A well-known fact is that science and engineering are significantly improved through the correct implication of Artificial Intelligence. Science and engineering Application of Artificial Intelligence (SEA-AI) has the capability to reduce, curb, and even prevent Climate Change. There are no doubts that Artificial Intelligence can significantly help providing Climate Change solutions. Carbon Capture and Storage (CCS) can significantly contribute to the minimization of Climate Change, then CCS-Analytics (Application of Artificial Intelligence for Carbon Dioxide Storage Systems) that incorporates SEA-AI, is a serious technology for reducing and even preventing Climate Change.

The title of this article represents the fact that science and engineering application of Artificial Intelligence is being used to assist Carbon Capture and Storage. There are significant differences in using Artificial Intelligence to solve science and engineering problems versus general (non-engineering) problems. Since Carbon Dioxide Storage Systems is an engineering related problem then the correct version of the Artificial Intelligence must be used to enhance the solution. This article will provide explanations about how science and engineering application of Artificial Intelligence must be used in CCS-Analytics (Application of Artificial Intelligence for Carbon Dioxide Storage Systems).

Carbon storage and sequestration in the geological formations that are thousands of feet under the surface can keep the Carbon Dioxide in such reservoirs (porous media) for hundreds of millions of years. The research of Carbon Storage (Sequestration) will significantly contribute to the enhancement of this process and makes it as realistic and applicable as fossil energy production in the past several decades. It must be noted that Carbon Storage (Sequestration) requires domain expertise in reservoir engineering as CCS-Analytics requires application of Artificial Intelligence in reservoir engineering. Carbon Storage (Sequestration) can be done in several types of geological formations that are tens of thousands of feet below surface.

Two of the most important geological formations that are used for Carbon Storage are saline aquifers and oil and gas reservoirs that have already been emptied through the production of the hydrocarbons. Saline Aquifers are “Green Fields”, and Petroleum Reservoirs are “Mature (Brown) Fields”. The main difference between these two types of formations is the availability of the field measurements that can represent their geological characteristics. In many cases Geo-Analytics (AI-based Geological Modeling) can provide the required enhancement for CCS-Analytics. Some details of the Geo-Analytics will also be mentioned in this article.

CCS-Analytics (Application of Artificial Intelligence for Carbon Dioxide Storage Systems) can be applied in two different types of applications. The first application is developing “Smart Proxy Model” of the Numerical Reservoir Simulations that is used to model the Carbon Dioxide Storage Systems to generate pressure and saturation distribution of the Carbon Dioxide in space and time throughout the reservoir. Smart Proxy Model that is the Artificial Intelligence application in Proxy Modeling is far more different and orders of magnitude enhanced, exact, and better than the traditional proxy models (Reduce Order Models – ROM, and Response Surface Models - RSM). These traditional proxy models of the numerical simulation (ROM and RSM) have been used in the engineering related numerical simulations in the past several decades. In this article case study of Smart Proxy Modeling for the Carbon Dioxide Storage Systems will be presented.

The second CCS-Analytics (Application of Artificial Intelligence for Carbon Dioxide Storage Systems) is a purely AI-based (Top-Down) Reservoir Simulation and Modeling that only uses actual field measurements to model the pressure and saturation distribution of the Carbon Dioxide in space and time throughout the reservoir. However, this second application of CCS will be provided and presented as soon as such research projects are completed.