The petroleum industry has a high demand for production forecasting under uncertainties, which is performed through probabilistic approaches. Although these approaches have been used very often in recent years, one of the challenges to their application is the development of a methodology with multiple scenarios that honor the dynamic data available (history matching procedures). Many works, which propose different probabilistic history matching approaches have been published in the literature but are based on methods that demand a very high number of reservoir simulation runs. Thus, to make this type of application feasible, these methods rely on the application of proxy models, which, however, are not able to capture the full physical behavior of the model. Different from those methods, this work proposes a methodology for uncertainties reduction of reservoir properties integrated with an assisted history matching procedure, in a multi-objective approach that does not employ proxies.

The methodology proposed is an extension of a procedure previously published by our group, which has shown to be an efficient way to perform probabilistic history matching. At each iteration of the process, the model uncertainties are combined through a robust sampling technique, which originates a set of model realizations that are then simulated. The results are evaluated for all Objective Functions (OF), namely, the quadratic deviations of all well rates and pressures. This work proposes the use of quantitative tools to perform the matching: (1) indexes that quantify the matching quality for each OF and allow prioritizing the worst OF to be matched and (2) a bi-dimensional matrix that supports the identification of sources to the highest deviations and gradual model properties updating. At the end of the procedure, good models (which must present satisfactory matching for all OF) are filtered and further employed in production forecasting under uncertainties.

The methodology is applied to a synthetic model (UNISIM-I-H) based on the Namorado field in Brazil. The results show not only the gradual improvement of all OF during the matching procedure but also and, more importantly, how the reservoir uncertainties are updated based on the tools proposed here.

The proposed indicators make the application systematic and can be used for automatizing the process. The methodology is able to deal with complex cases, especially those involving several wells and uncertainties with non-linear interaction among them. The developments provide robustness to the application, resulting in a more reliable production forecast and management of the field.

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