More Rapid and Robust Multiple History Matching With Geological and Dynamic Uncertainties: Heavy-Oil Case Study

Abstract

Abstract The generation of reservoir simulation models that match field production data has been and is still a long-time industry challenge, not only for the time spent on history matching studies but also because of the non-uniqueness of the solution. This paper presents a new approach called "Hybrid Models" to accelerate this process and get more realistic history match models. Hundreds of stochastic possible geological models are produced and tested in regard to the dynamic data. The Hybrid model is a composite geological model, not only constrained by the initial well data but also with selected parts of the first realizations matching around some wells. This technique allows a relatively quick history matching process and results in a series of matched geological models. This process was applied in part of a heavy oil field (14 horizontal wells in fluvial reservoirs were considered), after 3 years of production. The objective was to explain and reproduce the high water-cut, oil rates, GOR and bottom-hole pressures in this part of the field. A complete uncertainty workflow was applied with sedimentological and petrophysical uncertainties as well as fluids and dynamic uncertainties. Results showed that static uncertainties were essential to get a coherent match and "Hybrid Model" technology was applied with success. The Hybrid model technique gives several matched geological models. All models have been carried out through forecasting keeping the present development plan, evaluating the potential impact of remaining static uncertainties. Dynamic uncertainties were also considered on one geological matched model. Several combinations of dynamic parameters have been computed to keep a match. Corresponding models have been transferred through forecasting. Final conclusions were that at fixed development plan, dynamic uncertainties are more to be considered and combined for the forecast than static ones. The use of the "Hybrid models" technique and the integration of static and dynamic properties as matching parameters have been shown to be efficient to produce accurate multiple production history matched models. From those models, it has been possible to quantify the remaining uncertainties in terms of future production and to propose new developments.