1.
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ADVANCED LOGGING TECHNOLOGY COMBINED WITH INTEGRATED
FORMATION EVALUATION ANALYSES PROVIDES CONFIDENT
PETROPHYSICAL INFORMATION IN THE GIANT GAS DISCOVERIES OF
THE LEVANTINE BASIN
F. Brambilla, E. Tuyrin Baker Hughes,
This paper was presented at the 12th
Offshore Mediterranean Conference and Exhibition in Ravenna, Italy, March 25-27, 2015. It was
selected for presentation by OMC 2015 Programme Committee following review of information contained in the abstract submitted by
the author(s). The Paper as presented at OMC 2015 has not been reviewed by the Programme Committee.
ABSTRACT
Experience gained in recent activity in the Levantine basin has allowed for the development of a
formation evaluation strategy for accurate reservoirs description in this region. The proposed
evaluation approach considers operational issues of deep water wells, challenging borehole
conditions (high salinity mud, deep invasion) and other geological features of these clastic
reservoirs and their fluids.
Our case study highlights benefits of the integrated evaluation of new laterolog resistivity data
together with 2D NMR inversion results optimized for a gas bearing reservoir. Furthermore
borehole imaging logs are included in our evaluation approach.
The recently developed multi laterolog tool has an advantage of four multiple depths of
investigation. It provides a detailed high 1ft vertical resolution radial resistivity profile overcoming
the deep invasion often present in these reservoirs. The NMR acquired in gas oriented acquisition
mode exploits the multi-frequency capability of the logging device. Combined together multiple
G•TE and multiple TW experiments contribute to robust determination of the T1 and T2 reservoir
fluid properties. This acquisition sequence allows for continuous hydrocarbon typing applying the
T1/T2 vs T2 2D maps method, which is practical for these reservoirs given the T1 contrast
between gas and other fluids. Consequently we are able to perform accurate HI corrections and
therefore improve the estimates of NMR permeability and saturations. Further in the workflow we
compare NMR and Stoneley wave permeability’s and assess in details their differences.
The geological study performed with the combination of simultaneously acquired ultrasonic and
resistivity borehole images provides additional insight into the reservoir architectures, taking
advantage during the analysis of the different logging responses of the petrophysical factors to
acoustic and resistivity investigation for a detailed delineation of the productive beds.
The advantages of this integrated approach are illustrated with field data examples.
INTRODUCTION
The Levantine Basin is the most prolific offshore area of the Eastern Mediterranean and it has
been highlighted by several discoveries of giant natural gas fields, in the Neogenic subsalt
sequence that includes all reservoirs below the continuous Messinian-age salt, characterised by
clastic reservoirs, below thick sequences of evaporitic rocks (halite and anhydrite), and usually
drilled with salty saturated mud. The depositional paleoenviroment is composed of Tertiary incised
valleys, submarine canyons and channels, with associated deepwater siliciclastics sediments

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deposition in either confined or non-confined settings. Exploration targets are a combination of
structurally and stratigraphically trapped reservoirs (Gardosh et. Al 2009).
The gas is trapped in faulted anticlines and the lateral continuity of reservoirs is often difficult to
delineate due to the lateral variation of the sand bodies typical for this sedimentation.
Fig 1: Generalized sketch of the structural and stratigraphic asset
The main gas-bearing reservoirs are well identified by the GR and high resistivity readings, up to
200 Ohm.m, while in the water legs resistivity drops to 1.5 ohm.m. The clastic sandstones are
interbedded with claystone and contain minor occurrences of limestone stringers.
BOREHOLE ENVIRONMENT
The typical water depth for wells drilled in this area is greater than 1000 m.
To insure borehole stability reason in the evaporitic section a KCl saturated mud is used, replacing
sea water used to drill the post evaporitic sequence. The reservoir is drilled maintaining this salty
mud, with a density of 11.6 -11.8 ppg. The temperature of the target zones ranges between 60 -
85 degC, with a resulting mud resistivity less than 0.03 Ohm.m. Mud filtrate resistivity can
decrease to 0.016 Ohm.m, with a hydrogen index range of 0.82-86.

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RESERVOIR PROPERTIES
The formation water is relatively fresh, its resistivity is about 10 times greater than of mud filtrate,
indicating a salinity around 20-25 kppm NaCl, with hydrogen index close to 1.0. No water samples
have been recovered, so no more accurate data on composition or other parameters are available.
The hydrocarbon present is dry methane with a hydrogen index of 0.51 – 0.53 at reservoir
pressure and temperature.
The pay beds are comprised of homogeneous clean sands composed of quartz (up to the 95%)
complemented with additional detritic clastic components (Christensen et. al 2013). The porosity
can reach values greater than 22%, occasionally cemented levels are encountered with strong
reduction of porosity down to 4%. The sands exhibit excellent permeability up to 1000-2000 mD in
the cleanest intervals, the permeability decreases across cemented intervals. The GR response
delineates the sand beds, however across the beds with relatively constant GR values the porosity
can vary considerably.
RESISTIVITY LOG
The tool used for accurate resistivity measurements is a multi-laterolog device, the RteXplorer.
This device is based on the dual laterolog (DLL) principle, but is designed with four independent
focused measurements featuring different depths of investigation (ranging from 10 to 50 inches),
with a single enhanced vertical resolution of one foot (1 ft) providing a detailed high resolution
radial resistivity profile (Brambilla et al 2013).
Its capability to work with a wide range of formation resistivity and high conductivity mud, together
with the registration of four resistivity curves from the four sub-arrays with four different depths of
investigation from very shallow to deep allow to compute a reliable Rt and calculate invasion length
(Lxo) and flushed zone resistivity (Rxo). This is accomplished at wellsite by a simultaneous
inversion processing using a simplified radial 1-D model including six parameters: Rm – mud
resistivity, Bhd – borehole diameter, Ecc – tool eccentricity, Rt – formation resistivity, Rxo and Lxo
(Zhou et Al 2008, Maurer et Al 2009). Results of 1D inversion are shown on the Fig 2 and Fig 3.
Two of these parameters, namely Rm and Bhd, are known, being measured directly with the mud
resistivity sensor located at the head of tool string and the caliper. The remaining four unknowns
can be determined from the four independent measurements (MLR1, MLR2, MLR3, and MLR4).

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Fig 2: Composite plot showing resitivities and borehole images in a water bearing zones.
The presence of thin laminations is well defined with the images, showing also the
occurrences of cemented streaks, often not laterally continuous. These streaks are well
defined by the high definition resistivity-like curve computed from STAR microresistivity
image A sharp invasion profile, in front of the sand beds, is delineated from the lateral
resistivity curves and the invasion depth is computed from the 1D inversion processing.

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Fig 3: Composite plot showing resitivities and borehole images in a gas bearing zone
and gas/water transition at the bottom. The microresistivity is influenced by residual gas in
the invaded zone, as compared to the microresistivity image on Fig 2 (both images are
plotted using the same graphics scale). Observed also the difference of levels A and B at
the top of the gas sequence, both have similar GR values, but different resistivity, DTC and
density values
POROSITY DETERMINATION
The standard porosity logs, i.e. neutron and density alone are not sufficient to determine the
accurate true formation porosity. There are several factors affecting these tools response. As
highlighted on the Fig 2 and Fig 3 there is a considerable amount of invasion in the formation.
Therefore within the depth of investigation of density and neutron there are three types of fluids,
namely gas, formation water (may be at free or irreducible condition) and the filtrate. Considering
the specific mud properties used in this area, the accurate solution for porosity is not achievable
with the only two measurements, due to the 3 unknowns.
The neutron is mainly reading in the invaded zone and will be in general under-calling porosity in
the reservoir sections due to the low HI of mud filtrate, featuring even higher under estimation
across the gas bearing formations. Direct estimation of porosity from density log is even more
complicated, because mud filtrate features density higher than 1, formation water density is close
to 1, while gas density is significantly lower. Practically speaking it is required to know gas
saturation exactly at the DOI of the density tool for the reliable porosity calculation. Therefore,
using only these two logs it will not be possible to determine a quantitative pore fluid filling

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parameters in order to utilise them in a standard petrophysical approach to compute accurate
porosity values.
This challenge can be addressed with NMR log acquisition, optimised for gas bearing reservoir.
Acquired NMR data is used further, in a post-acquisition processing, namely 2D inversion, allowing
determining continuous volume of different fluids present in the pore space.
MREX is a side-looking tool with the sensitive volume reaching from 2.5” to 4.5” into formation,
depending on the operating frequency. This DOI is selected to be tolerant to borehole irregularities
and less affected by invasion, reading deeper than the fully flushed zone. This NMR device is a
gradient tool, where the gradient strength decreases with frequency. The gradient capability allows
to excite multiple slices of sensitive volumes in one single polarization time using the frequency
interleaving method. The entire operating frequency band ranges from mid-400kHz to upper-
800kHz with sufficient frequency separation between any two frequencies to avoid interference
from the excitations in neighboring sensitive volumes.
To reduce the environmental noise, common for NMR acquisition in high conductive mud, like in
this well, the device was run with a mud excluder sleeve, limiting the amount of mud volume
around the device while logging. The data stacking can be used further to improve signal-to-noise
ratio, however it will reduce the vertical resolution of the computed NMR volumetrics.
Because it is desirable to acquire NMR data simultaneously in a single logging pass, as well as to
continuously characterize fluids in the pore space, four basic objective-oriented acquisition (OOA)
pulse sequence packages: PoroPerm , PoroPerm + OIL, PoroPerm + GAS, and PoroPerm +
Heavy Oil has been developed for optimal characterization of relevant hydrocarbons. More specific
OOA in case of particular fluids are available. In gas reservoir the OOA sequence named
PoroPerm + GAS is recommended to be used, allowing for the relevant fluid typing in the DOI of
NMR tool and therefore accurate HI correction and true porosity calculation.
The sequence comprises multiple echo trains with different wait-times (TW) which facilitate
simultaneous recordings of the apparent T2 (T2,app) and T1 decay times. Further, in details, this
sequence uses 6 even frequencies (12, 10, 8, 6, 4, 2) to acquire full T2 echo trains and CBW1
packets, and 2 frequencies to acquire BVI2
echo trains (12, 10).
The difference between these spectras is generally caused by diffusion. As the diffusion of gas is
significantly higher than that of water and oil, the inequity between T2,app and T1 can be used for
gas detection.
Considered acquisition sequence provides sufficient amount of data to reconstruct two-dimensional
intensity maps in T2,app - T1 domain as T1/T2,app vs. T2,app . These maps feature much more valuable
information than one-dimensional T2,app or T1 spectral plots, allowing straightforward gas
identification complemented by more accurate quantification (Hursan. et Al 205)
The ratio R= T1/T2,app depends on the diffusivity, D, of the fluid, according to the following relation:
   
12
1
12
2
2
2
2
2
2
2
2
2
2
2
1 int
,
int
,
,
int
,
int
,
int
,
,
int
,
,
T
D
GTE
T
D
GTE
k
T
T
T
kT
T
kT
T
T
R
diff
app
app












The ratio T1/T2,app is close to 1 for liquid-phase but much greater than 1 for the gas-phase signal,
this distinctive feature between gas and liquids lets this method to be particularly useful for gas
identification and quantification.
1
CBW: Clay Bound Water
2
BVI: Bulk Volume Irreducible

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Once the gas volume is quantified is it possible to correct pore volume occupied by gas using the
hydrogen index computed for gas composition at reservoir temperature and pressure, as
highlighted on the Fig 4 and Fig 5.
As additional advantage the correct NMR volumes let to compute a more accurate permeability
using the Timur – Coates equation.
Fig 4: Composite plot showing NMR analysis, complemented with resistivity curves,
Stoneley permeability and borehole images in a water bearing zones. From the left the
following curves are presented: Track 1 shaded GR, Track 2 RTEX curves and high
definition resistivity-like curve computed from STAR microresistivity image, NMR
permeability, Stoneley permeability and its upper uncertainty limit, Track 3 NMR total
porosity and NMR effective porosity both hydrogen index corrected, neutron porosity and
density porosity both in sandstone matrix, Track 4 NMR T2,app spectrum, Track 5 NMR 2D
map computed by stacking data from 2.5 meters, Track 5 gas saturation in flushed zone,
Track 6 graphically stacked NMR fluids volumetric to produce the total NMR porosity
(MPHS), Track 6 NMR partial porosity distribution, Track 7 acoustic image. It is worth to
mention the acceptable consistency between NMR and Stoneley permeabilities and the
presence of negligible amount of residual gas on the 2D plot (see maps at xx90-xx92.5 and
xx06.5-xx09), that could be indication of the secondary gas migration in the reservoir.

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Fig 5: Composite plot showing NMR analysis complemented with resistivity curves,
Stoneley permeability and borehole images in the gas zone (same curves of Fig 4). In the
reservoir the match between NMR and Stoneley permeabilities is acceptable. The difference
of levels A and B at the top of the gas sequence, could be explained in term of variation on
lithology: the NMR volumetrics and the partially porosity suggest that the top of sequence
is composed by silt, with reduction of porosity but a relatively high amount of water
reducing its resistivity. The 2D NMR plot shows evident gas occurrence, it is worth to
remember that considered gas saturation is computed in the invaded zone, so it is
definitively inferior to the gas presence in the non-invaded reservoir.
BOREHOLE IMAGE LOGS
The formation evaluation is complemented by the simultaneous acquisition of ultrasonic and
resistivity borehole images, which provide additional insight in the reservoir architectures. The
reservoir analysis benefits from the different petrophysical factors influencing either the acoustic or
resistivity images, resulting in more detailed delineation of the productive beds.
The micro-resistivity imager (STAR-HD) provides high resolution formation resistivity imaging in
conductive mud systems even in wells drilled with salt saturated muds. The wide dynamic range of
the sensor arrays results in enhanced geological and petrophysical reservoir evaluation.
The six-arm independently articulated carrier and powered stand-off ensures optimal sensor to
formation contact even in highly deviated boreholes. Pads with 24 sensors are mounted on each of
the six articulated arms, resulting in a total of 144 microresistivity measurements, with a vertical
and azimuthal resolution of 0.2” (5 mm).

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The microresistivity data can be stacked, averaged and processed to provide a high resistivity-like
curve to be used for evaluation in conjunction with the RTEX curves (Fig 2 and Fig 3).
The acoustic imager (UltrasonicXplorer) provides high-resolution borehole acoustic images in
difficult wellbore conditions, including oil-based muds and large boreholes. These images provide
valuable insight for making difficult drilling, completion, and production decisions at the wellsite.
Full 360° image coverage is achieved due to the use of a rotating acoustic transducer operating in
the pulse-echo mode. The transducer scans the entire circumference of the borehole wall providing
sharp images and boundary delineation. The instrument operates reliably in both water-based and
oil-based muds. The lower operating frequency (250 kHz) along with downhole DSP processing
allows logging in larger boreholes and in highly attenuating muds when compared to similar
devices of previous generation. Two images, an acoustic amplitude image and a travel-time image
are provided by the tool. The travel-time image can be calibrated to provide a high-resolution
borehole shape.
Fig 6: Combined acoustic and resistivity image. Both images are dynamically normalized
for detailed sedimentological analysis. The images analysis shows sedimentary structures

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more complex than the GR curve alone would suggest. At xx40 the conglomeratic level is
better defined by the acoustic image, where the different impedance contrast between
pebbles and matrix let to recognize the imbrication structure, while there is no strong
variation of microresistivity.
The resistivity image is function of the resistivity of invaded zone, so it is dependent on the
composition and saturation of fluids present in the pores, being responsive to the residual gas
presence and the formation factor. The acoustic image, based on the measured amplitude of the
reflected echo from the wall of the hole, is function of the contrast of acoustic impedance between
mud and formation. For this reasons both images are petrophysically complementary.
Fig 6 provides a valuable example of integrated analysis of combined images.
STONELEY PERMEABILITY
Borehole Stoneley waves have been used routinely to estimate formation permeability. Fluid
contained in the pore space causes wave attenuation and velocity dispersion (Tang, Patterson
2004). Relationship of above mentioned parameters with permeability can be written as:
Attenuation
Dispersion pf
k
K





,
Where Kpf is the pore fluid incompressibility, η is pore fluid viscosity. Therefore Stoneley wave is
indicative of permeability index, not the absolute permeability. External measurements, such as
core, wireline formation testing, are required for the permeability calculation from Stoneley wave.
Furthermore, being a guided wave on the borehole-formation interface, Stoneley wave is also
affected by the tool presence. Tool replaces the large portion of the borehole fluid thus causing
later Stoneley arrivals than would be expected in the clean borehole. In the isotropic, non-
permeable formation, at zero frequency limit Stoneley slowness can be found as (Norris, 1990):
‫ܶܵܶܦ‬ଶ
=
ఘಳಹ
ఘಷಾ(ଵି௥)
‫ܵܶܦ‬ଶ
+ ቄ
ଵା௥
ଵି௥
‫ܨܤܶܦ‬ଶ
+
௥ఘಳಹ
(ଵି௥)ெ೅
ቅ,
where r is the tool-to-borehole area ratio and the symbol ρ with subscripts BH and FM denotes
borehole and formation density, respectively. Presence of the permeability in the formation or
anisotropy will increase or decrease respectively Stoneley wave slowness relatively to isotropic,
non-permeable case:
ቆ
‫ܶܵܶܦ‬௣௘௥௠
ଶ
‫ܶܵܶܦ‬்ூ
ଶ
ቇ =
ఘಳಹ
ఘಷಾ(ଵି௥)
ቆ
‫ܵܶܦ‬௜௦௢
ଶ
+ ߜܵ௣௘௥௠
ଶ
‫ܵܶܦ‬்ூ
ଶ
− +ߜ்ܵூ
ଶ
ቇ + ቄ
ଵା௥
ଵି௥
‫ܨܤܶܦ‬ଶ
+
௥ఘಳಹ
(ଵି௥)ெ೅
ቅ,
where the slowness increment δSperm and decrement δSTI relate to the formation permeability and
anisotropy, respectively.
Considering the above equations, four borehole and tool parameters are required for permeability
index calculation, and for this purpose they assumed to be constant throughout the logging section.
Therefore they can be calibrated in the known non-permeable isotropic zone.
As highlighted by Tang (Tang et. al 2004), the exact accurate values are not required. Normally
calibration is focused on the matching of measured and calculated Stoneley wave slownesses for a
given set of tool diameter, borehole fluid density and slowness with tool modulus adjustment.
Graphical presentation of the calibration is shown on the Fig 7, where X and Y axes are squared
shear and Stoneley wave slownesses and non-permeable, isotropic zones fall on a linear function.
Data points above this line represent permeable zones, whereas cluster below the line belongs to
anisotropic shales.

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Fig 7: Cross plot of the squared DTS and DTST.
Quantitative calculation of TI-anisotropy and permeability index from Stoneley wave is described in
Tang (2003) and Tang et. Al (1998). In this method the anisotropy estimation is performed first,
using the dipole-measured shear slowness. It is taken into account further in the simultaneous
fitting of measured travel time delay and frequency shift relative to the modelled non-permeable
case for permeability index calculation. The misfit between the two measurements allows for the
upper and lower error bounds estimation.
Let us discuss the practical aspects of Stoneley permeability calculation. Foremost in the sand –
shale sequences it is challenging to find isotropic and non-permeable zone. In the clastic
sediments this kind of formation can be the silt, however from the acoustic data alone it is difficult
to define it. Furthermore, thin bedded sand – shale laminations can be characterized with the same
type of response as silty formations. As indicative on the Fig 7 the permeability and TI-anisotropy
have effects that may compensate each other in thin bedded sequences, so this plot should also
be used with a special care for tool modulus calibration. In order to properly define the anisotropic,
non-permeable zone for Stoneley permeability calibration we use the image data. The relevant
zone is selected in homogeneous interval with the moderate GR values, likely to represent silty
formation (Fig 8, interval 1). In order to validate this selection, we compare corresponding TI
anisotropy with the clay bound water from NMR (Fig 8, Track 6). The approximate profile
agreements should be observed between the two curves, as they are both shale indicators.
In order to quantitatively estimate permeability from Stoneley wave, a calibration to another
measurement is required. Although core information is preferred, it was not available for our study
and NMR Timur - Coates permeability was used instead. The latter is giving absolute permeability
values, however an attention should be paid according to the model limitation. The porosity used in
the NMR permeability calculation should be HI corrected, as well as BVI fluid volume should be
calculated after the fluid typing, due to gas signal contamination of fast relaxing components
because of it’s high diffusivity. Furthermore, Timur – Coates permeability was developed on the
dataset of clean quartz sandstones, so relevant points should be selected for reliable Stoneley
permeability index calibration. Also, in thinly laminated intervals NMR based calculations will be
limited for reservoir identification and evaluation due the low vertical resolution limitation (Fig 8
intervals 2 and 3).

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Fig 8: Stoneley wave permeability analysis. Track 1 – GR, caliper and effective NMR
porosity; Track 2 – Stoneley wave permeability with computed upper and lower bounds of
the error, Timur – Coates permeability, True resistivity from 1D inversion, electrical image
mean value; Track 3 – Stoneley waveform, where reflections related artifacts have been

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removed; Track 5 – Measured and synthetic Stoneley slownesses; Track 6 – Measured
(vertical) and synthetic (horizontal) shear wave slownesses, computed TI-anisotropy, clay
bound water volume from NMR; Track 7 – wireline electrical image. Interval 1 was used for
the tool modulus calibration as assumed isotropic, non-permeable zone. Intervals 2 and 3
are the examples of thin bedded formations, where Stoneley permeability has better
sensitivity than NMR based permeability. In the NMR 2 only upper bound permeability is
indicative of reservoir response.
CONCLUSIONS
We have demonstrated an integrated approach required for comprehensive gas-bearing reservoir
evaluation in the Levantine basin. The optimized logging suite should comprise multi-laterolog
device, conventional triple combo, NMR, full monopole and dipole waves for acoustic and
combination of wireline electrical and acoustic images.
In this suite NMR 2D fluid typing will contribute to accurate porosity calculations as well as
complement and confirm other evaluations, namely shale index and permeability. Stoneley wave is
thought to be the optimal permeability estimator, inferring practical usage of the error bounds of the
calculation and calibration to the Timur – Coates permeability in the reasonably selected intervals.
The valuable response of Stoneley wave permeability in the thin beds is also highlighted.
The electrical image is of particular importance allowing for the thin bedded reservoir identification
and important selection of the intervals for referencing other measurements interpretation and
explanation of their character.
The combination of microresistivity and acoustic images let to improve the sedimentological
understanding of these complex reservoirs architecture.
ACKNOWLEDGEMENTS
The authors thank the management of ATP Oil & Gas and its partners for the release of their data
for publication. They also wish to thank the management of both ATP Oil & Gas and Baker
Hughes for permission to publish this work.
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Processing For A New Array Laterolog Tool”, SPWLA 50th Annual Logging Symposium,
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