首页|Data assimilation for uncertainty reduction using different fidelity numerical models
Data assimilation for uncertainty reduction using different fidelity numerical models
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NSTL
Data assimilation (DA) for uncertainty reduction based on reservoir simulation models is generally an intense and high time-consuming process due to the number of uncertain parameters involved and the computational time required to run the flow simulation models. Depending on the degree of model description (fidelity), DA can take days or even weeks to be executed because it can take several hundred (or thousands) of reservoir simulations, even applying efficient DA methods. Since quick decisions on reservoir development and management phases have to be made, the process must be carried out in a suitable and affordable computational time. There are two complimentary ways of dealing with this problem;; the first is reducing the model simulation time (trying to maintain the quality of results), and the second is the application of efficient and effective DA methods. In this work, we propose a methodology that consists of generating and using efficient and effective lower-fidelity models (LFM), combined with the application of the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) method which is the state-of-art on data assimilation combining efficiency and efficacy. The focus is to present a comprehensive and robust analysis of the DA process by assessing different model fidelities to achieve the best trade-oft between computational time and quality of results. We applied the methodology in a real field using six different fidelities;; an initial (fit-for-purpose) model named the medium-fidelity model (MFM) and five LFM. Their performances are benchmarked against the MFM results. For each fidelity model, we ran a DA process and compared the results, including (1) quality of data match and computational time, (2) prior and posterior attribute distributions, and (3) predictive capacity of the models. To conclude, we showed that the DA process using LFM reduced the computational time from days to hours, being up to about 11 times faster than the process using the original model with similar or even better results, making it clear the advantage of building, evaluating and selecting a lower-fidelity model based on the purpose of the study. This is important because, after the data assimilation, high number of reservoir simulations (typically thousands) is necessary for reliable decisions in the context of reservoir development and management.
NSTL
