  In petroleum production systems, continuous multiphase flow rates are
essential for efficient operation. They provide situational awareness, enable
production optimization, improve reservoir management and planning, and form
the basis for allocation. Furthermore, they can be crucial to ensure a fair
revenue split between stakeholders for complex production systems where
operators share the facilities. Yet, due to complex multiphase flow dynamics
and uncertain subsurface fluid properties, the flow rates are challenging to
obtain with high accuracy. Consequently, flow rate measurement and estimation
solutions, such as multiphase flow meters and virtual flow meters, have
different degrees of accuracy and suitability, and impact production decisions
and production allocation accordingly.
  We propose a field-proven, data-driven framework for reconciliation and
allocation. With data validation and reconciliation as the theoretical
backbone, the solution exploits measurement redundancy to fuse together
relevant flow rate information to infer the most likely flow rates in the
production system based on quantifiable uncertainties. The framework consists
of four modules: data-processing, uncertainty estimation, reconciliation, and
gross error detection. The latter, being the focus of this paper, is a means to
identify and mitigate the effect of measurements subject to systematic error,
which can invalidate the reconciliation.
  In this paper, we highlight that a combination of statistical tests and
supporting logic for gross error detection and elimination can be beneficial in
obtaining a more justifiable production allocation. Using the maximum power
measurement test, the module can be limited in its ability to pinpoint the
erroneous measurement. Yet, it is demonstrated that the detections can be
convenient indications of gross errors and where these might reside in the
production system.
