oa Using Integrated Machine Learning Property Modeling for Delineating Optimum CO2 Storage Sites

In this challenging era of deciding Carbon Capture & Storage (CCS) sites, it becomes not only important but also critical to use all available data for drilling better wells for injecting carbon into the underground storage. This abstract would summarize a case study where seismic inversion & machine learning property modeling data was used to effectively delineate the tight vs non-tight sands; this resulted in a better development strategy for the injection of CO2 into the field. The integration of machine learning & seismic inversion results resulted in deciding the best area for injection of CO2 and planning the development phase for CO2 injection across the entire project life for underground storage.

The objective of this study was to effectively delineate the non-tight sands through seismic inversion & machine learning for underground CO2 storage, map these non-tight sand intervals throughout the field and recommend new well locations in the cretaceous deltaic depleted reservoir for CO2 storage.

It is also important to mention here that the machine learning application for property modeling helped in creating 1000’s of scenarios with blind testing validation.