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A set of mathematical models relating surface production rate history of one well to its bottomhole pressure history and offset injection rates history.

In case the bottom-hole pressure data is not available it is considered constant over time.

The CRM is trained over historical records of production rates, injection rates and bottom-hole pressure variation.


The major assumptions in CRM model are:


Application



  • Assess current production performance

    • current distribution of recovery against expectations

    • current status and trends of recovery against expectations

    • current status and trends of reservoir depletion against expectations
       
    • current status and trends of water flood efficiency against expectations

    • compare performance of different wells or different groups of wells 

  • Identify and prioritize surveillance opportunities

  • Identify and prioritize redevelopment opportunities

Advantages



  • fast-track

  • based on the robust input data

  • does not involve full-field 3D dynamic modelling and associated assumptions

Limitations



Does not pretend to predict reserves distribution as dynamic model does
Only provides hints for misperforming wells and sectors which need a further focus
Only provides hints for misperforming wells and sectors which need a further focus

Can only be tuned for injector-producer pairing with a rich history of injection rates variations

Can only work at long times and only in areas with limited drainage volume


Technology



CRM trains linear correlation between variation of production rates against variation of injection rates with account of bottom-hole pressure history in producers.

against material balance and require current FDP volumetrics, PVT and SCAL models. 


The CRM has certain specifics for oil producers, water injectors, gas injectors and field/sector analysis. 



See Also


Petroleum Industry / Upstream /  Production / Subsurface Production / Field Study & Modelling / Production Analysis

Capacitance-Resistivity Model @model


References



https://doi.org/10.2118/147344-MS

https://doi.org/10.2118/177106-MS

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