A full-field dynamic simulation model has traditionally been seen as the benchmark for assimilating all available static and dynamic data to develop robust production forecasts. Santos’ experience modelling the Walloon Coal Measures in its Surat Basin acreage has shown that the performance of individual wells producing from this CSG reservoir is governed by reservoir variability at a fine-scale. This presents a fundamental challenge in developing full-field dynamic models that can accurately describe and predict production performance down to the scale of individual coal seams.

Current Queensland CSG projects have focussed on the most prospective acreage, however as subsequent developments move to more marginal areas a greater understanding of the subsurface will be required for optimum development. The target formations will increase in geological complexity, such as Santos’ Surat Basin acreage on the edge of the CSG fairway. Here wells produce from a greater number of distinct coal reservoir units, and how these reservoir units are structured and relate to each other governs reservoir connectivity and defines long-term production performance. Each reservoir unit is comprised of multiple coal plies, all with their own unique maceral distribution and cleating characteristics. These fine-scale properties define the reservoir's dynamic behaviour, and can be impossible to upscale such that these characteristics are preserved at a coarse scale. Consequently, accurately modelling individual well performance will require a fine-scale model to capture and characterise this variability.

In development areas where the quantity and quality of reservoir data gathered from exploration and appraisal is sparsely populated, these fine-scale models will need to be populated geostatistically. Without model-scale appropriate control data from production and pressure measurement in the development wells to provide constraints however, a probabilistic model will not accurately define fine-scale behaviour of specific reservoir units. These data requirements can help shape the appraisal scope for new areas and define an appropriate level of surveillance for producing assets.

Traditional full-field dynamic modelling has fundamental limitations for interrogating complex unconventional CSG reservoirs at a fine scale. Because of this, alternative workflows are required to answer the subsurface questions necessary to develop CSG assets such as the Surat Basin effectively. This paper details a selection of workflows explored to address this pragmatically, as well as their limitations and associated data requirements. This will also assist in identifying data gaps needed for optimum reservoir management and to aid in the development of these challenging CSG reservoirs.

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