A Deep-Learning Framework for the Optimization of CO2 Plume Geothermal Power Production - A Pathway to Carbon Net-Zero

Abstract

Abstract Several businesses have recently developed CO2 plume geothermal technology (CPG). The concept proposes generating geothermal energy using CO2 trapped in salty aquifers. Compared to traditional geothermal concepts, CPG is unique. In this instance, the feedstock makes use of CO2 as a carrier fluid to draw heat from the subterranean reservoir. Furthermore, the system may use a typical sedimentary substrate rather than only shallow natural hydrothermal areas. Finally, CPG may continue to produce energy in low-temperature settings where it is currently not feasible to do so using traditional geothermal methods. For the purpose of maximizing power generation from a CPG system, we introduce a novel deep-learning optimization methodology. The framework employs a modified N-BEATS methodology. The method is built on an interconnected stack of backcast and forecast connections and ensembled feedforward networks. The framework's versatility with regard to numerous input parameters and different forecastable time series is one of its benefits. This is especially crucial for CPG to effectively capture changes in the temporal dynamics and temperature responses across the numerous CO2 injection and production wells. We assessed the framework on a simulated CO2 storage reservoir based in the Taranaki basin in New Zealand. Given the existence of a sizable saline aquifer that may be ideally suited for CO2 storage and CPG energy generation, the Taranaki basin has been extensively explored for CO2 storage. As input to the Neural basis expansion analysis for time series (N-BEATS) framework, we modeled 3.5 years of CO2 generation and injection for geothermal energy production. The network demonstrated high training performance, and the model's effectiveness was assessed based on the ensuing two years' worth of energy output. The global optimization framework is then combined with the deep learning framework to maximize energy production while modifying CO2 injection. A novel approach to improving energy generation from CO2 storage reservoirs, the new deep learning N-BEATS optimization framework for CPG power generation offers a sustainable means to reduce carbon footprint while delivering electricity.