--> --> Abstract: A Probabilistic Approach to Solving Static Subsurface Uncertainty: Examples from Angola Block 0 Reservoirs, by Antonio M. Ingles, Sebastien Bombarde, and Dolores Evora; #90082 (2008)

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A Probabilistic Approach to Solving Static Subsurface Uncertainty: Examples from Angola Block 0 Reservoirs

Antonio M. Ingles1, Sebastien Bombarde1, and Dolores Evora2
1Chevron Africa/Latin America Exploration & Production, Luanda, Angola
2Sonangol E.P, Luanda, Angola

The Pinda Formation in Angola’s Block 0 has historically been described as a highly heterogeneous mixed clastic carbonate system that yields complex yet prolific reservoirs, for which rock quality is challenging to predict.

Deterministic static modeling techniques have historically been employed to resolve Pinda subsurface uncertainty. Lately the use of probabilistic methods has grown in use and rather than providing a unique solution to subsurface uncertainty, they provide a range of possible outcomes.

Key static subsurface uncertainties associated with Pinda reservoirs in Block 0 have been identified and can be summarized into two questions: how much oil is there? (Original Oil in Place) and how easily will it move around? (Reservoir Connectivity).
Thus OOIP and Reservoir Connectivity have been carried as key uncertainty parameters in this study.

The probabilistic approach taken uses an existing deterministic geologic model (base case) as starting point, from which low and high scenarios are created.

Five model variables were found to significantly affect OOIP and Reservoir Connectivity: External Porosity Histogram, Global Facies Proportions, Average Porosity Trend Map, Variogram Length and Porosity Trend Map Weighting. Uncertainty ranges (scenarios) for those variables have been developed using a variety of statistically valid methods. Then permutations of all OOIP/connectivity scenarios have been combined to produce nine geologic model permutations. Those are then Scaled Up and subjected to dynamic flow simulation.

A thorough discussion of the statistical methods employed to generate uncertainty variable ranges and the probabilistic approach workflow constitute the subject matter of our paper.

AAPG International Conference and Exhibition, Cape Town, South Africa 2008 © AAPG Search and Discovery