The US National Energy Technology Laboratory (NETL) has expanded the scope of its work in carbon management to include carbon dioxide capture at natural gas-fired power plants, direct-air capture technologies and blue hydrogen research, among others.
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And as in the oil and gas industry, digitalisation is increasingly essential to its mission, says Grant Bromhal, senior fellow for geosciences at NETL, the research and development organisation under the Department of Energy’s Office of Fossil Energy.
“There are capabilities that use computational approaches and predictive modelling and an AI-based approach to help more quickly upscale concepts,” he says, such as novel carbon capture technologies.
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Digitalisation “could accelerate how quickly these technologies go from concept, from small pilot to large pilot, and go out in the field”, he says.
Bromhal is technical director of the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications programme, or SMART Initiative, a 10-year, multi-organisational effort to improve subsurface visualisation and ensure the efficiency and effectiveness of large-scale carbon storage and unconventional oil and gas operations.
“Our goal here is to bring machine learning and predictive modelling capabilities... together with new sensing technology to help make better decisions through enabling real-time — or maybe what would be better called just-in-time — visualisation capabilities, real-time forecasting capabilities,” he says.
The initiative is in its early stages and Bromhal hopes to bring in industry partners in an upcoming second phase.
“In phase two, which will start next year, we are looking to work with partners in the industry who would be interested in having us shadow them in the development and build-out of a carbon storage site,” he says.
“We are hoping to help build the next generation of technology here. We are not looking to just tweak things a little. We are trying to come up with transformational approaches.”
The initiative is currently developing subsurface visualisation technology “to take real-time data and then do an automatic history match and a rapid prediction, so that we can have a platform where we can take the information and show it, in a much more intuitive way, to those who want to use it, from operators to regulators to the public”, he says.
“Then I expect in the next two years to have some opportunities where we can help build out some site-specific models that can show how this technology can work for any site. That’s what we hope to do with industry collaboration and participation."
