Numerical analysis of bio-reactive transport in CO2-H2 underground bio-methanation within depleted reservoirs

Underground bio-methanation (UBM) of CO2 and H2 in depleted hydrocarbon reservoirs presents a promising strategy that combines carbon recycling, large-scale subsurface energy storage, and renewable CH4 production. Despite its potential, the bio-reactive transport mechanisms underlying UBM remain poorly understood. To fill this knowledge gap, this study develops a numerical modeling framework. A coupled hydro-bio model was developed by integrating multicomponent multiphase flow with microbial growth and conversion processes, and was implemented numerically using the MATLAB Reservoir Simulation Toolbox (MRST). Key microbial kinetic parameters were calibrated using data from high-temperature and high-pressure conversion experiments conducted with formation water containing indigenous methanogenic microorganisms from the Shengli Oilfield, China. Within this this framework, the effects of operational and reservoir parameters, including shut-in duration, injection rate, and reservoir permeability, on gas transport, microbial conversion, and production performance were systematically investigated. Simulation results indicate that extended shut-in periods allow methanogens to continuously consume CO2 and H2, leading to greater pressure depletion and lower residual CO2 in the gas phase. Specifically, increasing the shut-in duration from 180 to 720 days raises the final microbial CO2 conversion from 43.5% to 96%. Higher injection rates extend the gas-front migration distance and stimulate a larger methanogen population, increasing the total CO2 conversion, although the overall conversion efficiency slightly decreases due to higher gas input. In high-permeability reservoirs, enhanced gravity segregation causes gases to accumulate in the upper reservoir, limiting contact with methanogens near the far-well region and thereby reducing conversion efficiency. This study provides new insights into the coupled transport and microbial processes in UBM and offers guidance for the optimization of its design and field-scale implementation.