AWS Autonomous Pipeline Solution for TC Energy Saves Money, Improves Reliability

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This case study details TC Energy’s need to improve operational efficiency by automating sensor data. AIM Consulting’s solution generated millions in monthly ROI for TC Energy while increasing reliability and promoting more efficient resource management.

TABLE OF CONTENTS:

Situation: TC Energy Bogged Down With Manual Processes

Canadian-based TC Energy is one of North America’s largest energy companies, with more than 60,000 miles of natural gas and crude oil pipelines operating in Canada, the United States, and Mexico.

Employing more than 7,000 workers across the continent, the company generated US $10.7 billion in 2021 revenue through its core businesses of energy solutions, natural gas, oil & liquids, and power & storage.

Approximately 75% of TC Energy’s power capacity is emission-less; the company takes pride in its development and operation of high-efficiency, natural gas-fired generating stations. Innovation has enabled TC Energy to lead in sustainability and green-energy initiatives.

The company’s field engineers have long monitored natural gas pipeline sensors to help determine real-time supply and demand. More gas is released from a storage facility when demand is high and conversely, in times of lower demand, more gas is stored.

Sensor data is relayed to corporate offices, where workers manually massaged the data from different geographical regions using a custom solution, an operation requiring considerable time and resources. Manual processes such as these remain a challenge in the energy industry.

Business Challenge: Automate Pipeline Data To Improve Efficiencies

Tasked with improving operational efficiency, IT leaders launched a project to automate the sensor data for three major U.S. gas pipelines, with the intent for faster and easier interpretation of the data and more intelligent business decisions.

The project was designed to improve efficiencies in equipment run-time, leading to fuel savings, lower greenhouse gas emissions, and confidence in reliable and safe operations. TC Energy brought in AIM to scale its autonomous pipeline solution.

Solution: AI & ML Models Deliver Effective, Unified View

The project had several components, one of which was a pilot where our data engineers and visualization experts digested and transformed power generation data. This was coupled with an intelligence solution for gas gathering and scheduling.

The pilot’s success ensured the solution would scale across additional pipelines, power plants, and storage facilities. The pilot and subsequent projects involved the integration of numerous technologies based in Amazon Web Services (AWS), TC Energy’s existing cloud partner.

Our data experts created artificial intelligence (AI) and machine learning (ML) models to make predictions for station pressures and produce operational recommendations to meet the predicted flow requirements.

These machine learning models worked by pulling together data from a variety of internal and external sources, including telemetry, nominations, weather, and sensor data – evaluating thousands of potential scenarios.

The machine learning models work 24/7 to make predictions for station pressures and produce operational recommendations to meet the predicted flow requirements.

AIM crafted the Machine Learning models to include:

  • Preprocessing and exploratory data analysis (EDA)
  • Feature selection
  • Data set builds
  • Model training and evaluation (36 models per pipeline)
  • Parameter selection
  • Hyperparameter tuning
  • Model deployment

AIM transformed TC Energy’s legacy data solution into a human-centric, connected experience for end users, turning existing models into highly-stylized user interfaces, which were deployed to all three pipelines.

The solution delivered a ‘single pane of glass’ unified view. Controllers can now look to one dashboard in real time to assess how best to optimize the pipelines.

Additionally, the UI/UX framework has set a baseline for all future designs to accelerate development and further increase the usability of the solution.

AWS tools leveraged by the models

Throughout the project, we introduced numerous technologies and methodologies to TC Energy’s IT staff, including various AWS services, to help refine their AWS architecture:

  • Amazon Redshift
  • Amazon S3
  • Amazon DynamoDB
  • Amazon Lambda
  • OSIsoft
  • Amazon Timestream
  • Amazon Quicksight
  • Amazon Sagemaker
  • Angular

“The Autonomous Pipeline project enabled the consolidation of diverse data into a central point, with the system’s design allowing gas controllers to make decisions easily and with confidence. This project started out as a proof-of-concept and was quickly embraced as a product.”

“AIM resources helped reduce “technical debt,” which occurs naturally in rapid prototyping application development. This was an essential step in the evolution of the application, and this activity not only helped create a more stable product but also enabled our business to expand the solution across multiple pipelines.”

Cristine Wells, Sr. Strategic Business Analyst at TC Energy

Results: Increased Reliability, Significant ROI

Our delivery of the Autonomous Pipeline Program has already paid for itself through a significant financial monthly ROI, and plans for future product development and program growth.

The project led to the following results:

  • Increased reliability
  • Reduced downtimes and other start-and-stop events
  • Fuel savings
  • Lower greenhouse emissions
  • More efficient resource management
  • Optimized profitability

Our guidance and training to ensure engineers understand and operate the product effectively also increased the IT department’s organizational maturity.

Additional business units at TC Energy are looking to apply the learning and technology from the Autonomous Pipeline Program to their projects, further spreading the value of AIM’s expertise throughout TC Energy.

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