Application of artificial intelligence for technical screening of enhanced oil recovery methods

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 nonregression 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.