The currently available PVT simulators predict the physical properties of reservoir fluids with varying degrees of accuracy depending on the type of the model utilised, the nature of the fluid and the prevailing conditions. Nevertheless, they all exhibit the significant drawback of lacking the ability to estimate the quality of their answers.

Artificial Neural Networks (ANNs), trained by large PVT databases, are increasingly utilized to provide accurate predictions of physical properties mainly due to their ability to learn from experience. The utilization of such models offers the unique capability of estimating the quality of their predictions as their degree of competence can be evaluated for each unknown test case1 -2 . The accuracy of the ANN based PVT simulators depends heavily on the density of the database compositional mapping around the coordinates of the unknown reservoir fluid. Unknown test cases found outside the available training space may lead to poor predictions.

In this work, a quality assurance tool is presented that is integrated to the ANN based PVT Expert model. This tool, for an unknown fluid, qualifies the predictions of the PVT simulator based on the evaluation of the affinity of the test case with the training data sets contained in the utilised database. Subsequently, the competence with which the ANN model has learned the general trend in the area around any new test case is assessed numerically.

This innovative approach was successfully tested against a large set of studies "unseen" by the PVT Expert. The ability of providing confidence for the accuracy of the PVT predictions and of assessing their quality, significantly upgrades the applicability of the PVT simulator as a valuable reservoir management tool.

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