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: Shale Analytics
    sub title: Data-Driven Analytics in Unconventional Resources
  name:
    name part: Mohaghegh, Shahab D.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 287 p. 243 illus., 235 illus. in color. online resource.
  abstract: This book describes the application of modern information technology to reservoir modeling and well management. Data Driven Analytics in Unconventional Resources looks specifically at reservoir modeling and production management of shale reservoirs, since conventional reservoir modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the absence of well-understood and well-defined physics of fluid flow in shale. Also discussed are important insights into completion practices of production from shale Abundant examples and computer code are given that illustrate the operation of Data-Driven Analytics. The flexibility and power of the technique is demonstrated in numerous real-world situations.
  table of contents: Data-Driven Formation Evaluation – Generation of Synthetic Geo-mechanical Well Logs in Shale -- Data-Driven Reservoir Characteristics – Impact of rock and completion parameters in -- Data-Driven Completion Analysis – Analysis, Design and Optimization of Hydraulic Fracturing in Shale -- Data-Driven Reservoir Modeling – Full Field Reservoir Modeling of Marcellus Shale -- Data-Driven Reservoir Modeling – Full Field Reservoir Modeling of Niobrara Formation, DJ Basin -- Data-Driven Reservoir Modeling – AI-Based Proxy of Numerical Reservoir Simulation of Shale.
  note: by Shahab D. Mohaghegh.
  subject:
    topic: Energy
  subject:
    topic: Fossil fuels
  subject:
    topic: Economic geology
  subject:
    topic: Mineral resources
  subject:
    topic: Geotechnical engineering
  subject:
    topic: Data mining
  subject:
    topic: Energy
  subject:
    topic: Fossil Fuels (incl. Carbon Capture)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Economic Geology
  subject:
    topic: Mineral Resources
  subject:
    topic: Geotechnical Engineering & Applied Earth Sciences
  classification: TJ163.13-163.25
  classification: TP315-360
  classification: 662.6
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319487533
  identifier: http://dx.doi.org/10.1007/978-3-319-48753-3
  record info:
    record creation date: 170210
    record change date: 20170210094830.0
    record identifier: 978-3-319-48753-3