Stock Changes in Oil Tanks with Floating Roof

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN9: Understanding stock levels and monitoring supply chains.

Description

The condition of crude oil reserves holds significant significance for the oil market and is a vital determinant in worldwide economic progress. Oil inventory reflects the balance between market supply and demand, directly impacting pricing Therefore, regular, and precise updates regarding the levels of reserves are of utmost importance. High frequent SAR VHR imagery (daily) can be utilized for daily assessments of oil production. SAR imagery is preferable as it provides a continuous time series regardless of weather conditions, day, and night. However, remote sensing sensors cannot penetrate walls and roofs. Therefore, they are only capable of measuring the volume of oil in storage tanks that are above ground and equipped with an external floating roof. The roof sits on the oil and goes up and down as the oil level changes in the tanks. This helps to make less space above the oil and reduce the vapour in that space. By measuring how tall and wide the floating tanks are, along with some other tank measurements, we can accurately figure out how much oil they can hold.

Spatial Coverage Target

Oil storage tanks with floating roof

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

By using a time series of daily or weekly VHR SAR imagery for the same region, it becomes feasible to detect the change that is occurring in an object (floating roof oil tanks).  Objects that haven't changed while being observed will appear identical every day. Objects that have undergone changes can be recognized and studied. With the VHR imagery, the change in height and area can be calculated enabling the volume of oil on a daily or weekly basis.

Input data sources

Optical:  N.A

Radar: VHR images from different sources like ICEYE and Capella space

Supporting data:   N.A

Accessibility

SAR VHR imagery: commercially available on demand from EO service providers.

Spatial resolution

SAR VHR: ≤ 0.5 m

Frequency (Temporal resolution)

SAR VHR: sub-daily to daily

Latency

≤ 1 day

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: 90%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

n Cost of time series of VHR images

n While VHR imagery provides detailed views, there might still be limitations in identifying very small details

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Ample

Knowledge: Ample

Similar Products

P14: Stock changes in oil tanks with floating roof

Download the product sheet gap analysis 

Maturity score

Mean: 2.5

STD: 0.50

Constraints and limitations

·  While VHR imagery provides detailed views, there might still be limitations in identifying very small details.

·  The product is only limited to oil tanks with floating roofs.

·  Cost of time series of VHR images.

Relevant user needs

UN9: Understanding stock levels and monitoring supply chains

R&D gaps

·  Few commercial missions with VHR SAR satellite imagery as this product is preferable to be developed using SAR image to provide data day for all weather conditions.

Potential improvements drivers

·  More VHR SAR constellations

·  Price models for commercial EO data

Utilisation level review

Utilisation score

Mean: 2.83

STD: 1.07

No utilisation

Unawareness of the existence of this EO product.

Low utilisation

Medium utilisation

Unawareness of the existence of the best available commercial EO product with better specifications.

High utilisation

Some financial organizations already purchase this EO product and use the data in their modelling of prices for trading

Critical gaps related to relevant user needs