Real-time optimization of oil and gas production requires a production model, which must be fitted to data for accuracy. A certain amount of uncertainty must typically be expected in production models fitted to data due to the limited information content in data. It is usually not acceptable to introduce additional excitation at will to reduce this uncertainty due to the costs and risks involved.

The contribution of this paper is twofold. Firstly, this paper discusses estimation of uncertainty in production optimization resulting from fitting models to production data with low information content, a concept that has previously mainly been applied in reservoir management. Secondly, this paper illustrates how system identification can be used to find production models which can be solved with little computational effort and which are designed to be easily fitted to production data.

The method is demonstrated on a synthetic example before being applied to a case study of a North Sea oil and gas field. In offshore oil and gas production, the suggested method is expected to have applications in the development of structured approaches to uncertainty handling, for instance excitation planning and real-time optimization under uncertainty.

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