Coal Thickness Modelling Across a Regional CBM Project: An Amended Workflow
- Simon Beaney (Arrow Energy) | Max Jeffries (Arrow Energy) | Saikat Mazumder (Royal Dutch Shell Plc) | Artem Sibgatullin (Arrow Energy)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18-19 November, Brisbane, Australia
- Publication Date
- Document Type
- Conference Paper
- 2019, Unconventional Resources Technology Conference (URTeC)
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- 43 since 2007
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Building on the successful results of the Surat Model presented by Zhang et al, SPE-186340-MS; this paper describes an amended methodology to simplify the distribution of coal bearing facies of the Walloon Coal Measures in the Surat Basin across a regionally significant Gas Project.
The workflow was created with the high vertical resolution to capture the detail of the coal horizons and decrease up-scaling and averaging artefacts in the 3D environment whilst honouring geological premise for fining-up sequences within stratigraphic units.
The model horizons were created using picks from six coal packages within the Juandah and Taroom Coal Measures. The coal seam packages divisions typically display a fining-up sequence with the top of the sequence most-often picked as coal. Unlike the initial workflow where a series of coal and sand sub-zones were picked in order to narrow statistical ranges to aid control of the lateral and vertical proportions within members. This workflow utilised a coal probability property to guide the spatial distribution of coals which resulted in a preserved statistical outcome from input log data to final modelled result that honoured well data whilst dramatically reducing dynamic run time. Coal distribution was modelled using the Truncated Gaussian Algorithm trended to this probability property. The non-coal proportion of the resulting property was populated with sands to create a final three facies property.
Net-to-gross was modelled as a separate property using moving average algorithm applied to up-scaled coal cells. This property was then multiplied against cell height filtering on coal cells from the facies modelling outcome in order to achieve a net coal outcome.
The use of a trend property to place coal within the model volume captures the natural fining-up sequences regionally through each stratigraphic unit without the need to tailor the depositional progressions using deterministic sub-horizon picks. As this property is dynamically updated with the introduction of new well data it removes the requirement for extensive manual statistical analysis.
A benefit of the use of a trending property is the honouring of regional variations in net coal and is defined by up-scaled well data. Importantly, the results yielded a seamless progression of coal thickness from areas of low data density to high data density.
This approach resulted in a highly detailed, geologically representative model that blends the requirements of delivering complexity of drainage architecture and a base case outcome.
The workflow presents an opportunity to model net coal regionally and locally within a single model with high resolution, honouring local data and regional distribution. This provides a model that characterises a credible geological outcome whilst representing a convincing dynamic simulation.
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