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.

主要应在实验室环境中评估在储层恶劣条件下提高采油率（EOR）的方法的可行性，以捕获可能威胁到作业性能的故障，尽管此类实验通常很昂贵且耗时。这项工作研究了人工智能在缓解对EOR方法的初始筛选方面的此类担忧方面的应用。因此，采用了三种机器学习算法，即自适应神经模糊推理系统（ANFIS），多层感知器-人工神经网络（MLP-ANN）和径向基函数-人工神经网络（RBF-ANN），来预测效率碳酸盐岩和砂岩岩心样品中的一组二氧化硅纳米流体驱替实验的结果。最初，确定了所采用模型的最佳结构。然后，比较他们的表演。通过ANFIS模型获得了最强的性能，其中在确定系数和训练，测试以及整个数据点的均方根误差方面，结果分别为0.9954和0.3395、0.9877和0.4793、0.9939和0.3793 。ANFIS模型还具有最短的执行时间和最少的过度拟合问题，因此可用于筛选silicon-EOR项目的效率。分别。ANFIS模型还具有最短的执行时间和最少的过度拟合问题，因此可用于筛选silicon-EOR项目的效率。分别。ANFIS模型还具有最短的执行时间和最少的过度拟合问题，因此可用于筛选silicon-EOR项目的效率。