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Small Business Research Collaboration

Small Business Research Collaboration Overview 


The Carbon Transport and Storage Program fosters collaboration between the National Energy Technology Laboratory (NETL) and small businesses through the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs. The SBIR/STTR programs are highly competitive programs that encourage domestic small businesses to engage in Federal Research/Research and Development (R/R&D) with the potential for commercialization. Through a competitive awards-based program, SBIR and STTR enable small businesses to explore their technological potential and provide the incentive to profit from its commercialization. By including qualified small businesses in the nation's R&D arena, high-tech innovation is stimulated, and the United States gains entrepreneurial spirit as it meets its specific research and development needs. Information about current Carbon Transport and Storage Program funded SBIR projects is presented below.

The goal of this project is to further develop rapid, cost-effective technologies to solve issues related to the collection, compression, and transmission of large volumes of subsurface sensory data. Phase I demonstrated the feasibility of novel compression algorithms for automated data collection and transmission of time-lapsed seismic data. Phase II will focus on the optimization of compression gain and reconstruction quality of collected data while also developing these technologies to be more cost-effective as they move toward future commercialization.


The goal of this project is to generate innovative user interfaces for government-supplied scientific software. During the Phase I, working prototypes of the software system and software distribution mechanism were constructed and demonstrated to show the feasibility of the approach. Phase II will focus on implementing the workflows, conducting stakeholder interviews, enabling processing of external reservoir simulator data, and developing simulation lifecycle management facilities. Quarterly alpha and beta test releases are planned to fully test the software in the field.


The goal of this project is to prove its concept of combining all three natural fracture characterization approaches (geological, geophysical and petrophysical) using ML techniques to create a workflow that accurately predicts fractured reservoirs before wells are drilled.

The purpose of this project is to develop a real-time passive seismic monitoring system by deploying specially engineered fiber-optic cables on the surface, in shallow boreholes and in injection wells. This creates a 3D sensing array with optimal sensing geometry and sensitivity, able to detect weak events.

The purpose of this project is to use the large amount of multiscale and multimodal data available in EDX for various formations to create machine learning-aided rock physics models. The aim is to use the data in the EDX platform to develop an end-to-end mapping between the rock and seismic properties. Based on the change in rock properties, the CO2 plume migration path can be visualized.

The purpose of this project is to support the development of a turnkey microseismic monitoring service that would help ensure the safe operation of industrial scale CO2 storage facilities. Specifically, this would enable operators of CCS facilities to make better operational decisions about the state of the storage formation, CO2 injection rates, and reservoir integrity. Ensuring safe operation of any storage facility is critical to the long-term effectiveness of this approach. Reducing or eliminating significant seismic events is also critical to public perception and acceptance. This Phase 1 proposal covers modeling, design, and planning for the installation and operation of a turnkey, long-term, real-time seismic monitoring service for CCS facilities in the USA, and the selection of a test site on which to validate the service.

The goal of this project is to adapt and optimize novel deep learning time-frequency denoising tools to distributed acoustic sensing (DAS) array data to enhance the signal-to-noise (SNR) ratios of DAS waveforms. Such denoising work is intended to improve the detectability of certain signals of interest, including local, regional and teleseismic earthquakes, microseisms, converted phases from local structures, and long-duration and emergent signals (e.g., tremor-like signals).

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