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

In case the bottomhole pressure data is not available it is considered constant over time.

The CRM is trained over historical records of production rate, injection rates and bottomhole pressure variation.


The major assumptions in CRM model are:


Goals

Objectives

Identify and prioritise production optimisation opportunitiesGenerate production and formation pressure forecasts based on the bottom-hole pressure and injection rates
Identify and prioritise redevelopment opportunitiesAssess productivity index of producing wells
Identify and prioritise surveillance candidatesAssess dynamic drainage volume around producing wells

Quantify connectivity between injectors and producers

Assess water flood efficiency against expectations and / or between wells or well groups



Advantages

Limitations

Fast-trackIt only models injector-producer system
Based on the robust input dataRequires eventful history of injection rates variations

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

Requires productivity index of producers to stay constant

Requires production sharing between producers stay approximately the same throughout the time


Technology


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

See Capacitance-Resistivity Model @model


InputsOutputs
Production rate historyProductivity Index for the focus producer
Bottom-hole pressure historyDrainage volume by the focus produce producer
Injection rate historyShare of injection going towards the focus producer
PVT model




CRM is a specific case of MDCV with the following unit-rate transient responses:


DTRCTR from offset producersCTR from offset injectors


UTR


p_{1nn}(t) =J_n^{-1} \left( 1 - \frac{t}{\tau_n} \right)



p_{1nm}(t) = 0



p_{1nm}(t) =  \frac{f_{nm}}{J_n \tau_n} \cdot t



Drawdown


\delta p_{1nn}(t) =  \frac{1}{J_n \tau_n} \cdot t



\delta p_{1nm}(t) = 0



\delta p_{1nm}(t) =  \frac{f_{nm}}{J_n \tau_n} \cdot t



Log Der


p'_{1nn}(t) = \delta p_{1nn}(t) 



p'_{1nm}(t) = 0



p'_{1nm}(t) = \delta p_{1nm}(t)



See also CRM as MDCV @model for derivation.



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




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