An adsorbed gas estimation model for shale gas reservoirs via statistical learning

TL;DR

This paper introduces a statistical learning-based model for estimating adsorbed shale gas content, aiming to improve accuracy and reduce costs compared to traditional methods in shale gas resource evaluation.

Contribution

The study develops a novel, low-cost, high-accuracy estimation model using geological parameters and statistical learning, addressing limitations of existing methods.

Findings

Model achieves higher accuracy than traditional empirical models.

Reduces reliance on expensive adsorption experiments.

Demonstrates effectiveness across different shale formations.

Abstract

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