect of having to rebuild and revalidate their
models from scratch.
Fortunately, experienced Raman spectroscopy providers are simplifying
the process by offering premade starter models that can be readily used for
most common measurements in the oil and gas segment. The models can then be modified and expanded,
as needed. This largely eliminates the time and cost required to conduct 30–50 reference analysis tests
during the development of new models.
By using a model, oil and gas companies can substantially lower
their costs by moving straight to the model maintenance phase where they
collect just one or two samples to verify and update the model. Once a model is
further developed, it can then be shared and deployed to additional analyzers.
A mathematical transformation allows the customer to continue
using their validated models.
Streamlining testing. Pre-developed models and a simplified conversion process facilitate
the widespread use of Raman as a supplement to more expensive, time-consuming
In one promising area of utilization, lab personnel have
used Raman spectroscopy to supplement the analysis of octane number when
testing gasoline blends. Instead of using the knock-engine to test a dozen
samples during the blending process, the lab runs the knock-engine on the final
sample. When Raman spectroscopy confirms the specified RON or MON target is
reached, the lab will then run the knock-engine test on that final sample to
validate the blend to ensure it conforms to ASTM standards. This approach significantly
improves the throughput and processing speed within the lab and delivers pertinent
operational information in seconds.
Raman spectroscopy, with the addition of validated models, allows
labs to achieve faster turnaround, higher throughput and near real-time results
for processes they need to monitor, quantify or blend.
There are many benefits to streamlining testing in the lab.
Utilizing Raman with validated modeling and chemometrics, for example, can help
processors achieve more accurate octane ratings without over blending.
Presently, oil and gas companies have to “give away” some octane
to ensure that when the actual samples are taken to the lab and analyzed, they
are not below strict specifications. If below the target value, they are
required to re-blend to bring the octane up to the spec. The incremental
savings of not having to over-blend by even minor amounts like 0.2 or 0.3 can
translate to a significant amount of money long term, not to mention time saved
on having to reprocess out-of-specification fuel.
Raman equipment. In the past, Raman instruments were less
reliable and required models specifically designed for the analyzer. Now, more
stable, solid-state systems allow core models to be applied to achieve reproducible results from unit to unit.
For example, in the author’s company’s Raman measurement systema,
each device is nearly an exact copy so common mathematical models can be
applied across multiple systems to produce consistent results.
The compact systema is designed in a package 80%
smaller than previous Raman instruments and has no moving parts. The system
works with a wide array of both contact and non-contact probes suitable for oil
and gas applications that can be changed in seconds without the need for
The instrument can be used on any samples (liquid, solid, gas)
that require Raman analysis, providing labs with tremendous flexibility in
measurement and capability.
Takeaway. As the oil and gas sector increasingly relies on chemometrics and
Raman spectroscopy to expedite quality assurance, processing and distribution, and
simplified predictive modeling will play an important role. The combination
allows labs to achieve faster turnaround, higher throughput and real-time
results for the processes that they are looking to monitor, quantify or blend.
a MarqMetrix All-in-One
BRIAN MARQUARDT is the Founder and CEO of Seattle,
Washington-based MarqMetrix. Founded in 2012 at the University of Washington,
the company specializes in compositional analysis utilizing Raman spectroscopy
and has pioneered advancements in Raman for use in process analysis.