A real-time autonomous adjusting process for fluid-fluid displacement in CO₂ geological sequestration

To achieve net-zero carbon emission, securely and permanently sequestrating CO₂ into deep underground is internationally assured as a robust solution, although a few technical challenges on complex in-situ storage process are yet to be overcome. Despite researchers are increasingly familiar with laboratory-scale CO₂-brine displacement and how to characterize and improve the process, field implementation is not that simple and of great challenge. In this article, an opportunity on an approach that utilizes fluid-fluid displacement fundamentals is discussed to predict CO₂ sequestration using artificial intelligence. A concept of machine learning is introduced, where computer programs can learn and improve automatically via previous experiences. With machine learning model, fluid displacement behaviors that are spontaneously monitored are emphasized to predict the displacement result, which is readily adjusted if needed while training the model from real-time CO₂ injection response. Such an approach is a real-time autonomous adjusting process, consisting of three main stages: Selection of first appraisal fluid for trial injection, real-time machine learning from in-situ injection response, and fluid adjustment if needed or continuation on the same injection until achieving a maximum CO₂ storage. This approach could play a vital role in the carbon capture and storage industry to develop CO₂ storage effectively with adequate resources, and yet has a potential to substitute a conventional design or fluid screening approach for subsurface fluid injection, including underground hydrogen storage and hydrocarbon recovery.