Production data back-allocation of fluids produced into the various wells can be affected by some uncertainties, especially when multiphase flow conditions limit the reliability of flowmeters measurements.

This uncertainty can have a considerable impact on the outcomes of Production Data Analysis (PDA) aimed at understanding the main fluid paths in reservoir.

In this context, a probabilistic Monte Carlo based approach is proposed to validate the production trends, especially in terms of WC and GOR, compensating the back-allocation issues.

An Eni in-house tool (IPRMC) is implemented in order to manage the risk on production rates. A probabilistic approach is run on the VLP of each well, generating thousands of gradient curves among which it is possible to select those that honour the wellhead and bottom hole pressures, the most reliable measurements. This subset of curves is characterized by a new probabilistic distribution for each uncertain produced fluid which is used to identify its most likely value/range.

The work describes the application of this methodology to an offshore deep-water field, where it proved to be a powerful tool to support detailed PDA. The risk analysis approach allows for more confidence in the fluids produced by each well, and better identification of the inter-well interactions and fluid dynamics in reservoir.


Effective reservoir management is aimed at maximizing asset value, increasing reservoir/network system efficiency and productivity.

Nowadays, oil field digitalization allows to access huge real time data, available via sensors and metering system located on wells and throughout the network. Integrated data analysis guarantees to understand the reservoir behaviour, identifying the main fluids paths and connections between wells.

The reservoir mechanisms knowledge provides a powerful way to identify optimization opportunities and reservoir management guidelines.

Measured and allocated fluids rates are key data for the above-mentioned analysis.

The quality of production data strongly affects the reliability of any reservoir analysis and consequently the identified reservoir management strategy.

Criticalities occur in case of lack of sensors (not available flow meters) or when the wells produce in multiphase flow conditions that limits the reliability of measurements.

The paper illustrates the application to a deep-water field of the probabilistic approach using an Eni in-house tool (IPRMC), implemented in order to manage the uncertainty on well performance parameters.

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