This document discusses an integrated source-free interpretation approach for evaluating storage capability in reservoirs using NMR and acoustic logging. [1] NMR logging directly measures hydrogen in pore fluids, providing a detailed porosity description independent of lithology or gas presence. Acoustic logging also responds to porosity but is indirect, relying on empirical models. [2] By calibrating the acoustic model using NMR porosity measurements, the two techniques can be combined to provide a more accurate total porosity evaluation, especially in gas-bearing formations where NMR may underreport porosity due to low hydrogen content. [3] This integrated approach improves storage capacity assessment, a key imperative for underground gas storage projects, while avoiding safety and regulatory

1. Baker Hughes
11th Annual Global Gas Village Summit 2011
Prague – 11, 12 & 13 April 2011
Evaluating storage capability of reservoir using an
integrated source-free interpretation approach
Fabio Brambilla
Senior Geoscientist Baker Hughes
Fabio.brambilla@bakerhughes.com

2. Agenda
1. Sub-Surface imperative of UGS
2. Added value of integrated source free approach
3. The challenge of storage capacity evaluation
4. NMR logging vantages
5. Acoustic porosity
6. Combined porosity
7. Summary
2

3. Sub-Surface Imperatives of UGS
3
 Maximize storage capacity
 Maximize deliverability
 Optimize cushion gas volume
 Mitigate project risk
 Well reliability
 Profitable project
Project
Economics
Storage
Capacity
Deliverabilit
y
Cushion
Gas
Risk
Reliability

4. Added value of integrated source-free
interpretation approach
4
• Improve the evaluation of storage capability
providing better porosity knowledge of the
reservoir
• The continuous permeability profile from
NMR service let to understand the
deliverability of the well
• Avoid any risk about utilization of radioactive
sources
Project
Economics
Storage
Capacity
Deliverability
Cushion GasRisk
Reliability

5. Storage capacity: the first imperative
• The evaluation the storage capacity of reservoir for UGS requires
running porosity logs, in order to have quantitative estimation of
space available in your reservoir to accommodate the injected gas

6. Evaluating the storage capacity: the challenge
• The traditional approach of evaluation requires running
density and neutron log devices in order to have
quantitative estimation of reservoir porosity
• Both logs response are affected by lithology and gas
presence
• Environmental regulations for UGS fields management
are more and more limiting the use of chemical
radioactive sources
• HSE nationals rules tend to made complex the logistic of
devices using radioactive sources

7. Evaluating the storage capability: the solution
• To overcome that, a more advanced approach have
been developed for porosity determination using
source-free tools, combining:
1. Nuclear Magnetic Resonance (NMR) logging
2. Acoustic logging
• Both devices rely on a comfortable physics:
– NMR: tool contains permanent magnet with magnetic field
– Acoustic: deals with acoustic waves
• The porosity from that combination is indipendent from
lithology and gas presence

8. NMR vantages
• HSE fully complaint !
• Advanced detailed porosity
description
• Continuous permeability
profile

9. NMR service
• This evaluation service is available either
– While drilling the well (LWD)
– At end of well drilling in open hole (WL)
MagTrak
MR Explorer (MREX)

10. NMR: what it is measured (a bit of physics)
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination
•

11. NMR how it works
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination
B=0, M=0

12. M0 B0
NMR how it works
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination

13. NMR how it works
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination
Tool emits radio
Frequency RF
pulse with field
strength B1
Spins are tipped 90
degrees by the RF pulse
and then begin to precess
in the B0 field
f =  B0

14. Spins precess in the
B0 field after tipping
by an RF pulse
f =  B0
NMR how it works
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination
Echoes signal
are recorded

15. NMR how it works
• NMR logging has the advantage of direct measuring the
hydrogen of fluids in pore space avoiding lithology effect
on porosity determination
Echoes signal
are recorded
TE : intercho spacing
TE Time
90°
x 180°
y 180°
y 180°
y 180°
y 180°
y
Amplitude
Echo Signals
RF Pulses

16. Volumetrics porosity distribution in the reservoir
according NMR exploration
0 100 200 300 400 500 600
Time (ms)
Porosity%
25
20
1
5
10
5
0
Superposition
Clay
Bound
Water
Capillary
Water
Movable
Water
Light
Hydrocarbon
0
1
2
3
4
0.1
PartialPorosity
1 10 100 1000
T2 cutoffs
T2
Movable
Water
Capillary
Water
Clay
Bound
Water
Light
Hydrocarbon

17. NMR porosity description
• The NMR logging offers a complete overview of
– porosity distribution: total porosity, clay bound water
volume, capillary water volume, mobile fluid volume
– a continuous permeability curve.
• The knowledge of these values allows:
– recognizing the best storage zones of the reservoir
– Better understand the deliverability
total porosity (ØT,NMR )
Matrix Rock
Dry
Clay
Clay-
bound
water
Free
water
Capillary
trapped
water
Hydro-
carbons
BVMCBW
e
BVI
t

18. Where default parameters are: C =10, m = 4 & n = 2
Coates-Timur Model :
MBVI
MBVM
C
k
n
=
m

MPHE
NMR Permeability

20. Shale indicator from NMR
• CBW: Volume of clay bound water (CBW) represents the
porosity in clay content in a formation rock
• From NMR logs, both the fractional porosity from CBW
(ØCBV) and the total porosity (ØT,NMR ) are obtained
NMRT
CBW
,

Vsh =
0
1
2
3
4
0.1
PartialPorosity
1 10 1001000

21. Porosity evaluation in gas bearing beds
• The gas occurrence affect all the
porosity logs
– Lower density: over call density
porosity
– Lower Hydrogen index: under call
porosity based on Hydrogen Index

22. Superior hydrocarbon typing
• Innovative NMR acquisition techniques provide comprehensive NMR data for
fluids analysis
– T1, T2 & Diffusion data acquired simultaneously while logging
• 2D NMR plots identify and quantify hydrocarbons
– Available from all hydrocarbon typing Objective Oriented Acquisitions
– Acquired as continuous logs (NOT stationary measurements!)
PoroPerm + Gas PoroPerm + Oil PoroPerm + Heavy Oil
2 32 512
T2,app (ms)
16
4
1
T1/T2,app
T2,int (ms)
e-8
e-9
e-10
e-11
D(m2/s)
2 128 1024
T2,int (ms)
e-8
e-9
e-10
e-11
e-12
D(m2/s)
162 128 102416
Gas
CBW BVI
Water
Oil
Heavy Oil
Water

23. Porosity evaluation in gas bearing beds
• The accuracy of NMR total porosity in gas-bearing
formations is affected by low Hydrogen Index (HI)
• Thanks to hydrocarbon typing analysis we can correct for
the HI effect ,

24. Porosity evaluation in gas bearing beds
• However in depleted levels or low pressure reservoir the
correction for HI is definitively an improvement but still an
estimate due to uncertainty of HI estimation
Porosity & HI correction
0
1
2
3
4
5
6
7
8
0 50 100 150 200 250 300 350
BAR
HI
0
5
10
15
20
25
30
35
40
Porosity

25. Porosity evaluation in gas bearing beds
To overcame this imprecision we suggest to exploit the
vantage of combine the porosity from NMR service with
the porosity from the acoustic service
NMR Acoustic

26. Acoustic vantages
• HSE fully complaint !
• This evaluation service is available either
– While drilling the well (LWD)
– At end of well drilling phase (WL) in open hole and cased
hole (CH)
SoundTrak
XMAC F1

27. Porosity from modified Raymer-Hunt-Gardner (1)
• Δt is the measured slowness of wave velocity,
• Δtma is the slowness of the dry matrix.
– Constant in clean reservoir (Δtma,clean )
– it changes with shale presence: type, distribution, and
percentage of shale (Δtma)
• C is the fitting parameter
C
t
tt ma
acoustic


=
(1 ) Alberty, M. 1994

28. Acoustic porosity
• The acoustic measurements respond to lithology and
porosity
• In addition respond to texture consequently acoustic
porosity is an indirect measurement based on semi-
empirical models, which often requires calibration of
parameters
• The Raymer-Hunt-Gardner function can be calibrated
using the NMR total porosity and NMR shale volume

29. {
Acoustic porosity calibration
• Calibrate the fitting parameter C
• The Raymer-Hunt-Gardner function is calibrated in a clean water zone
using the NMR total porosity
• (ØT,NMR ) = (ØT,Acoustic )
• Calibrate Δtma,clean
– Complex matrix
C
t
tt cleanma
NMRT


= ,
,
C
t
tt
a
cleanmaa
NMRTa


=
,
,
C
t
tt
b
cleanmab
NMRTb


= ,
,
cleanma
t
tt
C
NMRT
,
,

=

0
1
2
3
4
0.1
PartialPorosity
1 10 100 1000

30. Acoustic porosity calibration
• Calibrate the Δtma, in the shaly sand section
– Using the calibrated C and the NMR porosity
• A correlation can be established between Δtma and Vsh
• The matrix slowness is back-calculated over all the shaly
zones
t
C
tt
NMRT
ma =
,
GRvstma . dt_ma vs GR
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100
GR (gAPI)
dt_ma(us/ft)
)(gAPIGR
tma(s/ft)
GRvstma . dt_ma vs GR
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100
GR (gAPI)
dt_ma(us/ft)
)(gAPIGR
GRvstma . dt_ma vs GR
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100
GR (gAPI)
dt_ma(us/ft)
)(gAPIGR
tma(s/ft)
GR (gAPI)
tp,ma(µs/ft)
Vsh %
Δtma

31. Acoustic porosity calibration: summary
• The Raymer-Hunt-Gardner function is calibrated using the
NMR total porosity in a clean water zone.
• Subsequently using the shale volume, computed from the
clay bound water volume, the matrix slowness is back-
calculated over all the shaly zones
• The function, with the calibrated parameters is run over
the reservoir

32. Combined NMR log-calibrated acoustic porosity
• These steps let to compute the final porosity using
the correct parameter over the whole interval.
Compute Vsh,NMR
Calibrate Δtma
Calibrate C
NMR logging
Acoustic logging
Using modifies R-H-G function
Compute NMR-calibrated Acoustic Porosity
Permeability

33. Example of NMR log-calibrated acoustic porosity
• Example in shaly sand sequences
•

34. Where and when ?
• This approach is applicable from clean to shaly
sandstones, and carbonate reservoirs
• Necessary data can be gathered either using LWD at
drilling phase and or at wireline measurements phase

35. 35
Summary
 First UGS imperative: to be able to evaluate the
storage capacity
 Mitigate project risk
 Get information helping to maximize the
deliverability
 NMR log-calibrated acoustic porosity provides more
accurate and detailed description of reservoir
porosity
 Data can be acquired either while drilling or post
drilling phase.
Project
Economics
Storage
Capacity
Deliverability
Cushion GasRisk
Reliability

36. References
• Alberty, M. 1994. The influence of the borehole
environment upon compressional sonic logs. Paper 1994-
S, SPWLA 35th Annual Logging Symposium
• Raymer, L.L., Hunt, E.R., and Gardner, J.S. 1980. An
improved sonic transit time to porosity transform. Paper
1980-P, SPWLA 21st Annual Logging Symposium
• Chun Lan, Songhua Chen, Freddy Mendez, Rex Sy,
2010. Sourceless Porosity Estimation in Gas Reservoirs
Using Integrated Acoustic and NMR Logs, SPE ATCE
SPE 133487

37. Thank you