Trace Midstream Data Modernization – Optimizing Asset Performance and Driving Operational Efficiency with Azure

Executive Summary

QCG partnered with Tiger Analytics to build Quantum Energy Cloud, a centralized data platform that unifies financial, operational, and market data from across its portfolio, creating a single source of truth that’s secure, connected, and AI-ready. The platform facilitates real-time operations, streamlines back-office processes through automation, and supports advanced analytics, positioning portfolio companies to transition effectively to cloud-native environments. For Trace Midstream, one of QCG’s portfolio companies, Tiger implemented a cloud-based SCADA modernization, ingesting IoT sensor data from multiple compressor stations. This enabled real-time dashboards, faster decision-making, a reduction in per-unit operating expenses, and higher gas compression runtime efficiency. Modernization also created a foundation for predictive maintenance, conditional monitoring, and equipment performance management.

Following the 2024 acquisition of natural gas gathering assets in New Mexico, Trace integrated diverse operational systems. Trace created a secure, scalable data foundation that prepared them for AI by leveraging Microsoft Azure’s features alongside Tiger Analytics’ specialist knowledge.

Trace Midstream Operational Landscape and Challenges

Trace operates natural gas gathering and transportation assets across more than 160 miles of pipeline and 12 compressor stations, with a total design capacity exceeding 650 million standard cubic feet per day of natural gas. Trace inherited diverse systems and processes following the acquisition of new assets in New Mexico, creating a strategic opportunity to improve operational efficiency and data management.

This acquisition presented a variety of challenges:

• On-prem SCADA servers limited scalability and increased downtime risks
• Inconsistent data structures limited analytics, reporting, and scalability
• Reporting tools such as Power BI were not directly connected to operational data

These gaps constrained operational visibility, slowed decision-making, and complicated the monitoring of third-party operator performance. By migrating these systems into Azure and establishing robust pipelines into Azure Data Lake, Tiger helped Trace eliminate data “forking, establish strong data governance, and move towards a cloud-first operating model.

Our Solution: Modernizing SCADA for Real-Time Visibility

Cloud Migration

Trace’s Ignition SCADA instances were migrated to Azure, eliminating physical vulnerabilities and establishing a scalable platform for operations.

Scalable, Real-time Data Ingestion

High-frequency IoT sensor data from compressor stations, gas meters, and other devices undergo real-time data ingestion into Azure Data Lake. Data Pipelines built with Azure Data Factory and Databricks Auto Loader capture and store raw operational data in a durable Bronze Delta Lake layer, establishing a single resilient source of truth.

Data Transformation and Curation

Traces data foundation transforms raw, noisy telemetry into highly contextual, aggregated gold layer data in real-time, tailored for deep advanced analysis and visualization. Sophisticated Databricks pipelines use contextual data dictionaries and data models to clean, connect, and enrich complex sensor input. By incorporating dimension tables, these systems help maintain high levels of data quality. Meanwhile, Unity Catalog enforces strict data governance, security, and lineage, making trusted data available for sharing through federated connections.

Real-Time Business Intelligence

Power BI dashboards provide near real-time insights connected directly to these delta tables in Databricks. Security and access are managed consistently through Unity Catalog.

AI and Machine Learning Foundation

MLflow supports full lifecycle management of machine learning models, preparing the platform for scalable AI applications, including anomaly detection and alarm management.

Operational Transformation

Trace leveraged this modernized architecture to shift its operational culture towards increasingly automated and data-driven accountability. Power BI dashboards, fueled by highly contextual gold data, now facilitate transparent, evidence-based performance tracking with third-party providers, significantly reducing time spent on dispute resolution. This granular visibility empowers Trace to more strictly enforce Service Level Agreements (SLAs). Ultimately, this closes the loop on operational management, transforming raw telemetry into tangible cost savings and higher uptime.

Impact Delivered

• Operational Efficiency: Up to 45% increase in compressor unit volumetric throughput on certain machines. Measurable improvement in compressor fleet runtime
• Cost Optimization: Contextualized long-term historical data analysis helped weed out underperforming assets, driving a reduction in per-unit of throughput compressor operating expenses that was previously unattainable
• Customer Engagement: Automated dashboards improved transparency and responsiveness
• Scalability and Security: Cloud-native architecture supports business growth faster than headcount. Enterprise-grade security reduces dependency on physical IT infrastructure
• Data and Technology Architecture: Consolidation from 10 systems to 7 cloud and SaaS platforms by 2025 SCADA system fully transitioned to Azure. All key information systems now feed into Azure Data Lake and Power BI dashboards. Infrastructure is ready for enhanced anomaly detection and alarm rationalization

Next Phase: Advanced Insights and Automation

• AI and ML Expansion: Extend SCADA anomaly detection for predictive maintenance. Process unstructured data using LLMs.
• Physical Automation: Boost workforce efficiency by automating physical tasks like valve actuation and shutdowns.
• Enhanced Reporting: Expand Power BI dashboards for operational, financial, and emissions monitoring.
• Continuous Optimization: Drive equipment performance with Databricks: Automate analysis, maximize value.