Coalbed Methane Modelling Best Practices

Abstract

Determining the potential range of recoverable volumes for a Coalbed Methane (CBM) prospect is a necessary precursor to a successful development plan. Several key best practices were incorporated into a workflow to consistently assess the CBM potential of numerous prospective areas. For each area 3D static models were built based on available structural data. The models were geo-statistically populated with coal properties such as density and ash content. Correlations for other properties including gas content, permeability and Langmuir volume were developed. An analysis of the residual distribution between each correlation and its measurements was used to characterise the uncertainty in each. Several methods were considered to reproduce this uncertainty. These ranged from directly applying discrete trends, to geo-statistical property population. The effect of applying each on the predicted EUR was investigated. Reservoir simulation models of production pilots were built and history matched. Given the complexities of the coal reservoir and the non-uniqueness of the history match, further work was carried out to capture the remaining uncertainty and determine its impact on the model predictions. Experimental design (DOE) was used to generate a population of simulation models that sampled the uncertainty range. By using the measured pilot production as a filter, this population was reduced to include only those that matched the observed production. The final step was to optimise the placement of development wells. An algorithm that traded off the gain in gas recovery obtained by a tighter well spacing, against the increased cost associated with the extra wells was devised. The uncertainty in recovery given by this well spacing was tested using the reservoir simulation models. Although static and dynamic modelling of CBM reservoirs is quickly becoming routine in the industry, the best practices developed while building this workflow are novel solutions to several challenges that still confound the CBM modelling community. These best practices are not unique to the study area and could easily be applied to other areas. As such this paper should provide a useful reference to those about to undertake a CBM modelling project.

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

“…These findings align with these of prior research [5,18,35]. However, the sensitivity degrees on gas and water production of other parameters are controversial [6,[36][37][38] (Table 4). This may be because the high and low value setting approaches for the sensitivity parameters are different, e.g., empirical method, analogous method, testing data, or probability method.…”

New Integrated History Matching Approach for Vertical Coal Seam Gas Wells in the KN Field, Surat Basin, Australia

“…Permeability, which controls the fluid flow capacity in coal reservoirs, is a key property for commercial CSG development. , The permeability data for the JCMs used in the regional model show a decreasing trend along with depth, which can be described by an exponential equation (Figure a). A similar trend was observed for the permeability data for the TCMs.…”

“…When combined with the isotherm, the gas content gives the coal’s gas saturation, i.e., the fraction of gas content compared with the isotherm equilibrium amount at the given reservoir pressure. The gas saturation indicates the amount of required pressure reduction via dewatering before the gas desorption begins . Typically, the isotherm can be described by the Langmuir equation, which has two key parameters, i.e., V L and P L .…”

“…The gas saturation indicates the amount of required pressure reduction via dewatering before the gas desorption begins. 23 Typically, the isotherm can be described by the Langmuir equation, which has two key parameters, i.e., V L and P L . V L represents the maximum amount of gas that can be adsorbed at the infinite pressure, and it is correlated inversely with the coal's density.…”

Stochastic Modeling for Estimating Coalbed Methane Resources

“…Given the limitations of the available data and the inherent heterogeneity of coal, 25,26 the distributions of the simulated parameters in the structural model and the property model have a degree of uncertainty, which needs to be assessed. Structural Model Uncertainty.…”