Microbial contribution estimated by clumped isotopologues (13CH3D and 12CH2D2) characteristics in a CO2 enhanced coal bed methane reservoir.

Carbon capture and storage (CCS) of CO2 is a key technology for substantially mitigating global greenhouse gas emissions. Determining the biogeochemical processes in host rocks after CO2 injection informs the viability of carbon storage as a long-term sink for CO2, the complexity of reservoir CH4 cycling, as well as the direct and indirect environmental impacts of this strategy. The doubly substituted ('clumped') isotopologues of methane (13CH3D and 12CH2D2) provide novel insights into methane origins and post-generation processing. Here, we report the chemical compositions of hydrocarbons (C1/C2+ molecular ratios), and methane bulk and clumped isotopes (δ13C, δD, Δ13CH3D and Δ12CH2D2) of a CO2 enhanced coal bed methane recovery (CO2-ECBM) area in Qinshui basin, China and is an analogue for carbon capture and storage. The clumped isotopologue compositions observed in the study area are generally consistent with a range of temperatures spanning 73 to 193 °C. The range in apparent temperature and correlations among clumped and bulk isotopic indices are best explained by mixing between a high maturity thermogenic methane (high in δ13C and δD, with a clumped isotope composition equilibrated near ∼249 °C) and biogenic methane formed or processed in the reservoir (low in δ13C and δD, with a clumped isotope composition equilibrated near 16-27 °C). We hypothesize that the biogenic endmember may result from slow methanogenesis and/or anaerobic oxidation of methane (AOM). This study demonstrates that the potential of methane clumped isotope approach to identify in situ microbial metabolic processes and their association with carbon cycling in CO2-ECBM area, improving our understanding of biogeochemical mechanisms in analogous geological reservoirs.

Machine Learning Predicts the Methane Clumped Isotopologue (12CH2D2) Distributions Constrain Biogeochemical Processes and Estimates the Potential Budget.

Methane (CH4) is a matter of environmental concern; however, global methane isotopologue data remain inadequate. This is due to the challenges posed by high-resolution testing technology and the need for larger sample volumes. Here, worldwide methane clumped isotope databases (n = 465) were compiled. We compared machine-learning (ML) models and used random forest (RF) to predict new Δ12CH2D2 distributions, which cover valuable and hard-to-replicate methane clumped isotope experimental data. Our RF model yields a reliable and continuous database including ruminants, acetoclastic methane, multiple pyrolysis, and controlled experiments. We showed the effectiveness of utilizing a new data set to quantify isotopologue fractionations in biogeochemical methane processes, as well as predicting the steady-state atmospheric methane clumped isotope composition (Δ13CH3D of +2.26 ± 0.71‰ and Δ12CH2D2 of +62.06 ± 4.42‰) with notable biological contributions. Our measured summer and winter water emitted gases (n = 6) demonstrated temperature-driven seasonal microbial community evolution determined by atmospheric clumped isotope temporal variations (Δ 13CH3D ∼ -0.91 ± 0.25 ‰ and Δ12CH2D2 ∼ +3.86 ± 0.84 ‰), which in turn is relevant for future models quantifying the contribution of methane sources and sinks. Predicting clumped isotopologues translates our methane geochemical understanding into quantifiable variables for modeling that can continue to improve predictions and potentially inform global greenhouse gas emissions and mitigation policy.