In this paper we describe an approach to real-time decision support that is completely data-driven. The approach can be viewed as a sequence of transformations acting on available production data. The transformations form a data pipeline from sensors to operational advice, and can be summarized as follows. Historical and real-time production data, experimental data such as well tests, and any other operational metadata, enters the data pipeline on one end. The data is then synchronized, cleansed and compressed, while retaining important uncertainty measures such as standard errors. The resulting information is used to build models using regression analysis or machine learning. These models are updated in real time as new data enters the pipeline. In this fashion, the models are adjusted frequently to the operational situation, making up for their limited prediction capabilities compared to advanced first-principle models. This approach may reduce the need for advanced fluid modeling and model calibration efforts in real-time applications. Arguably, the approach may provide automation under uncertainty, and hence sustainability, to a higher degree than traditional approaches based on process simulation. A great advantage of the data-driven approach is that the uncertainty of measurements and models is tracked. By considering this information, production estimation and optimization may give operational advice with uncertainty measures. Furthermore, by tracking uncertainty, it is possible to advise experiments (e.g. step-tests) that may yield valuable operational information by entering unexplored operational regions. In our experience, information about the uncertainty of alternative production advice may ultimately alter the final decision. Furthermore, production advice without accompanying uncertainty measures may be misleading.
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