History-matching is commonly used to calibrate numerical simulation models to validate geological and reservoir engineering inputs and ensure the reliability of model predictions. While history-matching can yield a powerful forecasting tool, thermal SAGD history-matching presents a host of challenges, particularly in terms of computational time and numerical tuning difficulties associated with large thermal simulation models. Typically, single well or small multi-well models are developed and calibrated to generate meaningful production forecasts. However, these predictions are only valid prior to steam chamber coalescence and the models are insufficient for life-of-well forecasting. Hence, it is necessary to build and calibrate large multi-well simulation models within a reasonable timeframe.

The oldest SAGD wells at Suncor's Mackay River (MR) property are part of the initial Phase 1 development. This phase consists of 25 well pairs that commenced steam injection in September 2002. This paper presents a calibration process for Phase 1 simulation model, generated through Suncor's geostatistical modeling process, incorporates more than 9 years of production history and contains approximately 1.4 million active cells. The process described has several components: 1) rigorous data quality control, 2) establishing appropriate boundary conditions, 3) numerical tuning, and 4) a focus on global rather than detailed local changes.

Using the process described herein, a Phase 1 history match was achieved which honours actual field rate and pressure history. The history match results are presented on well/pattern/phase basis. This process has subsequently been applied to MR Phase 2 and 3 with significant efficiency improvements.

A process is presented that addresses many of the issues that arise in validating and history-matching large, complex simulation models. It is shown that history-matching multi-wells SAGD performance with a long operation history is feasible, practical, and useful. The calibrated modeling processes gives confidence in understanding the reservoir and reduces the uncertainty of future forecasting results.

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