challenges in the reservoir characterization of a
TRANSCRIPT
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SPWLA-India Symposium, November 19-20, 2009
1
Challenges In The Reservoir Characterization Of A Laminated Sand
Shale Sequence
Anil Kumar Tyagi1, Rupdip Guha
1, Deepak Voleti
1 & Kamlesh Saxena
1
ABSTRACT
In a deep-water channel over bank system, there lie
a lot of uncertainties, due to presence of thin beds,
primarily, sand, silt and shale or their combination
in term of their petrophysical properties and lateral
extant. A lack of adequate reservoir characterization
can cause significant amounts of oil and gas to
remain un-recovered or to be recovered
inadequately. Petrophysical parameters play an
important role in the development of a filed. The
lateral and vertical variations in the petrophysical
properties lead to different scenarios of the field
development.
The conventional logging tools, with low vertical
resolution, are not able to characterize the thin beds.
This implies that log values do not represent the true
bed-or layer properties, but rather an average of
multiple beds. Direct interpretation of the log
readings will therefore result in a significant
underestimation of reservoir quality and potential.
Similarly the productivity of formation is also
strongly dependent on the distribution of the shale
within the sand. A certain amount of dispersed (pore
filling) shale has a far more detrimental effect on the
permeability of the sand than the same amount of
shale concentrated into shale laminae between clean
sand. Therefore, it becomes important to identify the
type and distribution of the shale to estimate the
potential of the reservoir.
The current study integrated the core, Image and log
data. The contribution of the thin sand laminae to the
average log response underestimated the porosity (Ф)
and hydrocarbon saturation (Sh) The core porosities
(near total porosity) were much greater then the
average log-derived effective porosities. Therefore it
became important to compare the similar porosity
from both the data sets. Similarly, capillary pressure
curves obtained on plugs from the sand laminae
indicated greater hydrocarbon saturations than the
average log-derived values. All this may lead to
undue rejection of either the core or the log data set
as being “unreliable”. The special logs like the
resistivity anisotropy proved quite useful as the
vertical and horizontal resistivity across these beds
led to measurable electrical anisotropy. The
resistivity measured perpendicular to the bedding is
significantly higher than resistivity measured parallel
to the bedding. The situation occurs due to high
resistivity sand layers interspersed within low
resistivity shale layers. The true sand porosity and
hydrocarbon saturation were calculated using the
laminated sand shale sequence and calibrated with
core data. The study led to the realistic petrophysical
estimation of the sand shale laminae.
Keywords: thin beds, anisotropy, image logs
INTRODUCTION
In Petrophysical sense, “thin beds” are thinner than
the vertical resolution of the logging devices. This means
that each logging tool has its own definition of thin beds
starting from 2 – 4 ft in sonic and deep resistivity to 0.5in
in micro-imagers. Hence, in thin bedded reservoirs, the
conventional logging tools give the cumulative response
of the thin shale and sand laminae (Fig.1). One of the
common methods to derive water saturation is from
resistivity. The non-linear response of the resistivity to the
volume and distribution of shale imparts a strong effect
on the measured average resistivity of the formation. So,
the conventional interpretation methods
1Reliance Industries Limited, Petroleum (E&P)
lead to significant underestimation of results.
To overcome this problem, first we have to understand
why the conventional logging tools are not able to
identify and quantify the laminated shaly sands; i.e,
1. Resolution of the tool is less than the thin
beds.
2. Conventional Resistivity is dominated by
high
conductivity shale layers.
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The main problem in reservoir characterization of
laminated shaly sand sequences is the tool resolution,
which is less then the resolution of thin bed; due to which
tool averages out the physical properties of the formation
so we are not able to get true layer properties in thin bed
sequence.
Fig 1: Conventional and Image log data showing the
presence of thin beds.
One of the example (Fig 1) show how the thin beds
can be identified using image logs whereas the same were
not detected on the conventional Resistivity, Density and
Neutron logs. The increase in vertical Resistivity also
indicates the presence of thin hydrocarbon bearing sand
laminae .So to solve this problem world wide all major
petroleum companies are following two approaches.
1. Thin Bed Analysis Using Resistivity Bore Hole
Image Tools
2. Laminated Shaly Sand Analysis using resistivity
anisotropy tool.
To address these short comings, number of interpretation
techniques has been suggested for nearly a decade to
estimate the porosity, net thickness, net to gross and
irreducible water saturation. Many people would prefer to
use the image logs as identifier of thin beds, quantitatively
maximize the resistivity measured from the borehole
heterogeneity while others prefer to use the resistivity
anisotropy technique or both. In this paper, the focus is to
understand the challenge put forward by the two
techniques, their advantages and pitfalls of using image
logs and resistivity anisotropy techniques that form the
basis of thin bed reservoir characterization.
THIN BED ANALYSIS USING RESISTIVITY BORE
HOLE IMAGE TOOLS
This is one of the methods in reservoir characterization of
a laminated shaly sand sequences. This technique is used
to enhance standard log resolution with the help of high
resolution shallow resistivity log recorded by bore hole
imager. This technique enhances the normal log
resolution and tries to bring true layer property.
WORKFLOW
1. Input resampled depth matched SRES, VSH, RT,
GR, RHOB, and NPHI logs.
2. Identify the bed boundaries from
SRES log.
3. Classification and Identification of different litho
facies.
4. Creating filter for each facies.
5. Application of filters to each logs and generate
square logs.
6. Optimization of iteration
7. Output sharpened, RT, GR, RHOB, and NPHI
logs.
8. Volumetric computation of shale volume,
porosity and saturation.
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Fig 2: Thin bed analysis using Image log.
BED BOUNDARY IDENTIFICATION Bed boundaries are identified from inflection points in
SRES data. It can be done either by software or manually.
Software defines the bed boundaries based on maximum
slope change (second derivative method) in SRES log.
CLASSIFICATION AND IDENTIFICATION OF
DIFFERENT LITHO FACIES
Lithofacies can be classified into three main litho facies:
sand, silt, shale and two auxiliary facies wet and tight.
Lithofacies can be identified using normal logs and
volume of shale. Higher resistivity indicates sands,
moderate resistivity indicates silt and low resistivity
indicates shale. Tight streak and hydrocarbon bearing
sands can be differentiated from density-neutron and
other logs. OBM invaded wet sand and silt can be
differentiated by the deep resistivity curve. To
differentiate low invaded water wet sand and shale both
of which have low resistivity volume of shale curve can
be used. Based on the threshold values of volume of shale
and standard input logs, one can define a litho-facies
model of sand, silt and shale. Further auxiliary litho-facies
ca be defined as wet and tight based on deep resistivity
and bulk density.
CREATING AND APPLICATION OF FILTER FOR
EACH FACIES
For initial set of vertical filter, the data range (minimum,
maximum) and average value of physical parameter for
each log for each facies. If there is a large range in
physical property then the whole data set can be divided
into a number of zones and then the same process is to be
repeated. Once the filter is created, it can be convolved
with standard logs to generate the square (blocked) logs.
OPTIMIZATION OF ITERATION After generation of initial set of square logs optimizer
would check whether the squared logs are matching with
the standard log or not, if not then optimizer iteratively
changes the average value for each log for each facies to
get a best match.
Using the sharpened optimized data, processing is run
using the core derived parameters. Clay volume is
recomputed using the sharpened logs, which has gone as
an input in the processing.
FIELD EXAMPLE
The example shown below is from centimeter scale
alternation of sand – shale laminae charged with
hydrocarbon forming a classical thin bed reservoir. Well
was drilled vertically with oil base mud. Conventional
induction resistivity log was recorded along with density-
neutron in hi-resolution mode. Image log was also
recorded. The squared logs are generated from the
standard logs by the work flow shown in the figure 2, as
seen; a good match between squared log and standard
logs (Fig. 3a, track1, 5 & 6) is obtained. The red curve in
track5 is the sharpened resistivity and the blue and red
curves in track 6 are neutron and density with the yellow
shading represents the cross over. These curves were used
in petrophysical model to generate volumetric of clay,
clay bound water, quartz, free water and gas (track 9,
Fig3a).The same volumetric computation is done by
normal logs (Fig3b), where we can clearly observe the
difference. The gas saturation computed by sharpened log
is around 0.75-0.8 whereas, it is around 0-0.05 when we
compute it with the standard resolution logs.
Fig 3: Volumetric comparison of sharpened and normal
logs.
PIT FALLS OF THIN BED ANALYSIS USING
RESISTIVITY BORE HOLE IMAGE TOOLS
1. The computation is based on modeled curves, in
which the facies definition is completely depends
on interpreter.
2. Hard streaks are also picked as hydrocarbon
bearing sand zone.
3. Zones with sand lamina which are thinner less
then 1” are over looked hence resource is
underestimated (Fig4)
4. Zones with thin shale layers are also over looked
hence over estimate the net (Fig4)
5. The sampling interval of the final output
becomes 0.00254. This is extremely fine. It
cannot be used in geo-cellular model building for
property population as the number of cells in the
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model goes to 100‟s of million. The essence of
the technique is lost in up-scaling for realistic
and practically workable geo-cellular model.
Fig 4: Underestimation and Overestimation of net pay by
OBMI
RESISTIVITY ANISOTROPY METHOD
There are some sand beds which are still thinner than the
resolution of the image logs; it led to the development of
3D Resistivity anisotropy tools. It can measure resistivity
both parallel and perpendicular to the direction of sand
and shale layers (Fig 5).
Fig 5: Bimodal resistivity sand shale model
Rh is mostly affected by the presence of conductive shale
layers as it sees the resistors in parallel. Hence decreases
the resistivity. The vertical resistivity measures the
resistivity of sand and shale in series. Using the Rv and
Rh, we can solve the equations for the estimation of
Rsand, similarly Rshale and Rsand components in vertical
and horizontal direction can be computed.
1/Rh = (Vsh / R shale) + (Vsand/Rsand) (1)
Rv = (Vshale*Rvshale) + (Vsand*Rsand) (2)
Vshale+ Vsand =1 (3)
The above three equations are based on the assumptions
that sands are isotropic where as shales show anisotropy
(Transversely Isotropic). Rv shale and Rh shale are the
critical parameters for computation of Rsand which are
taken from the clean shale zone. The generalized work
flow of laminated shaly sand is shown in Fig 6.
Fig 6: Generalized flowchart of laminated shaly sands
Porosity, Volume of shale and water saturation is the
main parameters needed for resource estimation of sand
layers. All are interdependent on each other. Volume of
shale is the most critical parameter which controls rest of
the two. Therefore, it needs to be calculate using more
than one method and also needs to be validated using the
external data like core. Once the Vsh and Phit are
calculated, Thomas Stieber method can be used to find
shale distribution.
According to this model, there are three types of shale
distributions (Fig7).
1. Laminated- layer of shale within the sand.
2. Dispersed shale on sand grains or pore
filling.
3. Structural sand sized shale particles in load
bearing position with in the rock.
These shale distributions can severely effects permeability
and thus productivity (injectivity). For e.g., the
permeability of clean sand having 33% porosity will be
reduced to zero, if its pore is filled with shale ( Vshale =
33%). But, if same amount of shale is present in the
laminated form, two-thirds of its permeability is still
retained in the rock.
Fine laminations,of sand
which are not captured
in image log. leads to
underestimation of sand
Stack of shale
laminations, appearing
as a thick sand bed on
image log.leads to
over estimation of
sand
Fine laminations,of sand
which are not captured
in image log. leads to
underestimation of sand
Stack of shale
laminations, appearing
as a thick sand bed on
image log.leads to
over estimation of
sand
Fine laminations,of sand
which are not captured
in image log. leads to
underestimation of sand
Stack of shale
laminations, appearing
as a thick sand bed on
image log.leads to
over estimation of
sand
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Fig 7: Thomas Stieber shale distribution model
The shale distribution and porosity can be computed form
Thomas-Stieber cross-plot, in which Volume of shale is
plotted on X-axis and total porosity on Y-axis. Based on
the position of data points in this cross plot, laminar (Vl),
dispersed (Vd), structural (Vs) shale volumes and porosity
of sand laminae can be calculated using following
equations.
1. Laminated shale only Lsh VV
1maxmax TshLT V (4)
2. Dispersed shale only Dsh VV
TshDT V 1max (5)
3. Structural shale only Ssh VV
max T + TshSV (6)
4. Material balance for shale
SDLsh VVVV (7)
Depending on the local geological set-up it was assumed
that amount of structural shale is too small to reduce the
variable and simplify the shale distribution.
If the amount of dispersed shale is very less, then we can
directly use Archie‟s equation to calculate the
hydrocarbon saturation, else, we have to use other shaly
sand equations like Waxman-smith. In the current study
we have used Archie‟s equation since the amount of
dispersed shale is negligible.
FIELD EXAMPLE
The example shown in the Fig.9 consists of 2 major sands
first sand is highly laminated and second sands is thick.
The green and blue curves in track3 are vertical and
horizontal resistivities respectively. We can clearly see
the increase in vertical resistivity in sand1, indicates the
presence of thin laminated sand shale sequence. The blue
and red curves in track 4 are neutron and density
respectively and yellow shading between these curves
indicate crossovers. Laminated shaly sand analysis is
carried and output curves are plotted on track5, 6, 7, and 8
Core total porosity and log total porosity are plotted on
track 5 showing good agreement. The saturation obtained
from LSSA is plotted in track 6 shows good match with
the with the capillary pressure curves obtained on plugs
from the sand laminae .The average water saturation of
sand1 in both the cases shows around 10-20%.In sand 2
the same comes to be around 5-10%.The volumetric
distribution of sand shale is shown in the last track (track
8).Figure 8a &8b shows the distribution of shale in two
different sands shown in Fig 9.
Fig 8: Thomas Stieber cross plots of zone 1 and 2
showing the shale distribution
Fi Fig 8a Thomas stiber cross plot sand
1 Fig 8b Thomas stiber cross plot sand 2
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capilary pressure plot
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100sw
pc
capilary pressure plot
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
sw
pc
Fig 9c Capillary pressure curves of
thick sand.
Fig 9b Capillary pressure curves of thin
laminated shaly sand
Fig 9a volumetric presentation of lssa processed data.
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capilary pressure plot
0
200
400
600
800
1000
1200
1400
1600
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2000
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100sw
pc
capilary pressure plot
0
200
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600
800
1000
1200
1400
1600
1800
2000
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
sw
pc
Fig 9c Capillary pressure curves of
thick sand.
Fig 9b Capillary pressure curves of thin
laminated shaly sand
Fig 9a volumetric presentation of lssa processed data.
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Fig 9: Calibration of volumetric computed from LSSA
with core data.
PIT FALLS OF LAMINATED SHALY SAND
ANALYSIS
It is observed that there is a significant increase in
hydrocarbon saturation using the LSSA technique.
However, it may be remembered that it may overestimate
the saturation in the following circumstances.
1. If there is a calcareous sand streak or any other
electrically anisotropic layer (like calcite)
present then it will enhance the Rv. Hence will
lead to overestimation of Rsand. This higher
Rsand will give higher saturation.
2. The distribution is bimodal. It accounts the
presence of calcite as sand calcite cement in
sand will appear as pore fill as shown in Fig 10.
3. Silt is very common in clastic environment.
Presence of any third facies like silt or calcite is
not taken care by this method. Considering the
effect of third facies in Rv and Rh makes the
computation more difficult.
4. Sand is considered as isotropic in this method.
Laminations (planar and cross- bed) are very
common. Internal anisotropy is not considered
which leads to over-estimation of hydrocarbon
volume.
5. Higher shale anisotropy may also boost up the
Rv in the shaly sand reservoir and hence may
push down the contact by a few meters if
present near the contact as shown in Fig 11.
6. The output logs are standard resolution with
sampling interval of 0.154m. Using them in
construction of geo-cellular model is easy and
practically workable.
Fig 10: Limitation in bimodal distribution; calcite shown
as sand and calcite cement as pore fill.
Fig 11: Resistivity anisotropy effect on gas water contact
COMPARISON
An attempt was made to compare both the techniques in
one of the wells under study. The results are presented in
Fig 12. Improvement in hydrocarbon saturation was found
in LSSA when compared with the Sharp Elan process.
Figure 12 shows the comparison of the volumetrics
compared from Sharp Elan and LSSA. There is a
difference in the saturation computed in both the cases.
LSSA enhances the saturation. Fig12 consists of two
Fig12 (a) and 12(b). 12(a) shows the volumetrics of Sharp
Elan. 12(b) shows the volumetrics of LSSA. Sharp Elan
shows saturation of 75-80% and LSSA shows saturation
of 90-95%. The LSSA results matched nearly to the core
derived irreducible water saturation from capillary based
method.
Actual gas water
contact
Gas water contact
from LSSA
Actual gas water
contact
Gas water contact
from LSSA
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Fig 12: Comparison of volumetric computed from SHARP
ELAN and LSSA. LSSA shows better gas saturation than
SHARP ELAN.
CONCLUSION
Image logs forms an integral part for the characterization
of thin bed reservoirs. Within the tool resolution
limitations, it can be effectively used to constrain the net
thickness. However, using it as processing techniques like
SHARP ELAN pose lot of practical difficulties in terms
of net, porosity and water saturation. Laminated shaly
sand analysis using anisotropy approach is quite simple,
robust and forms workable solution. It should always be
constrained with image log facies characterization to
obtain the maximum benefits. Laminated shaly sand
analysis provides better control on the realistic estimation
of resource as well.
ACKNOWLEDGEMENT
The authors would like to thank the management of
Reliance Industries for providing the rich data set and
permission to publish the work. Special thanks for the
fruitful discussions with the technical experts from
Schlumberger and Baker Atlas during the course of the
study.
REFERENCES
Archie, G.E., 1942, “The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics”, Transactions, AIME
Vol. 31, pp. 350-366.
Anil Tyagi, Rupdip Guha, 2007, New Delhi, Formation Dips Computation Using Tri-Axial Induction Tool: An Alternate To Image
Logs, Petrotech.
Bastia et. al., 2005, Reservoir characterization and modeling of thin beds in a deep water gas field, offshore India, Petro-Tech, paper 223
Bastia, R., 2004, Depositional Model and Reservoir Architecture of
Tertiary Deep Water Sedimentation, Krishna-Godavari Offshore Basin, India, Journal, GSI, vol. 64, p.11-20
Gossenberg, P., Galli, G., Andreani, M., Klopf, W., 1996 "A New
Petrophysical Interpretation Model for Clastic Rocks based on NMR, Epithermal Neutron and Electromagnetic Logs" SPWLA 37th Annual
Logging Symposium, Paper M.
K. Saxena, Anil Tyagi, T. Klimentos, C. Morriss, A.Mathew (Schlumberger), 2006, Evaluating Deepwater Thin-Bedded
Reservoirs with the Rt Scanner, Petromin, Kaula Lumpur.
Kennedy, D.W. and Herrick, D.C., 2004, “Conductivity Anisotropy in Shale-Free Sandstone,” Petrophysics, Vol. 45, No. 1, pp. 38-58.
Kriegshauser, B., Fanini, O., Forgang, S., Itskovich, G.,
Rabinovich, M., Tabarovsky, L., Yu, L.,Epov, M., 2000. “A
New Multicomponent Induction Logging Tool To Resolve
Anisotropic Formation”, 41st Annual Logging Symposium
Transactions, Paper D. Mezzatesta, A.G., Rodriguez, E.F., Frost, E., Mollison, R. 2003. “A
Comprehensive Petrophysical Model For Laminated Shaly-Sand Reservoirs”, SPE Annual Technical Conference, SPE 81075.
Mollison, R.A., Schön, J.H., Fanini, O.N., Kriegshäuser, Meyer, W.H.,
and Gupta, P.K., 1999, “A Model For Hydrocarbon Saturation Determination From An Orthogonal Tensor Relationship In Thinly
Laminated Anisotropic Reservoirs,” SPWLA 40th Annual Logging
Symposium, Paper OO. Mollison, R.A., Ragland, T.V., Schön, J.H., Fanini, O.N., and van Popta,
J., 2000, “Reconciliation Of Waxman-Smits And Juhasz „Normalized
Qv‟ Models From A Tensor Petrophysical Model Approach Using Field Data,” SPWLA 41st Annual Logging Symposium, Paper YY.
Schön, J.H., Mollison, R.A., and Georgi, D.T., 1999, “Macroscopic
Electrical Anisotropy of Laminated Reservoirs: A Tensor Resistivity Saturation Model,” SPE Annual Technical Conference, SPE Paper
56509.
Thomas, E.C., and Stieber, S.J., 1975 “The Distribution of Shale in Sandstones and Its Effect upon Porosity,” SPWLA 16th Annual
Logging Symposium Transactions, Paper T.
Thomas, E.C., and Haley, R.A., 1977 “Log Derived Shale Distribution In Sandstone And Its Effects Upon Porosity, Water Saturation And
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CURVE MNEMONICS NAMES USED IN TEXT
HCAL Caliper
GR Gamma ray
RT True resistivity
SRES Synthetic resistivity
RV Vertical resistivity
RH Horizontal resistivity
NPHI Neutron porosity
TNPH Thermal Neutron porosity
RHOB Density
PHIT Total porosity
PHIE Effective porosity
PHIT_SS Sand normalized Total porosity
PHIE_SS Sand normalized Effective
porosity
SWT Total water saturation
SWE Effective water saturation
SWT_SS Sand normalized total water
saturation
BV_WF Bulk Volume Free Water
Fig 12a volumetric computed from SHARP ELAN
Fig 12b Volumetric computed from LSSA
Fig 12a volumetric computed from SHARP ELAN
Fig 12b Volumetric computed from LSSA
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POREFIL Volume of shale filled in pore
VOL_MTX Volume of matrix
VOL_DST Volume of dry structural shale
VOL_DDIS Volume of dry dispersed shale
VOL_DLAM Volume of dry laminar shale
CBW Volume of clay bound water
PIGN Effective porosity in SHARP
ELAN
SUWI Effective water saturation in
SHARP ELAN
VUWA Volume of water in uninvaded zone
VUGA Volume of gas in uninvaded
zone
VUQA Volume of quartz
VXBW Volume of water in flushed
zone
VMON Volume of montmorillonite
ABOUT THE AUTHORS
Anil Kumar Tyagi is working as Asstt. Vice
president with Reliance Industries Limited. He has
got an experience of 27 years as Petrophysicist. He
has expertise in carbonate as well as clastic reservoir.
He holds masters degree from Indian Institute of
Technology, Roorkee, India. He has been responsible
bringing in the concept of resistivity anisotropy to
Indian context. The concept has helped in unlocking
the potential of thin bedded reservoir form the deep
water basins of India. He is member of SPWLA, SPE
and SPG
Rupdip Guha is a senior petrophysicist with
Reliance Industries Limited, India. Since joining
Reliance, he has worked in onland and offshore
basins of India, Oman and Yemen. His expertise
includes thin bed reservoirs, shaly sand, gas hydrate
and deep water field development. He holds a
master‟s degree in Applied Geology from Jadavpur
University, Kolkata and a Master of Technology
degree from Indian Institute of Technology in
Bombay, India, with a specialization in the Geo-
Exploration. Currently he is responsible for
petrophysical data acquisition, interpretation for
KGD6, implementation of rock physics techniques
and field development. His current interests include
nuclear magnetic resonance and reservoir
characterization. He is a member of SPWLA and
SEG.
Deepak Kumar currently working as a
petrophysicist in Reliance Industries Limited (E&P
Business) holds M.Sc (tech) degree from Andhra
University, AP. He worked extensively in silliclastic
environment mainly in laminated shaly sands. He is
associated with the operations and interpretation of
Krishna Godavari basin .He is expertise in
deterministic and probabilistic methods of formation
evaluation and presently working on core log
integration and capillary pressure based saturation
analysis. He is a member of SPWLA Indian chapter.
Kamlesh Saxena Holds M.Tech degree in Applied
Geology from University of Saugar, Sagar M.P.
India. Joined Reliance Industries Ltd. (Petroleum
Business – E&P) in July 2001 and is currently
General Manager. Responsibilities include, planning
and execution of the LWD-MWD & Wireline
logging operations & Interpretation. Credited with
applying unconventional methods in petrophysical
analysis; especially thin beds, Image Logs
interpretation and optimizing logging combinations.
Prior to joining RIL, he has worked in different
technical and management positions for
Schlumberger in various countries from south-east
Asia to Europe and Middle East for 18 years.
Member of SPWLA & SPE and has presided on their
technical committees.
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