Artificial Intelligence

Subsurface Trend Analysis (STA) Method and Tool

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

NETL advanced the subsurface trend analysis (STA) workflow with an AI-informed image segmentation/embedding model.
The STA method was created to be a foundational technology, capable of assisting any subsurface predictive need.
The image embedding tool uses convolutional neural networks (CNNs) to:
- Extract images from unstructured data
- Categorically label the images
- Create a repository for geologic domain postulation
A case study on data available for the Gulf of Mexico shows the STA image embedding tool extracts and accurately labels images with 90% to 95% precision.
The STA 2D Tool is available on NETL’s Energy Data eXchange® (EDX).

Falyn Eisiminger2024-09-18T17:24:54+00:00September 18th, 2024|

Wafer-scale engine Field equation Application programming interface (WFA)

Cutting-Edge, advanced AI computing & modeling, accelerating solutions to real world challenges

The NETL-developed WFA programming interface for the wafer-scale engine allows researchers to tackle tough computational problems including materials, climate modeling, single-phase computational fluid dynamics, molecular dynamics, and AI-accelerated scientific computing.
The wafer-scale engine has demonstrated solving the Rayleigh-Benard (R-B) natural convection problem up to 470x faster than traditional HPC with 3 orders of magnitude less energy usage and emissions.

Falyn Eisiminger2024-09-18T16:08:03+00:00May 14th, 2024|

UNet Performance with Wafer Scale Engine (Optimization Case Study)

Romanov, V. (2023). UNet Performance with Wafer Scale Engine (Optimization Case Study). 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1–6. https://doi.org/10.1109/HPEC58863.2023.10363451

Falyn Eisiminger2024-04-08T14:23:34+00:00December 25th, 2023|

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

Hoover, B., Zaengle, D., Mark-Moser, M., Wingo, P., Suhag, A., and Rose, K., (2023). Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions. Frontiers. Big Data 6:1227189. https://doi.org/10.3389/fdata.2023.1227189

Falyn Eisiminger2024-07-16T16:18:13+00:00December 18th, 2023|

Development of an equation-based parallelization method for multiphase particle-in-cell simulations

Woo, M., Jordan, T., Nandi, T., Dietiker, J.F., Guenther, C., and Van Essendelft, D., (2022). Development of an equation-based parallelization method for multiphase particle-in-cell simulations. Engineering with Computers. https://doi.org/10.1007/s00366-022-01768-6

Falyn Eisiminger2024-04-08T16:02:55+00:00December 22nd, 2022|

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Romanov, V., (2022). Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys. JOM, 74(4). https://doi.org/10.1007/s11837-022-05163-w

Falyn Eisiminger2023-02-14T18:09:10+00:00February 14th, 2022|

Application of Artificial Intelligence to Computational Fluid Dynamics

Mohaghegh, S., Aboaba, A., Martinez, Y., Shahman, M., Guenther, C., & Liu, Y., “Application of Artificial Intelligence to Computational Fluid Dynamics.” In Advances in Subsurface Data Analytics, edited by Shuvajit Bhattacharya and Haibin Di, 281-352, Elsevier, 2022. https://doi.org/10.1016/C2019-0-04878-9

Falyn Eisiminger2023-02-14T18:09:36+00:00January 1st, 2022|

Latent Learning with pyroMind.2020

Romanov, V., (2021). Latent Learning with pyroMind.2020. 2021 IEE International Conference on Big Data, pp. 4624-4627, https://doi.org/10.1109/BigData52589.2021.9671643

Falyn Eisiminger2022-12-28T16:48:40+00:00December 15th, 2021|

Machine learning-informed ensemble framework for evaluating shale gas production potential: Case study in the Marcellus Shale

Vikara, D., Remson, D., & Khanna, V., (2020). Machine learning-informed ensemble framework for evaluating shale gas production potential: Case study in the Marcellus Shale. Journal of Natural Gas Science and Engineering, 84(12). https://doi.org/10.1016/j.jngse.2020.103679

Falyn Eisiminger2022-12-28T17:15:56+00:00December 28th, 2020|