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.