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AAPG Bulletin, V.
Bayesian assessment of favorability for oil and gas prospects over the Recncavo basin, Brazil
1Basin Analysis and Petrophysical Laboratory, Department of Geology, Federal University of Paran (UFPR), Curitiba, Paran, Brazil; present address: Universidade Federal do Paran, Setor de Cincias da Terra, Departamento de Geologia, Centro Politcnico, Jardim das Amricas, 81531-990, Curitiba, Paran, Brazil; email: firstname.lastname@example.org
2A. C. Mattana Consultoria e Sistemas, Curitiba, Paran, Brazil; email: [email protected]
3Basin Analysis and Petrophysical Laboratory, Department of Geology, Federal University of Paran (UFPR), Curitiba, Paran, Brazil; email: [email protected]
Sidnei P. Rostirolla is a professor of geology at the Federal University of Paran. He received his M.Sc. (1991) degree from the Federal University of Ouro Preto (UFOP) and Ph.D. (1996) from the State University of So Paulo (UNESP). He worked at the Petrobras Research Center as a structural geologist, where his research focused on balancing cross section, syntectonic sedimentation and field work. His current research interests are basin analysis, geomathematics, structural geology, and fractured reservoir characterization.
Antonio C. Mattana received his M.Sc. degree (2000) in exploration geology from the Federal University of Paran. His research interests are geographical information systems and object-oriented programming applied to geology and geophysics.
Marcelo K. Bartoszeck is a graduate student of geology in the Federal University of Paran. His research interests are reservoir modeling, seismic interpretation, and structural geology.
The authors thank John Lorenz, John Doveton, Robert Otis, Luciano Magnavita, and John Harbaugh for the profitable suggestions to improve the manuscript. Sidnei Pires Rostirolla thanks the Brazilian National Research Foundation CNPq, processes 520063/98-8 e 463002/00-8, for financial and scholarship support, and UFPR for institutional support.
This paper presents a Bayesian approach to evaluate remaining potential of oil and gas in the Recncavo basin, Brazil. The purpose is to test a new Bayesian weighting methodology and quantify the favorability for the existence of new fields in the basin. The methodology implies organization of petroleum system data in descriptive models with which results from drilling are manipulated statistically, including analysis of geologic factors that are spatially correlated with both producing and dry areas. In the first stage of modeling, the essential elements (reservoir, seal, and overburden rocks) that control the fundamental processes of generation, expulsion, migration, and entrapment of petroleum accumulation are defined throughout integration of previously published data. The petroleum accumulation models of Recncavo basin comprise generation from Neocomian shale rocks of the Gomo Member (Candeias Formation), vertical migration along extensional and transfer faults, and accumulation in tilted horsts with Upper Jurassic prerift reservoirs (Sergi Formation) or in Neocomian turbidite reservoirs in stratigraphic/combined traps (Candeias and Marfim formations). Probability distributions and weights are then calculated through Boolean operations among producing and dry areas and each diagnostic criterion evaluated through descriptive models such as source bed thickness, onset of organic maturation, presence or absence of faults and structural blocks, reservoir thickness, and seal distribution. The final stage of evaluation consists of spatial integration of raster maps that are weighted according to their necessity and sufficiency conditions, the results being presented as favorability maps. The characterization of favorable areas and their comparison with known fields suggest that such a Bayesian approach can contribute to the understanding of petroleum systems as a practical approach that considers the spatial nature of exploration variables.
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