Compared to using a state-of-the-art convolutional neural network for the task, U-FNO is twice as accurate while requiring just a third of the training data. Machine learning models provide similar accuracy levels while dramatically shrinking the time and costs required.īased on the U-Net neural network and Fourier neural operator architecture, known as FNO, U-FNO provides more accurate predictions of gas saturation and pressure buildup. Traditional simulators for carbon sequestration are time-consuming and computationally expensive. For a successful storage project, it’s also important to understand the carbon dioxide plume - the spread of CO 2 through the ground. Scientists use carbon storage simulations to select the right injection sites and rates, control pressure buildup, maximize storage efficiency and ensure the injection activity doesn’t fracture the rock formation. “Machine learning techniques such as those used in this work provide a robust pathway to quantifying uncertainties in large-scale subsurface flow models such as carbon capture and sequestration and ultimately facilitate better decision-making.” How Carbon Storage Scientists Use Machine Learning White, subsurface carbon storage manager at ExxonMobil. “Reservoir simulators are intensive computer models that engineers and scientists use to study multiphase flows and other complex physical phenomena in the subsurface geology of the earth,” said James V. U-FNO will be used to accelerate carbon storage predictions for ExxonMobil, which funded the study. Over a hundred carbon capture and storage facilities are under construction worldwide. It was unveiled this week in a study published in Advances in Water Resources, with co-authors from Stanford University, California Institute of Technology, Purdue University and NVIDIA.Ĭarbon capture and storage is one of few methods that industries such as refining, cement and steel could use to decarbonize and achieve emission reduction goals. While doing so, scientists must avoid excessive pressure buildup caused by injecting CO 2 into the rock, which can fracture geological formations and leak carbon into aquifers above the site, or even into the atmosphere.Ī new neural operator architecture named U-FNO simulates pressure levels during carbon storage in a fraction of a second while doubling accuracy on certain tasks, helping scientists find optimal injection rates and sites. A team of scientists has created a new AI-based tool to help lock up greenhouse gases like CO 2 in porous rock formations faster and more precisely than ever before.Ĭarbon capture technology, also referred to as carbon sequestration, is a climate change mitigation method that redirects CO 2 emitted from power plants back underground.
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