Uncertainty Analysis in a Mature Field: Dibi Field Case Study
Djuro Novakovic, Joy Roth, 2011. "Uncertainty Analysis in a Mature Field: Dibi Field Case Study", Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World, Y. Zee Ma, Paul R. La Pointe
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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.
Reservoir simulation of earth models is the most common tool to model fluid flow through the reservoir, with results used as a basis for business decisions, from sizing facilities, optimizing well count, evaluating well placement, and exploring enhanced recovery in the field, as well as estimating reserves and forecasting production profiles.
A common approach to assessing reservoir performance has been to build and match a single deterministic model representing the best earth science and engineering judgment. Upside and downside can be created by altering the best guess according to what the practitioner found to be adequate or uncertain. This approach does provide a range of outcomes, but the range is arbitrary, sometimes narrow, and is based on a personal bias. It is commonly hard to replicate because of its subjective nature (Figure 1). This type of approach is termed “deterministic.”