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In stagnant wellbore fluid conditions the pressure and temperature variations in time are linearly correlated:

(1) p(t) = a + b \cdot T(t)

and the regression coefficient is related to along-hole pressure gradient  \nabla p and along-hole temperature gradient  \nabla T as:

(2) b = \frac{d p}{ d T} = \frac{\nabla p }{ \nabla T }



In stationary conditions:

(3) T(z) = T_0 + \nabla T \cdot (z-z_0)
(4) p(z) = p_0 + \nabla p \cdot (z-z_0)

Assume very slow quasi-isothermal variations of fluid column z = z(t) then:

(5) T(z(t)) = T_0 + \nabla T \cdot (z(t)-z_0) \rightarrow dT(t) = \nabla T \cdot dz(t)
(6) p(z(t)) = p_0 + \nabla p \cdot (z(t)-z_0)\rightarrow dp(t) = \nabla p \cdot dz(t)

Dividing (6) by (5) leads to (2).


The analysis of wellbore pressure-temperature correlation helps in QC of gauge metrology and check for fluid stagnancy.

Non-linearity in  dp \ {\rm vs} \ dT is a sign of a poor gauge calibration or fluid movement.


In practical applications, the pressure and temperature gradients can be calculated form data readings of a pair of calibrated downhole pressure gauges:

{\rm Gauge_{up}} = \{ p_{\rm up}(t), \ T_{\rm up}(t) \}
{\rm Gauge_{down}} = \{ p_{\rm down}(t), \ T_{\rm down}(t) \}

with  \Delta l_z true vertical spacing between the sensors.


This allows calculating the pressure and temperature gradients along the hole as:

(7) \nabla p(t) = \frac{p_{\rm down}(t)-p_{\rm up}(t)}{\Delta l_z} = \frac{\Delta p(t)}{\Delta l_z}
(8) \nabla T(t) = \frac{T_{\rm down}(t)-T_{\rm up}(t)}{\Delta l_z} = \frac{\Delta T(t)}{\Delta l_z}


Building a linear regression with sliding window \delta_t:

(9) [ \Delta p(t) ]_{\delta_t} = \frac{dp}{dT} \cdot [ \Delta T(t) ]_{\delta_t}

provides a practical estimate of  \displaystyle \frac{dp}{dT} as function of time t.


For stagnant vertical water column  \rho_w= 103 kg/m3 with standard geothermal gradient  \nabla T= 0.025 K/m the derivative should be around  \displaystyle \frac{dp}{dT} = \frac{\rho_w \, g}{\nabla T}= 3.9 · 105 Pa/K.






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