challenges in the reservoir characterization of a laminated sand shale sequence

2nd

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





Stack of shale



image log.leads to

over estimation of

sand





Stack of shale



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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A

N

D

1

S

A

N

D

2

capilary pressure plot

0

200

400

600

800

1000

1200

1400

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1800

2000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100sw

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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.

S

A

N

D

1

S

A

N

D

2


0

200

400

600

800

1000

1200

1400

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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

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0

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600

800

1000

1200

1400

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

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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

Permeability,” Canadian Well Logging Society 6th Annual Formation

Evaluation Symposium, Calgary. Waxman, M.H., and Smits, L.J.M., 1968, “Electrical Conductivities In

Oil Bearing Shaly Sands,” SPE Jour., Vol.8, No.2, pp. 107-122.

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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challenges in the reservoir characterization of a laminated sand shale sequence

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