- journal_title：AAPG Bulletin
- Contributor：Sidnei Pires Rostirolla ; Antonio Carlos Mattana ; Marcelo Kulevicz Bartoszeck
- Publisher：American Association of Petroleum Geologists (AAPG)
- Date：2003-
- Format：text/html
- Language：en
- Identifier：10.1306/10070201068
- journal_abbrev：AAPG Bulletin
- issn：0149-1423
- volume：87
- issue：4
- firstpage：647
- section：Articles
This paper presents a Bayesian approach to evaluate remaining potential of oil and gas in the Recôncavo 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 Recôncavo 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.