2405214
Project Grant
Overview
Grant Description
SBIR Phase I: Tiered multi-satellite observation scheme for methane quantification and attribution.
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in being able to assess and mitigate company-level methane emissions from oil & gas operations across the globe.
Methane, with its global warming potential 85 times that of carbon dioxide over a 20-year period, represents a critical target in meeting the climate change goals.
Specifically, the proposed intervention could help flatten the methane emissions curve, cutting emissions of US oil & gas producers by 75% over five years.
This 75% reduction in emissions from fossil fuels aligns with the International Energy Agency’s goal for 2030 that would enable limiting global warming to 1.5°C.
Additionally, decreased global warming mitigates the frequency and severity of climate-related disasters such as wildfires, floods, and heatwaves.
These changes have profound implications for biodiversity, ecosystems, and human livelihoods, particularly in vulnerable regions which includes much of the U.S.
The proposed technical innovation is a computationally efficient, tiered multi-satellite monitoring system that tracks daily-to-weekly methane emissions from oil & gas assets across the globe.
These are then used to assess company-level emission performance and benchmark companies amongst their peers.
The technology integrates satellite observations from multiple sensors, deep learning models, and statistical data aggregation.
A crucial component is a deep learning model which automatically detects methane plumes in high-resolution imagery from satellites not designed to detect methane, like the Landsat suite and Sentinel-2.
These plumes are used to refine TROPOMI baseline observations.
Most quantification methods, and deep learning models in particular, are too computationally expensive to use at a global scale.
Thus, innovative, computationally efficient methods for emission quantification and statistical data aggregation must be developed.
A significant technical risk is that these new computationally efficient methods may sacrifice some accuracy in methane quantification.
Larger uncertainties using these methods could result in a data product that lacks meaningful insights.
The intellectual merit of the proposed project is in developing computationally efficient new methods which strike the appropriate balance between efficiency and accuracy that meet real-world information needs of key stakeholders.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in being able to assess and mitigate company-level methane emissions from oil & gas operations across the globe.
Methane, with its global warming potential 85 times that of carbon dioxide over a 20-year period, represents a critical target in meeting the climate change goals.
Specifically, the proposed intervention could help flatten the methane emissions curve, cutting emissions of US oil & gas producers by 75% over five years.
This 75% reduction in emissions from fossil fuels aligns with the International Energy Agency’s goal for 2030 that would enable limiting global warming to 1.5°C.
Additionally, decreased global warming mitigates the frequency and severity of climate-related disasters such as wildfires, floods, and heatwaves.
These changes have profound implications for biodiversity, ecosystems, and human livelihoods, particularly in vulnerable regions which includes much of the U.S.
The proposed technical innovation is a computationally efficient, tiered multi-satellite monitoring system that tracks daily-to-weekly methane emissions from oil & gas assets across the globe.
These are then used to assess company-level emission performance and benchmark companies amongst their peers.
The technology integrates satellite observations from multiple sensors, deep learning models, and statistical data aggregation.
A crucial component is a deep learning model which automatically detects methane plumes in high-resolution imagery from satellites not designed to detect methane, like the Landsat suite and Sentinel-2.
These plumes are used to refine TROPOMI baseline observations.
Most quantification methods, and deep learning models in particular, are too computationally expensive to use at a global scale.
Thus, innovative, computationally efficient methods for emission quantification and statistical data aggregation must be developed.
A significant technical risk is that these new computationally efficient methods may sacrifice some accuracy in methane quantification.
Larger uncertainties using these methods could result in a data product that lacks meaningful insights.
The intellectual merit of the proposed project is in developing computationally efficient new methods which strike the appropriate balance between efficiency and accuracy that meet real-world information needs of key stakeholders.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Sausalito,
California
94965-2231
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 7% from $275,000 to $295,000.
Geofinancial Analytics was awarded
Project Grant 2405214
worth $295,000
from National Science Foundation in July 2024 with work to be completed primarily in Sausalito California United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Tiered multi-satellite observation scheme for methane quantification and attribution
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in being able to assess and mitigate company-level methane emissions from oil & gas operations across the globe. Methane, with its global warming potential 85 times that of carbon dioxide over a 20-year period, represents a critical target in meeting the climate change goals. Specifically, the proposed intervention could help flatten the methane emissions curve – cutting emissions of US oil & gas producers by 75% over five years. This 75% reduction in emissions from fossil fuels aligns with the International Energy Agency’s goal for 2030 that would enable limiting global warming to 1.5°C. Additionally, decreased global warming mitigates the frequency and severity of climate-related disasters such as wildfires, floods, and heatwaves. These changes have profound implications for biodiversity, ecosystems, and human livelihoods, particularly in vulnerable regions which includes much of the U.S.
The proposed technical innovation is a computationally efficient, tiered multi-satellite monitoring system that tracks daily-to-weekly methane emissions from oil & gas assets across the globe. These are then used to assess company-level emission performance and benchmark companies amongst their peers. The technology integrates satellite observations from multiple sensors, deep learning models, and statistical data aggregation. A crucial component is a deep learning model which automatically detects methane plumes in high-resolution imagery from satellites not designed to detect methane, like the Landsat suite and Sentinel-2. These plumes are used to refine TROPOMI baseline observations. Most quantification methods, and deep learning models in particular, are too computationally expensive to use at a global scale. Thus innovative, computationally efficient methods for emission quantification and statistical data aggregation must be developed. A significant technical risk is that these new computationally efficient methods may sacrifice some accuracy in methane quantification. Larger uncertainties using these methods could result in a data product that lacks meaningful insights. The intellectual merit of the proposed project is in developing computationally efficient new methods which strike the appropriate balance between efficiency and accuracy that meet real-world information needs of key stakeholders.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
ET
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 5/5/25
Period of Performance
7/15/24
Start Date
6/30/25
End Date
Funding Split
$295.0K
Federal Obligation
$0.0
Non-Federal Obligation
$295.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2405214
Additional Detail
Award ID FAIN
2405214
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
W513L93SHXL7
Awardee CAGE
88V24
Performance District
CA-02
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 5/5/25