Oil-CO₂ MMP Determination in Competition of Neural Network, Support Vector Regression, and Committee Machine

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.

Keywords:

• Committee machine
• minimum miscible pressure
• miscible CO₂ injection
• neural network
• support vector regression

Notes

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ldis.