Inter-well and inter-field allocation: besides the benefit of zonal allocation improvements, the ROCM process can be performed under well/field allocation uncertainty. This can be particularly insightful when multiple platforms/fields have been combined into one metering scheme and uncertainty is propagated to wells and/or platforms/fields.

POSEIDON™ ALLOCATION will allow quantification of contributions of phases by layer. This module is designed in order to tackle problems in 3 different work processes:

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POSEIDON™ ANALYTICSPOSEIDON™ ANALYTICS module demonstrates a sweep pattern understanding and improvement: the combination of ROCM and well interactions (Analytics allows for the diagnosis both visually and quantitatively of the effectiveness of gas and/or water flood. Sweep factor maps can then be easily generated to visualize the efficiency of the fluid movement through the reservoir. This module is designed with focus on these aspects:

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POSEIDON™ ROCM  is a robust algorithm that allows to generate remaining oil maps honouring both the ‘known’ estimated localized phase distributions around the wells together with the material balance for each reservoir unit. It is naturally expected that this approach becomes increasingly reliable as reservoir maturity increases, and with greater production constraints (number of wells), but unlike reservoir simulation history-matching, the process remains very practical, resource-light and time-effective irrespectively of this increasing complexity.

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Infill and Behind Casing Opportunities: opportunities can be readily classified into Behind-Casing-Opportunities (BCO) if accessible from existing wells, or Infill Opportunities if they reside outside of the reach of existing wells. In order to understand the true nature of by-passed oil, it is recommended to generate ROCM maps at the end of the No Further Activity (NFA) forecast. The Estimated UltimateRecovery (EUR) of these incremental opportunities can be calculated either deterministically through a target volume method, or using Predictive Analytics, estimated and benchmarked to historical reservoir performance via a machine learning methodology.

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A total of 60+ validators allow to identify, quantify and map both suspicious production data behaviour and clear anomalies. To that effect, well events (zone changes, injection start/stop, etc) are incorporated in the analysis. A data quality index is generated and data corrective measures proposed for each well string. This step is the first in the reservoir management workflow, and can be used as a pre history-matching validation.

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