Efficient Gas Processing with AI-enabled Process Optimization

Introduction

In today’s energy landscape, gas processing plants face increasing pressure to enhance efficiency, maximize yield, and minimize downtime, while managing never ending variability in feedstock compositions and customer demands. 

Traditional process optimization methods rely on manual monitoring, reactive troubleshooting, and static models that struggle to keep up with dynamic plant conditions. However, AI-enabled process optimization is significantly impact gas plant operations by leveraging real-time data, hybrid-modeling capabilities, and predictive insights to drive smarter decision-making and process improvements.  

From minimizing sales gas losses to reducing operational costs in the process, AI-driven solutions provide a proactive, data-driven approach that solves the problems affecting the bottom line.

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AI-Enabled Technology for Minimizing Sales Gas Losses

Sales gas losses represent a significant issue for gas processing plants. Even small inefficiencies across different areas of the plant, including combustion, compression, and other operational processes, can result in considerable gas losses. When production rates fluctuate or equipment experiences slight inefficiencies, sales gas can be lost, leading to increased operational costs and lower profitability.¹

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AI-Powered Process Optimization for Sales Gas Promotion: Compression Optimization as a Real-World Example

Traditional methods of process control often react to issues after they occur, leading to unnecessary flaring, fuel gas overconsumption, and product losses. AI-driven solutions, however, enable proactive and predictive control that optimizes key operational variables. 

Compression–enabling the efficient transport of gas through pipelines– is a critical yet energy-intensive step in gas processing, often accounting for more than 90% of a facility’s total power or fuel consumption.² 

To minimize costs, operators must continuously optimize compression systems while managing key variables such as load balancing, heat management, and suction pressures. Any inefficiencies in these systems can lead to increased energy consumption, higher operating costs, and reduced throughput.³

AI-enabled predictive analytics and real-time control mechanisms help mitigate these inefficiencies by optimizing system performance. By analyzing factors such as inlet gas pressure, compressor load, and temperature variations, AI models adjust compression parameters dynamically to maintain optimal efficiency.⁴  This then prevents unnecessary gas losses, maximizing sales gas recovery and overall plant profitability.

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Reducing Unscheduled Gas Plant Shutdowns Through Predictive Maintenance

Unscheduled shutdowns often lead to flaring or venting of gas, which results in the loss of valuable product. Similarly, equipment failures in gas processing plants—particularly in compression and dehydration units—can halt production, leading to revenue losses and increased operational expenditures.⁵ 

On average, oil and gas facilities lose 32 hours of productivity per month due to unplanned downtime, at an estimated cost of $220,000 per hour—translating to approximately $84 million in annual losses per facility.⁶ Compressor trips, foaming in the amine system, and hydrate formation at dew point control stages are among the most common causes of unplanned shutdowns. 

Predictive maintenance and real-time monitoring solutions, like Basetwo, enhance reliability by identifying potential failures before they occur, reducing unplanned downtime.⁷ The result allows operators to:

• Detect early warning signs of compressor faults and dynamically adjust running speeds.
• Optimize anti-foam injection rates to reduce the risk of foaming incidents.
• Monitor temperature and pressure conditions to prevent hydrate formation and keep dew point control stable.

This proactive maintenance approach of identifying impending issues and implementing preventative actions, and can reduce unplanned downtime by up to 20-45%.⁸ 

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Reducing Operational Costs in Gas Processing with AI

Gas processing plants are constantly tasked with reducing operating costs while ensuring high levels of efficiency and maintaining safety standards. The operational complexity of gas plants, combined with rising energy costs and aging equipment, presents a significant challenge to plant operators. However, AI provides several solutions to reduce costs and improve overall efficiency in gas plant operations.

AI-Powered Process Optimization for Reducing Fuel Gas Consumption

Fuel gas consumption refers to the volume of gas used to power critical plant operations, including compressors, furnaces, and boilers.⁹ Since fuel gas represents a significant share of a plant’s overall energy use, excessive consumption directly impacts profitability by driving up operating costs. For instance, each 1% reduction in fuel efficiency can cost $26,300 per year for a 100 MMBtu/h process heater.¹⁰

AI-powered optimization provides a solution by continuously analyzing real-time operating conditions and dynamically adjusting process parameters to minimize fuel gas usage while maintaining system performance.

By optimizing key processes such as compressor efficiency, reboiler operation, and regenerator overhead temperatures, AI ensures that energy is utilized effectively without compromising production quality. 

AI-driven monitoring systems can detect inefficiencies, identify opportunities for improvement, and recommend immediate adjustments to reduce fuel gas consumption. These data-driven optimizations not only lower energy costs but also improve overall plant efficiency and maintain consistent gas quality. Explore a use case by Basetwo here.

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Acid Gas Removal (AGR) Optimization for Reduced Operational Costs and Energy Consumption in Gas Processing

Acid Gas Removal (AGR) systems play a crucial role in reducing operating costs by efficiently removing acid gases such as hydrogen sulfide (H2S) and carbon dioxide (CO2) from natural gas streams. These systems are designed to handle diverse gas compositions and flow rates effectively, ensuring precise pressure and temperature control throughout the process.¹¹ 

Real-time monitoring powered by AI enables continuous measurement of amine emissions and degradation products, helping operators minimize solvent losses and maintain proper circulation rates.¹² 

AI models also analyze complex data patterns to quickly identify sources of amine losses, foaming, or excessive hydrocarbon absorption—issues that traditionally require extensive manual troubleshooting.¹³

One real-world example of AI-driven process optimization comes from Basetwo, where a client applied AI to optimize their amine gas treating process. By leveraging historical plant data, they determined predictive setpoints aligned with the plant’s AGR optimization goals. The result was a process that not only maximized uptime and throughput but also ensured compliance with H₂S output specifications while minimizing energy consumption in the amine regeneration boiler.⁷ 

This kind of data-driven decision-making demonstrates how AI in AGR processes can increase efficiency and reduce energy consumption—directly contributing to lower overall plant operating costs.

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Gas Dehydration Optimization for Fuel Consumption and Emissions Reduction

Gas dehydration is a critical process that removes moisture to prevent pipeline corrosion and hydrate formation. Traditional dehydration methods, such as glycol-based dehydration (TEG systems), often operate with inefficiencies due to variability in gas composition and flow rates. 

This presents a significant opportunity for optimization. For example, optimizing TEG circulation rates can result in fuel gas savings of over $500,000 or nearly 300,000 GJ/y in a sample of operating dehydration plants.¹⁴

Machine learning algorithms are being employed for multi-objective optimization of triethylene glycol (TEG) dehydration processes, helping to minimize BTEX emissions while maintaining dry gas water content specifications.¹⁵ Studies have demonstrated that reducing glycol circulation rates by 50% can cut BTEX and VOC emissions by around 50%.¹⁶

One study also explored the use of feedforward artificial neural networks (FANNs) to predict equilibrium water dew points in TEG dehydration processes.¹⁷ By accurately forecasting these dew points, operators can optimize TEG circulation rates and other process parameters, resulting in reduced energy consumption and lower operational costs.¹⁸

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Conclusion

AI-driven process optimization is redefining how gas processing plants operate—from reducing sales gas losses to lowering operational costs and maximizing efficiency. By leveraging real-time data, predictive analytics, and machine learning, gas processing facilities can proactively prevent process upsets, optimize equipment performance, and reduce energy consumption.

As gas plant operators continue to adopt digital transformation strategies, AI-driven gas processing optimization will remain a key enabler of enhanced production performance. The integration of real-time optimization, digital twins, and predictive analytics is revolutionizing gas processing, ensuring a more sustainable and cost-effective future for the industry.

For a tailored approach to optimization within your gas plants, reach out to Basetwo today!

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References:

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