In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

An artificial intelligence technique based on five (5) layered feedforward backpropagation algorithm is applied in this study for technical screening of enhanced oil recovery (EOR) methods. Explicit knowledge pattern associated with the field data are extracted by taking advantage of the robustness of fuzzy logic reasoning and learning capability of neural networks. Associated field data from successful EOR projects include parameters such as depth, porosity, permeability, viscosity, oil API and oil saturation. These parameters were used as input and predicted output in the training and validation processes, respectively. The developed model was then tested by using data set from Block T of the Angolan oilfield. Sensitivity analysis was performed between the Mandani and the Takagi Sugero (TSK) model approach incorporated in the algorithm. The results of the sensitivity analysis have shown the robustness of the ANFIS approach in comparison to other approaches for the prediction of suitable EOR technique. Five non-regression models (linear, potential, logarithm, power and polynomial) were applied to evaluate the accuracy of the model between the trained and the tested data set. The results of simulation show that hydrocarbon gas, polymer, combustion and CO2 are the suitable EOR techniques and could be used for further experimental and numerical studies.