Gas storage potential and electrohydraulic discharge (EHD) stimulation of coal seam interburden from the Surat Basin

Abstract

This paper evaluates the potential methane storage capacity of six clay-rich interburden rock samples from coal seam gas (CSG) wells in the Surat Basin, Australia. Clay minerals identified in all samples included kaolinite, illite, smectite, and illite-smectite mixed-layers. The total organic carbon concentrations in these interburden rocks ranged from 0.66 -1.19 wt%, and thus these rocks can be classified as fair to good hydrocarbon source rocks. The effective porosity of the rocks determined from mercury intrusion porosimetry and helium pycnometry ranged from 6.8 % to 12.5 %, and included volumes of micropores and mesopores. The adsorption isotherm results indicated that the average adsorption capacity of six interburden was 3.64 cm3/g, a value corresponding to approximately 20% that of Surat Basin coal. Based on the clay compositions and porosity of the samples, the permeability of these Surat interburden rocks is estimated to be less than 5 nano Darcy using Yang and Aplin's empirical correlation, which was too low for reliable measurement in our laboratory core flooding apparatus even with a differential pressure of 10 bar applied over a shortened 20 mm length core. However, after stimulation by electrohydraulic discharge (EHD) shockwaves the permeability of one of the interburden samples (S2) increased to 0.6 ± 0.11 mD due to development of fractures and new pores by the EHD stimulation. We characterised the development of the fractures after EHD shockwaves using xray computer tomography. The findings of this study suggest that dynamic shockwaves such as those generated by EHD have potential to increase permeability of soft and clay-rich interburden layers in CSG reservoirs and other layered reservoirs. This potentially opens these ultra-tight gas resources to exploitation and recovery.

Electrical wire explosion as a source of underwater shock waves

“…Liu et al [171] verified the fracturing effect of SWs on shale samples and found a large number of cell-shaped multiple cracks and penetration cracks by interior crack morphology and fluorescent tracing. Ren et al [172,173] applied SWs generated by an underwater arc discharge to stimulate the coal seam gas reservoirs, increasing the permeability of the coal sample by several orders of magnitude from <5 nano-Darcy to 0.6 ± 0.11 micro-Darcy. It was demonstrated that the existing fractures in the coal were the easiest to lengthen and widen, and the new fractures were more likely to form parallel to the bedding plane (the lowest strength natural plane).…”

Affecting Factors on History Matching Field-Level Coal Seam Gas Production from the Surat Basin, Australia

“…The WCMs in the Surat Basin have been deemed to have highly heterogeneous coal properties and compose an average of ∼30 m of net coal embedded with abundant clay-rich interburden. 25 It is noted that the average coal ply thickness is only ∼30 cm, which causes significant challenges in seam correlation and prediction of reservoir properties. Nevertheless, the WCMs generally possess low-rank coal with Ro varying from 0.35 to 0.65%, whereas the gas content moderately correlates with the coal depth varying between 1 and 14 m 3 /t (d.a.f) with an average of 5.3 m 3 /t (d.a.f).…”

“…The WCMs in the Surat Basin have been deemed to have highly heterogeneous coal properties and compose an average of ∼30 m of net coal embedded with abundant clay-rich interburden . It is noted that the average coal ply thickness is only ∼30 cm, which causes significant challenges in seam correlation and prediction of reservoir properties.…”

A New Method for Numerical Simulation of Coalbed Methane Pilot Horizontal Wells—Taking the Bowen Basin C Pilot Area in Australia as an Example

“…Despite limited isothermal data availability, strong correlations exist for both VL vs. RD and PL vs. RD. In order to represent coal heterogeneity and the uncertainty arising from limited testing data [30], probability trends are established when predicting both the VL and PL distribution (Figure 2c,d).…”