Specific type of Production Analysis (PA) workflow based on correlation between injection rates history and production rate history with account of bottomhole pressure history.

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

Motivation



Production rate in producing well depends on its productivity index , current formation pressure  and current BHP :

q_1^{\uparrow}(t)=J \cdot \left( p_e(t) - p_{wf}(t) \right)

and as such depends on completion/lift settings (defining ) and how formation pressure is maintained  over time.

It keeps declining due to the offtakes:

p_e(t) = p_e[q_1^{\uparrow}(t), q_2^{\uparrow}(t), q_3^{\uparrow}(t), \dots]

and maintained by either aquifer or Fluid Injection and in the latter case depends on injection rates:

p_e(t) = p_e[q_1^{\downarrow}(t),q_2^{\downarrow}(t),q_3^{\downarrow}(t),\dots ]

The combination of  and  lead to the correlation between production rates, injection rates and bottomhole pressure variation.


The ultimate purpose of CRM is to correlate the long-term (few months or longer) injection rates history with production rates history and BHP history (recorded by PDG).

It is essentially based on the fact that production rate responds to changes in BHP and offset injection.


The major assumptions in CRM model are:


Assumption 2 means that interference between producers is fairly constant in time despite the rate variations and their impact on the dynamic drainage volumes.


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
Requires minimum input data (BHP history, fowrate history and FVF)Requires eventful history of injection rates variations

Robust procedure (no manual setups)

Requires productivity index of producers to stay constant
Does not involve full-field 3D dynamic modelling and associated assumptionsRequires the drainage volumes of all producers stay the same throughout the modelling period

Only applicable for specific subset of PSS fluid flow regimes


Technology



The 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


Pressure drop 

\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


Arthur Aslanyan, Mathematical aspects of Multiwell Deconvolution and its relation to Capacitance Resistance Model, arxiv.org/abs/2203.01319

Nguyen, A. P., Kim, J. S., Lake, L. W., Edgar, T. F., & Haynes, B. (2011, January 1). Integrated Capacitance Resistive Model for Reservoir Characterization in Primary and Secondary Recovery. Society of Petroleum Engineers. doi:10.2118/147344-MS

Holanda, R. W. de, Gildin, E., & Jensen, J. L. (2015, November 18). Improved Waterflood Analysis Using the Capacitance-Resistance Model Within a Control Systems Framework. Society of Petroleum Engineers. doi: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