Arabian Journal for Science and Engineering (2019) 44:417–428
https://doi.org/10.1007/s13369-018-3476-8
RESEARCH ARTICLE - EARTH SCIENCES
A Modified Approach for Volumetric Evaluation of Shaly Sand
Formations from Conventional Well Logs: A Case Study from the Talhar
Shale, Pakistan
Muhsan Ehsan1,2
· Hanming Gu1 · Zulfiqar Ahmad3 · Malik Muhammad Akhtar4 · Saiq Shakeel Abbasi1
Received: 4 May 2017 / Accepted: 15 July 2018 / Published online: 4 August 2018
© King Fahd University of Petroleum & Minerals 2018
Abstract
In potential shale-gas basins, due to limited data availability, volumetric evaluation of shaly sand formations is a challenging
task for researchers, as well as for exploration and production companies, with the use of conventional well logs. This paper
is intended to provide a better understanding of volumetric evaluation in unconventional shaly sand formations. Petrophysical
parameters derived from conventional well logs play a significant role in the volumetric estimation of shale. The assessment
was performed using numerical equations, well log indexes, and cross-plot analysis. Deep and shallow resistivity logs overlap
with each other; this phenomenon indicates that the Talhar Shale in Pakistan can be considered to be an unconventional
formation. The average values of shale volume, total porosity, effective porosity, and matrix volume are 29, 16, 12, and 59%,
respectively. The Talhar Shale is dominantly characterized by intergranular porosity. It has been observed that at low shale
volumes (10–20%), porosities (total and effective), and resistivity measurements (deep, shallow, and microlaterolog), well log
signatures have close contents with the shale volume. In contrast, for the remainder of the shale volume, there is significant
separation of log signatures. Well log indexes indicate significant responses in 10–20% shale volume zones.
Keywords Shaly sand formations · Volumetric shaly sand evaluation · Gamma ray logs · Porosity logs · Resistivity logs ·
Well log indexes
1 Introduction
B
Muhsan Ehsan
muhsanehsan98@hotmail.com
Hanming Gu
guhanming@263.net
Zulfiqar Ahmad
fz97@hotmail.com
Malik Muhammad Akhtar
drmalikma21@gmail.com
Saiq Shakeel Abbasi
saiqshakeel@gmail.com
1
Institute of Geophysics and Geomatics, China University of
Geosciences, No 388 Lumo Road, Wuhan 430074, Hubei,
People’s Republic of China
2
Department of Earth and Environmental Sciences, Bahria
University, Islamabad, Pakistan
3
Office of Research Innovation and Commercialization
(ORIC), University of Wah, Wah Cantt, Pakistan
4
Department of Environmental Sciences, FLSI, Balochistan
University of Information Technology, Engineering and
Management Sciences (BUITEMS), Quetta, Pakistan
Most of the developing countries are in the midst of
worst energy crises. Currently, to meet the world’s energy
requirements, exploration and production companies and
researchers are focusing on unconventional hydrocarbon
resources. Previously, data were acquired in potential shalegas basins for conventional oil and gas resources. Geoscientists are now facing challenges to accurately evaluate shale
volumetric data, due to their limited availability in these
basins. Conventional well logs are being used to estimate
and evaluate shale-gas potential. Geoscientists are trying to
perform volumetric evaluations of shale by applying theoretical and deterministic or statistical methods. Fortunately,
conventional well logs can be utilized for this purpose in
the absence of advanced logs and lithologic analysis of core
samples.
Mainly, the challenges exist due to the lack of knowledge
about porosity, matrix volume, and shale content in geological formations [1]. The typical behavior of conventional well
logs, i.e., natural gamma ray, neutron porosity, resistivity,
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bulk density, and sonic logs, is used to identify shale-gas
reservoirs with an integrated approach. Researchers suggest
that the trend of these logs in shale-gas-bearing geological
formations is helpful in the volumetric evaluation of shale
[2–5]. Researchers are mainly focused on shale-gas interpretation models which are developed on the basis of a
significant criterion and rules for volumetric evaluation of
shale from conventional well logs in the absence or lacking
of geochemical and other modern-day logs [6–8].
Cross-plot analyses and numerical equations are helpful
for better understanding of formation evaluation [9]. Several
types of deterministic linear and nonlinear approaches are
applied on well logs data for the estimation of the volume of
shale [10–12]. Szabó and Dobróka [13] conducted a study on
the regional basis to check the validity of a new multivariate
statistical method using nonlinear deterministic methods on
different sedimentary basins data sets. Findings of volume of
shale using the deterministic method on single well log give
accurate and reliable results, but provide the information of
only one measured variable. Integrated study of more well
logs sensitive to volume of shale using empirical formulas
provides more accurate estimation [12–15].
This study entails the latest techniques used for volumetric
evaluation of shaly sand formations. Conventional well log
data are extensively studied to check the behavior of volume
of shale on well logs numerical values and their indexes. Well
log indexes can be used to identify thin and low shale volume beds. The estimation of volume associated with different
components of the shale-gas reservoir including porosity,
volume of shale, and solid rock fraction plays a key role
in the evaluation of unconventional hydrocarbon resources.
It is mandatory to actively consider all of these parameters
when dealing with this kind of reservoir because the porosity system becomes very complex due to the development of
natural fractures porosity along with intragranular porosity
of host matrix rock. As the sedimentary rocks contain different volumes of shale, porosity, and matrix, the theoretical
value of volume falls between 0 and 1. The unit volume of
rock is divided into three parts, i.e., effective porosity, shale
volume, and matrix volume of rock for the material balance
equation.
The purpose of this study is to introduce a quick scheme
to volumetric evaluation of shale by applying the numerical equations, well log indexes, and cross-plot analyses
on conventional well log data in the absence of XRD data
and other modern-day logs like nuclear magnetic resonance
measurements, and image logs, etc. Techniques are adopted
for volumetric evaluation of shale from several conventional well logs for Talhar Shale. This study is a milestone
for researchers and exploration and production companies,
which are working in potential shale-gas basins, to achieve
a better understanding of hydrocarbons structures of shaly
sand formations with the available conventional well logs
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data. The present study demonstrates an efficient approach
to the volumetric evaluation of unconventional shaly sand
formations by conventional well logging data.
2 Geological Settings
The Southern Lower Indus Basin is geographically located
in the Sindh province (SE) of Pakistan. It is extended approximately between 24 and 28◦ N and 66◦ E to the southern
boundary of Pakistan as illustrated in Fig. 1a. In a broader
view, it is a north–south trending sedimentary basin. Its width
is approximately 250 km and remained relatively stable during Mesozoic [18]. Major oil and gas wells are drilled in the
western side of basin because tectonic subsurface structures
are favorable for the accumulation of hydrocarbons in the
strata of Mesozoic. Normal faulting which formed horst and
graben geometry was observed [19,20]. In the study area,
major oil and gas production is extracted from Mesozoic
to Cenozoic sedimentary rocks. The contribution of Mesozoic is more significant and almost 76% of production in the
Southern Lower Indus Basin comes from Mesozoic rocks,
while only 24% from Cenozoic rocks [21]. Current research
focuses on volumetric evaluation of shale in Mesozoic sedimentary formation.
By William [22], the name Goru Formation of Cretaceous
age was introduced. Lower Goru Formation is distributed
in the entire Indus Basin as described in Fig. 1b. Based
on lithological information, the Goru Formation has been
divided into Lower Goru and Upper Goru formations. Cretaceous petroleum plays a significant role from hydrocarbon
exploration point of view. Lower Goru Formation is a sandstone reservoir sealed by shale of Upper Goru Formation and
charged by underlying shale of Sembar Formation [23]. Oil
and Gas Development Company Limited (OGDCL), Pakistan and Union Texas Pakistan (UTP) have divided Lower
Goru Formation into further units, based on its lithology. The
main units include Upper Sand, Basal Sand, Talhar Shale,
and Massive Sand [24]. In this study, the main target is Talhar Shale which is around 79 m thick. The wireline log data
of Hakeem Daho well are used in the present study.
The Government of Pakistan (GOP) started a project with
the help of USA to evaluate the unconventional hydrocarbon
potential in its territory [25]. According to the Energy Information Administration (EIA) report, Pakistan has estimated
shale-gas potential about 105 trillion cubic feet (TCF) and 9
billion barrels of oil shale [26]. E&P companies are trying
to explore the shale-gas potential of the study area, and their
main target is Talhar Shale. In a recent study, Talhar Shale
is considered as potential shale-gas candidate in Southern
Lower Indus Basin, Pakistan. Possibly, hydraulic fracturing
will be used to produce shale gas. Talhar Shale possesses low
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419
Fig. 1 a Basins Architecture of Pakistan after [16], b Lower Goru Formation distribution map in which study area is mentioned after [17]
permeability which provides one of the strong evidence of
the presence of shale gas [27–29].
Table 1 Classification of formation in the process of evaluation of shale
according to the volume of shale [35]
Classification
3 Methodology
The volume of shale (Vsh ) is a key parameter in the process of
conventional formation evaluation because of its impact on
various petrophysical parameters interpretation results [30].
Generally, in shale gases, a correlation between gamma ray
index and (Vsh ). Occasionally, natural gamma ray (NGR) log
gives higher values of (Vsh ) due to the presence of uranium
(U) content of non-shale origin. High U content shifts the
NGRmin and NGRmax , which gives ambiguous results. To
eliminate the effect of U content in calculations, a computed
gamma ray (CGR) log was used. CGR serves better comparison to NGR in the estimation of (Vsh ). Various field studies
have proved that estimation of the volume of shale using well
logs data is being improved with CGR instead of NGR, for
a better reservoir description and accurate estimation of percentage of shale [10,31–34]. On the basis of volume, shale is
classified into three types: clean sand, shaly sand, and shale.
The volume of shale, less than 10%, is called clean sand,
10% < Vsh < 33% shaly sand, while Vsh > 33% refers to
shale as shown in Table 1 [35].
Volume of shale (%)
Clean sand
Vsh < 10
Shaly sand
10 < Vsh < 33
Shale
Vsh > 33
Al-Ruwaili [36] proposed a model, which is based on
well logs data for shaly sand formations evaluation, and it
facilitates to understand the basic components of shaly sand
formations. Passey et al. [37] developed a systematic model
for evaluation of shale. A systematic model for material balance Eq. 1 using both concepts of Al-Ruwaili [36] and Passey
et al. [37] is illustrated in Fig. 2.
Vsh + φE + Vma = 100
(1)
The major parts of bulk volume of shaly sand formation
includes the volume of shale (Vsh ), total porosity (φT ), effective porosity (φE ), and volume of the matrix (Vma ). It has
been divided into three parts for the material balance equation in the process of volumetric evaluation of shale. Firstly,
(Vsh ) is the sum of volume of clay and non-clay minerals, dry
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Fig. 2 General systematic bulk volume of shaly sand formations . Modified after [36,37]
clay, clay bound water, and organic matter. Secondly, φE is
the sum of hydrocarbons and free water; basically, it is pore
space volume, and thirdly, Vma is the summation of specific
type of volume of particles (quartz etc) excluding (Vsh ). The
theoretical bulk volume of shaly sand formation is expressed
as 0–100%.
Empirical formulas have been used for NGR, CGR, and
Gamma Ray Sonic (GRS) logs to estimate the volume of
shale with both the applied linear and nonlinear approaches.
Gamma ray index (IGR ) is calculated from well log by using
Eq. 4. The values of NGRmax and NGRmin are taken from well
log [38]. A relationship between IGR and (Vsh ) is a quantitative indication of shale content in the geological formations
[39]. Poupon and Gaymard [40] introduced linear Eqs. 2 and
3, while Steiber [41], Larionov [42], and Clavier et al. [43]
used nonlinear Eqs. 4–6, respectively, for the estimation of
Vsh . Percentage error in shale volume is computed by using
Eq. 7.
IGR
GRlog − GRmin
=
GRmax − GRmin
(2)
where IGR = gamma ray index, GRlog = gamma ray
log values, GRmax = gamma ray maximum values, and
GRmin = gamma ray minimum values
IGR = Vsh
Error (%) =
Vsh by XRD − Vsh by GR
∗ 100
Vsh by XRD
Total porosity (φT ) can be obtained from any porosity log in
shaly formation [44]. Therefore, a combination of three logs
comprising bulk density, sonic, and neutron porosity logs that
are called as φNDS has been used for total porosity to reduce
the effect of a single log to achieve better results [38]. Hence,
φNDS is equal to the total porosity (φT ).
φN = neutron porosity from well log
φD =
ρma − ρb
ρma − ρf
(8)
(9)
where φD = density-derived porosity, ρma = matrix density,
ρf = fluid density and ρb = bulk density values from log
φS =
tlog − tm
tf − tm
(10)
where φS = sonic-derived porosity, tm = matrix transit time,
tf = fluid transit time, and tlog = sonic transit time from
log
φNDS =
φN + φD + φS
3
(11)
(3)
φNDS = φT
0.5∗ IGR
Vsh =
1.5 − IGR
(7)
(4)
Lower Goru is of Cretaceous age; therefore, we used and
applied rock older than Tertiary using Eqs. 5 and 6.
(12)
Hill et al. [45] introduced empirical Eq. 13 to compute effective porosity using total porosity and shale volume.
φE = φT (1 − Vsh )
(13)
Vsh = 0.33(22IGR − 1.0)
(5)
The volume of the matrix is computed using material balance Eq. 1 by computing shale volume and effective porosity.
Hence, the volume of the matrix is computed using Eq. 14.
1
2
Vsh = 1.7 − 3.38 − (IGR + 0.7)2
(6)
Vma = 100 − (φE + Vsh )
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(14)
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421
Empirical Eq. 15 is used to compute well logs index.
Ix =
xlog − xmin
xmax − xmin
(15)
where x = any well log, I x = index of log, xmin =
minimum log value, and xmax = maximum log value
4 Volumetric Shale Evaluation Results
The results of the volumetric shale evaluation are described
below.
4.1 Shale Volume
An essential step to be taken in the process of formation
evaluation is estimation of the amount of shale present in
the geological formation because it is mandatory to calculate
effective porosity and fluid content [46,47]. To accurately
estimate the volume of shale (Vsh ), numerical equations are
used for well logs. In this study, three logs, NGR, CGR, and
GRSL, are used to estimate the volume of shale. The results
of these logs are given in Table 2. We accepted (Vsh ), which
is 29.25% computed using CGR and applied Steiber [41]
empirical equation as it is very close to the results of the
volume of shale calculated by XRD. The (Vsh ) calculated by
XRD data is 30% in Talhar Shale [29]. It is common practice
to use the natural gamma ray (NGR) log, which is a total
contribution from all three elements, uranium (U), potassium (K), and thorium (TH), as indicator of shale content.
The presence of highly radioactive black organic shale in the
geological formation showed a significant difference than the
X-ray diffraction data. This causes an overestimation of the
volume of shale [47].
Therefore, computed gamma ray (CGR) log is used whose
results appear to be compatible with X-ray diffraction results.
Estimation of volume of shale using CGR is considered to be
better than NGR and GRSL. GRSL overestimated the volume of shale as compared to NGR and CGR logs. It is noticed
that linear approach, Larionov, and Clavier et al. overestimated the volume of shale. The display of (Vsh ) calculated
using CGR log and applied linear and nonlinear approaches
is illustrated in Fig. 3a. The display of accepted volume of
shale (%) is illustrated in Fig. 3b. The results of the classification of shale are illustrated in Fig. 3c, where depth (m) is
plotted on x-axis and volume of shale (%) on the y-axis.
Talhar Shale is classified into three types, as shown in
Fig. 3c based on the volume of shale values. The classification of shale by volume curve is constructed using MATLAB.
Shaly sand is present at 2992–2996 and 3024–3048 m depth
(m) intervals while shale and clean sand on small depth (m)
intervals. The volume of shale frequency distribution is illustrated in Fig. 3d. The maximum data points are located at
shaly sand zone and its contribution is 65%, while the contributions of shale and clean sand are 29 and 6%, respectively.
On the basis of this classification, we are able to conclude
that Talhar Shale is a shaly sand formation.
4.2 Porosities and Volume of Matrix
A combination of neutron porosity logs with one or more
porosity logs gives accurate porosity estimation and better
results in the process of shaly sand formations evaluation.
Density is also a porosity log, which is helpful for geologists
to detect the gas-bearing zones. Neutron porosity and bulk
density logs are reliable and ubiquitous logs for the evaluation of shaly sand formations. Sonic-derived porosity is,
sometimes, overestimated due to the presence of shale with
high density. Hence, a combination of all porosity logs is
used to calculate total porosity [35,38,48].
Total porosity is a function of the neutron, density, and
sonic porosities. Neutron porosity is taken directly from well
log, while density and sonic are calculated from numerical
equations. Porosity logs are affected by various shale contents in geological formations. The interval (2982–3012 m)
indicates the high values due to the high values of the volume of shale. Rest of the interval (3012–3061 m) of porosity
logs has medium range values, and neutron, sonic, and total
porosity are closely compatible. Contrary to that, densityderived porosity indicates low values because it is affected
by matrix density. The average total porosity in Talhar Shale
is 16.52%. The output plots of all porosities are illustrated in
Fig. 4. Effective porosity is computed from Eq. 15 after the
computation of total porosity and volume of shale. The average effective porosity in Talhar Shale is 11.54%. The average
volume of the matrix computed using Eq. 16 is 59.21% in
Talhar Shale.
5 Relationship Between Porosities, Sonic,
Resistivity Measurements with Shale
Volume
The key parameter in this study is the volume of shale because
it is a function of porosities, sonic, and resistivity measurements. In the present study, a relationship between the volume
of shale with porosities, sonic, and resistivity logs and their
indexes has been extensively studied to check the behavior
of these logs and their indexes against the volume of shale
values. The results are discussed further in the coming sections.
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Fig. 3 a Volume of shale display which is computed by using CGR and applied linear and nonlinear approaches. b Accepted volume of shale
display. c Classification of Talhar Shale by using accepted volume of shale with marked zones. d Accepted volume of shale frequency distribution
Fig. 4 Output plot of neutron,
sonic, density, and total
porosities in which depth (m) is
plotted on x-axis and porosities
(%) values on y-axis
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Table 2 Results of volume of shale (%) by linear and nonlinear methods with percentage (%) error
Well logs
Linear approach
Nonlinear approach
Gamma ray index by Eq. 5
Steiber [41] by Eq. 6
Larionov [42] by Eq. 7
Clavier et al. [43] by Eq. 8
NGR
error (%)
51.28
− 70.93 (Rejected)
28.80
4.03 (Rejected)
34.68
− 15.6 (Rejected)
34.04
− 13.47 (Rejected)
CGR
error (%)
51.93
− 73.1 (Rejected)
29.25
2.5 (Accepted)
35.54
− 18.4 (Rejected)
34.56
− 15.2 (Rejected)
GRSL
error (%)
53.73
− 79.1 (Rejected)
30.99
− 3.3 (Rejected)
37.92
− 26.2 (Rejected)
36.39
− 21.2 (Rejected)
5.1 Porosities and Shale Volume
Relationships between the total and effective porosities with
shale volume have been studied. Total porosity is directly
proportional to shale volume, while effective porosity is
inversely proportional. The curves separation between total
porosity and effective porosity appears to be prominent in
shale zone. At shaly sand zone where shale volume is 10–
20%, they have shown close compatibility with volume of
shale at depth interval 3026–3045 m as illustrated in Fig. 5a.
An increase in the volume of bound water present in the
shale results in the increase of shale volume as well as total
porosity. Effective porosity is a shale-free porosity that will
decrease with the increase of shale volume [49].
Consequently, only with porosity values, it is difficult to
find out low shale volume zone in well logs interpretation.
To resolve this problem, we introduced and applied total and
effective porosities index technique. The output plot of these
indexes with shale volume is shown in Fig. 5b. The effective
porosity index indicates significant response on low shale
volume zone on thin beds; consequently, very high values
are encountered. Therefore, low shale volume zones can be
easily identified, which are inversely proportional to shale
volume. Total porosity index is directly proportional to shale
volume. It is therefore concluded that porosity indexes can
identify low shale volume zone. Porosity indexes give a much
better response as compared to porosity logs in low shale
volume zone.
5.2 Sonic Log and Shale Volume
Sonic log plays a significant role in porosity analysis. Geoscientists used sonic log in hydrocarbons exploration as well
as for well log interpretation. Sonic log numerical values
are directly proportional to the shale volume as illustrated
in Fig. 5c. The sonic log indicates sharp change similar to
shale volume. The sonic index is directly proportional to
shale volume. It has prominent separation at low shale volume 10–20%. The value for total porosity (φT ) was derived
from combining the readings of porosity logs [49,50]. The
φS − φT cross-plot is dependent on the porosity parameters φN , φD , φS and deduced from density, neutron porosity,
and sonic logs which are used for the estimation of the type
of reservoir porosity (primary or secondary) in a particular
zones [41]. The cross-plot of total porosity–sonic porosity
is illustrated in Fig. 5d, which indicates the dominance of
the high intergranular porosity (primary porosity) with lower
secondary porosity type. High porosity zone in cross-plot
indicates shale or gas effect present in Talhar Shale.
5.3 Resistivity and Shale Volume
The resistivity logs are directly proportional to the shale volume in unconventional formations. The resistivity logs values
are plotted with the shale volume as illustrated in Fig. 6a. At
depth interval 3026-3045 m, all resistivity logs have close
contents with the shale volume, while at high shale volume, it
indicates separation, as illustrated in Fig. 6a. Microlaterolog
has strong responses on thin beds as compared to deep and
shallow resistivity logs. Resistivity log indexes are plotted
with shale volume to check the response on the shale volume as illustrated in Fig. 6b. Resistivity log indexes are also
directly proportional to the shale volume. In thin beds, microlaterolog index response is more prominent as compared to
deep and shallow resistivity log indexes. At low shale volume
10–20%, indexes responses are more significant as compared
to logs responses. Microlaterolog index is the most important
to detect thin as well as low shale volume beds in geological
formations.
6 Conclusions
Conventional well logs can successfully be utilized in the
volumetric evaluation of shale (in terms of volume, total
porosity, effective porosity, and matrix volume). Natural
gamma ray, porosity, and resistivity logs have played a vital
role in this study. The proposed method is successfully
applied on the Talhar Shale, and also it can be considered
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Fig. 5 a Output plot of effective and total porosities with accepted volume of shale; b output plot of effective and total porosities indexes with
accepted volume of shale; c display of sonic log and sonic index with
accepted volume of shale; d total porosity versus sonic porosity crossplot with color-coded by effective porosity which indicates prominent
intergranular porosity in Talhar Shale
as a comprehensive interpretation of the conventional well
log data in the volumetric evaluation of shale.
The numerical character of all well logs provides very useful information during volumetric shale evaluation. Natural
gamma ray, porosity, and resistivity logs are function of shale
volume. The best estimation of the shale volume is computed
using computed gamma ray (CGR) log with Stieber nonlinear approach, which is calibrated with XRD with 2.5% error
which validates our findings.
The maximum data cluster is located on shaly sand zone
(65%), which indicates that Talhar Shale is a shaly sand formation. In a nutshell, we conclude that the extensive study
of well logs numerical character and their indexes have
shown significant responses as compared to their well logs
numerical character, on low shale volume (10–20%) zone.
Therefore, low shale volume zone should be identified by
well log indexes instead of well logs.
Microlaterolog gives the best indication of thin beds as
compared to the shallow and deep resistivity logs. It is
inferred that along with well logs interpretation, numerical
equations and deterministic methods and well log indexes
can also be used to extract valuable information from the
conventional well log data.
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7 Appendix
In this section, conventional well logs application is described
in the process of volumetric shaly sand evaluation.
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Fig. 6 a Display of resistivity logs with accepted volume of shale, b display of resistivity log indexes with accepted volume of shale
To understand the subsurface structure and characteristics, in terms of petrophysical, “Wireline Logs” are utilized.
Wireline logs do not directly measure petrophysical properties; they measure “Log Properties” of geological formations
like bulk density, sonic, and resistivity [51]. In the absence
of XRD and other modern logs data, researchers are trying a volumetric evaluation of shale by using theoretical and
numerical approach, which is applied to conventional well
log data. The basic tool is conventional well log data, to
investigate and evaluate subsurface geological formations of
hydrocarbons structures [52,53].
Conventional well logs: NGR [API] is natural gamma ray
log, CGR [API] is computed gamma ray log, GRSL [API]
is gamma ray sonic log, DT [µs/ft] is sonic log, CNCF [%]
is neutron porosity log, ZDEN [g/cm3 ] is bulk density log,
RMLL [ohm-m] is microlaterolog, RS [ohm-m] is shallow
resistivity log, and RD [ohm-m] is deep resistivity log, which
was run at the depth interval of 2982–3061 m (79 m thick) in
Talhar Shale, as illustrated in Fig. 7. Schlumberger made data
acquisition in 2002. The data were acquired from Directorate
General Petroleum Concession (DGPC), Pakistan.
mation of the volume of shale, we utilized NGR which is a
weighted sum of thorium (TH), potassium (K), and uranium
(U), as described in Eq. 16. Computed gamma ray (CGR)
is the sum of thorium and potassium, which is calculated in
API unit, shown in Eq. 17. We also used gamma ray sonic
(GRSL) to calculate the volume of shale in our study. Then,
we compared the results with XRD data volume of shale and
chose the best one among them called accepted volume of
shale.
NGR(API) = 4∗ T H (ppm) + 8∗ U (ppm) + 16∗ K (%) (16)
CGR(API) = 4∗ T H (ppm) + 16∗ K (%)
(17)
where the weights 4, 8, and 16 were chosen on the basis of
total gamma ray count for natural gamma ray spectrometry
apparatus to compute NGR and CGR by a simple formula.
7.2 Porosity Logs
7.1 Natural Gamma Ray
Natural gamma ray spectrometry (NGS) apparatus is capable of measuring the natural radioactivity of the geological
formations by measuring TH, K, and U concentrations [10].
Natural gamma ray measures the natural radioactivity in geological formations; its values are directly proportional to the
volume of shale content [54]. In general, the concentrations
of naturally occurring elements are high in shale as compared
to other sedimentary rocks. Sedimentary rocks are composed
of quartz sand, silt, and shale, so higher values indicate the
shale content [55]. In the conventional method for the esti-
Porosity is measured as a percent of total rock volume. It
is categorized into effective porosity (φE ) and total porosity
(φT ) in the process of evaluation of geological formations.
Total porosity bulk volume is mainly composed of hydrocarbons, non-clay water (free water), and clay bound water.
Effective porosity (a shale-free porosity) bulk volume is
mainly composed of hydrocarbons and non-clay water. The
main difference between φT and φE is the volume of free
water that is present in geological formations. Effective
porosity has a significant role in geological formations that
contain commercial hydrocarbons (oil and gas) deposits
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Fig. 7 Conventional well logs data displayed from left to right, CGR
[API] computed gamma ray, GRSL [API] gamma ray sonic, NGR [API]
natural gamma ray, DT [μs/ft] sonic, ZDEN [g/cm3 ] bulk density, CNCF
[%] neutron porosity, RS [ohm-m] shallow resistivity, RMLL [ohm-m]
microlaterolog and RD [ohm-m] deep resistivity logs, respectively
because in this type of porosity fluids can be moved and
recovered [56,57].
The geological formation has a variable density value;
due to this phenomena, the effect of shale on porosity logs
is also variable. Hence, it is crucial to check the behavior
of all available porosity logs for the volumetric evaluation
of shale. The porosity logs include neutron porosity, bulk
density, and sonic. These are used for formation evaluation.
Neutron porosity log is used principally for determination of
porosity of geological formations; it is a function of hydrogen content in geological formations, while in shaly sand
intervals it is a function of both shale and liquid fills.
7.2.1 Primary and Secondary Porosity
123
Neutron and density logs are responses to pores of all sizes
in geological formations. However, it has been observed
by field studies that sonic log is used to measure interparticle (intergranular and intercrystalline) porosity. Porosity
is derived from the sonic log by using Wyllie et al. [58]
equation. The cross-plot between sonic derived porosity and
total porosity provides information about primary porosity (intergranular) and secondary porosity. The cross-plot of
total porosity–sonic-derived porosity depends on the porosity parameters (φN , φD , φS , and φT ), which are deduced
from neutron, density, sonic logs, and their combinations,
respectively. Secondary porosity usually does not exceed as
Arabian Journal for Science and Engineering (2019) 44:417–428
427
Table 3 A summary of phenomenological interpretation of conventional well logs modified after [33]
Descriptor
Measurement
Shaly sand/shale
Natural gamma ray (NGR)
Vsh ↑ →
Porosity (φ)
Bulk density
φ↑ →
ρb ↓
Resistivity
Functional behavior
NGR ↑
Neutron
φ↑ →
φn ↑
Sonic (DT)
φ↑ →
DT ↑
Deep resistivity (RD)
Vsh ↑ →
Shallow resistivity (RS)
Vsh
↑ →
RS ↑
Microlaterolog (RMLL)
Vsh
↑ →
RMLL ↑
RD ↑
Where ρb = bulk density, φn = neutron porosity and Vsh = volume of shale
compared to primary porosity. Data points located in intergranular zone indicate shale or gas effect [41,50,59].
7.3 Resistivity Logs
The resistivity logs are used to measure the formation resistivity; the presence of hydrocarbon raises the resistivity
values. Resistivity has an inverse relationship with porosity. In our study, three types of resistivity logs are utilized,
which include deep resistivity, shallow resistivity, and microlaterolog. Deep and shallow resistivity logs are used to
evaluate the productivity of the geological formations. The
presence of shale content in geological formations has an
adverse effect on resistivity logs. The effect is related to
the content of shale volume. Deep resistivity and shallow
resistivity logs overlap each other in tight sandstone, shale,
and shaly sand beds. Geoscientists used microlaterolog for
best estimation and the correlation between thin beds and
reservoir analysis of the geological formations [54,57]. A
summary of phenomenological interpretation of conventional well logs is given in Table 3.
Acknowledgements I thank Directorate General Petroleum Concession (DGPC), Pakistan, for providing data for this research. I would
like to pay my regards to my elder brother Mr. Mohsin Raza from University of East London, UK, and my friend Mr. Ahsan Shafi from China
University of Geosciences, Wuhan, for their moral support and their
help to complete this work.
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