Recently, due to declination of oil production the importance of enhancement of oil recovery becomes highlighted. CO₂ injection as one of popular approaches because of economically and environmental friendly has wide applications in enhancement of oil recovery. Supercritical carbon dioxide is defined as CO₂ which is placed at the pressure and temperature above the critical pressure and temperature which is used widely in petroleum industry. After CO₂ injection to the reservoir, the light hydrocarbons of crude oil can be extracted by liquid CO₂ and these processes are affected by different parameters such as solubility, so this study was performed to investigate solubility of alkanes in supercritical CO₂. Two types of artificial neural networks, i.e., Radial Basis Function (RBF) and Networks Multi-layer Perceptron (MLP) were applied for this investigation. Results show that the MLP-ANN (artificial neural network) has better performance than RBF-ANN to predict solubility of n-alkane in supercritical carbon dioxide.

近来，由于石油产量的下降，提高石油采收率的重要性变得突出。由于经济和环境友好，CO ₂注入是流行的方法之一，在提高石油采收率方面具有广泛的应用。超临界二氧化碳被定义为在高于石油工业中广泛使用的临界压力和温度的压力和温度下放置的CO ₂。将CO ₂注入油藏后，可以通过液态CO ₂提取原油中的轻烃，并且这些过程受诸如溶解度等不同参数的影响，因此本研究旨在研究烷烃在超临界CO _2中的溶解度。。两种类型的人工神经网络，即径向基函数（RBF）和网络多层感知器（MLP）被应用于此研究。结果表明，MLP-ANN（人工神经网络）在预测正构烷烃在超临界二氧化碳中的溶解度方面优于RBF-ANN 。