Stock Changes in Oil Tanks with Floating Roof | ||||||||
(Left) Oil tanks with floating roof (Right) Floating oil tank roofs in the Port of Rotterdam, Netherlands using ICEYE’s Daily Coherent Ground Track Repeat (GTR) imagery (0.5 m) (Source: ICEYE). | ||||||||
Product Category | ||||||||
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Financial Domains | ||||||||
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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 |
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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 | |
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 | |
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