Artificial neural network, support vector machine, decision tree, random forest, and committee machine intelligent system help to improve performance prediction of low salinity water injection in carbonate oil reservoirs

Abstract

A large body of experimental research supports the effectiveness of Low Salinity Water Injection (LSWI) for enhanced oil recovery from carbonate reservoirs in laboratory scale. Development of robust predictive smart models connecting effective parameters controlling this complex process to Final Recovery Factor (RF_f), as the target parameter, is of a paramount importance. The main objective of this research work is to develop intelligent models using Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Committee Machine Intelligent System (CMIS) to forecast performance of LSWI in carbonates using experimental data reported in the literature. Random Search (RS) and Anneal (AL) algorithms were used for optimization of hyperparameters. After data collection from 47 reliable coreflooding studies (582 data points), a rigorous data preprocessing was conducted to ensure quality of the database. Features selection process was used to determine the main parameters controlling LSWI performance in carbonates: brine permeability (K_b), core diameter (d), porosity (Φ), and residual water saturation (S_wi) of the core, HCO₃⁻ concentration, and salinity (S) of the connate brine, the salinity (S) of the injected brine, and initial recovery factor (RF_i) which were used for development of the models. We considered initial oil recovery (RF_i) in this research work, which was not considered in previous works reported in the literature. The applicability domain analysis showed that training and testing response outliers were zero and 9, respectively, indicating acceptable quality of the database. Performance of the developed smart models was analyzed and compared using statistical and graphical error analysis methods. The best performance was obtained for the RF model with Root Mean Square Error (RMSE) of 2.497 and 5.757 for training and testing datasets, respectively, which exhibits a very good agreement with the experimental data.