Evaluating the CO2 Storage Well Connectivity at the Kemper CO2 Storage Site Utilizing AI

Sustainability objectives have played an important role in order to minimize carbon emissions and create value from carbon dioxide (CO2). A crucial objective is to extract the emissions from industrial processes before being released into the atmosphere and then stored in subsurface geologic formations. In order to minimize value creation and reduce carbon dioxide emissions into the atmosphere, carbon collection, storage, and utilization is essential. Since carbon capture, utilization, and storage allows businesses to continue operating while emitting less greenhouse gases, it may represent an opportunity for enhancing sustainability. However, storage must be practical, affordable, and secure. Storage formations may be found in both onshore and offshore settings, and each kind of geologic formation has pros and cons of its own. CO2 is stored for longterm, which requires safe storage, and this process includes a number of parameters being considered to make sure the gas is adequately contained. To identify the appropriate formations, a variety of variables are often needed to be considered. These include saline formations, oil and natural gas reservoirs, impermeable coal seams, organic-rich shales, and basalt formations. The Kemper CO2 Storage Project, which is being constructed in Kemper County, Mississippi, is essential to analyze and acquire experience in managing carbon storage projects as well as understanding how to sequester CO2 efficiently. It was first constructed by Mississippi Power, a branch of Southern Company, and is also known as the Kemper County Energy Plant or Plant Ratcliffe. The aim of the project was to reduce the amount of carbon emissions produced and released in the atmosphere by using CO2 sequestration. The location of the plant in Kemper County was selected to make use of the area’s untapped brown coal potential and provide regional diversity, which would help to balance the state’s energy demand and generate energy. In order to establish well connections for carbon storage, the research offers a unique directed well connectivity approach that uses similarity learning. Similarity learning can assist with connection determination for CCUS when evaluating the connectivity between the injectors and producers by classifying and categorizing data as well as by adding several non-Euclidean metrics to evaluate similarity. Similarity learning may also be used to identify anomalies and take remedial measures to anticipate future CO2 sequestration problems. The results demonstrated a strong connection between the injector and producer wells, supporting the relationship between increasing CO2 injection volumes and the subsequently detected CO2 levels in the characterization wells. The method might be a useful method for optimizing CCUS well placement and sequestration.