Skip navigation

Oil and Gas Tank Level Forecasting

Today, most facilities operate reactively to problems in tank levels. This 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.

Forecasts are created by harnessing the power of real-time and historical data readily available from sensors, meters and records, which helps to:

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

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 and then 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.

The Cortana Intelligence Suite is adaptable and can be customised to meet different requirements that facilities and corporations have.


Note: If you have already deployed this solution, click here to view your deployment.

For more details about how this solution is built, visit the solution guide in GitHub.

Estimated provisioning time: 20 minutes

The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure – data ingestion, data storage, data processing and advanced analytics components – all of the essential elements for building a tank level forecasting solution.

This solution combines several Azure services to provide powerful advantages. Event Hubs collects real-time tank level data. Stream Analytics aggregates the streaming data and makes it available for visualisation. Azure SQL Data Warehouse stores and transforms the tank level data. Machine Learning implements and executes the forecasting model. Power BI provides a visualisation of the real-time tank level as well as the forecast results. Finally, Data Factory orchestrates and schedules the entire data flow.

The ‘Deploy’ button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify. The solution includes multiple Azure services (described below) along with a web job that simulates data so that immediately after deployment you have a working end-to-end solution.

After deployment, see the post-deployment instructions here.

Technical details and workflow

  1. The data feeds into the Azure Event Hubs and Azure SQL Data Warehouse service as data points or events, which will be used in the rest of the solution flow.
  2. Azure Stream Analytics analyses the data to provide near real-time analytics on the input stream from the event hub, and publishes directly to Power BI for visualisation.
  3. The Azure Machine Learning service is used to make forecasts on the tank level of a particular region given the inputs received.
  4. Azure SQL Data Warehouse is used to store the prediction results received from the Azure Machine Learning service. 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 visualisation, so that users can monitor the tank level from a facility in real time and use the forecast level to prevent spillage.


©2017 Microsoft Corporation. All rights reserved. This information is provided “as is” and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third-party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets.

Related solution architectures

Energy Supply Optimization

In an energy grid, energy consumers are engaged with various types of energy supplying, trading and storage components such as substations, batteries, wind farms, solar panels, micro-turbines and demand response bids to meet their respective demands and minimise the cost of energy commitment. To achieve this, the grid operator must determine how much energy each type of resource should commit over a given time frame, given the prices of soliciting different types of resources and the capacities and physical characteristics of each of them.