Fundamental studies on kinetic isotope effect (KIE) of hydrogen isotope fractionation in natural gas systems

Fundamental studies on kinetic isotope effect (KIE) of hydrogen isotope fractionation in natural gas systems

Geochimica et Cosmochimica Acta

Based on quantum chemistry calculations for normal octane homolytic cracking, a kinetic hydrogen isotope fractionation model for methane, ethane, and propane formation is proposed. The activation energy differences between D-substitute and non-substituted methane, ethane, and propane are 318.6, 281.7, and 280.2cal/mol, respectively. In order to determine the effect of the entropy contribution for hydrogen isotopic substitution, a transition state for ethane bond rupture was determined based on density function theory (DFT) calculations. The kinetic isotope effect (KIE) associated with bond rupture in D and H substituted ethane results in a frequency factor ratio of 1.07. Based on the proposed mathematical model of hydrogen isotope fractionation, one can potentially quantify natural gas thermal maturity from measured hydrogen isotope values. Calculated gas maturity values determined by the proposed mathematical model using δD values in ethane from several basins in the world are in close agreement with similar predictions based on the δ13C composition of ethane. However, gas maturity values calculated from field data of methane and propane using both hydrogen and carbon kinetic isotopic models do not agree as closely. It is possible that δD values in methane may be affected by microbial mixing and that propane values might be more susceptible to hydrogen exchange with water or to analytical errors. Although the model used in this study is quite preliminary, the results demonstrate that kinetic isotope fractionation effects in hydrogen may be useful in quantitative models of natural gas generation, and that δD values in ethane might be more suitable for modeling than comparable values in methane and propane.

2011-05-15

Ni, Yunyan

Ma, Qisheng

Ellis, Geoffrey S.

Dai, Jinxing

Katz, Barry

Zhang, Shuichang

Tang, Yongchun

Journal Article

9UZKN3AM

0016-7037

10.1016/j.gca.2011.02.016