Definition
The Capacitance-Resistance Model (CRM) is a set of mathematical models relating production rate to the bottomhole pressure and offset injection rate.
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:
- productivity index of producers stays constant in time
- drainage volume of producers-injectors system is finite and constant in time
- total formation-fluid compressibility stays constant in time
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
- current distribution of recovery against expectations
- 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
CRM does not pretend to predict reserves distribution as dynamic model does.
It only provides hints for misperforming wells and sectors which need a further focus.
CRM can only be tuned for injectors with a rich history of rates variations.
CRM only works 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.
CRM – Single-Tank Capacitance Resistance Model
The CRM model is trying
The simulation is based on the following equation:
(1) | q^{\uparrow}(t) = f \, q^{\downarrow}(t) - \tau \cdot \frac{ d q^{\uparrow}}{ dt } - \beta \cdot \frac{d p_{wf}}{dt} |
where
q^{\uparrow}(t) | total surface production |
q^{\downarrow}(t) | total surface injection |
p_{wf}(t) | average bottomhole pressure in producers |
f | unitless constant, showing the share of injection which actually contributes to production |
\tau | time-measure constant, related to well productivity [ s/Pa ] |
\beta | storage-measure constant, related to reservoir volume and compressibility [ m3/Pa ] |
The \tau and \beta constants are related to some primary well and reservoir characteristics:
(2) | \beta = c_t \, V_\phi |
(3) | \tau = \frac{\beta}{J} = \frac{c_t V_\phi}{J} |
where
c_t | total formation-fluid compressibility |
V_\phi = \phi \, V_R | drainable reservoir volume |
V_R | total rock volume within the drainage area |
\phi | average effective reservoir porosity |
J | total fluid productivity index |
Total formation compressibility is a linear sum of reservoir/fluid components:
(4) | c_t = c_r + s_w c_w + s_o c_w + s_g c_g |
where
c_r | rock compressibility |
c_w, \, c_o, \, c_g | water, oil, gas compressibilities |
s_w, \, s_o, \, s_g | water, oil, gas formation saturations |
The target function is:
(15) | E[\tau, \beta, f] = \sum_k \big[ q^{\uparrow}(t_k) - \tilde q^{\uparrow}(t_k) \big]^2 \rightarrow \min |
The constraints are:
(16) | \tau \geq 0 , \quad \beta \geq 0, \quad 0 \leq f \leq 1 |
CRMP – Multi-tank Producer-based Capacitance Resistance Model
(17) | q^{\uparrow}_j (t) = \sum_i^{n_i} f_{ij} q^{\downarrow}_i(t) - \tau_j \cdot \frac{ d q^{\uparrow}_j}{ dt } - \beta_j \cdot \frac{d p_j}{dt} |
The target function is:
(18) | E[\tau, \beta, f] = \sum_k \sum_j \big[ q^{\uparrow}_j(t_k) - \tilde q^{\uparrow}_j(t_k) \big]^2 \rightarrow \min |
The constraints are:
(19) | \tau_j \geq 0 , \quad \beta_j \geq 0, \quad f_{ij} \geq 0 , \quad \sum_i^{N^{\uparrow}} f_{ij} \leq 1 |
ICRM – Multi-Tank Integrated Capacitance Resistance Model
(20) | Q^{\uparrow}_j (t) = \sum_i^{n_i} f_{ij} Q^{\downarrow}_i(t) - \tau_j \cdot \big[ q^{\uparrow}_j(t) - q^{\uparrow}_j(0) \big] - \beta_j \cdot \big[ p_j(t) - p_j(0) \big] |
The target function is:
(21) | E[\tau, \beta, f] = \sum_k \sum_j \big[ Q^{\uparrow}_j(t_k) - \tilde Q^{\uparrow}_j(t_k) \big]^2 \rightarrow \min |
The constraints are:
(22) | \tau_j \geq 0 , \quad \beta_j \geq 0, \quad f_{ij} \geq 0 , \quad \sum_i^{N^{\uparrow}} f_{ij} \leq 1 |