Probabilistic production forecasting at Tengiz is largely driven by reservoir uncertainty. Reservoir uncertainty is most effectively synthesized and quantified through simulation modeling. Early in the construction of a new Tengiz dynamic model, fundamental reservoir uncertainties were identified and evaluated. This allowed for model ‘building blocks’ to be developed with different characterizations to encompass key uncertainties.

Key uncertainties, which can significantly impact future production under primary depletion and sour gas injection, have been described. These include typical uncertainties such as porosity, irreducible water saturation, hydrocarbon fluid properties, oil-water contact levels, rock compressibility, geologic baffles, and relative permeability. Unique uncertainties specific to Tengiz include geometry and density of the natural fractures, and reservoir heterogeneity.

Considerable production history and a large reservoir surveillance database provided input for rigorously characterizing and subsequently validating the range of each uncertainty. After ranges were established, appropriate model realizations were created. A wide range of reservoir models were obtained by selecting combinations of high/mid/low realizations for each uncertainty. Using experimental design (ED), reservoir simulations were conducted to test uncertainty ranges against field history. A quantitative history match and statistical analysis were developed to objectively judge the appropriateness of uncertainty values. Uncertainties with the largest overall impact on the history match are: fracture density, platform horizontal permeability, compressibility, and platform heterogeneity.

This case study demonstrates how analysis of reservoir uncertainties can be: (1) captured in static and dynamic reservoir models and (2) validated through ED and quantitative history matching. This study employs state-of-the-art technologies to evaluate model uncertainties of a giant carbonate reservoir undergoing both depletion and miscible gas drives. The range of reservoir models subsequently developed will be of great value in creating robust probabilistic reservoir forecasts to optimize field operation and future development.

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