Application of Artificial Intelligence to Predict Enhanced Oil Recovery Using Silica Nanofluids

The feasibility of approaches to enhanced oil recovery (EOR) in harsh conditions of reservoirs should be evaluated primarily in the laboratory environment to capture possible failures that threaten the performance of an operation, although such experiments are commonly expensive and time-consuming. This work investigated the application of artificial intelligence in allaying such concerns regarding the initial screening of EOR methods. Accordingly, three machine learning algorithms, namely adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron-artificial neural network (MLP-ANN), and radial basis function-artificial neural network (RBF-ANN), were employed to predict the efficiency of a set of silica nanofluid flooding experiments in carbonate and sandstone core samples. Initially, the optimum structures of the employed models were determined. Then, their performances were compared. The strongest performance was achieved by the ANFIS model, where the results in terms of coefficient of determination and root-mean-square error for training, testing, and entire data points were 0.9954 and 0.3395, 0.9877 and 0.4793, and 0.9939 and 0.3793, respectively. The ANFIS model also has the shortest execution time and the least over-fitting problems, and thus it can be utilized for screening the efficiency of silica-EOR projects.

Automated summary

The study utilized artificial intelligence algorithms to screen EOR methods, improving experimental efficiency and accuracy, ultimately determining the ANFIS model as the best choice. This method can effectively predict the efficiency of silica nanofluid flooding experiments, saving time and cost.