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Oil and gas tank level forecasting

Azure Data Factory
Azure Event Hubs
Azure Machine Learning
Azure Stream Analytics
Azure Synapse Analytics

Solution ideas

This article describes a solution idea. Your cloud architect can use this guidance to help visualize the major components for a typical implementation of this architecture. Use this article as a starting point to design a well-architected solution that aligns with your workload's specific requirements.

Today, most facilities operate reactively to problems in tank levels. This reactivity often leads to spills, emergency shutdowns, expensive remediation costs, regulatory issues, costly repairs, and fines. Tank level forecasting helps manage and abate these and other problems.

Architecture

Architecture diagram shows data into Azure Event Hubs / Azure Synapse. Azure Stream Analytics analyzes data while Power BI monitors oil tank level.

Download a Visio file of this architecture.

Dataflow

  1. The data feeds into the Azure Event Hubs and Azure Synapse Analytics service as data points or events that will be used in the rest of the solution flow.
  2. Azure Stream Analytics analyzes the data to provide near real-time analytics on the input stream from the event hub and directly publish to Power BI for visualization.
  3. Azure Machine Learning is used to make forecast on the tank level of particular region given the inputs received.
  4. Azure Synapse Analytics is used to store the prediction results received from Azure Machine Learning. These results are then consumed in the Power BI dashboard.
  5. Azure Data Factory handles orchestration, and scheduling of the hourly model retraining.
  6. Finally, Power BI is used for results visualization, so that users can monitor the tank level from a facility in real time and use the forecast level to prevent spillage.

Components

Scenario details

The tank level forecasting process starts at the well input. Oil is measured as it comes into the facility via meters and is sent to tanks. Levels are monitored and recorded in tanks during the refining process. Oil, gas, and water output are recorded via sensors, meters, and records. Forecasts are then made using data from the facility; for example, forecasts can be made every 15 minutes.

Azure Cognitive Services is adaptable and can be customized to meet different requirements that facilities and corporations have.

Potential use cases

This solution is ideal for the energy, automotive, and aerospace industries.

Forecasts are created by harnessing the power of real-time and historical data readily available from sensors, meters, and records, which help with the following scenarios:

  • Prevent tank spillage and emergency shutdowns
  • Discover hardware malfunction or failure
  • Schedule maintenance, shutdowns, and logistics
  • Optimize operations and facility efficiency
  • Detect pipeline leaks and slugging
  • Reduce costs, fines, and downtime

Next steps

Product documentation:

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