rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial intelligence for a more sustainable oil and gas industry and the energy transition
    sub title: case studies and code examples
  name:
    name part: Ahmadi, Mohammadali
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and industries working on water resources and carbon capture and storage. This book contains fundamental knowledge on artificial intelligence related to oil and gas sustainability and the industry's pivot to support the energy transition and provides practical applications through case studies and coding flowcharts, addressing gaps and questions raised by academic and industrial partners, including energy engineers, geologists, and environmental scientists. This timely publication provides fundamental and extensive information on advanced AI applications geared to support sustainability and the energy transition for the oil and gas industry.
  table of contents: Front Cover -- Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition -- Copyright Page -- Contents -- 1 Artificial intelligence (AI) overview -- 1.1 Introduction -- 1.2 Types of AI in terms of autonomy level and capabilities -- 1.2.1 Reactive machines -- 1.2.2 Limited memory -- 1.2.3 Theory of mind -- 1.2.4 Self-aware -- 1.2.5 Strong AI -- 1.2.6 Weak AI -- 1.3 Types of AI in terms of applications -- 1.3.1 Machine learning -- 1.3.2 Deep learning -- 1.3.3 Robotics -- 1.3.4 Expert systems -- 1.3.5 Computer vision -- 1.4 Opportunities and challenges
  table of contents: 1.5 Summary -- Disclosure -- References -- 2 Machine learning -- 2.1 Introduction -- 2.2 Types of machine learning -- 2.2.1 Supervised learning -- 2.2.2 Unsupervised learning -- 2.2.3 Reinforcement learning -- 2.2.4 Semisupervised learning -- 2.3 Challenges of ML development in the oil and gas industry -- 2.4 Performance indicators of ML models -- 2.4.1 Confusion matrix -- 2.4.2 Classification accuracy -- 2.4.3 F1 score -- 2.4.4 Receiver operating characteristic curve -- 2.4.5 Area under curve -- 2.4.6 Mean absolute error -- 2.4.7 Mean squared error -- 2.4.8 Root mean square error
  table of contents: 2.4.9 Average relative error -- 2.4.10 Squared correlation coefficient (R2) -- 2.4.11 Variance accounted for (VAF) -- 2.5 Summary -- AI disclosure -- References -- 3 Classification -- 3.1 Introduction -- 3.1.1 Statistical methods -- 3.1.2 Rule-based methods -- 3.1.3 Instance-based methods -- 3.1.4 Neural network methods -- 3.1.5 Ensemble methods -- 3.2 Statistical methods -- 3.2.1 Support vector machine -- 3.2.1.1 Linear kernel -- 3.2.1.2 Polynomial kernel -- 3.2.1.3 RBF kernel -- 3.2.1.4 Sigmoid kernel -- 3.2.2 Discriminant analysis -- 3.2.3 Naive Bayes -- 3.2.4 Logistic regression
  table of contents: 3.3 Rule-based methods -- 3.3.1 Decision Trees -- 3.3.2 Rule-based classifiers -- 3.3.3 Associative classification -- 3.3.4 Bayesian networks -- 3.3.5 Decision Trees -- 3.3.6 Rule-based classifiers -- 3.3.7 Associative classification -- 3.3.8 Bayesian networks -- 3.4 Instance-based methods -- 3.4.1 K-Nearest Neighbor -- 3.4.2 Case-based reasoning -- 3.4.3 Locally weighted learning -- 3.5 Neural network methods -- 3.6 Ensemble methods -- 3.6.1 Bagging method -- 3.6.2 Boosting method -- 3.6.3 Random Forest -- 3.6.4 AdaBoost -- 3.6.5 Stacking -- 3.7 Case studies
  table of contents: 3.7.1 Enhanced Oil Recovery type prediction -- 3.7.1.1 Data preprocessing -- 3.7.1.2 Classification models and evaluation -- 3.7.2 Lithology identification -- 3.7.3 Data loading and preprocessing -- 3.7.4 Data splitting and feature scaling -- 3.7.5 Classification models -- 3.7.6 Model evaluation and hyperparameter tuning -- 3.7.7 Results and visualizations -- AI disclosure -- References -- 4 Regression -- 4.1 Introduction -- 4.2 Regression types -- 4.2.1 Linear regression -- 4.2.2 Polynomial regression -- 4.2.3 Ridge regression
  note: Mohammadali Ahmadi.
  subject:
    topic: Petroleum industry and trade
    topic: Data processing
  subject:
    topic: Artificial intelligence
    topic: Industrial applications
  subject:
    topic: Petroleum industry and trade
    topic: Environmental aspects
  subject:
    topic: Pétrole
    topic: Industrie et commerce
    topic: Informatique
  subject:
    topic: Intelligence artificielle
    topic: Applications industrielles
  classification: HD9560.5
  classification: 338.2728028563
  related item:
    identifier: (OCoLC)1423133826
  identifier: 0443240116
  identifier: 9780443240119
  identifier: 9780443240102
  identifier: 0443240108
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 240807
    record change date: 20260327180326.2
    record identifier: in00005773639
    language of cataloging:
      language term: eng