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Andreas Laake

Remote Sensing for Hydrocarbon Exploration

Ebook (PDF Format)

This book provides insights into the benefits of using remote sensing data from a geoscientist's perspective, by integrating the data with the understanding of Earth's surface and subsurface. In 3 sections, the book takes a detailed look at what data explorationists use when they explore for hydrocarbon resources, assess different terrain types for planning and hazards and extract present-day geologic analogs for subsurface geologic settings. The book presents the usage of remote sensing data in exploration in a structured way by detecting individual geologic features as building blocks for complex geologic systems. This concept enables reade… Mehr

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Produktdetails


  • ISBN: 978-3-030-73319-3
  • EAN: 9783030733193
  • Produktnummer: 37886136
  • Verlag: Springer
  • Sprache: Englisch
  • Erscheinungsjahr: 2021
  • Seitenangabe: 348 S.
  • Plattform: PDF
  • Masse: 49'597 KB

Über den Autor


Dr. Andreas Laake is a physicist and geoscientist with over 30 years of industry experience in processing, interpretation and integration of geological, geophysical, and remote sensing data from acquisition planning and data acquisition, to geological modeling from prospect to global scale. He is a Geophysical Advisor for Schlumberger Digital and Integration in Aachen, Germany, where he develops and executes digital exploration projects using analysis and interpretation of seismic data for geological modeling and reservoir characterization, integration of seismic, non-seismic, satellite and well data, and geological model building from basin to global scale. The author envisages processing, interpretation and integration of geoscience data as an art to unlock the information that is hidden in the raw data. The author's approach to the visualization of the results aims at the co-rendering of multiple data types in a form that inspires the intuitive interpretation of the viewer to draw comprehensive conclusions that are supported by the context of multiple data types, each of which provide a different facet of the studied features.

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