Scholars at home and abroad have studied the concentrations of Se in coal and the relationship between Se and sulfur assignment. However, little attention has been paid to the study of Se in floating coal, especially the distribution pattern of Se in floating coal of densimetric fractions and the influencing factors. A total of 138 coal samples were collected from 14 provinces and cities in China to test the Se and sulfur concentrations in coal, and we carried out the comparison of Se with forms of sulfur with 81 of them. A total of 10 coal samples were selected for float-and-sink analyses to investigate the distribution pattern of Se in floating coal. The results showed that the average Se concentration of Chinese coal was 2.26 μg/g, which is identical with the results of previous research studies, which found an average Se concentration of 2~3 μg/g. Selenium was not uniformly distributed in floating coals of densimetric fractions but it had some regularity. The main controlling factor was the sulfur content in the coal. The Se concentrations of the high-sulfur coal showed a “linear growth” distribution with an increasing density, the Se concentrations of the floating coal in each densimetric fraction increased slowly and the Se concentrations of the floating coal of a densimetric fraction > 2.00 g/cm³ increased sharply. The other controlling factor was the ash yield of the coal. The Se concentrations of the low–medium-sulfur floating coal showed a “parabolic-like” distribution, i.e., low Se concentrations at both ends and high Se concentrations at the middle densimetric fraction. With the increase in the ash content of the coal from 18.23% to 51.92%, the densimetric fraction with the highest Se concentration gradually progressed from 1.40 g/cm³ to 1.50 g/cm³, 1.60 g/cm³ and 1.80 g/cm³ in order. The correlation between the selenium of the raw coal from different regions and the total sulfur, pyrite sulfur and organic sulfur was not significant. However, there existed a significant correlation between Se, total sulfur, pyrite sulfur and organic sulfur in floating coals from the same mining area and depositional environment, among which pyrite sulfur was the most correlated with Se, followed by total sulfur and organic sulfur.

A new approach is presented for dynamic modeling of chemical and isotopic evolution of C1-3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for pyrolysis of various hydrocarbon sources(type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, asphalt, etc), the empirical evolution framework of chemical and isotopic composition of C1-3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting large amount of pyrolysis data, the chemical and isotopic composition of C1-3 derived from the pyrolysis experiments all follow the evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ13Cmixed) δ13Cmixed=(X〖*n〗_iA 〖*δ〗^13 C_iA+Y〖*n〗_iB 〖*δ〗^13 C_iB)/(X〖*n〗_iA+Y〖*n〗_iB ), it can be derived that the cumulative isotopic composition of alkane generated in certain maturity interval can be expressed by the integral of the product of instantaneous isotopic composition and instantaneous yield at certain maturity point, and then divided by the cumulative yield of alkane generated in corresponding maturity interval. Thus, the cumulative isotopic composition(A(X)), cumulative yield(B(X)), instantaneous isotope(C(X)),and instantaneous yield(D(x)) in the dynamic model comply with the following formula during the maturity interval of (X0-X) A(X)=(∫_(X_0)^X▒〖C(X)*D(X)dx〗)/(B(X)), where A(X) and B(X) can be obtained by fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of cumulative yield ,instantaneous yield, cumulative isotope and instantaneous isotope of C1-3 with increasing maturity.The dynamic model can quantify the yield of methane, ethane and propane as well as δ13C1, δ13C2 and δ13C3 respectively during the hydrocarbon generation process, which is of great significance to evaluate the natural gas resources of hydrocarbon source rock with different maturity and to identify the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of isotope series of C1-3 (including reversed isotopic series), which is hopeful to reveal the mechanism responsible for isotopic reversal when combined with post-generation studies.

A new approach is presented in this paper for the dynamic modeling of the chemical and isotopic evolution of C_1–3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for the pyrolysis of various hydrocarbon sources (type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, and asphalt, etc.), the empirical evolution framework of the chemical and isotopic composition of C_1–3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting a large amount of pyrolysis data, the chemical and isotopic composition of C_1–3 derived from the pyrolysis experiments all follow evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ¹³C_mixed), δ¹³C_mixed = $\frac{\mathrm{X}{\times \mathrm{n}}_{\mathrm{i}\mathrm{A}}{\times \delta }^{13}{\mathrm{C}}_{\mathrm{i}\mathrm{A}}+\mathrm{Y}{\times \mathrm{n}}_{\mathrm{i}\mathrm{B}}{\times \delta }^{13}{\mathrm{C}}_{\mathrm{i}\mathrm{B}}}{\mathrm{X}{\times \mathrm{n}}_{\mathrm{i}\mathrm{A}}+\mathrm{Y}{\times \mathrm{n}}_{\mathrm{i}\mathrm{B}}}$, it can be derived that the cumulative isotopic composition of alkane generated in a certain maturity interval can be expressed by the integral of the product of the instantaneous isotopic composition and instantaneous yield at a certain maturity point, and then divided by the cumulative yield of alkane generated in the corresponding maturity interval. Thus, the cumulative isotopic composition (A(X)), cumulative yield (B(X)), instantaneous isotope (C(X)), and instantaneous yield (D(x)) in the dynamic model, comply with the following formula during the maturity interval of (X₀~X). A(X) = $\frac{{\int }_{{\mathrm{X}}_0}^{\mathrm{X}}\mathrm{C}\left(\mathrm{X}\right)\times \mathrm{D}\left(\mathrm{X}\right)\mathrm{d}\mathrm{x}}{\mathrm{B}(\mathrm{X})}$, where A(X) and B(X) can be obtained by the fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of the cumulative yield, instantaneous yield, cumulative isotope, and instantaneous isotope of C_1–3 with increasing maturity. The dynamic model can quantify the yield of methane, ethane, and propane, as well as δ¹³C₁, δ¹³C₂, and δ¹³C₃, respectively, during the hydrocarbon generation process. This model is of great significance for evaluating the natural gas resources of hydrocarbon source rock of different maturities and for identifying the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of the isotope series of C_1–3 (including reversed isotopic series), which is promising for revealing the mechanism responsible for isotopic reversal when combined with post-generation studies.