Invalid PVT data provide high uncertainty in field development studies such as reservoir fluid compartmentalization, reserve estimation, reservoir simulation, production engineering, and surface facility design. Then, consistency check should be conducted to ensure the validity of PVT data and to identify the most representative PVT sample(s). In this paper, preliminary and complementary consistency check of PVT laboratory methods was applied to design a new comprehensive validity check procedure for verification of the reservoir fluid properties. This procedure would be ascertained whether or not the data could be used as the most representative fluid sample in further studies. The data used in this study were collected from seven undersaturated oil reservoirs, and challenges were observed through the 28 full set of available PVT laboratory data. In our study quality checking procedure of PVT samples consists of different methods such as graphical method, recombination and material balance check for well stream composition test, Buckley, modified Wilson and Hoffman plot, Watson characterization factor, Y-function of CCE test, Y-function of DL test, compositional material balance of DL test, overall mass balance for density, Bo and GOR of DL test and density check for separator test. After implementing the applicable proposed checking procedure to detect the validity of laboratory PVT sample, only 9 out of 28 PVT samples were satisfied all the preliminary and complementary check methods. Furthermore, a practical strategy, to select representative reservoir fluid sample, was also provided and discussed based on the consistency check, well condition check and comparison of fluid properties.

The majority of the geostatistical realizations ranking methods disregard the production history in selection of realizations, due to its requirement of high simulation run time. They also ignore to consider the degree of linear relationship between the “ranks based on the ranking measure” and “ranks based on the performance parameter” in choosing the employed ranking measure. To address these concerns, we propose an uncertainty quantification workflow, which includes two sequential stages of history matching and realization selection. In the first stage, production data are incorporated in the uncertainty quantification procedure through a history matching process. A fast simulator is employed to find the realizations with consistent flow behavior with the production history data in shorter time, compared to a comprehensive simulator. The selected realizations are the input of the second stage of the workflow, which can be any type of the realization selection method. In this study, we used the most convenient realization selection method, i.e., ranking of the realizations. To select the most efficient ranking measure, we investigated the degree of the linear correlation between the ranks based on the several ranking measures and the performance parameter. In addition, due to the shortcomings of the traditional ranking methods in uncertainty quantiles identification, a modified ranking method is introduced. This modification increases the certainty in the probability of the selected realizations. The obtained results on 3D close-to-real synthetic reservoir models revealed the capability of the modified ranking method in more accurate quantification of the uncertainty in reservoir performance prediction.

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