Applications of artificial intelligence techniques in the petroleum industry / Abdolhossein Hemmati-Sarapardeh, Aydin Larestani, Menad Nait Amar, Sassan Hajirezaie.
Imprint
[Place of publication not identified] : Gulf Professional Publishing, 2020.
Front Cover -- Applications of Artificial Intelligence Techniques in the Petroleum Industry -- Copyright Page -- Contents -- About the author -- 1 Introduction -- 1.1 Overview -- 1.2 Preprocessing of data -- 1.2.1 Data cleaning -- 1.2.2 Data integration -- 1.2.3 Data transformation -- 1.2.4 Data reduction -- 1.2.5 Data discretization -- 1.2.6 Data statistics -- 1.2.6.1 Skewness -- 1.2.6.2 Kurtosis -- 1.3 Processing of data -- 1.3.1 Data training -- 1.3.2 Data validation and testing -- 1.4 Postprocessing of data -- 1.4.1 Statistical analyses for models' evaluation
1.4.1.1 Average percent relative error (APRE) -- 1.4.1.2 Average absolute percent relative error (AAPRE) -- 1.4.1.3 Root mean square error (RMSE) -- 1.4.1.4 Standard deviation (SD) -- 1.4.1.5 Coefficient of determination (R2) -- 1.4.2 Graphical error analysis for models' evaluation -- 1.4.2.1 Error distribution curve -- 1.4.2.2 Crossplots -- 1.4.2.3 Cumulative frequency plots versus absolute percent relative error -- 1.4.2.4 Group error -- 1.4.2.5 3-D plots -- 1.5 Applicability domain of a model -- 1.5.1 Identification of experimental data outliers -- 1.6 Sensitivity analysis on models' inputs
1.6.1 Relevancy factor analysis -- 1.7 The areas of intelligent models applications in the petroleum industry -- References -- 2 Intelligent models -- 2.1 Artificial neural networks -- 2.1.1 Multilayer perceptron neural network -- 2.1.2 Radial basis function neural network -- 2.2 Fuzzy logic systems -- 2.3 Adaptive neuro-fuzzy inference system -- 2.4 Support vector machine -- 2.4.1 Ordinary support vector machine -- 2.4.2 Least-square support vector machine -- 2.5 Decision tree -- 2.5.1 Random forest -- 2.5.2 Extra trees -- 2.6 Group method of data handling
