Aerated underbalanced drilling system development
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Texas A&M University recently has established a new method to develop a drilling expert system to provide consultations related to best practices and trouble shooting in aerated underbalanced drilling. The drilling expert system is based on Bayesian network. A Bayesian network is defined as "probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG)." To the best of the authors' knowledge, this paper is the first study that shows the use of such method in aerated underbalanced drilling engineering. The paper demonstrates the use of Bayesian network to develop the advisory system for aerated underbalanced drilling. To develop this system, we reviewed the literature and interviewed experts to find best practices in aerated underbalanced drilling. The advisory system can be used as a training tool for engineers to show them basic designing steps for aerated UBD. Copyright 2012, Society of Petroleum Engineers.
