1

LOW RESISTIVITY LOW CONTRAST PAY OF CLASTIC RESERVOIRS

WITH A STUDY CASE OF TERTIARY BASINS IN MALAYSIA

By: Yulini Arediningsih

I. Introduction

This paper presents an overview of how petrophysical analysis applied in low

resistivity low contrast pay (LRLCP) in clastic reservoirs. The paper also reviews a study

case of low resistivity low contrast pay in some Tertiary basins in Malaysia. First chapter

includes historical background, some theoretical concepts on low resistivity low contrast

pay. Second chapter presents geologic point of view on low resistivity low contrast

formations, concepts on shaly sand and the causes related to low resistivity low contrast

pay occurrence. Third chapter focuses on petrophysical analysis in evaluating typical pay

zones. The chapter also reviews problems in recognizing and evaluating low resistivity

pay zones by well logs. In this part, contribution of NMR logging tool is briefly

discussed. Fourth chapter mainly presents a LRLCP study case in Malaysian basins.

Low resistivity low contrast pay (LRLCP) is a global challenging phenomenon in

formation evaluation for over three decades, taking place in basins from the North Sea,

Europe, Middle East, West Africa and Alaska to Malaysia, Indonesia and Australia

(Boyd et al. 1995, Worthington, 2000). Problems of identifying low-resistivity pay in log

data have been recognized since first low resistivity low contrast formation discovered in

Texas and Louisiana Gulf Coast of the United States (Tixier et al. 1968). Big numbers of

documented records of low resistivity low contrast pay fields worldwide have been listed

based on their causes in Worthington (2000). Low resistivity low contrast pay may not be

2

identifiable through conventional log analysis. This may make it difficult to evaluate. Its

potentiality is often bypassed because of its over estimation on Sw values.

“Low resistivity” refers to its characteristic of low value in deep resistivity logs

ranging from 0.5 to 5 ohm-m. The formations with such characteristics may occur in

sandstone and carbonates (Saha, 2003, Riepe et al, 2008), but they are described often in

sandstones, that mostly associated with thinly bedded low-resistivity shaly sand

formations. The zones may have a combined resistivity only a few tenths of an ohm-m

higher than the adjacent shales. “Low contrast pay” is used as frequent concurrence with

low resistivity, indicating a lack of resistivity contrast between sands and adjacent shales

(Fanini et al., 2001; Boyd at al. 1995). Inadequate vertical resolution of conventional

resistivity data that are applied to determine properties of the individual beds, makes the

potential intervals are difficult to distinguish from adjacent shales. Its potentiality is

normally underestimated or even bypassed, resulted by inadequate vertical resolution of

conventional resistivity data to determine the properties of the individual beds. The log

analysis gives high saturation as given by lower resistivity than would be obtained from a

thick, hydrocarbon bearing sandstone.

The resistivity values noted earlier have evolved with time from initial range as

low as 1–3 ohm-m (Murphy and Owens, 1972) to less than 0.5 ohm-m (Boyd et al. 1995).

This signifies that uncertain numbers of low-resistivity pay reservoirs have been

discarded earlier over the years. Nowadays, there are no acceptable cut-off values given

to the resistivity of economical pay zones (Worthington, 2000).

3

II. Geologic Point of View on Low Resistivity Low Contrast Formations

2.1 Characteristics

Occurrence of high clay or shale within sand beds is considered as the major

cause of low-resistivity pay. Clay contribution to low-resistivity readings depends on the

type, volume and distribution of clay in the formation (Worthington, 1985). Other

geological causes of low resistivity low contrast pay include conductive minerals (such as

pyrite), low salinity or fresh formation waters, grain size or pore size effects, bioturbation

effects (considerable bioturbated fine silts and shale), internal micro porosity and

superficial micro porosity (Boyd et al, 1995; Worthington, 2000; Riepe et al, 2008). Saha

(2003) also identifies that low resistivity low contrast pay can be brought about by deep

invasion by conductive mud, presence of fractures and capillary bound water, and high

angle wells due to anisotropy effect.

2.2 Basics on Shaly sands

As pointed out earlier that occurrence of the high amount clay or shale within

sand beds, known as shaly sand, is considered as the major cause of low-resistivity pay.

Problems in analysing and interpreting shaly-sand log data have challenged log analysts

and petrophysicists since 1950. Numerous efforts have been made in developing more

than 30 shaly sand interpretation models in the last 60 years (Worthington, 1985).

Difficulties in interpretation become apparent whenever clastic formations have

appreciable content of clays. Their presence in the formation may add up the overall

conductivity. Their conductivity becomes as essential as the conductivity of the formation

4

water (Worthington and Johnson, 1991). In fact, this also makes the shaly sand analysis

becomes complicated because of a wide variety of clay minerals and their distribution

within the pore and rock structure. The analysis becomes more complex when conditions

of the shale content increases and the porosity and formation water salinity decreases.

That explains the absence of a unique universally accepted approach to shaly sand

analysis (Worthington, 1985). Key parameters in hydrocarbon potential evaluation are

porosity and water saturation. In a clay free formation comprising sand matrix, water,

and gas, water saturation and porosity can be estimated accurately based well log

data using the Archie equation. Archie equation, the most renowned water saturation

model, is empirically formulated, validated for sandstones that are free of clay minerals

and are (fully or partially) saturated with a high-salinity electrolyte (Archie, 1942). The

equation is expressed as :

Sw n

= Rw

φ m

.Rt

Where Sw = formation water saturation, fraction

Rw = resistivity of formation water, ohm-m

Rt = resistivity of formation rock, ohm-m

φ = porosity, fraction

n = saturation exponent

m = cementation exponent

The conditions for the Archie equation to relate resistivity solely to water

saturation no longer apply when clay is present significantly. The problem in analysing

shaly sand formation is complicated by the difficulty of accurately estimating the

shaliness from well log data. Slight changes, in the estimates of shaliness, can result in

large changes in the derived values of saturation. Potentiality of formation bearing

5

hydrocarbon is frequently underestimated, due to clay effect negligence, which gives

higher estimation in water saturation than the actual value. Therefore, when clay is

present, the Archie equation must be modified to generate appropriate shaly sand models

to compensate the effect of clay minerals on log response. Normally, the corrected

equation will give more accurate results when more log data are available (Worthington,

2005).

Based on their different concept, the shaly-sand models can be divided into two

main groups: fractional volume of shale (Vsh) group and Cation Exchange Capacity

(CEC) group. Simandoux model is commonly used in Vsh group while Waxman and

Smits and Dual Water models are in Cation Exchange Capacity (CEC) group. The main

pitfall of Vsh models is that they disregard all aspects related to clay mineralogy such as

distribution, textures and composition of different clay types. These parameters

essentially may give different shale effects for the same volume of shale fraction (Vsh).

To tackle this problem, CEC models were developed, which consider electrochemical

properties of clay mineral-electrolyte interfaces to produce more reliable models in shaly-

sand interpretation.

Terminology of “shale” and “clay” has been used synonymously in formation

evaluation by log analysts or petrophysicists. In fact, in geologic term, they are different.

Shale is a clastic sedimentary rock, composed of complex minerals. It is made up by

almost 60% of clay minerals and other constituents including minor amount clay to silt-

sized grains of quartz, feldspar and other minerals (Blatt, 1982). In contrast, clay usually

refers to a grain size with diameter less than 0.004 mm. It may also refer to

aluminosilicate minerals including illite, smectite, montmorillonite, chlorite, and

6

kaolinite. Shaly sand itself, in simple terms, is clay rich sand or sandstone. It also can be

defined as sandstone in which quartz is present as the primary mineral, but clay and other

associated minerals may be present in varying amounts, distributions, and particle sizes.

When clay minerals are present in sandstone, type, volume, and distribution of the clay

will affect the well log response to that sandstone (Worthington, 1985; Passey et al,

2006). Increased volume of clay decreases the effective reservoir capacity. Concurrently

the conductive clay may reduce the formation resistivity. It is a crucial task for the

petrophysicists to determine the effects of clay upon porosity, permeability and fluid

saturations.

The clay minerals contained in sandstones can be from detrital origin or

diagenetic origin (Almon, 1977). The former is mainly present as discrete clay-size

particles to sand-size aggregates, and usually incorporated into the sandstones at or

shortly after the time of deposition. The latter is naturally formed, mainly as clay cement

that develops after burial as product precipitation or recrystallization during diagenesis.

As diagenetic or authigenic clays, they may occur as any of three types of

growths, shown in Figure 2.1. These authigenic clays are formed; mainly as disperse

Figure 2.1. Formation of authigenic clays (Almon, 1979).

7

materials throughout the pore system of the sandstones from the formation water or are

the products of the interaction between formation water and the mineral components of

the rock, mainly within the sandstone pore system. Consequently, their occurrence can

indicate the pore water chemistry at the time of clay mineral formation.

Clay or shale in sandstones can also occur as laminar clay, structural clay and

disperse clay (Frost and Fertl, 1981) (Figure 2.2). Laminar shale can be present as detrital

origin, between clean sand layers. It tends to affect permeability and or porosity.

Structural shale usually replaces matrix or detrital grains or feldspar. This type may not

affect porosity or permeability. Dispersed shale is usually formed as authigenic or

diagenetic origin spread throughout the sand. Volume and type of clay mineral may

determine the degree of porosity and permeability reduction.

Figure 2.2 Distribution of clays in relation to porosity volume (Frost, and Fertl, 1981)

2.3 Geologic depositional environments

Favourable stratigraphic settings of low resistivity pay are usually related to

laminated or thinly bedded sand-shale sequences. The most common depositional

environments associated with the low resistivity pays are shown in Figure 2.3.

8

Figure 2.3 Model of the most common depositional environment of low resistivity low

contrast pays (After Darling and Sneider, 1993 cited in Boyd et al 1995).

A. Low stand basin floor

fan complexes

B. Deep water levee-

channel complexes and

over bank deposits

C. Transgressive marine

sands

D. Lower parts (toes) of

delta front deposits and

laminated silt-shales and

intervals in the upper

parts of alluvial and

distributary channels

9

In relation to deepwater environment, prospects of turbidite exploration are

geostatistically found to be worldwide at an undeveloped stage and provide a significant

part in the future projects of hydrocarbon exploration and production (Pettingill, 1998).

For that reason, in general, it can be assumed that a noteworthy proportion of the world’s

undiscovered hydrocarbon reserves is most likely associated with laminated, low-

resistivity, low contrast, shaly sand formations (Fanini et al, 2001). Kuecher and

Millington (2000) describe that turbidite sand deposits bearing low resistivity low

contrast pay extend over a wide range of depositional energy environments. Typical

thinly bedded, laminar sands and shales are commonly found in the sub-systems of

channel levee and over bank -levee environment and middle-to-distal fan complexes.

They significantly contribute overall net pay and oil-in-place determination of most

deepwater exploration plays as they are extremely prolific.

III. Petrophysical Analysis of Low Resistivity Low Contrast Pay

The challenge for interpreting low resistivity low contrast pay zones of thinly

bedded shale-sand sequence focuses on estimating shaliness, extracting the correct

resistivity measurement of formation and accurately deriving water saturation, Sw.

Shaliness (clay volume) is typically calculated using appropriate shaly sand models,

selected based on information of clay characteristics, types, compositions and

distribution, as discussed earlier. Improved vertical resolution of logging tools and data

processing techniques are essentially helpful in getting reliable resistivity data especially

in the thin beds.

10

Historically, in 1968 when Gulf Coast became a focus of frontier exploration in

low resistivity pay, their pay sands were not always noticeable on conventional resistivity

logs. Tixier et al. (1968) note that the pay sands commonly are high in porosity, clay

content but low Rw values. The finer-grain and silty sands are characterized by high

irreducible water saturations. The clean water sands have resistivities ranging from 0.2 to

1.0 ohm-meter; moreover shaliness increases this R value. Thus, identifying pay zones

with only a resistivity log is often difficult. However, the problem can be resolved by

resistivity logs combined with three porosity logs of density, sonic and neutron integrated

with SP and Gamma Ray curves, and sidewall samples. This implementation of this

integrated logs and core data is beneficial in the study of shaly sands.

Log evaluation in thin bedded sand-shale sequences is difficult because only bulk

density and resistivity that are directly measured. Other important reservoir properties

need to be deduced using those two earlier properties. Other reasons are incapability of

logging tools to measure beds that are too thin to be measured individually and

anisotropic petrophysical properties (Passey et al. 2006).

The petrophysical techniques for evaluating low resistivity low contrast pay can

be grouped into two, namely low resolution and high resolution techniques. Other

methods include Nuclear Magnetic Resonance (NMR) and multi component induction. In

the low-resolution techniques, properties of each individual thin bed are not necessarily

to be resolved, dissimilar to the high resolution techniques. NMR techniques are briefly

discussed in the next section. A summary of those techniques especially applied in shaly-

sand thin beds of low resistivity low contrast pay is given in Table 3.1, adapted from

reviews by Passey et al. (2006) and Hamada et al.(2001).

11

In some points of view, when performing log analysis of shaly sand reservoirs,

improper procedures sometimes result in overestimation of Sw (Riepe et al. 2008), as

follows :

• Improper correction of resistivity logging tools, including borehole, shoulder bed and

invasion effects, high dips or high well deviations, and thin bed effects (laminations,

anisotropy). These may lead to underestimate the Rt values.

• Incorrect value given to the resistivity of the formation water Rw,

• Incorrect saturation equation and parameters, such as relationships between Sw and

resistivity in Non Archie formations become more complex, as reflected by unknown

variables of cementation exponent (m), saturation exponent (n), Cation Exchange

Capacity (CEC).

Overall, the solution becomes more complex, when formation has more than one

of these effects. However, as soon as the cause of low resistivity low contrast pay is

recognized and well understood, integrated logging tools and/or interpretation techniques

can be applied to compute accurate Sw.

On the basis of particular reasons, related the occurrence of the low resistivity low

contrast pay, Saha (2003) provides quite straightforward solutions, summarised in Table

3.2 below.

12

Techniques Objectives Advantages Limitations

Low

resolution

Volumetric

Laminated

Sand analysis

using

conventional

well logs

To investigate the

effects of thin beds

on standard

resolution log data.

Suitable for bed with

thickness < 1 or 2 ft

No need to identify

thin boundaries

• Provide general output of

interval - average

solution

Depth alignment

logs not required

• Valid only in certain

limited assumptions on

the log response

• Confirmation of the bed

existence is needed.

High

Resolution

Log forward

modelling

To detect bed

boundaries using

high-resolution data

and try to unravel

true log values in

each thin bed.

Suitable for bed with

thickness > 1 or 2 ft

Able to show

detailed

distribution of thin

beds and pay zone

• Require high resolution

logs to identify the

boundaries if each thin

sand -shale beds.

Inversion • Uncertainty in solution

Other

special

techniques

Nuclear

magnetic

resonance

1)To help confirm the

presence of thin beds

2) Directly indicate

presence of pay

zone 3) To differentiate

between bound and

free water.

• Provide strong

evidence for

indicator of pay

zone even without

any high

resolution data.

• Can estimate

directly thickness

of the pay zone

• Distribution of the T2

can be influenced by

many difference factors

aside from pore size.

• Require many

consideration and other

knowledge to apply the

NMR

Multi

component

induction

To measure sensitive

perpendicular

component in

conductivity.

• Can reduce

uncertainty in the

low-resolution

evaluation of a

thinly bedded

reservoir.

• Ability to provide

influential evidence

for indicator of pay

zone.

• The multi component

induction logs are

sometimes unavailable

as not widely used.

• Accuracy on transverse

resistivity measurement

is unknown, and

environmental effects

are also uncertain

Table 3.1. Summary of low and high resolutions techniques (After Passey et al, 2006 and

Hamada et al., 2001)

13

Reasons Facts

Possible solutions

Invasion of

conductive

mud

Deep mud invasion, low reading

in Rt and computed Sw high 1) Run array laterolog or array induction log.

2) Run resistivity logging-while-drilling (LWD)

High clay

content

Common in shaly sand

formations

1) Run Gamma ray spectroscopy and Elemental

Capture Spectroscopy tools help estimate clay type

2) Combine with lab based clay mineralogical

analysis

Presence of

high capillary

bound water

Mainly related to grain

size.

Affect resistivity logs to read low

1) Run NMR tools and even combined with

resistivity LWD will greatly aid in this

interpretation.

Presence of

fractures

Mainly due to penetration of

conductive muds into open

fractures causing low reading in

Rt.

Common in carbonates

1) Run borehole imaging tools with LWD, can be in

water based and oil based mud.

Micro

porosity

Common in carbonate rock.

May reduce reducing the

resistivity.

Run NMR and or LWD

Presence of

conductive

minerals

Example pyrite, may conceal the

resistivity log reading.

Various, uncertain effect based on

its distribution.

1) Run photoelectric factor log

2) Run elemental spectroscopy log will help

effectively

High angle

wells

Makes resistivity logs become

apparent and tend to read low.

1) Implement an newly developed interpretation

method in induction type tools.

Laminated

formations

Averaging resistivity value in thin

bed.

Unable to resolve characteristics

of individual thin beds.

1) To run higher vertical resolution tools with

deeper depth of investigation, or both.

2) integrate with borehole imaging tools, with water

and oil based mud environments

Table 3.2 Solutions with regards some causes of low resistivity low contrast pay

(Adapted from Saha (2003).

Following is a generalized work flow given by Saha (2003) for solution

approach to low resistivity low contrast pay evaluation:

14

1. Carefully identify and define the pay zone, based on various data such as mud log and

shows, wireline formation pressure and sample tests, or other tests such as drill stem

or production tests.

2. Find out the cause. This is the most important stage in the work flow because it

determines selection of suitable solution or models to apply or develop to get reliable

results.

3. Make correction on the original high water saturation (Sw) to get lower a lower water

saturation, unless Sw is high because of high capillary bound water

4. Validate the results, preferably with core data.

3.1.1 Nuclear Magnetic Resonance Technique

Integrated log analysis of density, neutron and resistivity logs is proven to be

very effective in the evaluation of normal reservoirs. For low resistivity low contrast pay

zones, however, an accurate determination of the petrophysical parameters with the

conventional logs is very difficult and frequently failed. Nuclear magnetic resonance

(NMR) log has played an important role in providing advanced information on the

producibility of this typical reservoir. The technique provides a valuable measurement to

help determine when the presence of thin beds of sand-shale sequences is assumed in a

light oil bearing reservoir (Passey et al, 2006). NMR technique is applied to assist the

petrophysical evaluation especially to detect thin beds, determine fluid type, and establish

the hydrocarbon type and volume (Hamada et al. 2001).

15

The main limitation of NMR is related to its high cost and time consumption

during data collection. In the analysis of NMR data, several aspects of NMR technique

that are used include:

1) Fluid identification based on T1/T2 ratio (Figure;

2) The types of clay minerals can be determined based on the porosity value

difference between NMR derived porosity and total porosity;

3) NMR relaxation properties to identify fluids nature and rock properties.

NMR technique has significantly contributed in identifying the producibility of

pay zones in low resistivity formations. It helps to verify lithology independent porosity

and to differentiate between bound and free water. For the case of low contrast resistivity

reservoir in which small resistivity variation exists between water bearing formation and

oil bearing formation, interpretation on high contrast of NMR relaxation parameters has

enabled identification of the fluid nature of those formations as well as the oil column

thickness (Hamada et al., 2001).

Figure 3.1. Distribution of T2 showing small and large pores (Hamada et al., 2001)

16

IV. A Study Case of Low Resistivity Low Contrast Pay in Tertiary Basins in

Malaysia

This study case focuses on investigation of low resistivity low contrast zones in

clastic reservoir of Tertiary basins in Malaysia. The basins are PETRONAS operated

fields including Malay, Sarawak and Sabah basins. These basins, among the most

productive in South East Asia are moderately mature (Ghosh et al 2010) (Figure 4.1).

The hydrocarbon exploration and exploitation within the areas were extensively

commenced in 1882 when oil was discovered in Miri, Sarawak.

Malay Basin is known to be one of the deepest basins (12 km at the center) in this

part of SE Asia. The lithology bearing the low resistivity low contrast pay zone, mainly

comprises of a thinly laminated sand-shale sequence. The other basins discussed include

Sarawak (late Eocene to recent) and Sabah (mid-Miocene to recent). In general, reservoir

rocks in Sabah basin are similar to Malay Basin (Ghosh et al, 2010).

Low resistivity low contrast pay zones in these three basins specifically have

resistivity values ranging from 2-4 Ohm-m. These values are similar to the resistivities of

the nearby shale beds. The values are within the resistivity value range (1-2Ohm-m) of

the fresh formation water contained in the zones (Riepe et al, 2008). The pay zones were

not noticeable, so they were bypassed, due to insufficient conventional logging tools and

formation evaluation techniques.

17

Figure 4.1. Location of Malay, Sabah and Sarawak basins (After Ghosh et al, 2010)

4.1 Integrated Modern Petrophysical Techniques

The revisited study by Riepe et al (2008) to investigate the low resistivity low

contrast pay zones in these basins, aims at determining Sw cut-off. It is because of the

zones significantly contain a high volume of “capillary bound” water. Geological facts

causing the existence of low resistivity low contrast pay zones in the basins include in

grain size, high amount of bioturbated fine silts and shales and relatively high clay

content with high Cation Exchange Capacity. Recognition of the causes of the low

resistivity low contrast pay zone beneficially provides a guideline on selection of

advanced petrophysical techniques to assess the zones.

Sabah

Basin

Sarawak Basin

Malay Basin

18

The study is performed based on petrophysical analysis of advanced log data

including Nuclear Magnetic Resonance (NMR) and Borehole Imaging. The log data are

incorporated with Special Core Analysis (SCAL) data which consist of electrical,

hydraulic and NMR properties. The study results in enhanced concepts and work flows

that are established for the identification of cut-off criteria for “net pay”, log evaluation

parameters and possible adjustment in saturation equations. The results provide

guidelines for further evaluation in other PETRONAS basins bearing low resistivity low

contrast pay zones.

4.2 Work flow

The study comprises three stages covering:

1) Well selection: with a focus on wells representing LRLC zones. The wells should have

sufficient amount of log and core data. If available, image logs were used to identify

horizons with thinly bedded sand/shale sequences.

2) Special Core Analysis: to assess three various independent measurements i.e. NMR

T2-Spectra at different Sw; capillary type; and NMR properties. The schematic process

of this stage is portrayed in Figure 4.2.

3) Well log analysis: resistivity and NMR logs are set up as focus of the analysis to get

and compare saturation profiles. Some corrections are carried out in resistivity data to

produce realistic profiles of Rt for the Sw evaluation from different saturation models and

equations. In detailed, the steps of the analysis are shown in Figure 4.3.

19

Figure 4.2. Flow chart showing the process of evaluation of Swirr performed in the

stage of Special Core Analysis (Riepe et al., 2008)

Figure 4.3. Flow chart showing the process of evaluation of Swirr performed in the

stage of Well log Analysis (Riepe et al., 2008).

20

To simplify, the Sw cut-off is essentially set based on its irreducible water

saturation (Swirr) so that the reservoir will be productive to verify permeability

predictions. The permeability is analyzed based on capillary pressure and relative

permeability data. The study applies NMR technology to obtain T2 spectra and correlate

it with the Swirr data. The correlation is subsequently applied to NMR log derived

continuous Swirr and permeability profiles that have been calibrated.

V. Conclusions

Low resistivity low contrast pay (LRLCP) is a challenging universal phenomenon

faced in evaluating hydrocarbon bearing formations, for over three decades. Difficulty in

identifying low-resistivity pay in log analysis has been recognized since the first

discovery of major low resistivity low contrast pay in USA. Insufficient vertical

resolution of conventional resistivity data and unsuitable techniques in log analysis cause

bypassing the hydrocarbon potentiality due to overestimation on Sw values.

Low resistivity low contrast pay is commonly found in formations associated with

thinly bedded sand-shale sequences, normally characterised by low value in deep

resistivity logs ranging from 0.5 to 5 ohm-m. The occurrence of low resistivity low

contrast pay can be caused by a range of different factors including formation waters (low

or fresh); conductive minerals; grain or pore size effects; bioturbation effects, invasion of

conductive muds, presence of fractures and capillary bound water, and high angle wells

due to anisotropy effect.

When evaluating the shale-sand sequence in the low resistivity low contrast pay,

appreciation on detailed information about clay minerals, such as type, volume, and

21

distribution is essential. It is because those clay parameters will greatly affect the log

response. By understanding those clay parameters, interpretation on log response will

provide better and reliable solution. are present in the sequence tone, type, volume, and

distribution of the clay will affect the well log response to that sandstone.

Various techniques can be applied to resolve problems in the low resistivity low

contrast pay, comprising low and high resolution techniques. Above all, NMR technique

appears to be the powerful one, mainly because its ability to identify fluids nature

whether free and clay bound water using T1/T2 ratio as the major cause of low resistivity

low contrast pay. The main workflow of solution approach that can effectively help cope

with low resistivity low contrast pay is identification and definition of the pay zone,

identification the causes of the pay zone which determine proper techniques to apply and

validation the results with core data.

References

Almon, W.R., 1977, Sandstone diagenesis is stimulation design factor: Oil and Gas

Journal, 13, 56-59.

Almon, W.R., 1979, A Geologic Appreciation Of Shaly Sands : SPWLA 20th

Annual

Logging Symposium.

Archie, G.E., 1942, The electrical resistivity log as an aid in determining some reservoir

characteristics: Transactions of the American Institute of Mining and

Metallurgical Engineers, 146, 54-62.

Blatt, H., 1992, Sedimentary Petrology, W H Freeman & Co (Sd) , 2nd

ed. 514 pages

22

Boyd, A., Darling, H., Tobano, J., Davis, B., Lyon, B., Flaum, C., Klein, J., Sneider, R.J.,

Sibbit, A., Singer, J., 1995, The Lowdown on Low-Resistivity Pay : Oilfield

Review, Autumn edition, 4-18.

Darling H.L. and Sneider R.M., 1993, Productive Low Resistivity Well Logs of the

Offshore Gulf of Mexico: Causes and Analysis,” in Moore D (ed), 1993:

Productive Low Resistivity Well Logs of the Offshore Gulf of Mexico. New

Orleans, Louisiana, USA: Houston and New Orleans Geological Societies.

Fanini, O. N., Kriegshäuser, B. F., Mollison, R. A., Schön, J.H., and Yu, L., 2001,

Enhanced, Low-Resistivity Pay, Reservoir Exploration and Delineation with the

Latest Multicomponent Induction Technology Integrated with NMR, Nuclear,

and Borehole Image Measurements: OTC 13279, Offshore Technology

Conference

Frost, Jr., E. and Fertl, W.H., 1981, Integrated core and log analysis concepts in shaly

clastic reservoirs : Log Analyst, 22, 3-16

Ghosh, D., M., Halim, FAH., Brewer M., Viratno, B., and Darman, N., 2010,

Geophysical issues and challenges in Malay and adjacent basins from an E & P

perspective: The Leading Edge, 29 (4), 436-449,

Hamada, G.M., Al-Blehed, M.S., Al-Awad, M.N., Al-Saddique, M.A., 2001,

Petrophysical evaluation of low-resistivity sandstone reservoirs with nuclear

magnetic resonance log: Journal of Petroleum Science and Engineering 29,

129–138.

Kuecher, G., and Millington , J., 2000. Turbidites Hold Great Potential for Deepwater

Exploration : Depth , 6 (1), 30-35.

Murphy, R. P., and Owens, W. W., 1972. A new approach for low-resistivity sand log

analysis :Journal of Petroleum Technology, 24, 1302–1306.

Passey, Q. R., Dahlberg, K. E. , Sullivan, K. B., Yin, H. , Brackett, R. A. , Xiao, Y. H.

and Guzmán-Garcia, A. G., 2006, Petrophysical Evaluation of Hydrocarbon

Pore-Thickness in Thinly Bedded Clastic Reservoirs, AAPG Archie Series, 1, 1

– 197.

Pettingill, H.S., 1998, Worldwide Turbidite E&P: A Globally Immature Play with

Opportunities in Stratigraphic Traps: SPE paper 49245.

Riepe. L., Hamid, A.S.B.A, Hamzah, M.H.R.B., and Zain, Zain, M.N.B.M., 2008,

Integrated Petrophysical Analysis to Evaluate Low Resistivity Low Contrast

(LRLC) Pays In Clastic Reservoirs In Se Asia: International Symposium of the

Society of Core Analysts held in Abu Dhabi, UAE 29 October-2 November,

2008

23

Saha. S., 2003, Low-Resistivity Pay (LRP) : Ideas for Solution, SPE 85675

Tixier, M. P., Morris, R. L., and Connell, J. G., 1968, Log evaluation of low-resistivity

pay sands in the Gulf Coast : The Log Analyst, 9(6), 3–20.

Worthington, P., 1985, The Evolution of Shaly-sand Concepts in Reservoir Evaluation :

The Log Analyst, Jan-Feb, 23-40.

Worthington, P.F., and Johnson, P.W., 1991, Quantitative Evaluation of Hydrocarbon

Saturation in Shaly Freshwater Reservoir : The Log Analyst, v.32, no.4, 356-368.

Worthington, 2000, Recognition and evaluation of low-resistivity pay : Petroleum

Geoscience, 6 , 77–92

Worthington, P.F., 2005, An Electrical Analog Facility for Hydrocarbon Reservoirs: SPE

96718