Applications of artificial intelligence techniques in the petroleum industry /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Hemmati-Sarapardeh, Abdolhossein, Larestani, Aydin, Amar, Menad Nait, Hajirezaie, Sassan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : Gulf Professional Publishing, 2020.
Temas:
Artificial intelligence > Industrial applications.
Petroleum industry and trade > Data processing.
Intelligence artificielle > Applications industrielles.
P�etrole > Industrie et commerce > Informatique.
Artificial intelligence > Industrial applications
Petroleum industry and trade > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 2.6.1 Hybrid group method of data handling
  • 2.7 Genetic programming
  • 2.7.1 Multigene genetic programming
  • 2.8 Gene expression programming
  • 2.9 Case-based reasoning
  • 2.10 Committee machine intelligent system
  • References
  • 3 Training and optimization algorithms
  • 3.1 Overview
  • 3.2 Genetic algorithm
  • 3.3 Differential evolution
  • 3.4 Particle swarm optimization
  • 3.5 Ant colony optimization
  • 3.6 Artificial bee colony
  • 3.7 Firefly algorithm
  • 3.8 Imperialist competitive algorithm
  • 3.9 Simulated annealing
  • 3.10 Coupled simulated annealing
  • 3.11 Gravitational search algorithm
  • 3.12 Cuckoo optimization algorithm
  • 3.13 Gray wolf optimization
  • 3.14 Whale optimization algorithm
  • 3.15 Levenberg-Marquardt algorithm
  • 3.16 Bayesian regularization algorithm
  • 3.17 Scaled conjugate gradient algorithm
  • 3.18 Resilient backpropagation algorithm
  • References
  • 4 Application of intelligent models in reservoir and production engineering
  • 4.1 Reservoir fluid properties
  • 4.1.1 One-phase properties
  • 4.1.2 Two-phase properties
  • 4.2 Rock properties
  • 4.3 Enhanced oil recovery
  • 4.3.1 Enhanced oil recovery processes

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