000			01863nab a2200205 4500
005			20260520000312.0
008			260224s2011 xxu
245	0	0	_aBayesian networks for prospect analysis in the North Sea
260			_a _b _cago. 2011
270			_a19/09/2011 ; 19/09/2011
300			_a20 p. ; 1423-1442
520			_aTranscripción del resumen del autor. We propose a flexible framework for evaluating prospect dependencies in oil and gas exploration and for solving decision-making problems in this context. The model uses a Bayesian network (BN) for encoding the dependencies in a geologic system at source, reservoir, and trap levels. We discuss different evaluation criteria that allow us to formulate specific decision problems and solve these within the BN framework. The BN model offers a realistic graphic model for capturing the underlying causal geologic process and allows fast statistical computations of marginal and conditional probabilities. We illustrate the use of our BN model by considering two situations. In the first situation, we wish to gain information about an area where hydrocarbons have been discovered, and use the value of perfect information to determine which locations are the best to drill. In the second situation, we consider the problem of abandoning an area when only dry wells are drilled. For this latter, we use an abandoned revenue criterion to determine the drilling locations. The application is from the North Sea. Our main focus is the description, visualization, and interpretation of the results for relating the statistical modeling to the local understanding of the geology.
581			_a8
773	0		_tAAPG Bulletin _g95
942			_cARTICULO
100	1		_aMartinelli, Gabriele _951587
100	1		_aEidsvik, Jo _951588
100	1		_aHauge, Ragnar _951589
999			_c185591 _d185591