QGC's current full-field reservoir model comprises hundreds to thousands of CSG wells. This presents a considerable challenge from a history-matching standpoint compared to a conventional workflow where well-level adjustments may be made on one well at a time. In QGC, a model with an improved well-level match is desired as the resulting well forecast will enable decisions on a well-level to be made more confidently, such as the prioritization of well workovers. Previously a field-level history-match was deemed acceptable when the model was only used for field development planning. The method parameterizes the well-level relative error in simulated production from the model versus observed production. The workflow utilizes this data, known as well-level modifiers, to alter subsurface properties. This has been achieved with a semi-automated workflow to make the process efficient and repeatable, but also to enable engineering judgement to be incorporated in the history-matching process. The feedback loop is also an essential component of the workflow as it allows the well-level modifiers to be sense checked against the regional geological trends. This further encourages collaboration within a multi-disciplinary team. These well-level modifiers can also be used to create history-match metrics, which can be spatially mapped to help target specific areas for improvement in history-match quality. Some powerful use of visualization techniques discussed in this paper has not only minimized the mismatch but ensures the characteristics of the production history and geological trends are honoured to assure the robustness of the history-match and the resulting model predictability. The workflow has significantly reduced the time and efforts spent in delivering an improved well forecast when required. The technical development community in QGC has actively nurtured a culture of ideas sharing and innovation, which made the development of this workflow possible.

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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