Building a representative static model for predicting and monitoring the performance of coal-seam gas (CSG) fields presents several complex and unique challenges. The individual reservoirs possess different coal architectures, often with highly complex seam splitting, amalgamating, and structural deformation. Our objective was to develop an alternative approach that honored log and core data capturing both the lateral heterogeneity and the vertical signature of the Bowen Basin coals in Central Queensland.

In some areas of the Bowen Basin, coals can be thick and laterally continuous; picking the top and base of each seam works well in small models with homogeneous coals. As seam geometries begin to increase in complexity and coals become more heterogeneous in nature with thinner seams in multiple packages, then a net‐to‐gross (NTG) approach is often more appropriate. Each method has its merits. The former approach describes the reservoir architecture but implies a certain degree of confidence in coal correlation. In a vast field with complex seam splitting and merging with abundant drilling data, it might not be a practical technique. The NTG method disregards coal‐seam architecture and reservoir connectivity.

The proposed workflow is a hybrid approach using discrete modeling algorithms and data‐analysis tools on continuous NTG properties. The process operates on a relatively coarse chronostratigraphic framework in which coal is captured as contiguous discrete‐NTG “facies.” The use of the truncated Gaussian model ensures the ordering of NTG facies and mimics transitions between coals and the surrounding interburdens. With the adoption of facies vertical proportion trends, we are able to replicate a similar coal‐seam signature laterally away from the wellbore. The definition of a categorical coal model allows the proper scaling of seams with different coal‐quality characteristics.

With the successful geocellular‐model construction and history match of two historical CSG fields in the Bowen Basin, the discrete‐NTG truncated Gaussian simulation (TGS) approach has proved to be a valid alternative CSG‐modeling technique.

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