Development of heavy oil fields presents increasing complexity directly associated to the high levels of uncertainty in the fluid and reservoir characterization. Particularly in offshore scenarios, where the difficulties for well testing, fluid and core sampling demand much more efforts and limit the availability of information. Thus, when considering the development of an offshore heavy oil field, a probabilistic analysis, instead of a deterministic one, is the natural way to evaluate reservoir production performance and project feasibility.
This work considers the application of experimental design techniques to the problem of uncertainty quantification and risk analysis for heavy oil offshore projects. Initially, the parameters that have a large impact on the cumulative oil production response are specified. Then, based on the given uncertainty distribution of these parameters and on the application of an experimental design technique the assessment of uncertainty on the cumulative oil production is achieved. The computational effort when estimating production uncertainty is reduced by considering a response surface methodology, approximating the simulator by a simple regression model that fits the simulator outputs.
The methodology proposed in this work was applied to a synthetic case, representative of an actual heavy oil offshore field scenario, which considered as uncertain parameters porosity, absolute permeabilities, area of the accumulation and well productivity index.
Reservoir engineering requires managing sources of uncertainty on the physical reservoir description parameters due to three main causes: the model, because it is an imperfect representation of reality; geologic parameters, because of a limited sampling in space and/or time, and measurement errors in the experiments performed to determine inputs.
With rapid changes in political and economic conditions, it seems that smaller fields with complex geology, reduced economic margin and less robust projects (EOR, infill drilling, thermal recovery, heavy oil offshore fields), are typical petroleum engineering issues in many hydrocarbon producing regions of the world. For this reason, oil companies need a systematic method for quantifying the composite technical uncertainties (in production rates and reserves) and its associated economic risks (NPV and other economic indicators) connected with field developments and incremental projects.
In the evaluation and planning of a reservoir development the common approach is first to build the expected geological model, using the most representative set of dynamic parameters, and then determine the best set of well locations given the geological model. The platform, in the case of an offshore field, and production facilities are optimized (with respect to NPV) for this model. This combination of geological model, dynamic parameters and technical design constitutes the base (or reference) case. A reservoir simulation is then performed, giving the base case production profile and recovery factor. This production profile is finally combined with a fixed scenario for the future oil and/or gas prices and investment interests to obtain the economic indicators (NPV, PI, IRR) for the project.
To study the influence of the various parameters that enter the process on the final results, a sensitivity study is usually performed.
