Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

A comparative study of the predictive capabilities of recent advances in computational intelligence (CI) is presented. This study utilised the machine learning paradigm to evaluate the CI techniques while applying them to the prediction of porosity and permeability of heterogeneous petroleum reservoirs using six diverse well data sets. Porosity and permeability are the major petroleum reservoir properties that serve as indicators of reservoir quality and quantity. The results showed that the performance of support vector machines (SVM) and functional networks (FN) is competitively better than that of Type-2 fuzzy logic system (T2FLS) in terms of correlation coefficient. With execution time, FN and SVM were faster than T2FLS, which took the most time for both training and testing. The results also demonstrated the capability of SVM to handle small data sets. This work will assist artificial intelligence practitioners to determine the most appropriate technique to use especially in conditions of limited amount of data and low processing power.