Artificial Intelligent Approaches in Petroleum Geosciences by Constantin CranganuArtificial Intelligent Approaches in Petroleum Geosciences by Constantin Cranganu

Artificial Intelligent Approaches in Petroleum Geosciences

byConstantin CranganuEditorHenri Luchian, Mihaela Elena Breaban

Hardcover | May 4, 2015

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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.

Constantin Cranganu is a professor of geophysics and petroleum geology at Brooklyn College of the City University of New York. He obtained a PhD degree (ABD) from the University of Bucharest, Romania (1993) in geophysics and another PhD from the University of Oklahoma (1997) in geology.Before coming to Brooklyn College, he worked at "A...
Title:Artificial Intelligent Approaches in Petroleum GeosciencesFormat:HardcoverDimensions:290 pagesPublished:May 4, 2015Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319165305

ISBN - 13:9783319165301

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Table of 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.

Editorial Reviews

"The book 'Artificial Intelligent Approaches in Petroleum Geosciences' is a comprehensive presentation on the basic methods and approaches for surveying, measuring and monitoring geoscience and petroleum basins. . The graphics in this book are exceptional, in color and aid the readers greatly. . directed specifically to high-level modellers and analysis professionals, advanced students will understand the discussion and methods through text." (Jeff Thurston,, May, 2015)