Quality assurance tool for PVT simulator predictions

The currently available pressure/volume/temperature (PVT) simulators predict the physical properties of reservoir fluids with varying degrees of accuracy depending on the type of model used, 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 used to provide accurate predictions of physical properties mainly because of their ability to - learn from experience. The use of such models offers the unique capability of estimating the quality of their predictions, as the degree of competence can be evaluated for each unknown test case. 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 PVT Expert,* which is an ANN-based PVT properties prediction model. In the case of an unknown fluid, this tool 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 database used. Subsequently, the competence with which the ANN model has learned the general trend in the area around any new test case is assessed numerically through the development of one ANN sextuple model per property. Finally, the use of the average predicted value eliminates the risk of considerable deviations. This innovative approach was tested successfully against a large set of studies unseen by the ANN model. The ability to provide confidence for the accuracy of the PVT predictions and to assess their quality significantly upgrades the applicability of the PVT simulator as a valuable reservoir management tool.