RESUMO
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 (γ and σ 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.
RESUMO
This paper presents an important new approach to improving the timeliness of Total Petroleum Hydrocarbon (TPH) analysis in the soil by Gas Chromatography - Flame Ionization Detector (GC-FID) using the CCME Canada-Wide Standard reference method. The Canada-Wide Standard (CWS) method is used for the analysis of petroleum hydrocarbon compounds across Canada. However, inter-laboratory application of this method for the analysis of TPH in the soil has often shown considerable variability in the results. This could be due, in part, to the different gas chromatography (GC) conditions, other steps involved in the method, as well as the soil properties. In addition, there are differences in the interpretation of the GC results, which impacts the determination of the effectiveness of remediation at hydrocarbon-contaminated sites. In this work, multivariate experimental design approach was used to develop and validate the analytical method for a faster quantitative analysis of TPH in (contaminated) soil. A fractional factorial design (fFD) was used to screen six factors to identify the most significant factors impacting the analysis. These factors included: injection volume (µL), injection temperature (°C), oven program (°C/min), detector temperature (°C), carrier gas flow rate (mL/min) and solvent ratio (v/v hexane/dichloromethane). The most important factors (carrier gas flow rate and oven program) were then optimized using a central composite response surface design. Robustness testing and validation of model compares favourably with the experimental results with percentage difference of 2.78% for the analysis time. This research successfully reduced the method's standard analytical time from 20 to 8min with all the carbon fractions eluting. The method was successfully applied for fast TPH analysis of Bunker C oil contaminated soil. A reduced analytical time would offer many benefits including an improved laboratory reporting times, and overall improved clean up efficiency. The method was successfully applied for the analysis of TPH of Bunker C oil in contaminated soil.
