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In this paper we analyze the relationship between capital and energy through cross price elasticities. First, we extend Thomsen's (2000) methodology in order to link the short and long run in a panel data setting, by including an equation for the motion of capital. Then, by using an expansive industry-level data set and two functional forms, we show clear evidence of long run complementarity in all the analyzed industries, and with respect to the different types of capital that we consider (buildings and machinery). We identify the industries with the greatest degree of dependence between energy and capital. These are therefore, the industries in which a policy of increasing energy prices via taxes to reduce energy consumption may have a serious effect, reducing their investment levels. Hence we recommend that a better governmental policy would be to encourage technological diffusion.

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.

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