1 research outputs found
DATA-DRIVEN CONNECTIONIST MODELS FOR PERFORMANCE PREDICTION OF LOW SALINITY WATERFLOODING IN SANDSTONE RESERVOIRS
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 reservoir