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.
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