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Hydrocarbon exploration risk evaluation through uncertainty and sensitivity analyses techniques

Author

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  • Ruffo, Paolo
  • Bazzana, Livia
  • Consonni, Alberto
  • Corradi, Anna
  • Saltelli, Andrea
  • Tarantola, Stefano

Abstract

The evaluation of the exploration risk in the oil industry is a fundamental component of the decision process related to the exploratory phase. In this paper the two basic components of the exploratory risk: trap geometry and trapped hydrocarbon quantities (fluid), are compounded in a single coherent uncertainty and sensitivity approach. The results clarify that the model geometry influences each Petroleum System Modeling step and that the geometric uncertainty is correlated with the fluid uncertainty. The geometric uncertainty evaluation makes use of geostatistical techniques that produce a number of possible realizations of the trap geometry, all compatible with available data. The evaluation of the fluid uncertainty, through a Monte Carlo methodology, allows us to compute the possible quantities of oil and gas, generated in a basin and migrated from the hydrocarbon source location to each single trap. The final result is the probability distribution of oil and gas for each trap in the basin, together with other useful indicators like: the hydrocarbon filling probability map, the closure probability map, the drainage area probability map, the spilling paths probabilities, the trap-filling scenarios.

Suggested Citation

  • Ruffo, Paolo & Bazzana, Livia & Consonni, Alberto & Corradi, Anna & Saltelli, Andrea & Tarantola, Stefano, 2006. "Hydrocarbon exploration risk evaluation through uncertainty and sensitivity analyses techniques," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1155-1162.
  • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1155-1162
    DOI: 10.1016/j.ress.2005.11.056
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    References listed on IDEAS

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    1. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
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    1. Roux, Sébastien & Loisel, Patrice & Buis, Samuel, 2019. "A filter-based approach for global sensitivity analysis of models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 119-128.
    2. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.

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