SAGD (Steam Assisted Gravity Drainage) is an efficient and proven technology to recover vast reserves of Alberta's oil sands. Because of its thermal and compositional effects, numerical simulation of the SAGD process requires extensive computational run time, especially in a history matching framework. Therefore, it is beneficial to use an optimization technique that yields faster convergence and better match-quality solutions.
This paper presents a new population-based optimization technique, called differential evolution, in the assisted history matching process. Differential evolution belongs to the class of evolutionary algorithms in the continuous parameter space that has been used successfully in a large range of engineering optimization problems outside the oil industry. Differential evolution converges faster than many other global optimization methods. It requires fewer control variables, is robust and easy to use, and lends itself very well to parallel computing.
We applied the differential evolution technique to a SAGD case study to history match saturation and temperature profiles as well as cumulative oil and water production and cumulative SOR. The results show that it is an excellent optimization technique for obtaining multiple good history matched models, which allow the assessment of uncertainty for the forecast stage. The match-quality of the history matched models obtained with differential evolution has been compared to the results of the particle swarm optimization method that is widely used in history matching. The comparison shows that differential evolution offers much better match-quality solutions with much lower number of simulation runs.