Hybrid connectionist model determines CO2-oil swelling factor

被引:12
作者
Ahmadi, Mohammad Ali [1 ]
Zendehboudi, Sohrab [1 ]
James, Lesley A. [1 ]
机构
[1] Mem Univ Newfoundland, Fac Engn & Appl Sci, St John, NF A1C 5S7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
CO2; injection; swelling; Genetic algorithm; Predictive model; Least-squares support vector machine; ENHANCED OIL-RECOVERY; SUPPORT VECTOR MACHINES; MINIMUM MISCIBILITY PRESSURE; CYCLIC CO2 INJECTION; PLUS CARBON-DIOXIDE; INTERFACIAL-TENSION; PHASE-BEHAVIOR; HEAVY OIL; STORAGE; PREDICTION;
D O I
10.1007/s12182-018-0230-5
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2-oil swelling factor. A genetic algorithm is used to optimize hyperparameters (gamma and sigma (2)) of the LS-SVM model. This model showed a high coefficient of determination (R (2) = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2-oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2-oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2-oil swelling factor when adequate experimental data are not available.
引用
收藏
页码:591 / 604
页数:14
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