COMPOSITIONAL VARIABILITY IN BITUMENS FROM EOLIC SEDIMENTARY FACIES OF PIRAMBÓIA FORMATION, PARANÁ BASIN, BRAZIL: CHARACTERIZING BIODEGRADATION

• Main Authors: Laercio Lopes Martins, Eliane Soares de Souza, Hélio Jorge Portugal Severiano Ribeiro, Georgiana Feitosa da Cruz
• Format: Article
• Language: English
• Published: Sociedade Brasileira de Geoquímica 2016-05-01
• Series: Geochimica Brasiliensis
• Subjects: petroleum biomarker; heavy oil; tar sand; paraná basin
• Online Access: https://geobrasiliensis.org.br/geobrasiliensis/article/view/422/pdf_1
• ISSN: 0102-9800; 2358-2812

Bitumen accumulations were identified within eolic sedimentary facies of Pirambóia Formation, in the eastern margin of Paraná Basin in Brazil. This basin is known to have great mineable oil sand accumulations (tar sands), with one of the 19 occurrences with amount of oil in place estimated in 6 million barrels. The compositional variability in a series of this tar sand bitumens taken from four identified eolic sedimentary facies (foreset and bottomset of dunes, dry and wet interdune) was determined by different techniques to characterize and evaluate biodegradation. Results obtained suggest that bitumens from dry interdune facies have suffered the most severe biodegradation and the bottomset facies contains the least biodegraded oil. Furthermore, some compounds (C23 tricyclic terpane, C29 hopane, gammacerane and C27 diasteranes) show high resistance to microbial degradation, proving that these biomarkers can be useful to biodegradation analysis in highly biodegraded oils, such as bitumens. Aromatic biomarker analysis, however, show that this class of compounds is not useful for biodegradation analysis of outcrops samples and tar sands, because they are more susceptible to other secondary alteration processes, such as water washing and evaporative fractionation. The major significance of this study is to enable more accurate estimation of the chemical composition from different sedimentary facies in biodegraded reservoirs, leading to improved predictions of fluid quality in different regions of this reservoir, improving production decisions.