The inflow performance relationship (IPR) for a coalbed methane (CBM) reservoir is relatively complex as the production behaviour is different from conventional gas wells, as gas in coal is stored by means of adsorption. Most often coal reservoirs are relatively under saturated, meaning the pressure in the reservoir needs to be substantially depleted prior to gas desorption. Gas from coal is produced at relatively low reservoir pressures, thus requiring the pseudo pressure function to be included in any deliverability calculations. A two phase flow system in a coal reservoir adds to that complexity as relative permeability, reservoir geo-mechanics and saturation needs to be taken into account within the IPR correlation. The IPR correlation for coalbed methane wells is an important part of any well performance optimisation and nodal analysis. A simplistic IPR correlation for multi-phase coalbed methane wells was generated based on the history matching of field production data. The correlation has been tested both for a multi seam completion in the Surat Basin and for horizontal wells in low permeability coals within the Bowen Basin. Furthermore the empirical CBM IPR correlation for single and multi-phase reservoir was compared. A sorption isotherm was used to limit the maximum recovery and generate recovery efficiency based on the IPR correlation. One of the potential risks to a CBM development is the likely loss in recovery efficiency due to higher backpressures on wells as a result of suboptimal gathering network sizing due to a chosen surface concept. This risk needed to be further evaluated and addressed for potential outcomes when the field starts production. The study was initiated by means of using the CBM field IPR's to evaluate this risk in more detail, identify mitigation measures and capture any associated opportunities that may be available.

Development of coal seam gas fields is conceptually simple but complexity arises with: the stochastic nature of coal reservoirscontinually changing work scopethe large number of wells required to meet gas contracts. In the current environment, the cost of developing thousands of wells and hundreds of kilometres of associated gathering is a key driver to the success or failure of CSG projects. Continuous reduction in cost/funding with limited resources drives companies to derive an integrated approach to the field development. Subsurface models now form an integral part of production forecasting and decision making. Companies have benefited from the computation technological advances in the recent past, whereby it is possible to run large-scale models in a reasonable timeframe. Several tools and approaches are available today to integrate complex 3D reservoir models with surface networks to generate an integrated production forecast. In this paper we focus on using advanced geospatial applications with integrated system models to derive a development concept which is optimal, realistic and capable of adapting to changes in work scope as the development progresses. Gathering routes and associated material take off (MTO) points are generated in geographic information system (GIS) tools, using constraints and criteria such as: access and approvalssub-surface data (scope of recovery maps, net coal and permeability)maximum use of existing infrastructure (Roads, Tracks, etc.)environmental constraints (overland flow, vegetation, etc.)well spacing. Seamless integration of GIS tools and sub-surface modeling tools is what makes this workflow unique. GIS tools acts as a key integrator, forcing different disciplines and departments to work together in a common platform. It also functions as a common database used across an entire organisation. GIS toolbox gives a significant head-start to the project by first defining what is achievable. It is then finessed with the best value sub-surface outcome and a final forecast is derived in a significantly shorter time scale. With the approach presented in this paper, the forecasting cycle, involving full economic run, is substantially reduced– from several months to just weeks, if not days. The final outcome is an achievable well sequence which is derived along a realistic gathering route. With this, the MTO and the production forecasts are aligned and the associated costs can be easily traced to source. This workflow is automated and can be easily repeated if scope or project premise changes. Last but not least, this approach can be applied to any onshore unconventional or conventional plays.

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Upscaling of a reservoir model is normally conducted when a high resolution model has been generated and it is practically impossible to run the simulation using a high resolution model. Reservoir model upscaling is done in order to have a reasonably coarse model for reservoir history matching and forecasting purposes without losing some of heterogeneity in the reservoir model. A high resolution static coal reservoir model (around 0.1 metre thickness) is generated based on the detailed single coal ply correlation in order to capture complexity and heterogeneity of the coal sediment. A case study is presented in this paper. Several upscaling methods for reservoir properties such as permeability and gas content were tested and analyzed. The upgridding process on the geocellular model mainly focused on vertical direction in order to identify which ply could be merged into the sub layer level. The next step identifies whether the ply can be merged with neighboring ply individually. The results of the upscaling and upgridding are analyzed using the combination of recovery ratio, simulation running time ratio and cell count ratio plot. Histogram and area map analysis is also used to evaluate the result of the upscaling work. The expected results of the upscaled model are lower running time, lower cell count and very close recovery value to the high resolution model, while still capturing the heterogeneity. The results of the upscaling exercise for this case are reduction of running time by 72%, reduction of the cell count by 64% and recovery difference of 3%. Some of the plies in one of the layers cannot be merged into a sub layer level which indicates a high degree of heterogeneity. This upscaling technique for the coal reservoir model provides more information about the reservoir characterization methodology for unconventional reservoirs, and coal bed methane reservoirs specifically.