The Resource Artificial intelligent approaches in petroleum geosciences, Constantin Cranganu, Henri Luchian, Mihaela Elena Breaban, editors

Artificial intelligent approaches in petroleum geosciences, Constantin Cranganu, Henri Luchian, Mihaela Elena Breaban, editors

Label
Artificial intelligent approaches in petroleum geosciences
Title
Artificial intelligent approaches in petroleum geosciences
Statement of responsibility
Constantin Cranganu, Henri Luchian, Mihaela Elena Breaban, editors
Contributor
Editor
Subject
Genre
Language
eng
Summary
This book presents several intelligent approaches for tackling and solving challenging practical problems facing those in the petroleum geosciences and petroleum industry. Written by experienced academics, this book offers state-of-the-art working examples and provides the reader with exposure to the latest developments in the field of intelligent methods applied to oil and gas research, exploration and production. It also analyzes the strengths and weaknesses of each method presented using benchmarking, whilst also emphasizing essential parameters such as robustness, accuracy, speed of convergence, computer time, overlearning and the role of normalization. The intelligent approaches presented include artificial neural networks, fuzzy logic, active learning method, genetic algorithms and support vector machines, amongst others. Integration, handling data of immense size and uncertainty, and dealing with risk management are among crucial issues in petroleum geosciences. The problems we have to solve in this domain are becoming too complex to rely on a single discipline for effective solutions, and the costs associated with poor predictions (e.g. dry holes) increase. Therefore, there is a need to establish a new approach aimed at proper integration of disciplines (such as petroleum engineering, geology, geophysics, and geochemistry), data fusion, risk reduction, and uncertainty management. These intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and mining, data analysis and interpretation, and knowledge discovery, from diverse data such as 3-D seismic, geological data, well logging, and production data. This book is intended for petroleum scientists, data miners, data scientists and professionals and post-graduate students involved in petroleum industry
Member of
Cataloging source
GW5XE
Dewey number
662.60285/63
Illustrations
illustrations
Index
index present
LC call number
TN870.5
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Cranganu, Constantin
  • Luchian, Henri
  • Breaban, Mihaela Elena
http://library.link/vocab/subjectName
  • Petroleum
  • Artificial intelligence
  • TECHNOLOGY & ENGINEERING
  • Artificial intelligence
  • Petroleum
  • Energy
  • Fossil Fuels (incl. Carbon Capture)
  • Artificial Intelligence (incl. Robotics)
  • Geotechnical Engineering & Applied Earth Sciences
  • Mathematical Modeling and Industrial Mathematics
  • Mineral Resources
  • Artificial intelligence
  • Economic geology
  • Mathematical modelling
  • Mineralogy & gems
  • Fossil fuel technologies
Label
Artificial intelligent approaches in petroleum geosciences, Constantin Cranganu, Henri Luchian, Mihaela Elena Breaban, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-16531-8
Instantiates
Publication
Note
Includes index
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Intelligent Data Analysis Techniques -- Machine Learning and Data Mining -- On meta-heuristics in optimization and data analysis. Application to geosciences -- Genetic Programming Techniques with Applications in the Oil and Gas Industry -- Application of Artificial Neural Networks in Geoscience and Petroleum Industry -- On Support Vector Regression to Predict Poisson's Ratio and Young's Modulus of Reservoir Rock -- Use of Active Learning Method to determine the presence and estimate the magnitude of abnormally pressured fluid zones: A case study from the Anadarko Basin, Oklahoma -- Active Learning Method for estimating missing logs in hydrocarbon reservoirs -- Improving the accuracy of Active Learning Method via noise injection for estimating hydraulic flow units: An example from a heterogeneous carbonate reservoir -- Well log analysis by global optimization-based interval inversion method -- Permeability estimation in petroleum reservoir by artificial intelligent methods: An overview
Dimensions
unknown
Extent
1 online resource (xii, 290 pages)
File format
unknown
Form of item
online
Isbn
9783319165318
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-16531-8
Other physical details
illustrations (some color)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)907947183
  • (OCoLC)ocn907947183
Label
Artificial intelligent approaches in petroleum geosciences, Constantin Cranganu, Henri Luchian, Mihaela Elena Breaban, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-16531-8
Publication
Note
Includes index
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Intelligent Data Analysis Techniques -- Machine Learning and Data Mining -- On meta-heuristics in optimization and data analysis. Application to geosciences -- Genetic Programming Techniques with Applications in the Oil and Gas Industry -- Application of Artificial Neural Networks in Geoscience and Petroleum Industry -- On Support Vector Regression to Predict Poisson's Ratio and Young's Modulus of Reservoir Rock -- Use of Active Learning Method to determine the presence and estimate the magnitude of abnormally pressured fluid zones: A case study from the Anadarko Basin, Oklahoma -- Active Learning Method for estimating missing logs in hydrocarbon reservoirs -- Improving the accuracy of Active Learning Method via noise injection for estimating hydraulic flow units: An example from a heterogeneous carbonate reservoir -- Well log analysis by global optimization-based interval inversion method -- Permeability estimation in petroleum reservoir by artificial intelligent methods: An overview
Dimensions
unknown
Extent
1 online resource (xii, 290 pages)
File format
unknown
Form of item
online
Isbn
9783319165318
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-16531-8
Other physical details
illustrations (some color)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)907947183
  • (OCoLC)ocn907947183

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