Abstract
Oil-CO2 minimum miscible pressure (MMP) has significance in selecting appropriate reservoir for miscible gas injection and greatly governs performance of local displacement. Accurate determination of MMP is very expensive, time-consuming, and labor intensive. Therefore, the quest for a method to determine MMP accurately and save time and money is necessary. This study held a competition between neural network and support vector regression models and assessed their performance in prediction of MMP for both pure and impure miscible CO2 injection. Subsequently, a committee machine was constructed based on divide and conquer principle to reap benefits of both model and increases the precision of final prediction. Results indicated committee machine performed more satisfyingly compared with individual intelligent models performing alone.
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