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1. Motivation



One of the most important objectives of the well testing is to assess the drainable oil reserves and reservoir properties around tested well.

This particularly becomes important in appraisal drilling as well testing is the only source of this information.


BUS – Build-up Survey 


Conventional single-well testing is based on long-term monitoring of downhole pressure response to the step change in flow rate (usually shut-in or close-in).


The primary hard data deliverables are:
 

  • formation pressure  P_i

  • skin-factor S
     
  • average transmissibility in drainage area  \sigma

  • time to reach the reservoir boundary  t_e


The conditional deliverables from build-up survey would be:

DeliverablesDescriptionNon-BUS Input ParametersKey Uncertainties
(1) V_o = \frac{4 \, \sigma \, t_e \, (1-s_{wi})}{c_t}

where   c_t is total compressibility:

(2) c_t = c_r + (1-s_{wi}) \, c_o + s_{wi} \, c_w

and \{ c_r, \, c_o \, c_w \} are rock, oil and water compressibility.


Drainable oil reserves


The rock compressibility c_r(\phi) is defined from core lab study or empirical porosity correlations

Fluid compressibility \{ c_o , \, c_w \} from PVT

Initial water saturation s_{wi} from SCAL


Rock compressibility c_r(\phi)


Initial water saturation s_{wi}

(3) A_e = 4 \, \chi \, t_e

where  \chi is pressure diffusivity:

(4) \chi = \big< \frac{k}{\mu} \big> \, \frac{1}{\phi \, c_t}

where \phi is reservoir porosity, \big< \frac{k}{\mu} \big> is fluid mobility:

(5) \big< \frac{k}{\mu} \big> = k_a \, \bigg[ \frac{k_{rw}}{\mu_w} + \frac{k_{ro}}{\mu_o} \bigg]

k_a is absolute permeability to air,

k_{rw}, \, k_{ro} are relative permeabilities to water and oil,

\mu_w, \mu_o are water and oil viscosities


Drainage area


Formation porosity \phi

Absolute permeability to air k_a from core study


Relative permeabilities \{ k_{rw}, \, k_{ro} \} from SCAL

Fluid viscosities \{ \mu_w, \mu_o \} from PVT


Absolute permeability to air k_a


Relative permeabilities \{ k_{rw}, \, k_{ro} \}
(6) h = \sigma \, \bigg< \frac{k}{\mu} \bigg>^{-1}


Effective reservoir thickness


Absolute permeability to air k_a from core study


Relative permeabilities \{ k_{rw}, \, k_{ro} \} from SCAL

Fluid viscosities \{ \mu_w, \mu_o \} from PVT


Absolute permeability to air k_a


Relative permeabilities \{ k_{rw}, \, k_{ro} \}

As one can see, the drainage area and the reservoir thickness are conditioned by core data which may not be representative of the whole drainage area.


SPT – Self-Pulse Testing


The single-well self-pulse test is based on long-term monitoring of downhole pressure response to the sequential rate step change (usually shut-in or close-in).


The primary hard data deliverables are:

  • formation pressure  P_i

  • skin-factor S
     
  • near  \sigma_{near} and far  \sigma_{far} zone transmissibility 

  • near  \chi_{near} and far  \chi_{far} zone pressure diffusivity

  • time to reach the reservoir boundary  t_e


The SPT is correlating pressure variation with pre-designed flowrate variation sequence and tracks:

  • pressure response amplitude which depends on formation transmissibility  \sigma 

and

  • time lag between flowrate variation and pressure response which depends on formation diffusivity  \chi.


This allows estimating effective formation thickness  h directly from field survey without assumptions on core-based permeability (compare with (6)) and consequently leads to assessing the drainange area  A_e, fluid mobility  \bigg< \frac{k}{\mu} \bigg>  and absolute permeability  k_a with lesser uncertainties than in BUS: 

DeliverablesDescriptionNon-BUS Input ParametersKey Uncertainties
(7) h = \frac{\sigma}{\phi \, c_t \, \chi}


Effective reservoir thickness


Formation porosity \phi

Rock compressibility c_r(\phi)

Initial water saturation s_{wi}

Fluid compressibility \{ c_o , \, c_w \}


Rock compressibility c_r(\phi)

(8) A_e = \frac{4 \, \sigma \, t_e}{c_t \, h}


Drainage area


Rock compressibility c_r(\phi)

Initial water saturation s_{wi}

Fluid compressibility \{ c_o , \, c_w \}




Rock compressibility c_r(\phi)

(9) \bigg< \frac{k}{\mu} \bigg> = \chi \, \phi \, c_t


Fluid mobility


Rock compressibility c_r(\phi)


Initial water saturation s_{wi}


Fluid compressibility \{ c_o , \, c_w \}


Rock compressibility c_r(\phi)


Initial water saturation s_{wi}

(10) k_a = \frac{\bigg< \frac{k}{\mu} \bigg>}{\bigg[ \frac{k_{rw}}{\mu_w} + \frac{k_{ro}}{\mu_o} \bigg]}


Absolute permeability


Rock compressibility c_r(\phi)


Initial water saturation s_{wi}


Relative permeabilities \{ k_{rw}, \, k_{ro} \}

Fluid viscosities \{ \mu_w, \mu_o \}

Fluid compressibility \{ c_o , \, c_w \}


Rock compressibility c_r(\phi)


Initial water saturation s_{wi}


Relative permeabilities \{ k_{rw}, \, k_{ro} \}


The absoluite permeability from SPT  k_a |_{SPT} is usually stacked up against core-based permeability  k_a |_{CORE} to validate the core samples and assess the effects of macroscopic features which are overlooked at core-plug size level.


Running SPT in two different cycling frequences allows assessing the near and far resevroir zones spearately.

The usual SPT workflow includes several cycling tests with different frequencies, the lower the frequency the longer the scanning range.

This captures variation of permeability and thickness when moving away from well location.

Together with deconvolution, the SPT is reproducing conventional PTA information and providing additional data on pressure diuffusivity.

This maybe used as estimation of permeability and thickness separately and their variation away from well location.



The effect of the pressure response delay to flow rate variation in single well test is so small (seconds) that conventional build-up can not capture it reliably due to a high pressure contamination and wellbore instability at early build-up times and hence pressure diffusivity normally can not be assessed.

In SPT the rate undergoes sequential step changes which allows data stacking and more accurate measurement of pressure-rate time lag and through this assess pressure diffusivity. 

This effect is accurately described by analytical solution of diffusivity equation and meets practical observations.

In order to numerically reproduce a short-term pressure-rate time lag in single-well survey one needs a dedicated numerical solver since the required mesh size is very small and comparable to the well size and conventional Peaceman well model does not work (see also Numerical solutions of single-phase diffusion models).


2. Objectives


  • Assess reservoir volume around well

  • Assess reservoir permeability and thickness variation around well


3. Deliverables




VhcPotential hydrocarbon reserves
Ve

Drainage volume

AeDrainage area
knearPermeability of the near-reservoir zone
hnearEffective thickness of the near-reservoir zone
kfarPermeability of the far-reservoir zone
hfarEffective thickness of the far-reservoir zone
SSkin-factor
Pu(t)Deconvolution of the long-term unit-rate response


4. Inputs


PropertyDescriptionData Source
BoOil Formation Volume FactorPVT samples
coOil compressibilityPVT samples
cwWater compressibilityPVT samples
crRock compressibilityPVT samples
swiInitial water saturationCore samples

\phi

PorosityCore samples




5. Procedure



Test = Test 1 + Test 2 + Test 3



  1. Test 1 = high freq pulsations (10 pulses with period T)

  2. Test 2 = mid freq pulsations (10 pulses with with period 5T)

  3. Test 3 = Low freq pulsations  (10 pulses with period 25 T)

So that total duration of the test is 310 T.


Typically T = 3 hrs and total test duration is around 40 days.


6. Interpretation


  1. Numerical model

    1. Single well with circle boundary

    2. High density LGR

    3. High density time grid (seconds)

  2. Automated pressure match in PolyGon software



References



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