99/00372 Study of coal gasification via neural networks

Abstract

Coalbed methane is an important resource of energy. Meanwhile CO2 sequestration in coal is a potential management option for greenhouse gas emissions. An attractive aspect to this process is that CO2 is adsorbed to the coal, reducing the risk of CO2 migration to the surface. Another aspect to this is that the injected CO2 could displace adsorbed methane leading to enhanced coalbed methane recovery. Therefore, in order to understand gas migration within the reservoir, mixed-gas adsorption models are required. Moreover, coal reservoir permeability will be significantly affected by adsorption-induced coal swelling during CO2 injection. Coal swelling is directly related to reservoir pressure and gas content which is calculated by adsorption models in reservoir simulation. Various models have been studied to describe the pure- and mixed-gas adsorption on coal. Nevertheless, only the Langmuir and Extended Langmuir models are usually applied in coal reservoir simulations. This paper presents simulation work using several approaches to representing gas adsorption, implemented into the coal seam gas reservoir simulator SIMED II. The adsorption models are the Extended Langmuir model (ELM), the Ideal Adsorbed Solution (IAS) model and the Two-Dimensional Equation of State (2D EOS). The simulations based on one Australian and one American coal sample demonstrated that (1) the Ideal Adsorbed Solution model, in conjunction with Langmuir model as single-component isotherm, shows similar simulation results as the ELM for both coals, with the IAS model representing the experimental adsorption data more accurately than the ELM for one coal and identically with the ELM for the other coal; (2) simulation results using the 2D EOS, however, are significantly different to the ELM or IAS model for both coal samples. The magnitude of the difference is also dependent on coal swelling and the well operating conditions, such as injection pressure.