Mass flow rate prediction of shale gas considering gas diffusion and water film evaporation

Abstract

The difference between organic and inorganic matters in shale matrix has significantly influence on the transport capacity of shale gas and the total production of shale gas further. Previous studies have demonstrated that the organic pores are hydrophobic and the inorganic pores are hydrophilic. Therefore, the water film can be adsorbed on the walls of the inorganic pores. However, the evaporation of water film in inorganic pore is often overlooked due to the analytical challenge and the effect of the water film evaporation on gas transport capacity is rarely discussed. Thus, it is important to accurately predict mass flow rate of the shale gas through a single nanopore whether it is organic or inorganic. In this article, the mass flow rate prediction model of the organic pore is developed firstly, which considers the interactions of slip flow, gas adsorbed at pore walls, surface diffusion for adsorbed gas and gas diffusing from the kerogen which contains dissolved shale gas in organic pores at nanoscale. This model is then used to investigate the influences of initial pressure, pressure gradient, and thickness of kerogen on the gas mass flow rate. For inorganic pores, the mass flow rate prediction model is developed secondly and the interactions of water film and the evaporation of water film have been considered. The influence of evaporation, initial humidity, water–methane diffusivity is investigated on the gas mass flow rate. Results show that diffusion from kerogen plays an important role in the transport capacity of shale gas. Both higher pressure gradient and thicker kerogen can contribute to higher gas mass flow rate and higher diffusion from kerogen. Higher initial pressure and thicker kerogen can contribute to the lower shale gas desorption. The thicker kerogen can contribute to the higher surface diffusion. The evaporation in inorganic pore should not be ignored, because it impacts gas mass flow rate and can contribute to the higher flow rate. Experimental data was compared with our prediction model, which proves the correctness and validity of our model.