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Application of Artificial Intelligence in the Upstream Oil and Gas Industry (pp.1-38) $100.00
Authors:  (Shahab D. Mohaghegh, Yasaman Khazaeni, Intelligent Solutions, Inc., and West Virginia University, Morgantown, West Virginia, USA)
This chapter starts with an introduction to Artificial Intelligence (AI) including some
historical background of the technology. Artificial Intelligence may be defined as a collection
of several analytic tools that collectively attempt to imitate life (1). In the last twenty years
Artificial Intelligence has matured to a set of analytic tools that facilitate solving problems
which were previously difficult or impossible to solve. The trend now is the integration of
these tools, as well as with conventional technologies such as statistical analysis, to build
sophisticated systems capable of solving challenging problems.
Artificial Intelligence is used in areas such as medical diagnosis, credit card fraud
detection, bank loan approval, smart household appliances, subway systems, automatic
transmissions, financial portfolio management, robot navigation systems, and many more. In
the oil and gas industry these tools have been used to solve problems related to pressure
transient analysis, well log interpretation, reservoir characterization, and candidate well
selection for stimulation, among others.
Artificial neural networks, evolutionary programming and fuzzy logic are among the
paradigms that are classified as Artificial Intelligence. These technologies exhibit an ability to
learn and deal with new situations by possessing one or more attributes of "reason", such as
generalization, discovery, association and abstraction (2).
This chapter is organized in four sections. First three sections are focused on the
definition of some of the AI tools which are most commonly practiced and show some of their
applications in the upstream oil and gas industry. In the first section artificial neural networks,
are introduced as information processing systems that have certain performance characteristics
in common with biological neural networks. This section will conclude with detail
presentation of an application of neural networks in the upstream oil and gas industry.
Second section is intended to provide an overview of evolutionary computing, its
potential combination with neural networks to produce powerful intelligent applications, and
its applications in the oil and gas industry. The most successful intelligent applications
incorporate several artificial intelligence tools in a hybrid manner. These tools complement
each other and amplify each other's effectiveness. An overview of evolutionary computation
and its background is presented, followed by a more detailed look at genetic algorithms as the
primary evolutionary computing. The article will continue and conclude by exploring the
application of a hybrid neural network/genetic algorithm system to a petroleum engineering
related problem.
Fuzzy logic is the focus of the third section. An overview of the subject is provided
followed by its potential application in petroleum engineering related problems. In this
section, application of fuzzy logic for re-stimulation candidate selection in a tight gas
formation in the Rocky Mountains will be reviewed. This particular application was chosen
because it uses fuzzy logic in a hybrid manner integrated with neural networks and genetic
In the fourth and final section, some other applications of these tools in reservoir
characterization, production modeling and performance prediction are presented. 

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Application of Artificial Intelligence in the Upstream Oil and Gas Industry (pp.1-38)