Multiphase flow meters are indispensable tools for achieving optimal operation and control of wells as these meters deliver real-time information about their performance. For example, multiphase flow meters located downhole can improve the production of multilateral and multizone wells by timely allocating the zone where a gas or water cone occurs. However, multiphase meters are either expensive, inaccurate, or cannot be used downhole due to the harsh conditions. An alternative that can be used to overcome these disadvantages is to use multiphase soft-sensors, i.e. to estimate holdups and flow rates from relatively cheap and reliable conventional meters, such as pressure and temperature measurements, and a dynamic model connecting these measurements with the unknown quantities. The aim of this paper is to demonstrate, via two simulation based case studies, some possibilities and limitations of such multiphase soft-sensors. In the first case study the question is adressed whether it is possible to use only downhole pressure and temperatures measurements to estimate in real-time the water, oil and gas flow rates in a well. This question is of practical importance as these measurements are relatively cheap and reliable. The second case addresses the question whether it is possible to allocate the gas cone in a well with multiple inflow points or zones. This question is relevant as the estimated flow rate and holdup profiles can be used to manipulate Inflow Control Valves in such a way that gas breakthrough is prevented.

Using amongst others OLGA data as "real-life" data, an additional question addressed here is what the influence is of soft-sensor model error and measurement noise on the quality of the estimates.

From the first case study it can be concluded that, due to bad observability, pressure and temperature measurements alone are not sufficient to accurately estimate in real-time well flow composition parameters in a practically relevant situation. The preliminary results discussed in the second case study indicate that a soft-sensing solution to the gas cone allocation problem may very well be feasible.

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