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: Machine learning guide for oil and gas using Python
    sub title: a step-by-step breakdown with data, algorithms, codes, and applications
  name:
    name part: Belyadi, Hoss
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Haghighat, Alireza
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xiii, 462 pages)
  abstract: Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
  note: Hoss Belyadi, Alireza Haghighat.
  note: Includes bibliographical references and index.
  subject:
    topic: Petroleum engineering
    topic: Data processing
  subject:
    topic: Natural gas
    topic: Data processing
  subject:
    topic: Machine learning
  subject:
    topic: Technique du pétrole
    topic: Informatique
  subject:
    topic: Gaz naturel
    topic: Informatique
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Machine learning
  subject:
    topic: Natural gas
    topic: Data processing
  subject:
    topic: Petroleum engineering
    topic: Data processing
  classification: TN871
  classification: 622.3380285631
  related item:
    title info:
      title: Machine learning guide for oil and gas using Python
    name:
      name part: Belyadi, Hoss.
    identifier: (OCoLC)1195448296
  identifier: 9780128219300
  identifier: 0128219300
  identifier: 9780128219294
  identifier: 0128219297
  identifier: 6546221 Proquest Ebook Central
  identifier: 9780128219300 O'Reilly Media
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 210416
    record change date: 20260327170238.6
    record identifier: in00005768150
    language of cataloging:
      language term: eng