Quantifying Geological Uncertainty for Complex Integrated Production Systems with Multiple Reservoirs and Production Networks

Pathak, Varun (Computer Modelling Group Ltd.) | Hamedi, Yousef (Computer Modelling Group Ltd.) | Martinez, Oscar (Computer Modelling Group Ltd.) | Vermeulen, Stephen (Computer Modelling Group Ltd.) | Kumar, Anjani (Computer Modelling Group Ltd.)

OnePetro 

Abstract

Integrated production systems models are very valuable for predicting the performance of complex systems containing multiple reservoirs and networks. In addition, the value of quantifying uncertainty in reservoirs and production systems is immense as it can build confidence in operational investments. However, traditionally it has been extremely tedious to incorporate uncertainty assessments in the context of integrated production systems modelling. This has been addressed in the current work with the help of a case study.

In the current work, a complex integrated production systems model is presented - from Pre-Salt carbonates reservoir offshore of Brazil. The model includes multiple reservoirs with unique fluid types and complex fluid blending in the production network, multiphase and thermal effects in flowlines and risers, gas separation, gas processing, gas compression, and re-injection for either pressure maintenance or for miscible EOR.

The operational strategies, well placement, and well and network configurations are often based on a single geological realization. With the case study presented in this paper, an integrated way of quantifying geological uncertainty has been presented. A new multi-user, multi-disciplinary tool was used for this study that removed any discontinuities and inconsistencies that typically occur in such projects when multiple standalone tools are used for individual tasks. When quantifying uncertainty on production, the dependence on a single realization was eliminated as uncertain parameters were identified and used for creating robust probabilistic forecasts. Probability distribution curves were generated to represent the uncertainty in overall production from this asset, and the risk associated with operational investments was minimized.

Typically, an end-to-end uncertainty assessment is missing from the traditional Integrated Modelling workflows. With this new approach, the challenge of achieving a truly integrated uncertainty assessment for integrated reservoir and production models has been addressed successfully.