April 2026

Process Optimization

Digitalizing chemistry-driven quality management for intelligent operations—Part 1

IP: 10.1.228.95

In the oil and gas industry, the chemical integrity of amine solvents used in gas sweetening processes is a key operational intelligence driver for maintaining the efficiency of amine plant operations, as well as compliance with pipeline gas specifications. Amine-based units are essential to remove acidic contaminants like hydrogen sulfide (H₂S) and carbon dioxide (CO₂) from sour gas streams. However, the long-term effectiveness of these units hinges on the chemical stability and purity of the amine solvent. 

Over time, amine solvents degrade due to thermal and oxidative reactions and become contaminated with heat-stable amine salts, suspended solids, hydrocarbons and other impurities. These changes not only reduce the acid gas absorption capacity of the amine solution, but also contribute to equipment corrosion, foaming, fouling and increased operational costs. Therefore, continuous monitoring and proactive management of amine quality are essential to avoid process upsets and maintain safe and efficient plant operations. 

To address these challenges, the Amine Healthiness Management Index (AHMI) was developed as a quantitative, real-time indicator of amine degradation and contamination risks. The AHMI integrates key chemical parameters derived from laboratory testing data, including amine concentration, degradation byproducts, suspended solids and acid gas loading. 

By consolidating these critical factors into a single, standardized index, the AHMI enables operators to assess the overall health of the amine system from a chemistry-centric perspective. This integrated approach supports early detection of deteriorating conditions and data-driven decision-making to optimize amine system performance. 

Field trials and operational data have demonstrated that the AHMI correlates strongly with system anomalies such as solvent foaming and downstream contamination, allowing for predictive interventions before severe upsets occur. Furthermore, the index serves as a valuable tool for benchmarking amine quality across units, identifying root causes of degradation and guiding solvent reclamation or replacement strategies. 

AHMI development. In gas processing plants, amine-based gas sweetening (FIG. 1) is a cornerstone technology for the removal of acidic gases such as H₂S and CO₂. However, amine degradation due to heat, oxidation and contamination with hydrocarbons and metals poses a significant operational challenge. Degraded amine solutions can lead to reduced gas treating efficiency, increased corrosion, foaming and higher chemical consumption, ultimately affecting plant reliability and safety.  

FIG. 1. Gas sweetening (amine solvent-based) process flow diagram. 

Traditionally, amine quality has been assessed using isolated parameters such as concentration, pH and degradation product levels. These metrics, while useful, often fail to provide a holistic view of the system’s health. To address this gap, the AHMI was developed as a composite indicator that integrates multiple amine quality parameters into a single value, enabling real-time monitoring and early warning of deteriorating conditions. 

The AHMI is inspired by similar methodologies, such as the Hansen Solubility Parameter (HSP) and Water Quality Index (WQI), which combine multiple physical and chemical factors into a unified index. The AHMI aims to provide a comparable framework for amine systems, facilitating proactive maintenance, chemical optimization and asset integrity management. This article presents the development, application and validation of the AHMI for the Shedgum Gas Plant, focusing on its correlation with amine performance indicators. 

METHODOLOGY 

Amine quality domains. The AHMI is based on the integration of five core amine quality indicators: 

1. Amine concentration (key driver of acid gas absorption capacity) 
2. Degradation byproducts [e.g., heat stable salts (HSS)] indicators of amine breakdown and associated corrosion risks 
3. Suspended solids (measuring particulate contamination that can cause foaming and fouling) 
4. Acid gas loading (reflects amine activity and degradation status) 
5. Foaming tendency (process indicator of instability caused by contaminants or degradation). 

Each parameter is normalized on a scale of 0 to 1, where 1 represents optimal condition and 0 represents a critical failure threshold. 

Once samples are received from the plant, they are entered into a laboratory information management system (LIMS), with each sample assigned a unique identification number. The above parameters are assigned to every sample. This sequence of samples eventually creates a repository of the sample’s location, date of collection, analysis results and a comparison of the results with active specification limits.  

AHMI fundamentals. Eq. 1 represents the mathematical formula developed in reference to the WQI for the AHMI: 

where:  

• The AHMI is the Aggregated Amine Healthiness Index value (dimensionless, ranging from 0 to 100). 

• n is the number of amine quality variables considered in the index. 

• aᵢ is i-th sub-index value, representing the normalized chemical condition value for each variable. 

• mᵢ is i-th correlated weight factor determined to the corresponding variable, based on its relative impact on solvent performance. 

• T₁ is the temperature correction factor (adjusts sub-index values based on process temperature deviations from reference conditions) 
• P₂ represents the contamination correction factor (adjusts the index based on the presence of non-chemical stressors such as mechanical debris or microbial contamination). 

The resulting AHMI score ranges from 0 to 100, where:  

• 80–100: Excellent condition 

• 60–80: Acceptable, some monitoring required 

• 40–60: Marginal, corrective action recommended 

• 0–40: Critical, immediate intervention required. 

In this case, the total number of amine quality variables (n) is five, and the temperature correction factor (T₁) is regarded as 0.5 when the temperature is less than 140°F (60°C), otherwise 1. Whereas the contamination correction factor (P₁) is either 0.5 or 1, depending on the degree of pollution that created color or the odor nuisance, and this includes the formation of sludge, deposits, the presence of oil, debris, foam, etc. 

Integration with laboratory management data. The AHMI is integrated with the plant’s LIMS. Real-time data from inline sensors and lab results can be developed by measuring all five parameters of the amine system, and are then parsed into the AHMI engine, enabling dynamic updates and alarms for low AHMI values (FIG. 2).  

FIG. 2. LIMS workflow integration. 

EXPERIMENTAL  

Data collection and instrumentation. The AHMI model was deployed for the Shedgum Gas Plant, focusing on the amine sweetening section treating sour gas streams. Data collection spanned 7 mos (January 2025–July 2025), with the following parameters continuously monitored (FIG. 3): 

• Amine concentration  

• Foaming tendency (foam height test with nitrogen sparging) 

• HSS levels (grab samples analyzed in the plant’s lab) 

• Total suspended solids (periodic filtration tests) 

• Acid gas loading (grab samples analyzed in the plant’s lab). 

FIG. 3. Parameters monitored at the Shedgum Gas Plant.  

AHMI implementation. The AHMI model was implemented as a web-based dashboard accessible to operators, engineers and plant management. The model was trained using historical data from the past 2 yrs to establish optimal and critical thresholds. The weighting of each parameter was determined through sensitivity analysis and expert input from process engineers. 

Validation metrics. The AHMI was validated against: 

• Foaming incidents recorded in the plant’s alarm system 

• Corrosion rates from inline corrosion probes 

• Lean amine efficiency (measured by H₂S residual in sweet gas) 
• Chemical consumption (antifoam and amine makeup). 

RESULTS AND DISCUSSION  

AHMI trends and parameter influence. Over the 7-mos trial, AHMI values ranged from 54 to 93, with an average of 81. The lowest AHMI was observed during a period of high HSS and suspended solids, correlating with a foaming incident in the amine absorber. FIG. 4 shows the AHMI trend plotted alongside foaming events and corrosion rates. The clear inverse trend is visible: as the AHMI decreases, foaming and corrosion increase. 

FIG. 4. AHMI trend vs. foaming incidents and corrosion rates over 7 mos. 

Correlation analysis. Pearson correlation coefficients were calculated between the AHMI and operational indicators (TABLE 1).  

These results indicate that the AHMI is a strong predictive indicator of system health, particularly in relation to foaming and absorption efficiency. 

Operational insights.  

• Foaming mitigation: During a scheduled maintenance period, the AHMI was used to identify a unit with low scores due to high suspended solids. Preemptive filtration restored the AHMI to optimal levels, avoiding a potential foaming event. 

• Chemical optimization: When the AHMI dropped due to elevated HSS, the model triggered an increase in reclaimer operation, resulting in a 15% reduction in amine top-up and antifoam use. 

• Early warning capability: The AHMI provided a 48-hr lead over traditional lab sampling in identifying a degradation trend, allowing proactive adjustments to the regeneration system. 

Takeaways. The AHMI offers a novel, data-driven approach to managing amine system health in gas processing environments. By integrating multiple quality indicators into a single, dynamic index, the AHMI enables: 

• Early detection of degradation and contamination risks 

• Improved operational decision-making 

• Reduced chemical consumption and foaming incidents 

• Enhanced asset integrity and process reliability. 

The trial at the Shedgum Gas Plant demonstrated a strong inverse correlation between the AHMI and foaming incidents, as well as moderate correlations with corrosion and chemical use. These findings support the adoption of the AHMI as a key performance indicator for amine systems, particularly in large-scale processing facilities. 

Future work will focus on expanding the AHMI model to include machine-learning-based prediction and integration with SAP-based process monitoring systems, further enhancing its value as a digital twin for amine system management. 

Correlations between AHMI scores and solvent foaming incidents, amine contaminations and lean chemistry amine efficiency were evaluated over a 7-mos trial at the Shedgum Gas Plant. A strong inverse correlation (R = – 0.91) was observed between the AHMI and foaming incidents, while a moderate correlation (R = – 0.76) was found with downstream contaminations rates. The results suggest that the AHMI can serve as a robust decision-making tool for amine system optimization, enabling early detection of degrading conditions and supporting data-driven maintenance strategies. 

In summary, the AHMI enhances the monitoring and management of amine systems by transforming complex chemical data into actionable insights, thereby safeguarding process efficiency, equipment longevity and compliance with gas quality standards. 

ACKNOWLEDGMENTS 

The authors want to thank the Saudi Aramco Process Engineering and Plant Operations teams for their support during the implementation and validation of the AHMI. Special thanks to the Shedgum Gas Plant engineering team for ensuring data quality and system integration. 

The Authors

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