The Australia Pacific LNG Project is scheduled to come online in 2015 and consists of the extensive development of substantial coal seam gas resources across the Surat Basin. A variety of unique and complex challenges are presented when undertaking the geological and dynamic modelling and performance prediction of the laterally discontinuous and thin coal seams within the Walloons package. These challenges require a targeted approach and evolving solutions.

Specific challenges that have been assessed and addressed in the characterisation and modelling workflow of the complex Walloons coal seams include:

  • Complex coal packages exhibiting thin coal seams with high degrees of lateral and vertical heterogeneity

  • Appropriate prediction of uncertainty

  • Coal body distribution and connectivity

  • Application of learning from fine scale sector modelling to larger regional coarse scale models

  • Significant reservoir dynamics across large distances within Surat acreage

  • Production performance variation over time resulting from stress impacting the matrix

  • Vast scale of the Surat Walloons development combined with fast-cycle decision making

  • Adopting appropriate and unique workflows for regions of different production maturity

The developed workflow addresses coal seam gas modelling challenges both within the history matching phase and in the subsequent probabilistic forecasting. The workflow identifies uncertain reservoir properties and their expected maximum ranges based on available data and previous studies. An uncertainty analysis is conducted with statistical approaches including Tornado Chart and Latin Hypercube (LHC) to identify the most influential reservoir parameters and fine-tune their ranges in order to optimize the probabilistic history matching process. Subsequently, assisted history matching and optimisation techniques follow a stochastic algorithm of experiments to reduce the mismatch and create convergence with the production history. A number of representative, non-unique history matched models are identified which satisfy matching criteria and capture the uncertainty of the subsurface.

The selected acceptable history matched models, together with forecasting parameters and their ranges, including operational variables, are used in forecasting. Random sampling of uncertainties by LHC is used with assisted history matched cases resulting in thousands of forecasts based on which P10-P50-P90 probabilistic forecasts are selected.

This paper presents solutions to address uncertainty, assisted history matching and performance prediction of the Walloons coal measures for rigorous forecasting, and incorporation of learnings from localized fine scale models into larger coarse scale modelling for time efficient, regional performance prediction.

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