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We found that supercritical CO2 and the availability of protein-rich matter in depleted oil reservoirs can result in the biogenic production of H2 from oil hydrocarbons by indigenous microbial communities. Our experimental results support the hypothesis that a decrease in pH to acidic levels due to the dissolution of supercritical CO2 into the formation water and availability of protein-rich matter favors the activity of H2-producing microbial communities over the activity of H2-using microbial communities. To determine where, when, and how much H2 could be produced in a depleted oil reservoir injected with CO2 and produced water (PW) supplied with protein-rich matter, we simulated the biogenic production of H2 for the Morrow B sandstone reservoir. Simulations were conducted using CO2Bio, a program developed to simulate the multiphase bio-geochemical reactive transport of CO2-CH4-H2-H2S gases in geological carbon storage (GCS) sites. The microbiological capabilities of CO2Bio are validated against batch reaction experimental results. Our field-scale simulation results indicate that 154 – 1673 kg of H2 could be produced after 100 days of CO2 and PW co-injection into a single well of radial flow, and that sandstone reservoirs are more suitable than carbonate reservoirs to produce H2 from dissolved hydrocarbons. Based on the obtained experimental and simulation results, we propose a new H2 production method that couples GCS and PW disposal in depleted oil reservoirs to attenuate environmental and energy issues related to global warming derived from atmospheric pollution with CO2, risk of freshwater resources contamination with PW, and depletion of energy resources. 

Pore-scale modeling is essential in understanding and predicting flow and transport properties of rocks. Generally, pore-scale modeling is dependent on imaging technologies such as Micro Computed Tomography (micro-CT), which provides visual confirmation into the pore microstructures of rocks at a representative scale. However, this technique is limited in the ability to provide high resolution images showing the pore-throats connecting pore bodies. Pore scale simulations of flow and transport properties of rocks are generally done on a single 3D pore microstructure image. As such, the simulated properties are only representative of the simulated pore-scale rock volume. These are the technological and computational limitations which we address here by using a stochastic pore-scale simulation approach. This approach consists of constructing hundreds of 3D pore microstructures of the same pore size distribution and overall porosity but different pore connectivity. The construction of the 3D pore microstructures incorporates the use of Mercury Injection Capillary Pressure (MICP) data to account for pore throat size distribution, and micro-CT images to account for pore body size distribution. The approach requires a small micro-CT image volume (7–19 mm3) to reveal key pore microstructural features that control flow and transport properties of highly heterogeneous rocks at the core-scale. Four carbonate rock samples were used to test the proposed approach. Permeability calculations from the introduced approach were validated by comparing laboratory measured permeability of rock cores and permeability estimated using five well-known core-scale empirical model equations. The results show that accounting for the stochastic connectivity of pores results in a probabilistic distribution of flow properties which can be used to upscale pore-scale simulated flow properties to the core-scale. The use of the introduced stochastic pore-scale simulation approach is more beneficial when there is a higher degree of heterogeneity in pore size distribution. This is shown to be the case with permeability and hydraulic tortuosity which are key controls of flow and transport processes in rocks.