The University of Texas at Austin (UT Austin) developed a prototype modular computational approach for monitoring the location of the CO2 plume as it moves through the subsurface during the injection process—the period when the CO2 is pumped through an injection well into the targeted rock formation. The approach utilized project injection rate and pressure data as a basis for the modeling input. This enabled modeling and monitoring capabilities at negligible incremental cost because injection rate and pressure data is recorded for operational reasons in every carbon storage project. A goal of the modular computational approach is to take advantage of the inherent flexibility it provides, allowing for other types of data, such as surface deflection or seismic imaging, to be easily included with the rate/pressure data to reduce the uncertainty of the inferred plume location.
This project focuses on the development of a prototype of a new computational approach for monitoring the location of CO2 plumes during injection that is based on pressure and injection rate data. This approach allows for other types of data, such as surface deflection or seismic imaging, to be easily included with the rate/pressure data, which, in turn, will reduce the uncertainty of the inferred plume location and improve monitoring capabilities by improving the ability to predict the expected plume location, contributing to better storage technology thus reducing CO2 emissions to the atmosphere. Specifically, The software will be tested on synthetic data sets and validated with field data obtained from CO2 injection projects.
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