Case Study: Early Detection of Compressor Anomalies at TotalEnergies

TotalEnergies, one of the largest integrated energy and petroleum companies on the planet, recently undertook a transformative project to enhance the reliability and efficiency of their critical CNG compressors. Recognizing the imperative of predictive maintenance to reduce operational downtime and maintenance costs, TotalEnergies leveraged the Tangent to implement an advanced solution for asset health monitoring and early detection of upcoming failures.

This case study examines the successful integration of Tangent into TotalEnergies' field maintenance service operations, highlighting how it is enabling early anomaly detection for reduced downtime resulting in substantial cost savings.

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Introduction

Compressor systems are vital to numerous processes in the oil and gas industry, making their reliability and efficiency essential for business continuity. TotalEnergies recognized the significance of predictive maintenance and data-driven insights to maximize asset lifespan and minimize unplanned downtime.

In this context, TotalEnergies leveraged Tangent to implement predictive maintenance capabilities for their CNG compressors.

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Success criteria

TotalEnergies established the following primary objectives for this roll-out:

1. Early Anomaly Detection: implement a predictive maintenance solution to identify and predict compressor anomalies before they escalate into critical failures.

2. Data-Driven Insights: utilize advanced analytics and machine learning to gain valuable insights into compressor performance and behavior.

3. Optimized Operations: improve overall system efficiency, reduce maintenance costs, and enhance operational availability and reliability.

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Implementating Predictive Maintenance with Tangent

The project's implementation unfolded through a series of pivotal steps. Initially, data was collected from over 100 CNG compressors, encompassing a range of parameters such as temperature, pressure, and process data. This phase was crucial for building a comprehensive dataset.

Following this, data integration efforts structured various data sources into a centralized IoT platform provided by Siemens, which was instrumental in ensuring data accuracy and consistency.

With a solid data foundation in place, Tangent's predictive AI models were deployed, leveraging historical data to identify patterns and devise predictive maintenance algorithms.

This allowed for the continuous analysis of incoming data, enabling the system to trigger alerts whenever anomalies or irregularities were detected, thereby facilitating real-time monitoring and proactive maintenance strategies.

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Benefits Realized: Enhanced Efficiency and Safety

The implementation of Tangent yielded significant benefits for TotalEnergies:

1. Early Anomaly Detection: Tangent enables TotalEnergies to detect most anomalies in their compressor systems well in advance of critical failure, reducing unplanned downtime.

2. Data-Driven Insights: Tangent provides valuable insights into compressor performance and behavior, facilitating more informed decision-making.

3. Optimized Operations: with early anomaly detection and proactive maintenance, TotalEnergies is reducing maintenance costs and enhanced operational reliability, resulting in improved profitability.

4. Safety Improvements: Tangent contributes to enhanced safety by identifying potential issues that could pose risks to personnel or the environment.

5. Integration Success: The project successfully integrated Tangent into existing systems and processes, demonstrating the feasibility of predictive maintenance at scale.

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Future Outlook

TotalEnergies plans to expand the use of Tangent beyond compressor systems. The success of this project has spurred exploration into applying Tangent's predictive maintenance capabilities to other critical assets within the organization, such as pumps, turbines, and pipelines.

TotalEnergies also intends to continue enhancing Tangent's AI models and integrating additional data sources for improved results and deeper insights into their operations.

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Conclusion

TotalEnergies' implementation of the Tangent Information Modeler (Tangent) for early detection of compressor anomalies stands as a testament to the power of predictive maintenance and data-driven decision-making. The project has improved operational reliability, reduced costs, and enhanced safety while providing actionable insights for informed decision-making.

Tangent's scalability and adaptability position TotalEnergies to continue reaping the rewards of predictive maintenance and data-driven excellence in the future, serving as a blueprint for asset management in process industries.

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Article by Sam Verdonck, Chief Growth Officer at Tangent Works

Connect with Sam via LinkedIn!

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