In linear formation approхimation the pressure response to the varying rates in the offset wells is subject to convolution equation:

p_n(t) 
= p_{0,n} +  \sum_{k = 1}^N  \int_0^t  p^u_{nk}(t - \tau) \, \dot q_k \, d\tau
\approx p_{i,n} + \sum_{k = 1}^N  \sum_{\alpha= 1}^{N_k} \big( q^{(\alpha)}_k - q^{(\alpha-1)}_k \big) \ p^u_{nk}(t - t_{\alpha k})

where


1		pressure at -th well at arbitrary moment of time
2		initial pressure at -the well
3		rate value of -th transient at -th well
4		pressure transient response in -th wel to unit-rate production from -th well
5		starting point of the -th transient in -th well
6		number of wells in the test
7		number of transients in -th well

with assumption:

– for any well
at for any pair of wells

Hence, convolution is using initial formation pressure , unit-rate transient responses of wells and cross-well intervals and rate histories to calculate pressure bottom-hole pressure response as function time :

\big\{ p_{i, n}, \{ p^u_{nk} (t),   q_k (t)   \}_{k = 1 .. N} \big\} \rightarrow  p_n(t)

The is a reverse problem to convolution and search for functions and numbers using the historical pressure and rate records and provides the adjustment to the rate histories for the small mistakes :

\big\{ p_k(t), q_k (t) \big\} _{k = 1 .. N}   \rightarrow  \big\{  p_{i, n}, \{ p^u_{nk} (t),  \tilde  q_k (t)   \}_{k = 1 .. N}  \big\}

The solution of deconvolution problem is based on the minimization of the objective function:

E(\{ p_{i,n}, p^u_{nk}(\tau), q^{(\alpha)}_n \}_{n=1..N}) \rightarrow {\rm min}

where

E(\{ p_{i,n}, p^u_{nk}(\tau), q^{(\alpha)}_n \}_{n=1..N}) = \sum_{n=1}^N \Big(p_{i,n} + \sum_{k = 1}^N  \sum_{\alpha = 1}^{N_k} (q^{(\alpha)}_k - q^{(\alpha-1)}_k ) \ p^u_{nk}(t - t_{\alpha k})- p_n(t) \Big)^2 
+ w_c \, \sum_{n = 1}^N \sum_{k = 1}^{N_k} {\rm Curv} \big( p^u_{nk}(\tau) \big) + 
w_q \, \sum_{k = 1}^N  \sum_{\alpha = 1}^{N_k} \big( q^{(\alpha)}_k - \tilde q^{(\alpha)}_k \big)^2

and objective function components have the following meaning:


	is responsible for minimizing discrepancy between model and historical pressure data
	is responsible for minimizing the curvature of the transient response (which reflects the diffusion character of the pressure response to well flow)
	is responsible for minimizing discrepancy between model and historical rate data (since historical rate records are not accurate at the time scale of pressure sampling)

In practice the above approach is not stable.

One of the efficeint regularizations has been suggested by Shroeter

One of the most efficient method in minimizing the above objective function is the hybrid of genetic and quasinewton algorithms in parallel on multicore workstation.

The also adjusts the rate histories for each well to achieve the best macth of the bottom hole pressure readings.

The weight coefficients and control contributions from corresponding components and should be calibrated to the reference transients (manuualy or automatically).

The XDCV methodology constitute a big area of practical knowledge and not all the tricks and solutions are currenlty automated and require a practical skill.

See also