QGC's current full-field reservoir model comprises hundreds to thousands of CSG wells. This presents a considerable challenge from a history-matching standpoint compared to a conventional workflow where well-level adjustments may be made on one well at a time. In QGC, a model with an improved well-level match is desired as the resulting well forecast will enable decisions on a well-level to be made more confidently, such as the prioritization of well workovers. Previously a field-level history-match was deemed acceptable when the model was only used for field development planning. The method parameterizes the well-level relative error in simulated production from the model versus observed production. The workflow utilizes this data, known as well-level modifiers, to alter subsurface properties. This has been achieved with a semi-automated workflow to make the process efficient and repeatable, but also to enable engineering judgement to be incorporated in the history-matching process. The feedback loop is also an essential component of the workflow as it allows the well-level modifiers to be sense checked against the regional geological trends. This further encourages collaboration within a multi-disciplinary team. These well-level modifiers can also be used to create history-match metrics, which can be spatially mapped to help target specific areas for improvement in history-match quality. Some powerful use of visualization techniques discussed in this paper has not only minimized the mismatch but ensures the characteristics of the production history and geological trends are honoured to assure the robustness of the history-match and the resulting model predictability. The workflow has significantly reduced the time and efforts spent in delivering an improved well forecast when required. The technical development community in QGC has actively nurtured a culture of ideas sharing and innovation, which made the development of this workflow possible.

Use of p/Z versus cumulative produced gas plots is an industry standard practice to estimate gas initially in place (GIIP) and expected ultimate recovery (EUR) for conventional volumetric gas reservoirs that behave as a "tank". In this paper applicability of coal seam gas (CSG) material balance technique as proposed by King (ref 1) on Surat Basin undersaturated coals was investigated. A modified King's material balance technique, as well as the Jensen and Smith approach, were successfully applied to production data from undersaturated coals, producing robust dynamic GIIP and EUR estimates based on observed reservoir depletion trends. Stability of produced GIIP estimates was tested by means of sensitivity analysis and robustness of the technique demonstrated by comparison with results of numerical reservoir simulation. Finally, reservoir tank models were linked with the well inflow relationship to produce well by well production forecasts. The key difference of CSG wells, compared to conventional gas wells is that well productivity considerably changes during the life of a well. This gas productivity increase phenomenon was captured in the forecast by means of well PI versus recovery factor relationship. Thus, a practical CSG well by well production forecasting approach is proposed.

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