CBM reservoirs are known to exhibit a large degree of variability in production characteristics and performance at the inter-well space. This is a consequence of the variable nature of the fractured permeability network, but also other important measurable parameters such as gas content, isotherms, and much more difficult to measure ones such as porosity, relative permeability and matrix shrinkage.

Once the field is on production for a period of time, operators often wish to perform a history-match of the asset, to determine the remaining production potential, and the most adequate future development activities. Traditionally, this exercise is conducted with numerical models.

An unfortunate reality of history matching is the existence of multiple solutions, which will behave differently when forecasted in the future. This is a consequence of the fact that multiple parameters influence the match and their inter-dependency produces effects that are may compensate each other; many of these parameters are generally either poorly understood and/or highly variable at the inter-well scale. In addition, most of the critical parameters for dynamic performance are not measured in well logs, nor seismic (which often is not available) – only through more expensive activities such as coring and production testing.

Given this reality, using the classical conventional gas workflows for the implementation of 3D history matched models for CBM fields is shown to be inefficient since the transition from the reservoir characterization to the static modelling is insufficiently constrained, and doesn't include the production performance of the existing wells.

In order to adapt the classical workflows to the complex case of CBM fields, we introduce in this paper a filtering process based on geo-statistical constraints which uses the single well stochastic history matches to generate field matches and invest the matched drainage areas on the generation of "average properties" maps.

These maps can thereafter be used as input into the infill drilling analysis and the resource assessment studies, but also introduced into the static model to condition the property model, and achieve a field history-match which much more closely reproduces the production behavior of individual wells.

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