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Abstract

A procedure determines the relative importance of uncertainties in input information and in multiple parameter estimation to all outputs from two-dimensional basin modeling codes. The procedure does not rely on Monte Carlo methods, but on some simple properties of the cumulative probability distribution of output variations related to uncertainties. As a consequence, only a couple of computer trials are needed to evaluate the relationship of the variability of outputs to input uncertainties.

The procedure is applied to a two-dimensional cross section with evolution of the section with time. Attention is focused first on mainly geologic input uncertainties, and then on uncertainties of thermal factors and of hydrocarbon kinetic factors. Each group is initially taken separately, and then all three groups of uncertainties are combined and used simultaneously. The influence of each group of uncertainties on a suite of different outputs from the basin model is explored at different times across the evolving section. At each lime step, the relative sensitivity is examined of the uncertainty in a specified output to each group of input uncertainties, as is the relative importance of the uncertainty in a specified input to the suite of all outputs at each time step. In addition, the global relative importance of input uncertainties to output variabilities is considered, thereby providing a measure of output uncertainty effects, no matter where and when they occur, as a consequence of input uncertainties.

This work enables one to assess which inputs need to be more tightly constrained, and also to determine by how large a factor they need to be better constrained if the uncertainties on a suite of specified outputs are to remain within given tolerance limits.

The advantage to this rapid procedure is that one can focus more quickly on those factors of dominance in controlling, say overpressure development or hydrocarbon charge in a basin, without having to spend an inordinate amount of time, effort, or financial or staff resources on providing narrower limits of uncertainty to those input factors that provide but little change in output uncertainties.

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