The experimental investigation and data–driven modeling for thermal decomposition kinetics of Green River Shale

Given that kerogen is a source of vast amount hydrocarbons and organically–bound inorganic elements, it’s important to understand the thermal decomposition kinetics kerogen. Green River Shale contains significant immature (Type I), which can be an ideal for sample experimental study. In this study, Thermogravimetric Analysis Derivative Thermogravimetry (TGA/DTG) was used quantify weight loss during pyrolysis process from Utah subsequently establish kinetic model based on Friedman method followed by data–driven modeling approach. A two–step reaction mechanism components production were determined implementing Analysis–Differential Scanning Calorimetry–Gas Chromatography (TGA–DSC–GC). The chemical bonds analyzed with Fourier–Transform Infrared spectroscopy (FTIR) before after pyrolysis. From experiments, we observed two–stage reactions in hydrocarbon evolution window when heating rate lower than 30 °C/min, while only one stage higher rates. C14 generated continuously evolution, indicated contained plentiful it. parameters obtained organic mixture matter (kerogen) only. Artificial Neural Network (ANN) implemented train TGA/DTG experiment. prediction extrapolated cases showed good performance smaller 5 °C/min. proxy coupled various physical models simulate high accuracy.