This document discusses petrophysical analysis techniques for evaluating low resistivity, low contrast pay zones in clastic reservoirs, with a case study from tertiary basins in Malaysia. Key points: 1) Low resistivity, low contrast pay zones are challenging to identify due to insufficient resistivity contrast between pay sands and shales. They are often caused by high clay content, which reduces resistivity. 2) Evaluating these zones requires modified shale models to account for clay effects and improved vertical resolution from tools like NMR logging. Integrating multiple well logs is also important. 3) Common depositional environments for low resistivity pay include thinly bedded sand-shale sequences in deepwater turbidite

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
1

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).
2

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
3

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 = R w
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
4

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
5

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).
6

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.
7

8. 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
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).
8

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.
9

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).
10

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.
11

12. Techniques Objectives Advantages Limitations
• Provide general output of
No need to identify
interval - average
Volumetric To investigate the thin boundaries
solution
Laminated effects of thin beds
Low Sand analysis on standard • Valid only in certain
resolution using resolution log data. limited assumptions on
conventional Suitable for bed with Depth alignment the log response
well logs thickness < 1 or 2 ft logs not required
• Confirmation of the bed
existence is needed.
To detect bed • Require high resolution
Log forward boundaries using logs to identify the
modelling high-resolution data Able to show boundaries if each thin
High and try to unravel detailed sand -shale beds.
Resolution true log values in distribution of thin
each thin bed. beds and pay zone
Inversion Suitable for bed with • Uncertainty in solution
thickness > 1 or 2 ft
1)To help confirm the • Provide strong • Distribution of the T2
presence of thin beds evidence for
can be influenced by
2) Directly indicate indicator of pay
many difference factors
Nuclear zone even without
presence of pay aside from pore size.
magnetic any high
zone • Require many
resonance resolution data.
3) To differentiate consideration and other
• Can estimate
between bound and knowledge to apply the
directly thickness
free water. NMR
of the pay zone
Other
• Can reduce
special
uncertainty in the • The multi component
techniques
low-resolution induction logs are
evaluation of a sometimes unavailable
To measure sensitive
Multi thinly bedded as not widely used.
perpendicular
component reservoir. • Accuracy on transverse
component in
induction • Ability to provide resistivity measurement
conductivity.
influential evidence is unknown, and
for indicator of pay environmental effects
zone. are also uncertain
Table 3.1. Summary of low and high resolutions techniques (After Passey et al, 2006 and
Hamada et al., 2001)
12

13. Reasons Facts Possible solutions
Invasion of Deep mud invasion, low reading
1) Run array laterolog or array induction log.
conductive in Rt and computed Sw high
2) Run resistivity logging-while-drilling (LWD)
mud
Common in shaly sand 1) Run Gamma ray spectroscopy and Elemental
High clay formations Capture Spectroscopy tools help estimate clay type
content 2) Combine with lab based clay mineralogical
analysis
Mainly related to grain 1) Run NMR tools and even combined with
Presence of
size. resistivity LWD will greatly aid in this
high capillary
Affect resistivity logs to read low interpretation.
bound water
Mainly due to penetration of
conductive muds into open
Presence of fractures causing low reading in
1) Run borehole imaging tools with LWD, can be in
fractures Rt.
water based and oil based mud.
Common in carbonates
Common in carbonate rock.
May reduce reducing the
Micro
resistivity. Run NMR and or LWD
porosity
Example pyrite, may conceal the
Presence of resistivity log reading. 1) Run photoelectric factor log
conductive Various, uncertain effect based on 2) Run elemental spectroscopy log will help
minerals its distribution. effectively
Makes resistivity logs become
High angle 1) Implement an newly developed interpretation
apparent and tend to read low.
wells method in induction type tools.
Averaging resistivity value in thin
1) To run higher vertical resolution tools with
bed.
Laminated deeper depth of investigation, or both.
Unable to resolve characteristics
formations 2) integrate with borehole imaging tools, with water
of individual thin beds.
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:
13

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).
14

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)
15

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.
16

17. Sabah
Basin
Malay
Basin
Sarawak
Basin
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.
17

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.
18

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).
19

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
20

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.
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