As production in very deep waters becomes a crucial challenge for many oil companies, a better management of the production is constantly required. This paper presents two complementary methodologies for operation support and improvement of the production conditions. The first one is based on data reconciliation between process measurements and flow modelling. It brings an additional level of information to the problem of continuous metering of deepwater subsea wells. As periodic well testing is required to achieve this predictive metering, the second methodology provides the optimal test sequences of well permutations. It involves flow process simulation and algorithmical sorting, according to production constraints and operating strategies. Finally, comparisons between numerical simulations and plant data demonstrate the ability of these two methodologies to provide strong and reliable information with deep offshore producers.


The knowledge of the phase flow rates coming from each individual well of an oil field is mandatory for a better production and reservoir management. Generally, this information comes from a series of direct well testing, where a single well flows directly to a test separator

In deep offshore, this procedure turns out to be inappropriate: production developments are based on gathering network, where manifolds merge the production from several wells into a single flowline. This is the case on the Girassol oil field in Angola, see Fig. 1. Moreover, direct well testing implies deferred production, valve reliability and flow assurance issues: hydrate formation in dead branches, slugging at low flow rates, etc.

Whereas conventional solutions, such as hardware multiphase metering, supply a limited information, our methodology works as an overall field supervisor for:

  • estimating individual well production with respect to appropriate pressure and temperature measurements;

  • detecting abnormal behavior (sensor drift for instance);

  • validating hardware measurements, and replacing them in case of failure. Typically, in deep offshore production, hardware sensors are not replaced in case of failure for financial and feasibility reasons.

This methodology is based on data reconciliation. It assumes that measurements are not necessarily correct and can be corrected within a confidence interval. Meanwhile, unmeasured variables derive from redundancy between flow modelling and field data.

Data reconciliation has been already successfully applied to a small production network, see Ref. 1. Our paper intends to go further in the study of this innovative technology and presents its application to the Girassol field.

Well monitoring

Given a set of temperature and pressure measurements, our methodology aims to provide an estimate of the phase flow rates produced by each individual well of an oil field.

Problem modeling.

Real-time plant data are completed with a global steady state simulation of the production network involving:

  • mass, force, and heat balance equations;

  • thermodynamic calculations;

  • hydrodynamic modelling.

For instance, assuming process data at both ends of a choke and an estimate of the fluid composition, one can derive a local estimate of the liquid and gas mass flow rates from hydrodynamic and thermodynamic calculations.

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