  Shale gas plays an important role in reducing pollution and adjusting the
structure of world energy. Gas content estimation is particularly significant
in shale gas resource evaluation. There exist various estimation methods, such
as first principle methods and empirical models. However, resource evaluation
presents many challenges, especially the insufficient accuracy of existing
models and the high cost resulting from time-consuming adsorption experiments.
In this research, a low-cost and high-accuracy model based on geological
parameters is constructed through statistical learning methods to estimate
adsorbed shale gas content
