Retrospective Validation of the Robustness of Reservoir Simulation Predictions

Abstract Reservoir simulation projects are ubiquitous across the oil and gas industry. A less common practice is retrospective validation of the accuracy and robustness of the original model's predictions. This paper highlights the importance of validating previous simulation work with field data in order to test predictability and acquire more confidence in future simulation work. A fully implicit coupled wellbore-reservoir simulator was used to history match the performance of four steam-assisted gravity drainage (SAGD) well pairs after one year of operation. The purpose of this effort was to build a representative model that mimicked the observed field behavior and captured the key performance drivers such as reservoir quality, completion type, and operating strategy. A representative history matched model was achieved with an overall accuracy within 9.5% of actuals after one year of operation. This model was used to forecast well pair performance after three years. After three years of field operation, the predicted simulation forecast was compared against the actual field data. The accuracy and robustness of model's predictions remained valid and improved to within 7.5% of actuals after three years of operation. For two out of the four well pairs, the observed trends between the actuals and the simulation appeared to deviate due to completion type and operating strategy changes that occurred after the original history match was completed. Once these changes were reflected in the simulation model, predictability was restored and improved reaching an accuracy within 4.7%. This paper aims to deliver two key learnings and best practices. First, a coupled wellbore-reservoir simulation approach was successfully applied to build a representative model that captures the key performance drivers and observed field behavior. Second, a retrospective validation of the model's predictions was completed and resulted in more accurate, robust, and confident performance predictions. These robust models can be used to better understand, forecast and optimize the productive potential of oil and gas operations over their life cycle.

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