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The AAPG/Datapages Combined Publications Database

AAPG Special Volumes


Novakovic, Djuro, and Joy Roth, 2011, Uncertainty analysis in a mature field: Dibi field case study, in Y. Z. Ma and P. R. La Pointe, eds., Uncertainty analysis and reservoir modeling: AAPG Memoir 96, p. 121136.


Copyright copy2011 by The American Association of Petroleum Geologists.

Uncertainty Analysis in a Mature Field: Dibi Field Case Study

Djuro Novakovic,1 Joy Roth2

1Chevron Nigeria Limited, Lagos, Nigeria
2Chevron Nigeria Limited, Lagos, Nigeria


We thank Chevron Nigeria Limited and our joint venture partner Nigerian National Petroleum Corporation for supporting the publication of this work. We also thank Sebastien Strebelle, Marge Levy, Sebastien Bombarde, and Nancy Harris for discussions on earth modeling approaches; Chris Denison for his block diagram; Bryan Bracken and Brian Willis for depositional environment discussions; Jim Logan and Jegede Olamide for their previous log-facies indicator work, and Ed deZabala for previous work on the core data and numerous discussions on its significance. Kameron Mitchell is thanked for support and pragmatism during the development of the dynamic part of the assessment process. The editors of this AAPG book are also thanked for interesting insights and possible future development direction.


The use of reservoir simulation in conventional reservoir management commonly involves a single history-matched geologic model. Sometimes an upside or downside case is created by basing the uncertainty range on the ability to maintain the already-achieved history-match quality. Basing reserves estimates on the outcome of such a deterministic reservoir simulation yields variations in booked reserves, as well as failure in longer term forecasts. A workflow similar to a new (green) field uncertainty assessment has been used. The methodology includes using multiple faulted geocellular models (S grids) covering a range of uncertainty in input parameters with multiple-point statistical simulation facies-based modeling, combined static and dynamic experimental design, probabilistic history matching, and use of model building and analysis automation tools. The result is a continuously narrowing range on all input uncertainties, reproducible history-matched models, and associated forecasts.

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