Company
Portfolio Data
Back to Company SearchZanskar Geothermal & Minerals, Inc.
Address
90 South 400 WestSalt Lake City, UT, 84101
USA
UEI: EUN8RSK38WS4
Number of Employees: 120
HUBZone Owned: N/A
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2022
2
Phase I Awards
1
Phase II Awards
50%
Conversion Rate
$449,849
Phase I Dollars
$1,614,946
Phase II Dollars
$2,064,795
Total Awarded
Awards
Developing and Applying Deep Learning Time-Frequency Denoising Tools to DAS Data to Enhance Passive Seismic Signals for Seismic Hazard Analysis and Reservoir Characterization
Amount: $1,614,946 Topic: C54-23c
DAS (distributed acoustic sensing) has been widespread in the fields of glaciers, volcanoes, fault zones, and geothermal, etc. due to its high spatial and temporal resolution, which also shows big potential to be used for seismic characterization and monitoring during carbon storage. However, generally the large DAS data highly contaminated by serious noise appear to have a very low signal-to-noise ratio, which prevents the wide application of DAS data. The team is developing an efficient and effective DAS denoising tool based on autoencoder deep learning neural networks by assembling and labeling borehole DAS seismic events from different sites. In Phase I, the team developed an effective cascade workflow for labeling field borehole DAS seismic events, and end-to-end autoencoder deep learning DAS denoising tool with high-efficiency targeting on borehole DAS data. The case study on the earthquake and microseismic events from FORGE site indicate that the developed deep learning DAS denoising tool is superior to the conventional industry spectral filtering method. In Phase II, the team will 1) further optimize the deep learning based DAS denoising tool by expanding more types (especially surface DAS array) of assembled and labeled DAS seismic events; 2) develop a workflow to monitor the induced seismicity during carbon storage using the denoised DAS seismic data; 3) Build carbon storage DAS array design tool for general use. The DAS denoising and seismicity monitoring tool is critical for the carbon storage site characterization and monitoring in Phase 3 of field case study. Additionally, the developed tool is naturally to be extended to other fields for DAS applications, such as seismology, geothermal, etc.
Tagged as:
SBIR
Phase II
2023
DOE
Developing and applying deep learning time-frequency denoising tools to DAS data to enhance passive seismic signals for seismic hazard analysis and reservoir characterization
Amount: $249,935 Topic: C54-23c
Better seismic characterization tools are needed to assess the seismic hazards of those sites before major investments are made to prevent large economic losses (and possible human life losses) that would occur if moderate to large earthquake events are induced. Fiber optic sensing for seismic monitoring (distributed acoustic sensing; DAS) is revolutionizing seismology today but despite undeniable advantages, distributed acoustic sensing remains an underutilized tool, due to hurdles associated with data management and processing. The proposer plans to adapt and optimize novel deep learning time-frequency denoising tools to 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). The Phase I effort will adapt and optimize a recently developed, convolutional neural network (CNN) applied to time-frequency domain data. During the Phase I effort, the proposer will assemble a large database of a diverse range of signals of interest (e.g., local, regional and teleseismic earthquakes, microseisms, converted phases from local structures, and tremor-like signals) recorded by publicly available DAS deployments that will be used to create training, validation, and test datasets. These datasets will be used to modify, optimize and train Convoluted Neural Networks (CNN) to produce masks for each signal of interest that can be applied to raw DAS data to improve signal-to-noise ratios (SNR) without distorting waveforms. We will assess CNN performance against industry standard spectral filters and a pre-trained version of a CNN by utilizing the test DAS dataset and comparing filtered waveform properties. The Phase I effort will lead to a Phase II DAS field campaign, to be conducted at a proposed deep saline formation carbon sequestration site in the Appalachian Basin of SW Pennsylvania. Commercial and Other Benefits: Industry and market users in the carbon storage sector that will benefit from improved characterization include those looking to develop deep saline, depleted hydrocarbon, deep coalbeds, or young mafic rock reservoirs (which can overlap geothermal reservoirs, see CarbFix) for geologic CCS. Potential customers or beneficiaries of this development include oil and gas majors (e.g., ExxonMobil, Chevron, and Shell), several of whom are currently partnered on developing one of the largest geologic CCS sites in the world (the Gorgon Carbon Dioxide Injection Project in Australia, which is modeled to capture 3-4 million tons of CO2 per year). Significantly, the de-risking of CCS projects will contribute to the administration’s effort to achieve net-zero emissions by 2050.
Tagged as:
SBIR
Phase I
2022
DOE
Predicting naturally fractured reservoirs for subsurface characterization using machine learning and the EDX database
Amount: $199,914 Topic: C53-21d
Characterizing naturally fractured reservoirs is essential for subsurface energy applications, including hydrocarbon exploration, geothermal development, and carbon storage evaluation. However, predicting fracture properties (density, aperture, permeability, etc.) is difficult, especially in areas where there are few existing wells. There are three general approaches for characterizing naturally fractured reservoirs: Geological, Geophysical and Petrophysical. Individually, these methods fail to provide universal fracture predictions with high levels of confidence in areas where there are few well calibrations. This Phase I project aims to prove its concept of combining all three natural fracture characterization approaches using ML techniques to create a workflow that accurately predicts fractured reservoirs before wells are drilled. The subsurface EDX dataset will be used as training data for the machine learning model. Seismic data will be used to generate seismic attributes; horizon and fault interpretations will be used to generate structural restorations, strain calculations, and other structural attributes; petrophysical information from wells, outcrop analogs, and seismic inversions will be used as petrophysical attributes. Combined, these attributes will constitute the feature data. The machine learning model will be trained on these feature data using label data from wells. There is an urgent need to bring on additional sources of carbon-free energy sources if the United States is going to meet its carbon reduction targets. Predicting these fractures is the primary subsurface technical challenge needed to discover more geothermal resources that then can be developed into power plants. Unlocking fractured carbon storage and geothermal resources will also create more green energy jobs, particularly for geoscientists and the recently displaced oil and gas workforce. Specific groups in the carbon storage sector that can benefit from such a predictive tool include those looking at developing deep saline, oil and gas, or geothermal reservoirs for carbon storage sites. These include the oil and gas majors (e.g., ExxonMobil, Chevron and Shell), all of whom are currently partnered on developing one of the largest carbon capture and storage sites in the world.
Tagged as:
SBIR
Phase I
2022
DOE