Quantifying economic viability and carbon mitigation potential of carbon-dioxide sequestration in shale reservoirs using machine learning

The increasing concentration of atmospheric CO2 since the Industrial Revolution has driven research into subsurface storage as a viable solution. This study focuses on developing machine learning models to estimate net present value and carbon footprint in a combined gas production and CO2 sequestration scenario in shales. The dataset comprised a large set of numerical simulation scenarios, which were run using PSU SHALECOMP, a 3-dimensional, compositional and multiphase simulator, which incorporates an equation of state to capture the effects of pressure and temperature variations. A horizontal production/injection well with multiple hydraulic fractures was modeled using the stimulated reservoir volume approach which represents the volume impacted by hydraulic fractures as well as the induced fractures through the natural fracture network in the reservoir. The results of these scenarios were used to calculate net present value and carbon footprint associated with each scenario. Exploratory data analysis and feature engineering revealed that the net present value is primarily governed by stimulated reservoir volume’s fracture permeability, original gas in place within the stimulated reservoir volume, and injection constraints, whereas the carbon footprint is predominantly controlled by total production duration and injected CO2 volume. Machine learning models were trained to build robust forecasting tools for net present value and carbon footprint. These models revealed that the selected neural network model outperformed multiple linear regression and random forests models in predicting both net present value and carbon footprint, with R2 values of 0.99 and 0.96, respectively, for the testing sets. To further refine these estimates and improve the robustness of predictions, future research should focus on improving the certainty in deterministic and probabilistic estimations of net present value and carbon footprint by gathering more comprehensive data, and conducting detailed analyses of carbon emissions and operational costs. This research represents a significant step toward understanding the economic and environmental implications of CO2 sequestration in shale reservoirs, contributing valuable insights for future developments in this field.