In this interview, industry expert Janam Pandya explains how process Raman supports real-time oil and gas analysis, from changing feedstocks to fuel quality and hydrogen applications.
Can you give an overview of why real-time analytics are becoming more important in oil and gas operations?
Janam Pandya: Oil and gas operations are becoming more dynamic, constrained, and complex. Across the industry, we are seeing market volatility, changing fuel prices, shifting demand, the move toward cleaner and lower carbon fuels, and increasing regulatory complexity, especially around emissions and compliance.
At the same time, operators are dealing with supply chain disruptions and more variable feedstocks.
Individually, these pressures can be managed, but together they create significant operational challenges. Maintaining stable and efficient performance is becoming harder, especially when there is less room for inefficiency.
This is why faster analytics are becoming essential. Faster data enables quicker decisions, improves operational efficiency, reduces the risk of off-spec production, and helps teams identify issues before they affect product quality.
How have conditions inside plants and manufacturing facilities changed?
Janam Pandya: Inside plants and manufacturing facilities, the shift is just as significant; inputs are no longer as stable as they once were. For example, natural gas compositions and blended fuels can vary, and that variability directly affects performance. Operators are also under pressure to meet higher efficiency expectations and optimize every part of the process.
At the same time, modern facilities are becoming more automated. Many plants are moving toward advanced closed-loop control systems, which rely heavily on timely and accurate data. Uptime is also more critical than ever because downtime now has a much higher cost impact.
As a result, operations have moved from stable inputs, manual adjustments, and periodic measurements to variable inputs, automated systems, and continuous monitoring. The key question is whether measurement and control systems are keeping up with this shift.
Why are traditional analytical methods struggling to keep up?
Janam Pandya: Traditional measurement approaches were not designed for real-time control. Every step, from sampling to lab analysis to decision-making, introduces delays that can range from minutes to hours. By the time the results are available, verified, and acted on, the process may have already changed.
This creates delayed insight, limited visibility between measurements, and reactive decision-making. Instead of preventing issues, teams are often forced to respond after the fact. That gap between when an event occurs and when action is taken is where time, efficiency, and opportunity are lost.
In practice, this leads to several operational pain points, including slow turnaround times from offline lab analysis, higher costs from frequent manual sampling, and increased downtime risk because quality issues are not detected early enough.
From an analytical standpoint, there are also budget pressures, consumables, maintenance requirements, and variability in conventional measurements.
How does real-time process insight change operational decision-making?
Janam Pandya: Real-time process insight changes operations by allowing teams to continuously measure and analyze data directly from the process. Instead of waiting for offline results, operators can detect deviations immediately and respond while there is still time to influence the outcome.
This enables a true closed-loop system. Once a deviation has been detected, adjustments can be made in real time to maintain optimal performance. The result is reduced process variability, because operations can be stabilized under dynamic conditions rather than corrected after the fact.
Ultimately, real-time process insight supports a faster decision cycle. It helps shift operations away from reactive firefighting and toward proactive control. The value is not just better data, but having that data quickly enough to make decisions that improve process performance, quality, and efficiency.
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What is process Raman spectroscopy, and how does it work?
Janam Pandya: Process Raman spectroscopy uses Raman spectroscopy for real-time process monitoring. In most cases, fiber-optic-based Raman spectrometers are used. In a typical setup, laser light is transmitted from the instrument to the sample through a fiber-optic cable, which is connected to optics installed on the process line.
When the system is operating, the light interacts with the sample. As the hydrocarbon molecules vibrate, Raman-scattered light travels back through the optics and returns to the instrument through the fiber-optic cables. The instrument reads this scattered light and generates a Raman spectrum.
That spectrum can look somewhat similar to gas chromatography results. A chemometric model deployed on the instrument then converts the single spectrum into results for different properties of interest. These results are displayed in real time as a function of time, giving operators immediate process insight.
What advantages does process Raman offer over conventional analytical methods?
Janam Pandya: Process Raman offers several advantages compared with conventional analytical approaches. It is typically much faster than traditional techniques and provides results in real time. It is also non-destructive and requires no sample preparation, consumables, or carrier gases.
Another major advantage is that Raman spectroscopy can provide accurate quantitative and qualitative information. It can measure multiple properties in a single scan, which reduces the need for multiple disconnected tools and workflows. Modern Raman spectrometers can also work with many sample types, including solids, liquids, gases, gels, slurries, and more.
In process environments, this can reduce operational complexity and maintenance burden while improving process visibility. By providing immediate results, process Raman helps engineers make informed decisions quickly, supports tighter process control, reduces off-spec production, and helps maintain consistent product quality.
How can process Raman be used across oil and gas operations?
Janam Pandya: Process Raman can support real-time insight across many points in oil and gas operations. At the source, such as natural gas and crude oil, it can provide compositional insight. In refining, it enables continuous compositional analysis directly in the process, rather than relying only on periodic lab testing.
In blending and quality control, process Raman can support ASTM-compliant specification testing and property monitoring with quick cycle times. This is important because operators do not have to wait until after blending to verify quality. Instead, they can adjust in real time to stay within specification.
Further downstream, process Raman can support midstream transport and custody transfer by providing immediate verification of product composition.
In power generation and petrochemicals, it can continuously monitor key parameters that affect process performance and efficiency. The main value is that one technology can deliver continuous real-time compositional insight across multiple points in the operation.
How can process Raman support power generation and hydrogen blending?
Janam Pandya: In power generation, hydrogen blending into natural gas is becoming more common, but it introduces new variability. Hydrogen affects heating value, flame speed, and overall combustion behavior. Even small variations in blend composition can influence turbine stability, efficiency, and emissions.
Real-time insight is essential because operators need continuous visibility into blend ratios. Process Raman is useful here because it provides the specificity needed to measure hydrogen alongside the key hydrocarbon components of natural gas simultaneously.
This allows operators to maintain combustion stability and optimize turbine performance in real time. As energy systems move toward cleaner fuels and more flexible fuel blends, this type of continuous compositional monitoring becomes increasingly important.
What role can process Raman play in custody transfer and refined fuel blending?
Janam Pandya: In natural gas custody transfer, the goal is to ensure fair and accurate transactions between parties. This depends on precise measurement of gas composition because properties such as BTU and specific gravity directly affect energy value and billing.
Gas composition can vary even within the same pipeline, so real-time monitoring is important for understanding composition and detecting even small amounts of components such as carbon dioxide and nitrogen.
This is traditionally performed using online gas chromatography (GC). While GC is accurate, it can involve long analysis times, frequent calibrations, carrier gases, and ongoing maintenance.
Process Raman adds value by providing fast in-line measurement with minimal maintenance, no hardware calibrations, no sampling, and no consumables. A single Raman analyzer can measure multiple components, such as C1 to C5 hydrocarbons, and calculate derived properties such as BTU and specific gravity in real time.
In refined fuel blending, process Raman can help maintain tight control over key properties such as RON and MON. These properties are critical for fuel quality, regulatory compliance, and operational efficiency.
Because blending is highly dynamic, real-time measurement can help operators stay within specification, minimize product giveaway, and maintain compliance across fuel grades.
How is process Raman deployed in the field, and what makes it practical for industrial environments?
Janam Pandya: One of the key advantages of process Raman is its flexibility in installation. For processes without a sampling system, direct in-line probes, such as Process BallProbes, can be installed with a bore-through flange adapter. These probes can withstand harsh process environments, including pressures up to 6000 psi and temperatures from cryogenic conditions to 300 °C.
For processes that already have a sampling system, a FlowCell configuration can be used and integrated into existing setups. Fiber-optic jumpers also allow the analyzer to be placed close to the process or in a safer location, such as an analyzer shack, while maintaining high signal quality with minimal loss.
From an implementation standpoint, process Raman is designed to fit into existing operations. It uses in-line noninvasive probes, delivers high-frequency multi-component measurements, requires no consumables or carrier gases, and needs minimal maintenance.
It can also connect to existing process control systems through Modbus or OPC communication protocols. This makes it a scalable and robust solution for harsh industrial environments where real-time process intelligence is needed to support better control and efficiency.
About Janam Pandya 
Janam Pandya holds a PhD and a Master’s degree in Food Science from the University of Massachusetts Amherst and a Bachelor’s in Food Processing Technology. At Thermo Fisher Scientific, he progressed from Staff Scientist in Chemistry to Application Scientist for Spectroscopy and Product Manager, supporting Raman applications and Process Raman portfolio growth.
This information has been sourced, reviewed, and adapted from materials provided by Thermo Fisher Scientific – Portable and Handheld Process Raman Spectroscopy.
For more information on this source, please visit Thermo Fisher Scientific – Portable and Handheld Process Raman Spectroscopy.
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