| dc.description.abstract | Knowledge of the flow rates from individual wells in a subsea production system can
greatly improve decision-making processes. This thesis investigates flow estimation in
the subsea template Tilje in the BP-operated Skarv field, with automatic rate control in mind. Two optimization-based estimation methods, one with static models and one
with dynamic models, are developed and compared against each other by means of
benchmark data from an OLGA model and historical field data from Tilje. Ideas from
recent developments in production optimization are applied in the derivation of the
production network models. This amounts to representing the flow network as individ-
ual network components with B-spline approximated models., leading to a transparent
model which allows for easier estimator tuning, easier constraint handling and faster
solution times.
The results presented in this thesis show that the method with static models is able
to predict flow rates with acceptable accuracy, has good robustness properties and fast solution times, indicating that the method has potential for use in an automatic rate control system. The method with dynamic models is less robust, more complex, and
does not seem to improve the flow rate estimates. However, it does include estimates of mass and holdup and could potentially be useful if the method was improved. In addition to providing feedback for automatic rate control systems, the methods described in this thesis could potentially be used as cornerstones in advanced decision support tools, such as flow assurance systems and condition monitoring systems. | |
