Article · Mediterranean Journal of Social Sciences · December 1, 2014
Investigating Methane Adsorption Potential of Malaysian Coal for Coal Bed Methane (CBM) Study
Saleem Qadir Tunio, Swapan Kumar Bhattacharya, Khalil Rehman Memon, Sonny Irawan, Aung Kyaw, Mohd. Faisal Abdullah
Abstract
Methane in coal can be present as free gas or as an adsorbed layer in the internal surfaces of the coal micropores. Methane gas is adsorbed to the internal surface area of the coal associated with high pressure and exists as a monomolecular layer on the internal surface of the coal matrix. The only way to produce these gasses is by reducing the pressure within the coal matrix until the gas can be desorbed. A comparative study is made on the adsorption potential of methane gas in Malaysian coal with other producing CBM basins. The Langmuir isotherm shows that the amount of methane adsorbed in Sarawak coal is 14.11 scf/tonne at 36 psia where as in Piceance basin it amounts to 30 scf/tonne at 36 psia and in Powder River Basin is 6.52 scf/tonne at 36 psia. This suggests Sarawak coals of Malaysia also possess comparable methane adsorption potential to other producing CBM fields. Results also suggest that the adsorption amount depends on depth only for particular coal but the amount does not depend on the depth of varieties of coal. Therefore Malaysian coal will be successful CBM producer only if it has got the source of methane at greater depth. DOI: 10.5901/mjss.2014.v5n27p178
More by Sonny Irawan
- An explainable physics-informed neural network prototype for synthetic proppant transport in a two-dimensional fracture domainPhysics of Fluids · 2026
- Physics‐Guided Neural Regression Modeling of Energy‐Balance Proxies for Hydraulic Fracture Propagation in 1D–3DInternational Journal for Numerical and Analytical Methods in Geomechanics · 2026
- Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference SystemJournal of Earth Energy Science Engineering and Technology · 2025 · Citations: 1
- Physics-Informed Machine Learning for Predicting Drilling Fluid Losses in Naturally Fractured Caspian FormationsSPE Annual Caspian Technical Conference and Exhibition · 2025 · Citations: 4
