The effect of ionic strength on oil adhesion in sandstone--the search for the low salinity mechanism.
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ABSTRACT:
SUBMITTER: Hilner E
PROVIDER: S-EPMC4405700 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:Low salinity waterflooding (low salinity-EOR) has attracted great interest from many giant oil producers and is currently under trial in some of the oil fields of the United States, Middle Eastern countries, and North Sea reservoirs. Most of the reported studies on this process were carried out for medium to relatively heavy oil with significant polar contents. In this work, we have investigated low salinity waterflooding performance for light paraffinic crude oil with a low acid number. This study has been performed using crude oil from an Indian offshore oilfield and Indian offshore seawater. Oil recovery efficiencies of seawater and its diluted versions (low salinity seawater) were evaluated through core-flooding experiments performed on a silica sand pack containing small amounts (2 wt %) of bentonite clay saturated with crude oil. Interfacial tension and wettability studies were performed to understand the associated low salinity effects on the crude oil/brine/rock properties. Effluent brine produced during the flooding experiments was also analyzed to obtain a clearer insight into the low salinity-enhanced oil recovery (EOR) mechanism. The results showed that injection of low salinity seawater can significantly increase the waterflood recovery in comparison with high salinity seawater injection. Interfacial tension and contact angle studies revealed that there is an optimum dilution level at which the interfacial tension and wettability are the most favorable for enhanced oil recovery even in the case of light paraffinic crude. These results are in line with the results obtained from the core-flooding experiments. The possible reason behind recovery improvement based on the interfacial tension and wettability studies in conjugation with the effluent brine analysis has been discussed in detail. In this study, we have observed that the enhanced oil recovery efficiency could be achieved by applying low salinity seawater flooding even in the case of light paraffinic oil with a low acid number.
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Project description:Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RFf) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80-20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca2+ in the connate water, Na+ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RFf. The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoirs.
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