The determination of economical sweet spots in a mature thin oil rim reservoir is an arduous task. To improve the chances of operational successes, a surveillance program was put in place for the Seligi field, consisting of the acquisition of data in the wells via static gradient surveys and behind-casing saturation logs. These surveillance data are designed to determine current contacts and this publication discusses their use as objective functions in the integrated uncertainty-centric ensemble-based modeling of the main reservoir J18/20 of this complex field.

The probabilistic approach implemented for the Seligi field relies on building a prior ensemble considering uncertainties in static properties and dynamic parameters. Then, using a Kalman-family algorithm known as Ensemble-Smoother with Multiple Data Assimilation (ES-MDA), the production data are assimilated and the ensemble converges towards all observed well data, including well logs, while quantifying the uncertainties. In the case of Seligi field contact acquisition program, the available contact data must be converted to water saturation logs and gas saturation logs. In total, 15 water saturation logs and 20 gas saturation logs taken at different times were included in the study.

The ensemble approach allowed a reasonable history-match of the 33 years of production data for all seven groups and over 170 wells after four iterations. It was also able to converge on the historical contacts supplied as objective functions. After review, adjustments were made to further improve the contact matching results on account of fluid allocation issues, especially with respect to the gas injection, and the simulated well control. Finally, 11 out of 100 posterior cases were selected based on their qualitatively superior match to the historical contacts. The ensemble provided a statistical understanding of the contacts presently in the wells and therefore of the location of the remaining mobile oil.

This subset was used to estimate the reserves associated with barrel-adding operational decisions such as idle well reactivations (IWR), workovers. It was also used for the design of infill wells, with a quantification of the subsurface uncertainties that are key to the assessment of the risks. Over 26 IWR opportunities and 10 infill locations were identified from the calibrated realizations. Finally, the ensemble provided some guidance for upcoming surveillance campaigns, to further de-risk these opportunities.

This study demonstrates that time-dependent data, such as contacts acquired with high density logs or electrical behind casing saturation tools, can be used efficiently in the calibration of an ensemble, thereby increasing the representability of the ensemble, the understanding with respect to the remaining oil column and the confidence in the next phases of development.

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