SBIR/STTR Research Award

11b InFormer (Induced Seismicity Transformer)

Award identifier e33a195f0e429ffe4fb5f74889b88fc4d4e7650fb7a6ee8011e9cb08bcc348aeAwarding agency Department of Energy

Award amount

$199,950

Program and phase

SBIR · Phase I

Award year

2024

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
STOTTLER HENKE ASSOCIATES, INC
Proposal award date
2024-02-12
Contract end date
2024-12-11
UEI
FFEAH6Z5CK27
Solicitation number
DE-FOA-0003110
Topic code
C57-11b

Research abstract

Induced seismic activity is a category of seismic activity that is caused by human activity, which includes hydraulic fracturing, deep and shallow salt water disposal, and carbon sequestration, among others. In the past decade, induced seismic activity has increased in the United States, especially in the central US, causing continued danger and disruption to local communities as well as to commercial operations. While it is known that changes in pore pressure and/or poroelastic stress from injection or extraction of fluids is the primary trigger mechanism for induced seismicity, the broader causes for these changes are complex and multi-dimensional; meanwhile, current regulatory attempts are broad and reactive in nature and thus expose local communities to continued risk. Machine learning techniques like transformers have demonstrated the ability to learn complex, multivariate relationships that have been traditionally difficult to study. The proposed InFormer system will use an approach inspired by Spacetimeformer, a transformer-based multivariate time series forecasting technique which will be adapted to analyze the complex spatial and temporal relationships between well injection and production activity and induced seismicity. InFormer aims to find latent connections between variables; the tool will be useful to regulators and industry to evaluate existing and proposed operations on their seismic hazard before they occur. In the proposed effort, key seismological features from an existing set of injection data will be cleaned and engineered. This dataset will then be used to train a prototype machine learning pipeline based on the SpacetimeFormer architecture. The end-to-end pipeline will be evaluated on both in-distribution data and out-of-distribution data, i.e., data at the same geographical location as the training data and data at a different geographical location. This partition will demonstrate the efficacy of the proposed methods as well as their generalizability and domain adaptation capabilitiesŚthe evaluation results will be used to produce visualizations and reports representative of the full Phase II system. The proposed solution will help inform Government agencies how to best regulate injections (primarily saltwater disposal), which will help reduce environmental and societal impacts related to induced seismicity. InFormer will also help private companies evaluate new injection sites and potential risks associated with the locations. This will provide a method for prospectors to target construction in lower- impact areas and inform buyers of expected future site instabilities.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

428 SBIR/STTR records share this UEI (1990–2026). Reported award amounts total $136,164,855. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Health and Human Services, Department of Energy, Department of Commerce, National Science Foundation

Agency intelligence

Department of Energy

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