High-Definition Spectroscopy—Determining
Mineralogic Complexity
Neutron-induced capture spectroscopy tools measure the concentrations of specific
elements in downhole rocks. From these data, petrophysicists can derive mineralogic,
lithologic and matrix properties. Early spectroscopy tools lacked the spectral sensitivity
to derive total organic carbon—an important measurement for understanding
unconventional resource plays. A new tool delivers total carbon, from which organic
carbon concentrations can be determined. This tool also has the ability to resolve
complex lithology with a degree of accuracy never before possible.
Manuel Aboud
Rob Badry
Calgary, Alberta, Canada
Jim Grau
Susan Herron
Cambridge, Massachusetts, USA
Farid Hamichi
Jack Horkowitz
Sugar Land, Texas, USA
James Hemingway
Houston, Texas
Robin MacDonald
Saudi Aramco
Al-Khobar, Saudi Arabia
Pablo Saldungaray
Al-Khobar, Saudi Arabia
Don Stachiw
Northern Cross (Yukon) Ltd.
Calgary, Alberta
Christian Stoller
Princeton Junction, New Jersey, USA
Richard E. Williams
BHP Billiton
Houston, Texas
Oilfield Review Spring 2014: 26, no. 1.
Copyright © 2014 Schlumberger.
CMR-Plus, ECS, ELANPlus, GST, Litho-Density,
Litho Scanner, Minitron, Platform Express, RST, SpectroLith
and TerraTek HRA are marks of Schlumberger.
LECO is a mark of the LECO Corporation.
34
Rocks comprise an assortment of minerals and
fluids. Many processes combine to form the complex mixtures found in the subsurface, including
the transport mechanisms that delivered sediments and rock fragments to their current resting place, heat and pressure applied during
burial and subsequent lithification and a myriad
of internal and external forces acting on the
rocks. Using downhole spectroscopy tools, also
referred to as geochemical tools, geologists can
unravel the composition of sedimentary, metamorphic and igneous formations and better
understand their stratigraphy, mineralogy, diagenesis and hydrocarbon potential.
In the early days of well logging, geologists
and petrophysicists developed models to help
identify the presence of hydrocarbons, estimate
their quantities and determine production potential. Saturation models such as those described in
equations proposed by Gus Archie, later modified
to account for the influence of shale, usually
assume homogeneous, isotropic formations.1
These methods provide reasonable results when
computing hydrocarbon saturations in conventional reservoirs; however, to determine the oil
and gas potential in complex reservoirs and
unconventional resource plays, petrophysicists
have replaced simple models with techniques
Oilfield Review
Pulsed Neutron Generator
Highvoltage
supply
n
Controls
Ion source
Americium-Beryllium Source Reaction
γ (60 keV)
On-Off
switch
Main
power
Target
241
Am
237
237
Np*
Np
γ (4.4 MeV)
α (5.5 MeV)
n
p+
+
Deuterium
2
H
n n
p+
n n
p+ p+
Tritium
3
H
Helium
4
He
+
n
Neutron
n
+
Kinetic
energy
E (17.6 MeV)
9
Be
13
C*
12
C*
n (4 MeV, average)
12
C
> Neutrons from a pulsed neutron generator (PNG) and an americium beryllium [AmBe] radioisotopic source. PNGs (top left) are self-contained particle
accelerators that produce neutrons from a fusion reaction (bottom left). The neutrons leave with high kinetic energy of approximately 14 MeV of the total
17.6 MeV released. Typical PNG output is 3 × 108 neutron/s. AmBe sources, on the other hand, generate neutrons as by-products of nuclear reactions
(right). The AmBe source contains a mixture of americium [ 241Am] and beryllium [ 9Be]. When 241Am decays to an excited state of neptunium [237Np*]—the *
denotes an excited state—it emits 5.5-MeV alpha particles (α). To reach its final ground state, the excited 237Np* emits a 60-keV gamma ray (γ). A small fraction
of the alpha particles from the 241Am react with the 9Be, resulting in the formation of an excited state of carbon [13C*], which emits 4-MeV neutrons (n) to reach
an excited state of 12C*. The 12C* reaches its stable state through the emission of a high-energy gamma ray (approximately 4.4 MeV). A typical AmBe source
generates 4 × 107 neutron/s.
that require greater understanding of the composition and mineralogy of the rocks.
In the laboratory, scientists have an array of
instruments at their disposal to peer into the
rock structure. Using these tools, they can determine the chemical and mineral composition of
the rocks, hypothesize about their origins and
diagenesis and establish empirical relationships
of rock properties that affect generation, accumulation and production of hydrocarbons. In the
downhole environment and in words familiar to
most petrophysicists, “All interpretations are
opinions based on inferences from electrical or
other measurements.” 2 However, as technologies
and techniques advance, service companies are
providing a number of laboratory-grade measurements from tools at the end of a wireline cable or
attached to drillpipe.
Spectroscopy measurements, which are crucial to understanding complex reservoir rocks
and unconventional resource plays, have been
used by scientists in the laboratory for several
decades. Downhole spectroscopy tools have been
available since their introduction in the 1980s,
but the recently introduced Litho Scanner highdefinition spectroscopy service delivers geochemical data at a level of precision and accuracy
that has never before been available downhole.
The tool acquires measurements of a greater
number of elements than were available from
earlier tools and includes an accurate measurement of carbon, from which total organic carbon
(TOC) can be derived. For understanding unconventional resources such as oil- and gas-bearing
shales, TOC is crucial.
Spring 2014
This article reviews the basic theory of spectroscopy measurements and the development of
neutron-induced capture spectroscopy tools,
including advances in spectroscopy measurements introduced by the Litho Scanner tool. Case
studies from an Arctic wildcat well, an oil-bearing resource play in the US and an unconventional resource play with complex lithology in the
Middle East demonstrate various applications of
spectroscopy data.
Spectroscopy—Capturing Complexity
Two families of downhole spectroscopy tools are
used in the oil and gas industry: spectral natural
gamma ray tools and neutron-induced gamma ray
spectroscopy services. Geoscientists primarily
use spectral gamma ray tools to quantify the concentrations of naturally occurring thorium,
potassium and uranium in rocks by measuring
the energy level of gamma rays emitted as these
radioactive elements decay. From these data, log
analysts can estimate clay type, quantify radioactive mineral effects on natural gamma ray measurements and identify radioactive deposits.
Neutron-induced gamma ray spectroscopy,
which is a more comprehensive measurement
technique than that of spectral gamma ray tools,
delivers concentrations of the most common elements found in the minerals and fluids of reservoir
and source rocks. A neutron-induced spectroscopy
tool records transitory effects—lasting a few
microseconds to a few milliseconds—from formations bombarded with neutrons from a source:
either an electronic pulsed neutron generator
(PNG) or an americium [241Am] and beryllium
[9Be] radioisotopic source [AmBe] (above).3 The
AmBe chemical sources used for downhole logging
output a relatively stable number of neutrons with
a predictable energy level. Compared with AmBe
sources, PNGs generate many more neutrons and
at much higher energy levels, but their output can
vary with temperature, tool power and PNG age.
Unlike AmBe sources that are always generating
neutrons, when electrical power is removed from
PNGs, neutron generation ceases.
Laboratory spectroscopy tools such as X-ray
diffraction (XRD) and X-ray fluorescence (XRF)
spectrometers bombard rock samples with X-rays
or gamma rays and measure the resulting emissions. To determine mineralogy, technicians use
XRD devices; to perform elemental analysis, they
use XRF equipment. The XRF equipment in the
laboratory can measure more elements than can
downhole tools. However, the subset of elements
measured downhole includes the common mineral-forming elements, which are sufficient for
geologists to determine the mineralogic composition of most reservoir and source rocks.
1. For more on the Archie water saturation equation:
Archie GE: “The Electrical Resistivity Log as an Aid in
Determining Some Reservoir Characteristics,”
Petroleum Transactions of AIME 146 (1942): 54–62.
2. For many years, these words, or a similar statement,
appeared on the printed logs provided by most service
companies.
3. For more on PNGs used as neutron sources: Allioli F,
Cretoiu V, Mauborgne M-L, Evans M, Griffiths R,
Haranger F, Stoller C, Murray D and Reichel N:
“Formation Density from a Cloud, While Drilling,”
Oilfield Review 25, no. 2 (Summer 2013): 4–15.
35
Inelastic Neutron
Scattering
Electronic source
High energy
Excited
nucleus
Traditional source
10 6
Neutron energy
leaving source
n
Inelastic
region
Deexcited
nucleus
n
Neutron energy, eV
Intermediate energy
10 4
Inelastic
gamma rays
10 2
Epithermal energy
Neutron Capture
Capture
gamma ray
emitted
10 0
Excited
nucleus
Deexcited
nucleus
n
Average
thermal
energy
0.025 eV
10 –2
Thermal
neutron
Neutrons with thermal energy
Capture
gamma ray
200
400
Time, μs
> Life of a neutron and neutron scattering. Both electronic (PNG) and traditional (radioisotopic) sources emit high-energy
neutrons. Neutrons from the PNG used in the Litho Scanner tool have approximately 14 MeV initial kinetic energy, whereas
AmBe sources emit neutrons with around 4.4 MeV (left). These fast neutrons rapidly reach thermal energy level (approximately
0.025 eV). During those first few microseconds, before their energy falls below about 1 MeV, the neutrons experience inelastic
interactions (top right). Inelastic neutron scattering occurs when high-energy, fast neutrons collide with, pass closely by or are
absorbed by atomic nuclei. The now excited nucleus emits inelastic gamma rays to return to a deexcited state. Neutron capture
(bottom right) occurs when thermal neutrons are absorbed by atomic nuclei. The capturing atom generates gamma rays to
return to a deexcited state.
The first geochemical logs were created by
combining measurements from several existing
tools. In the late 1980s, scientists at the
Schlumberger Houston Product Center, supported by researchers at the Schlumberger-Doll
Research Center in Ridgefield, Connecticut, USA,
combined the data from an NGT natural gamma
ray tool, a GST gamma ray spectrometer tool and
an AACT aluminum activation clay tool.4 From
these data, they computed simple elemental concentrations for the following: aluminum [Al], calcium [Ca], iron [Fe], gadolinium [Gd], potassium
[K], sulfur [S], silicon [Si], thorium [Th], titanium [Ti] and uranium [U]. These elemental
concentrations provided information about mineralogy and rock composition.
Although early geochemical tools provided
geologists with information about the geochemistry of the rocks, first-generation tools suffered
from some inherent limitations. These limitations
include slow logging speeds, lack of combinability
with other logging tools, degradation of both quality and resolution of the measurements in downhole environments, an inability to differentiate
organic carbon from inorganic carbon and a lack
of sensitivity for some elements that are essential
for understanding complex lithology. For instance,
36
geologists use magnesium [Mg] to differentiate
dolomite from calcite, and an accurate Mg measurement was difficult to obtain with older generation tools.
Many geologists and petrophysicists consider
geochemical logging data crucial for accurately
characterizing reservoir rocks, but the tools were
not universally included in traditional evaluation
suites for many reasons. Among these were the
facts that the tools were long, could not be combined with other services and had to be run
slowly; also, the information could be obtained
from core data. The application of the ECS elemental capture spectroscopy tool for shale gas
exploration revolutionized the service.5 Because
of its ability to provide the mineralogic composition of the rocks, a geochemical tool was frequently included in logging programs for
unconventional reservoir evaluation and completion design.
Elements of Neutron Capture Spectroscopy
Of the many types of nuclear radiation, two are of
particular interest for spectroscopy measurements—gamma rays and neutrons. Gamma rays
are similar to X-rays and visible light and are the
highest energy form of electromagnetic radiation. Visible light has a wavelength range of about
400 to 700 nm; gamma rays, with wavelengths
much smaller than 1 nm, have a range of frequencies. Wavelengths typically encountered in downhole measurements are roughly 0.001 nm.
However, gamma rays are not usually described
by their wavelength, but by their energy level,
expressed in electron volts (eV) or the larger
units of keV (thousand eV) and MeV (million eV).
Induced neutron spectroscopy tools count
gamma rays over a range of discrete energy
bins—the gamma ray spectrum. In essence, they
measure the energies of artificially induced
gamma rays emitted by elements in the formation that have been bombarded by high-energy
fast neutrons supplied by the tool. These neutrons collide with other particles and rapidly lose
energy until they eventually reach thermal
energy level of about 0.025 eV. Because neutrons
are similar in mass to hydrogen’s single proton,
the maximum energy transfer and the neutron’s
most rapid slowing occur from collisions between
neutrons and hydrogen atoms (above).6
Thermal neutrons are eventually absorbed—
captured—by the atomic nuclei of various elements found in the formation, the borehole and
the tool. These now excited nuclei emit gamma
Oilfield Review
Spring 2014
Si Inelastic
Probability
Si Capture
0
2
4
6
8
10
0
2
4
Gamma ray energy, MeV
Capture Gamma Ray Spectrum
H
Gd
Cl
6
8
10
Gamma ray energy, MeV
Inelastic Gamma Ray Spectrum
K
Si
Fe
CTB
Al
Ca
Fe
Ca
S
ITB
O
Counts
rays—referred to as capture gamma rays
because they are a product of neutron capture—
to return to their lowest stable energy state.
Capture gamma rays have energy levels that are
characteristic of the element from which they are
emitted. Elastic scattering and eventual capture
can take place over a few tens to hundreds of
microseconds. Most downhole neutron capture
spectroscopy tools rely on neutron-induced capture gamma rays for their measurements.
Prior to reaching thermal energy level, highenergy neutrons that have not yet been significantly slowed may cause inelastic reactions.
Inelastic reactions differ from elastic scattering
and occur in about a microsecond after neutron
bombardment. These interactions are characterized by atomic nuclei that become excited by
encounters with neutrons with energy levels
above 1 MeV. During inelastic interactions, neutrons may collide with an atomic nucleus, transfer energy to that nucleus and then emerge with
reduced energy, or the fast neutron may be
absorbed after first knocking a subatomic particle from the nucleus. As in neutron capture,
nuclei become excited by these encounters and
emit one or more gamma rays to return to a deexcited state; however, the gamma rays resulting
from inelastic reactions have specific energy levels that differ from those of neutron-induced capture gamma rays for the same element (right).
Only PNG-based tools can accurately distinguish between the effects of capture and inelastic neutron interactions, but not all PNG-based
tools can make this measurement. To measure
inelastic interactions, the neutron generator
must be turned on and off rapidly, emitting pulses
of high-energy neutrons. Furthermore, for accurate measurements, the pulse must have a welldefined, repeatable burst shape, meaning the
neutron emissions have a constant and identical
output for each pulse of neutrons. Most spectroscopy tools, including the ECS tool, detect gamma
rays from inelastic reactions but cannot accurately determine elemental yields from these
measurements. Some downhole logging tools may
offer qualitative inelastic scatter data, but without hardware and measurement techniques to
take advantage of the inelastic interactions,
quantitative measurements are not possible.
Measurements from inelastic interactions are
less sensitive to environmental effects than those
from capture interactions. For instance, chlorine
[Cl] has a high thermal neutron capture cross
section and can significantly reduce the number
of thermal neutrons available for capture by
other elements.7 Reducing the pool of available
C
Mg
S
Ti
Al
Gamma ray energy
Si
Mg
Gamma ray energy
> Gamma ray spectra. Most neutron capture gamma ray spectroscopy logging tools rely on capture
gamma rays to determine elemental yields. After absorbing thermal neutrons, atomic nuclei emit
capture gamma rays with characteristic energies. For example, silicon [Si] (top left ) emits gamma rays
with several emission energies, but the highest probabilities are approximately 3.5 and 4.8 MeV. The
full capture gamma ray spectrum (bottom left ) is the combination of contributions from all the elements
generally found downhole. Inelastic gamma rays are generated when fast neutrons—those with
energies above 1 MeV—interact with nuclei in the formation, mud and the tool and result in the
emission of gamma rays. These inelastic gamma rays have an energy spectrum (bottom right ) that
looks similar to the capture gamma ray spectrum, but the characteristic energies differ. The Si inelastic
gamma ray energy (top right ) is about 1.8 MeV. The Litho Scanner tool takes advantage of both
spectra, which gives enhanced resolution to some elements, such as Mg and Fe, and adds others
such as C, which is not available from the capture spectrum. Capture tool background (CTB, bottom
left ) and inelastic tool background (ITB, bottom right ) are contributions to the measurement from the
tool and the borehole environment detected during spectral acquisition.
thermal neutrons for capture increases the statistical variability of the measurement. Because
the inelastic measurements are not affected by
neutron absorbers, they can serve to enhance the
resolution or precision of some capture data in
the presence of high Cl levels.
The Litho Scanner tool utilizes capture
gamma rays to determine concentrations of Al,
Ca, Fe, Gd, K, S, Si and Ti, as other tools have
done, but also quantifies concentrations of barium [Ba], Cl, hydrogen [H], Mg, manganese [Mn],
sodium [Na] and metals such as copper [Cu]
4. Hertzog R, Colson L, Seeman B, O’Brien M, Scott H,
McKeon D, Wraight P, Grau J, Ellis D, Schweitzer J and
Herron M: “Geochemical Logging with Spectrometry
Tools,” SPE Formation Evaluation 4, no. 2 (June 1989):
153–162.
5. For more on the ECS tool: Barson D, Christensen R,
Decoster E, Grau J, Herron M, Herron S, Guru UK,
Jordán M, Maher TM, Rylander E and White J:
“Spectroscopy: The Key to Rapid, Reliable Petrophysical
Answers,” Oilfield Review 17, no. 2 (Summer 2005): 14–33.
6. Radioisotopic neutron sources emit neutrons with energy
levels on the order of 4 million eV and typically output
4 × 107 neutron/s. PNGs emit neutrons with energies
around 14 million eV and typically output 30 × 107 neutron/s
and higher. Thermal neutrons are defined as those with
energy of 0.025 eV.
7. Neutron capture cross section is a relative measurement
of the probability of a nucleus capturing a neutron and
has the unit of barns (1 barn = 10–24 cm2). Of the elements
commonly encountered downhole, Cl is one of the most
receptive for absorbing thermal neutrons, thus has
a high capture cross section of 35 barns. Thermal
neutron capture cross section is low for most other
common downhole elements such as O (0.00019 barns),
C (0.0035 barns), Si (0.17 barns) and Ca (0.43 barns). Its
low capture cross section is one reason C concentrations
are determined using inelastic interactions.
37
Element
Element Name
Al
Aluminum
Ba
Barium
C
Carbon
Ca
Calcium
Cl
Chlorine
Cu
Copper
Fe
Iron
Gd
Gadolinium
H
Hydrogen
K
Potassium
Mg
Magnesium
Mn
Manganese
Na
Sodium
Ni
Nickel
O
Oxygen
S
Sulfur
Si
Silicon
Ti
Titanium
Capture
Inelastic
> Elements determined through capture and inelastic gamma ray
spectroscopy. (Adapted from Radtke et al, reference 9.)
and nickel [Ni]. The tool uses inelastic data primarily to quantify carbon [C] and Mg (above).
With an accurate Mg measurement, petrophysicists can differentiate calcite [CaCO3] from dolomite [CaMg(CO3)2]. The accurate C measurement
is crucial for determining TOC levels.
Hidden in the Spectra
Most downhole gamma ray logging tools use scintillation crystal detectors. When a gamma ray
encounters the detector crystal, the energy of that
Gamma rays
Scintillation crystal
Light
output
gamma ray is converted into a flash of light—
hence the name scintillation—and the magnitude of the light pulse is proportional to the energy
transferred to the crystal by the incident gamma
ray. A photomultiplier tube converts the flash of
light to a current, which it amplifies many times
before sending it to additional electronics, where
the analog signal is further amplified and converted to a digital value. The amplitude of the signal is determined by a pulse height analyzer, and
these data are combined with all the other pulses
Photomultiplier
tube (PMT)
Amplification,
pulse shaping and
pulse height analyzer
Dynodes
Gamma ray counts
Photo cathode
Anode
100
10
1
0.1
0.01
0
2
4
6
8
Gamma ray energy, MeV
> Scintillation detector. Gamma rays enter the scintillation crystal (top left ) causing a flash of light. The
intensity of the flash is directly related to the energy transferred to the crystal by the incident gamma
ray. The photomultiplier tube receives the light, converts it to a current, amplifies the current through
a series of dynodes and passes the signal along for additional amplification, shaping and pulse height
analysis (top right ). The information from all the gamma rays is combined, and counts are plotted
versus discrete energy levels (bottom right ).
38
that arrive at the detector to produce a gamma ray
spectrum (below left).
Sodium iodide [NaI] crystals doped with thallium [Tl] are used as detectors in most conventional
gamma ray logging tools, including some neutroninduced spectroscopy tools. Although the NaI crystal is robust, it is not efficient enough, nor is its
resolution great enough to separate the spectra of
all the desired elements. The ECS tool uses a bismuth germanate [Bi4Ge3O12], or BGO, crystal,
which, because of its high density and atomic number, produces a unique gamma ray spectrum.
However, the BGO scintillator is temperature sensitive; its spectral response broadens and loses definition or resolution, at elevated temperatures. The
Litho Scanner tool uses a cerium-doped lanthanum
bromide [LaBr3:Ce] crystal, which has a fast decay
time that permits high counting rates as well as
stable yields up to 200°C [400°F]. The light output
of the crystal is 50% brighter than that of NaI crystals, the benchmark for scintillation crystals; at
room temperature, its brightness is an order of magnitude higher than that of BGO crystals. The use of
the LaBr3:Ce scintillator marks a significant
increase in the ability to detect and count gamma
rays, and thus when combined with the high neutron output of a PNG, constitutes a major advance in
spectroscopy logging.
To be useful to petrophysicists, the gamma ray
spectrum measured by spectroscopy tools must
be translated into relevant mineralogy, a multistep process. The first step is the acquisition of
the gamma ray spectrum, which is a measure of
gamma ray counts versus energy bins as determined by the scintillation detector. After the
spectral response has been recorded, the spectrum must be converted to elemental yields.
Each element detected by the tool has a unique
signature, or elemental standard (next page, top).
8. Sedimentary minerals contain single or multiple oxides.
Examples are quartz [SiO2], calcite [CaCO3] and dolomite
[CaMg(CO3)2]. Clay minerals can also be treated as
complex mixtures of oxides. Examples are illite {(K,H3O)
(Al,Mg,Fe)2(Si,Al)4O10[(OH)2,(H2O)]} and montmorillonite
[(Na,Ca)0.33(Al,Mg)2(Si4O10)(OH)2·nH2O]. Concentrations
are expressed in weight %; the mass and not the volume
of any given element contributes to the spectrum.
For more information about the oxide closure method:
Grau JA, Schweitzer JS, Ellis DV and Hertzog RC: “A
Geological Model for Gamma-Ray Spectroscopy Logging
Measurements,” Nuclear Geophysics 3, no. 4 (1989):
351–359.
9. Radtke RJ, Lorente M, Adolph B, Berheide M, Fricke S,
Grau J, Herron S, Horkowitz J, Jorion B, Madio D,
May D, Miles J, Perkins L, Philip O, Roscoe B, Rose D
and Stoller C: “A New Capture and Inelastic
Spectroscopy Tool Takes Geochemical Logging to the
Next Level,” Transactions of the SPWLA 53rd Annual
Logging Symposium, Cartagena, Colombia, June 16–20,
2012, paper AAA.
10. Radtke et al, reference 9.
Oilfield Review
Capture Standards
Capture Standards
Counts, arbitrary log scale
Fe
Ca
Inelastic Standards
Cl
O
Na
Mg
K
Al
S
Ti
Si
Si
Mn
S
Al
Ba
Ca
Mg
Gd
Fe
C
H
Gamma ray energy, MeV
Gamma ray energy, MeV
Gamma ray energy, MeV
> Elemental standards and tool calibration. Engineers characterized the Litho Scanner tool at the Schlumberger Environmental Effects Calibration Facility in
Houston. The tool was placed in slabs of formation rocks (left ) and laboratory-prepared, simulated formations (right ) with known geochemical and
lithologic composition. Standards were derived for 18 elements using capture spectroscopy and 13 elements using inelastic spectroscopy (center, not all
shown). These standards are the basis for computing elemental yields.
tists apply the oxide closure model to the dataset.8
The closure model assumes that dry rock consists of a
set of oxides or compounds, and the sum of the proportions of all the oxides must equal 100%, or unity.
This closure requirement produces a unique normalization factor at each depth level, which in turn is
applied to the relative spectral yields to produce the
dry weight concentrations of specific elements.9
Dry weight elemental yields are then converted to mineralogy and lithology using software
modeling programs. SpectroLith lithology pro-
These elemental signatures can be used to
decompose the total measured spectra—which
are first corrected for environmental and electronic factors that distort them—into the contributions from the elemental standards. In the
case of the Litho Scanner tool, these standards
were established in test formations at the
Schlumberger Environmental Effects Calibration
Facility in Houston.
To obtain elemental weight fractions and produce
realistic mineralogic models of the formation, scienSpectral Acquisition
• Inelastic
• Capture
Spectral Stripping
• Elemental yields
Oxide Closure
• Elemental weight
fractions
Interpretation
• Minerals
• Total organic carbon (TOC)
• Matrix properties
C
t d
Corrected
Density
Inelastic
Normalized counts
Normalized counts
Al
Ca
Fe
S
ITB
Si
Ca
Fe Mg
S
Al
K
Na Mn
Ti
Gd
C
Illite
Quartz
K-feldspar
Na-feldspar
Calcite
Dolomite
Anhydrite
Pyrite
Kerogen
Inelastic
cessing for spectroscopy tools is one example. It
is an empirical model developed from hundreds
of laboratory measurements in known rock
types.10 ELANPlus advanced multimineral log
analysis is another technique. This analysis program computes the most probable formation
mineralogy and pore volume based on inputs
from several tools, including Litho Scanner yields
(below). Geologists may use knowledge of
expected rock types to guide the modeling software toward the correct mineralogic solution.
2 g/cm3 3
Matrix
Density
TOC
2 g/cm3 3
O
C
Si
Mg
Energy channel
Energy channel
Capture
Capture
Normalized counts
Normalized counts
H
Energy channel
Gd
Cl
K
Si
Fe
CTB
Ca
Mg
S
Ti
Al
Energy channel
> From acquisition to interpretation. Capture gamma ray and inelastic data (left ) are acquired with the Litho Scanner tool. Using elemental standards
established for the tool, spectral stripping converts data to elemental yields (center left ). Software computes elemental weight fractions from these elemental
yields based on the oxide closure model (center right ). Elemental analysis programs convert the yields or weight fractions to mineralogy (right, Track 1).
The Litho Scanner tool also directly measures carbon, from which TOC is computed (Track 2). Petrophysicists can use matrix density computed from the
elemental weight fractions and corrected for TOC (Track 3) to improve computed properties such as density porosity.
Spring 2014
39
Litho Scanner
tool standard
ECS tool standard
Room Temperature
Fe
Nal (Tl)
BGO
LaBr3:Ce
Density, g/cm3
3.67
7.13
5.29
Effective atomic number
50.8
75.2
46.9
Primary decay time, ns
230
300
25
8.2
61
Property
43.0
Light yield, photon/keV
Gamma ray counts
Si
Ca
S
H
LaBr3:Ce at 150°C, BGO at 60°C
1.8
LaBr3:Ce
1.6
Fe
1.2
Si
1.0
Nal (Tl)
0.8
Gamma ray counts
Relative light yield
1.4
0.6
0.4
BGO
0.2
0
0
50
100
150
Ca
S
200
Temperature, °C
H
0
2
4
6
8
10
Gamma ray energy, MeV
> Crystal scintillator comparisons. Several types of scintillation crystals are used in gamma ray logging tools; the NaI crystal is
the most common because of its ruggedness and low cost. A BGO scintillator is used in the ECS tool. For the Litho Scanner tool,
engineers chose the LaBr3:Ce scintillator because of its superior qualities compared with those of other scintillators. The quick
response time of the LaBr3:Ce scintillator—based on primary decay time—compared with that of other detectors (top left )
translates into greater efficiency and much higher counting capability. The relative light yield is stable from 0°C to 175°C [32°F to
350°F] (bottom left ), a clear improvement over the BGO scintillator, which can operate up to only about 60°C [140°F] before the
output drops below a usable level. The light yield of the LaBr3:Ce detector is higher than that of either the NaI or BGO crystals.
The LaBr3:Ce crystal detector is also more immune to thermal degradation than other detectors (right ). The clearly defined peaks
for elemental standards at room temperature (top right, green) are similar to those at 150°C (bottom right ). The elemental
standards response for the BGO crystal used in the ECS tool (red) broadens and loses definition at 60°C.
Litho Scanner Development
Neutron-induced capture spectroscopy data have
proved their value in characterization of complex
lithologies in both conventional reservoirs and
unconventional resource plays. However, petrophysicists who use these data have recognized
some of the limitations of early spectroscopy
tools. Engineers and scientists at Schlumberger
worked for many years to develop a spectroscopy
tool to address these concerns and correct issues
that affect data accuracy and precision.
40
Since the raw spectra measured by the tool
are the foundation upon which all other information is based, engineers searched for an
alternative to BGO detectors used in the ECS
tool, the gadolinium orthosilicate (GSO) [Gd2SiO5]
detector used in the RST reservoir saturation tool
and NaI detectors used in many other tools. One
of the major operational reasons for replacing
BGO detectors is their temperature sensitivity.
BGO crystals are sealed in a Dewar flask and
cooled with carbon dioxide [CO2] to keep the
tool internal temperature below 60°C [140°F]
for the entire logging operation. The BGO crystal output drops dramatically with temperature—light output when the crystal temperature
is greater than 60°C is too low to make acceptable logging measurements. This severely limits
the use of the ECS tool for long duration logging
such as drillpipe-conveyed or tractoring operations.
Schlumberger design engineers chose a largediameter LaBr3:Ce gamma ray detector for use in
the Litho Scanner tool. Compared with NaI and
Oilfield Review
Neutron output, relative counts
Litho Scanner Tool, Zero-Porosity Limestone Example
-2
0
2
4
6
8
10
Time, μs
> Stable and rapid neutron output. The hot
cathode method used by the Minitron PNG
delivers a rapid response when current is applied
to the PNG and an even faster decay when power
is switched off. This repeatable, controlled output
allowed design engineers to develop the inelastic
measurement that complements traditional
neutron capture gamma ray spectroscopy.
Gamma ray count, arbitrary scale
High count rate
Low count rate
Pileup corrected
0
1
2
3
4
5
6
7
8
9
10
Gamma ray energy, MeV
BGO crystals, this scintillator has an order of
magnitude faster response time. The faster
response enables high counting rates, improving
the tool’s precision compared with that of other
devices. The brighter output compared with that
of NaI and BGO scintillators translates to
improved spectral resolution. The LaBr3:Ce scintillator has a stable response from 0°C to 150°C
[32°F to 300°F], and even above 150°C, the light
yield is not significantly reduced (previous page).
The engineers also focused on the neutron
source when developing the Litho Scanner tool.
The PNG in the Litho Scanner tool includes a
Minitron PNG tube that uses a proprietary hot
cathode technology to produce crisp 8-μs bursts
with 400-ns rise and fall times (above). The rapid
response of this neutron generator allows precise
separation of inelastic and capture interactions.
Rated to 175°C [350°F], the PNG is capable of
producing 3 × 108 neutron/s; this high output
makes full use of the LaBr3:Ce scintillator’s fast
counting capabilities, as the count rate can
exceed 2.5 million count/s.
Engineers designed a new state-of-the-art
photomultiplier tube that is able to handle the
output from the high count rates now possible
from combining the LaBr3:Ce scintillator and the
new PNG. The Litho Scanner tool also incorporates specialized electronics to process the high
rate signals to avoid pileup, a condition in which
more counts arrive than can be separated by the
detector or the electronics (above right).11 Using
fast signal processors to handle the load avoids
spectral distortion caused by nearly coincident
Spring 2014
> Pileup distortion. When more gamma rays arrive at the detector than can
be counted, pileup occurs, and the result is spectral distortion. The problem
is more evident during high count rates (red) than low count rates (blue).
Because the Litho Scanner tool utilizes a high neutron output PNG and an
efficient LaBr3:Ce detector, pileup is most pronounced during inelastic
spectrum measurements. Algorithms have been developed to remove the
pileup degradation from the field spectrum based on the count rate (purple).
gamma ray arrivals. Unprecedented spectral resolution and precision are attained with the coupling of the scintillator, PNG, downhole electronics
and signal processing. The combination of these
enhancements results in the Litho Scanner tool as
a high-definition, third-generation neutron capture gamma ray spectroscopy service.
Spectroscopy, Rocks and TOC
Because of the increased development of unconventional resources, the ability to quantify TOC
in organic-rich rocks may be one of the most
important features of the new tool. TOC is the
weight % of organic carbon that resides within
the pore space of rocks. TOC includes carbon in
kerogen, bitumen and other solid, volatile and
liquid hydrocarbons trapped within the pore
space. Kerogen is the insoluble organic matter
from which hydrocarbons are generated.
Kerogen density is slightly higher than that of
fluids that fill the pore space; using only bulk
density measurements, petrophysicists have difficulty differentiating between liquid-filled pore
volume and the presence of immovable bitumen
in pores or kerogen in the rock framework.
Computing the true porosity of organic-rich
shales requires the removal of solid hydrocarbon
from the porosity measurement, which can be
accomplished with accurate TOC data combined
with other measurements such as those from
magnetic resonance tools.
For organic-rich shale exploration, geologists
and petrophysicists target formations that have
TOC values between 1.5 and 10 weight %. Rocks
with more than 10 weight % TOC from kerogen
only are usually considered too immature for
development.12 TOC values are typically derived
from core samples using a combustion technique
in which inorganic carbon is removed using phosphoric acid. The remaining sample material is
combusted in an oxygen-rich environment, and
the resulting CO2 is measured in an infrared
detection cell such as the LECO carbon analyzer.
A limitation of determining TOC from cores is that
11. Pileup occurs when more gamma rays arrive at the
detector than can be resolved by the system. Because
of the high output of the PNG used in the Litho Scanner
tool, pileup can be problematic during inelastic
processing. If the response of the system to pileup can
be characterized, the condition may be correctable.
12. Alexander T, Baihly J, Boyer C, Clark B, Waters G,
Jochen V, Le Calvez J, Lewis R, Miller CK, Thaeler J and
Toelle BE: “Shale Gas Revolution,” Oilfield Review 23,
no. 3 (Autumn 2011): 40–55.
41
Litho Scanner TOC
0
%
20 0
%
Core TOC
Depth, 0
m
%
Schmoker TOC
ΔLogR TOC
Litho Scanner TOC
20 0
Core TOC
20 0
%
Litho Scanner TOC
%
0
20
Core TOC
20 0
%
ΔLogR TOC
%
Schmoker TOC
20
Schmoker TOC
0
%
20
ΔLogR TOC
20
0
%
20
Calculated TOC, weight %
Schmoker TOC
20
15
10
5
0
-5
0
10
Core TOC, weight %
20
Measured TOC, weight %
Litho Scanner TOC
XX,000
20
15
10
5
0
-5
XX,025
0
10
Core TOC, weight %
20
Calculated TOC, weight %
ΔLogR TOC
XX,050
20
15
10
5
0
-5
0
10
Core TOC, weight %
20
> Comparison of methods to determine TOC. Several techniques have been developed to quantify organic carbon indirectly from well
logs. The Schmoker method utilizes density logs, and ΔlogR is based on sonic and resistivity data. The logs (left ) compare continuous
outputs for Schmoker TOC (Track 1, blue), Litho Scanner TOC (Track 2, purple) and ΔlogR TOC (Track 3, tan) with core-derived TOC
values (red dots). The three methods are shown together for direct comparison (Track 4). The crossplots (right ) compare calculated TOC
weight % with core-derived TOC weight % values. The TOC data from the Litho Scanner tool (center right ) compared most favorably with
core-derived TOC values, especially in rocks with high TOC weight %.
the core sample may not be representative of the
rest of the reservoir; TOC can vary considerably
across a reservoir section, which can be tens or
even hundreds of meters thick.
The Litho Scanner tool offers a continuous
carbon measurement from which TOC data can
be computed. A continuous dataset of TOC is a
more cost-effective and statistically accurate
option than measuring TOC on hundreds of core
samples. Many log-derived techniques—such as the
Schmoker and ΔlogR methods—have been used to
estimate TOC.13 The uncertainty can be high for the
various indirect measurement techniques and most
require calibration to core data (above).
Log analysts use the carbon component from
the Litho Scanner inelastic spectral measurements to quantify TOC. The carbon measurement
from the formation includes both inorganic (car-
bon in minerals) and organic carbon. The inorganic carbon can be quantified by assigning it to
the calcium and magnesium measurements,
which are associated with calcite and dolomite;
the amount of carbon bound up in these rocks
can be computed by first quantifying these elemental weight fractions. Some calcium and magnesium may be associated with minerals other
than carbonates. To address this situation, an
13. Gonzalez J, Lewis R, Hemingway J, Grau J, Rylander E
and Schmitt R: “Determination of Formation Organic
Carbon Content Using a New Neutron-Induced Gamma
Ray Spectroscopy Service That Directly Measures
Carbon,” Transactions of the SPWLA 54th Annual
Logging Symposium, New Orleans, June 22–26, 2013,
paper GG.
For more on the Schmoker technique: Schmoker JW:
“Determination of Organic-Matter Content of
Appalachian Devonian Shales from Gamma-Ray Logs,”
AAPG Bulletin 65, no. 7 (July 1981): 1285–1298.
For more on the ΔlogR method: Passey QR, Bohacs KM,
Esch WL, Klimentidis R and Sinha S: “From Oil-Prone
Source Rocks to Gas-Producing Shale Reservoir—
Geologic and Petrophysical Characterization of
Unconventional Shale-Gas Reservoirs,” paper SPE 131350,
presented at the CPS/SPE International Oil and Gas
Conference and Exhibition in China, Beijing,
June 8–10, 2010.
14. Gonzalez et al, reference 13.
15. Several varieties of calipers are used to measure
borehole diameter. An X-Y caliper measures the
borehole diameter with two sets of arms positioned 90°
apart and can more accurately describe the borehole
geometry than can single-axis calipers.
16. “Canada’s Arctic,” Alberta Online Encyclopedia,
Canada’s Petroleum Heritage, http://www.albertasource.
ca/petroleum/industry/historic_dev_canada_arctic.html
(accessed March 24, 2014).
17. Some historians consider Norman Wells—discovered
around 1910—at 65° 16’ 52” N latitude in Northwest
Territory, the first arctic oil field in Canada; however,
it is located just south of the Arctic Circle, which is the
defining line of the Canadian Arctic at 66° 33’ 44” N
latitude. For reference, the Eagle Plain basin, located in
Yukon, Canada, straddles the Arctic Circle.
18. For more on Arctic exploration: Bishop A, Bremner C,
Laake A, Strobbia C, Parno P and Utskot G: “Petroleum
Potential of the Arctic: Challenges and Solutions,”
Oilfield Review 22, no. 4 (Winter 2010/2011): 36–49.
42
Oilfield Review
extensive set of Litho Scanner rock matrix
measurements has been developed. Other less
common minerals with inorganic carbon that
might be encountered in oil and gas exploration
include siderite [FeCO3], rhodochrosite [MnCO3]
and ankerite [Ca(Fe, Mg, Mn)(CO3)]. Litho Scanner
tools measure the elemental concentrations necessary to correct for the presence of these carbonbearing minerals.14
Remaining carbon can then be considered
organic in nature and is equivalent to TOC.
The organic carbon determined using this technique includes carbon in the kerogen, bitumen
and any hydrocarbons—solid, oil and natural
gas—in the pore volume.
Correcting for Borehole Fluids
Borehole fluids are another potential contributor
of carbon to computed TOC. Determining TOC in
wells drilled with water-base mud (WBM) systems is fairly straightforward. In the absence of
organic-based additives, the organic carbon computed from tool measurements can be associated
with solid, liquid or gaseous hydrocarbons.
Additives in a WBM system may contribute to the
total carbon measurement, and a constant correction is often applied to compensate for it. Oilbase mud (OBM) systems present a different
challenge, and applying a constant offset may not
always account for the borehole contribution,
which is sensitive to borehole size and shape and
to environmental effects.
Scientists at Schlumberger-Doll Research,
working in collaboration with engineers in the
field for a solution to the OBM contribution to
TOC, discovered that the correlation between the
borehole carbon contribution and TOC is not a
simple linear relationship. Because the composition of mud in the borehole can vary considerably
from TD to surface, application of a simple offset
correction may not be valid. The researchers
were, however, able to develop a correction algorithm that has been successfully tested in both
OBM and WBM systems.
This new method computes an empirical carbon offset from the Litho Scanner carbon measurement as a function of borehole geometry
determined from caliper data. Software then
determines a correction factor to normalize
results for the specific mud system. For the purpose of computing this final correction, an X-Y
caliper is preferred, especially in hole sections
prone to ovality or enlargement.15 The correction
is applied at each depth frame (right). This technique recently proved its value in an Arctic exploration well in Yukon, Canada.
Spring 2014
Arctic Exploration
Indigenous peoples in the Canadian Arctic were
aware of oil seeps in that region for centuries and
used pitch from these seeps to waterproof their
fishing boats.16 But it wasn’t until 1974 that the
first Canadian Arctic oil field was discovered.17 In
the recent past, oil, rather than natural gas, has
often been the target of exploration in the Arctic
because of oil’s portability; today, however, both
natural gas and oil are viewed as targets.18
Litho Scanner TOC (Borehole Offset)
0
%
20
Litho Scanner TOC (Constant Offset)
Litho Scanner TOC (Constant Offset)
–2.5
Depth,
m
%
Effective Hole Size
200
mm
0
20
0
325
%
20
Core TOC
%
20
Correction Difference
X,X00
X,X50
X,Y00
> TOC correction for borehole contribution. Early methods of compensating
for borehole fluid TOC contributions applied a constant offset to the TOC
output; however, this method is sensitive to changes in borehole geometry.
For example, the TOC computed with a constant offset (Track 1, black)
generally follows the effective borehole size (magenta) when the hole is
washed out. Because borehole integrity is often difficult to maintain while
drilling in shales, data quality problems may be encountered. Recognizing
this limitation, Schlumberger scientists developed a more effective method
to compensate for TOC contributions from the mud system. This method
computes the TOC contribution in an in-gauge hole section, uses X-Y axis
calipers to model enlarged boreholes more accurately and applies a
realistic depth-by-depth offset. The TOC computed with the new method
(Track 2, blue) no longer reflects borehole geometry. The yellow shading
indicates the difference between the constant offset correction (gray curve)
and the borehole offset correction (blue curve) methods.
43
Ar
ct
ic
Cir
cle
Eagle Plain
Y u k o n
Arctic Circle
C
A
N
A
D
A
> Exploration in Arctic regions. Northern Cross (Yukon) Ltd. is exploring an area near the Arctic Circle in Yukon, Canada. Only 34 wells
had been drilled in the company’s 5,000-km2 lease in the Eagle Plain basin prior to the operator’s recent activity. Harsh conditions in
and around the Arctic Circle limit the drilling season and can potentially increase exploration and development costs. (Photograph
courtesy of Don Stachiw.)
Northern Cross (Yukon) Ltd. has recently begun
a campaign to actively explore the Eagle Plain
region in northern Yukon, Canada, a basin covering more than 5,000 km2 [2,000 mi2] (above).
Northern Cross speculates that Eagle Plain has
the largest oil and gas potential of any onshore
basin in the Yukon.
The Arctic region is a harsh environment for
drilling and exploration operations. Unlike locations in more temperate climates, vast areas in
the Arctic have seen little to no drilling activity
because of logistical difficulties. Across the
expanse of the Eagle Plain basin, only 34 wells
had been drilled prior to the Northern Cross
exploration campaign, and these were drilled
mostly in the 1960s and 1970s. The existing seismic data were 2D legacy surveys acquired before
many of the recent advances in high-resolution
3D techniques. From previous drilling programs,
engineers with Northern Cross knew that the
basin was geologically complex, and drilling
through some sections, including organic-rich
shales, posed operational difficulties.
Northern Cross targeted formations that
include conventional reservoirs and unconventional resource plays. A strong potential for structural and stratigraphic traps in the basin exists,
44
and these traps may provide opportunities for
conventional hydrocarbon production. For the
initial exploration phase, the operator planned
six wells, of which four had been drilled by the
end of 2013. Because of their close proximity to
the Dempster Highway, three wells are accessible
year-round and have been drilled. Typical of
many wells drilled in northern Canada, the other
three locations are accessible only during winter
months; one of these locations was drilled during
the 2012–2013 drilling season.
In addition to the logistical problems attributed to weather, operators exploring in the Arctic
face other challenges. In developing petrophysical analysis programs, geologists must decide
which tools and techniques should be utilized to
best evaluate exploratory wells. These geologists
face a daunting task, especially in complex reservoirs such as those of the Eagle Plain basin
because there are few wells with legacy datasets
for correlation and little state-of-the-art information about subsurface geology. Acquiring as much
data as is economically feasible is the norm,
which often includes taking conventional core.19
However, these are rank wildcat wells, and there
are no offset wells to offer guidance in determining which intervals to core. To avoid the expense
of coring rock that has no production potential,
engineers with Schlumberger suggested a traditional logging suite augmented with data from
the Litho Scanner tool. These data could then be
processed using the TerraTek HRA heterogeneous rock analysis service to determine optimal
sidewall core points, which could be taken using
a rotary coring tool.20
The output of the TerraTek HRA software is
commonly used for determining geomechanical
rock properties, but it also groups similar rock
types.21 Engineers and geologists used the rocktyping feature to pick rotary core depths, thus
ensuring desired rock types were represented in
the sampling program while avoiding oversampling in rocks with similar properties. The geologists also used TOC data from the Litho Scanner
tool to help further define core points. Because the
wells were drilled with a water-base mud system,
any conventional zones that displayed elevated
TOC values should correspond to hydrocarbons in
the pore space and be further evaluated.
Because the processing was conducted in real
time, the geologists were able to cross-reference
rocks identified from Litho Scanner data as having high TOC content with superior reservoir
Oilfield Review
quality rock types identified from TerraTek HRA
software (right). Rotary sidewall cores were taken,
and recovery was considered excellent in both
quality and quantity. Data from the high-graded
rotary coring program helped confirm results
from the Litho Scanner tool and provided lithology information similar in quality to that obtainable from whole core without the expense and
operational inefficiencies associated with cutting
conventional core. In addition, the operator
avoided costly conventional coring over intervals
of little interest.
While processing the Litho Scanner data during the initial log evaluation, petrophysicists
observed some puzzling results; a few intervals
exhibited elevated TOC values where none were
expected. These intervals generally corresponded
to borehole washouts, pointing to the mud system
as the source of the organic carbon. A review of
the mud report revealed the culprit of the elevated TOCs. In some wells, the mud engineer
occasionally used a lignite-based additive to
improve drilling performance. Lignite, a low-rank
coal, is a source of organic carbon, and its presence explained the elevated readings. The additive was not uniformly dispersed in the wells and
was not present across all intervals. Schlumberger
researchers had developed a borehole correction
technique to account for organic carbon in oilbase mud systems. Engineers used the technique
to correct for the presence of lignite, resolving
the problem.
In addition to the effects of mud additives
encountered by log analysts evaluating these
Arctic wells, operational issues related to drilling
affected the logging programs. During the course
of drilling two of the exploration wells, openhole
logs were acquired prior to a planned casing
point. Drilling deeper, the operator encountered
difficulties in a shale section that necessitated
drilling with a technique referred to as casing
drilling, in which the drill bit and mud motor are
attached to the casing. The interval is drilled,
and rather than being pulled from the well
when the rig reaches TD, the casing is cemented
in place.22
Schlumberger and Northern Cross petrophysicists and geologists acquired Litho Scanner data
in the cased section. Although spectroscopy data
can be acquired in cased hole, the influence of
the steel and cement behind the casing create
data offsets that require corrections. No openhole logs over the casing-drilled section existed
for comparison, but portions of the cased section
overlapped some previously logged openhole
intervals. By comparing openhole data to well
logs obtained inside the casing, engineers were
Spring 2014
Litho Scanner
Mineralogy
Anhydrite
Density Porosity
Siderite
Core TOC
Pyrite
Dolomite
0
Calcite
Depth,
m
30
Quartz+Feldspar+Mica 0
Clay
%
12
30
Litho Scanner TOC
%
TOC
%
–10
Neutron Porosity
%
–10
Corrected Porosity
12
30
%
–10
TerraTek HRA
Rock Types
Rotary
Core
Depth
X,700
X,750
> The Litho Scanner tool as an alternative to conventional coring. Because of cost and drilling
efficiency, conventional coring may not be an ideal choice for Arctic exploration wells; sparse offset
well data may provide little guidance for determining coring intervals. Northern Cross geologists used
the continuous mineralogy data from the Litho Scanner tool (Track 1) and TOC content computed from
carbon data (Track 2, gray shading) to identify zones with hydrocarbon potential. They then applied
TerraTek HRA software to identify similar rock types (Track 4) and determine the best depths for rotary
sidewall coring (Track 5, black dots). TOC measurements from those cores (Track 2, red dots) compare
favorably with Litho Scanner TOC measurements. The integration of these various data types resulted
in sampling that provided representative cores without needless oversampling. Neutron porosity
(Track 3, blue), density porosity (red) and Litho Scanner corrected porosity (black) computed using the
true mineralogy are also presented; the lithology-corrected porosity demonstrates how Litho Scanner
data can enhance petrophysical measurements.
19. For more on conventional coring: Andersen MA,
Duncan B and McLin R: “Core Truth in Formation
Evaluation,” Oilfield Review 25, no. 2 (Summer 2013):
16–25.
20. For more on rotary sidewall coring: Agarwal A,
Laronga R and Walker L: “Rotary Sidewall Coring—
Size Matters,” Oilfield Review 25, no. 4 (Winter
2013/2014): 30–39.
21. For more on the TerraTek HRA technique: SuarezRivera R, Deenadayalu C, Chertov M, Hartanto RN,
Gathogo P and Kunjir R: “Improving Horizontal
Completions on Heterogeneous Tight Shales,”
paper CSUG/SPE 146998, presented at the Canadian
Unconventional Resources Conference, Calgary,
November 15–17, 2011.
22. For more on the casing drilling technique: Fontenot KR,
Lesso B, Strickler RD and Warren TM: “Using Casing to
Drill Directional Wells,” Oilfield Review 17, no. 2
(Summer 2005): 44–61.
45
0
Openhole Mineralogy
Cased Hole Mineralogy
%
%
100 0
Anhydrite
Cased Hole Gamma Ray
0
Depth,
m
0
100
Pyrite
Pyrite
Dolomite
Dolomite
Calcite
Calcite
Openhole Gamma Ray
Quartz+Feldspar+Mica
Quartz+Feldspar+Mica
gAPI
Clay
Clay
gAPI
150
150
Cased Hole
Litho Scanner TOC
Anhydrite
–3
%
12
Openhole
Litho Scanner TOC
–3
%
12
TOC
X,600
X,650
> Spectroscopy data from inside casing. While drilling an exploration well in the Eagle Plain basin in
Yukon, Canada, Northern Cross drilling engineers encountered hole problems that necessitated drilling
with casing to reach TD. The cased interval included sections previously logged in open hole and
sections not logged before setting casing. Geologists decided to acquire data inside casing with the
Litho Scanner tool and compare it with the data from openhole runs. The gamma ray logs (Track 1)
from the openhole (magenta) and cased hole (black) passes were corrected for casing and cement
effects. Lithology and mineralogy data from the Litho Scanner tool run in open hole (Track 2) and cased
hole (Track 3) have good agreement. The TOC data from openhole (Track 4, magenta) and cased hole
(black curve, gray shading) measurements differ to some degree but are within the statistical limits of the
measurement precision.
able to apply offsets and correct for the contributions from the steel and cement (above). Satisfied
with the comparison of data from the previously
logged openhole section and logs from the now
cased section, Northern Cross had confidence
that the data faithfully represented the lithology
and TOC in the newly drilled portion.
Northern Cross plans to continue its exploration program in the Yukon and is in the process of
acquiring 3D seismic data across its lease position. Interpretation of log data indicates both oil
and natural gas potential in the basin.
What’s in a Name?
When referring to resource plays, some industry
professionals broadly apply the term shale to
unconventional reservoirs. Although many unconventional reservoirs may not necessarily meet the
46
standard geologic definition of shale, the term is
used to describe reservoir rocks that are often
rich in clay and have very low permeability.23 The
targets for exploration are generally referred to
as organic-rich shales because they have relatively high volumes of kerogen, a source of hydrocarbons. To have the potential for hydrocarbon
production, these rocks must possess the proper
mineralogy, porosity, hydrocarbon saturation,
organic content and thermal maturity.24 One
other aspect of most successful plays is the presence of large volumetric quantities of nonclay
components such as quartz, feldspar and carbonate. In contrast to clay, which tends to possess
low strength and may be highly ductile, these
nonclay minerals have high strength and contribute to a rock’s ease of fracture.
Most shale developments, such as the Barnett
Shale, the Marcellus Shale and the Haynesville
Shale, focus on rocks with a large proportion of
quartz, feldspar and mica (QFM)—an assemblage of silicate minerals common in sedimentary rocks. An abundance of these minerals
within the shale matrix may translate into successful unconventional wells. An exception to the
QFM-rich reservoir model is the Eagle Ford
Formation—or Eagle Ford shale—in south Texas,
USA. This formation, which is the source rock for
the prolific Austin Chalk formation, has produced
both liquids and gas in relatively large volumes.
The Eagle Ford Formation differs from many
other shale plays because of its high carbonate
content. As a result, the formation is amenable to
hydraulic fracture stimulation.25
The Eagle Ford Formation extends from south
Texas into northeast Mexico and is roughly 80 km
[50 mi] wide and 644 km [400 mi] long (next
page, top left). The average thickness is 76 m
[250 ft] at reservoir depth, which is approximately 1,220 to 3,660 m [4,000 to 12,000 ft] deep.
The Eagle Ford is sandwiched geologically
between the Austin Chalk and the Buda
Limestone formations. In some areas, the Maness
Shale may lie between the Eagle Ford and the
Buda Limestone.
Results from a well recently drilled by BHP
Billiton demonstrate the value of spectroscopy
data for evaluating the complex mineralogy of the
Eagle Ford Formation, especially when these
data are combined with information from the
CMR-Plus combinable magnetic resonance tool.
The CMR-Plus tool was operated in a newly developed 50-burst enhanced precision mode that
resolves small pores typically found in unconventional reservoir rocks.26 The TOC computed from
23. Shales are fine-grained rocks that form from the
compaction of silt and clay-sized particles. Because
they are formed from mud, they are also referred to as
mudstones. Shales are differentiated from other
claystones and mudstones in that they are laminated—
finely layered—and fissile, which means they can be
broken or split into sheets along their laminations.
For more on shales and shale exploration: Alexander
et al, reference 12.
24. For more on characteristics for targeting organic shale:
Glaser KS, Miller CK, Johnson GM, Toelle B,
Kleinberg RL, Miller P and Pennington WD: “Seeking
the Sweet Spot: Reservoir and Completion Quality in
Organic Shales,” Oilfield Review 25, no. 4 (Winter
2013/2014): 16–29.
25. For more on oil-prone source rocks and their evaluation:
Passey et al, reference 13.
26. For more on the 50-burst enhanced precision mode:
Hook P, Fairhurst D, Rylander E, Badry R, Bachman N,
Crary S, Chatawanich K and Taylor T: “Improved
Precision Magnetic Resonance Acquisition: Application
to Shale Evaluation,” paper SPE 146883, presented at
the SPE Annual Technical Conference and Exhibition,
Denver, October 30−November 2, 2011.
Oilfield Review
Water
Oil
TOC
Pyrite
Dolomite
Eagle
Ford
Formation
UNITED STATES
0
0
km
300
miles
300
Depth,
ft
Formation Name
T e x a s
Calcite
Pyrite
Quartz+Feldspar+Mica 0
Dolomite
Bound Water
Calcite
Montmorillonite
Kaolinite
Quartz+Feldspar+Mica
Clay
Illite
Total CMR-Plus
Porosity
%
25
Bound Water
Free Water
Oil
Kerogen
E
X
X,450
I
C
O
> The Eagle Ford Formation. The Eagle Ford Formation is the oil and gas
source rock for the prolific Austin Chalk formation. In Mexico, it lies along
the Mexican border with the US (red) and then extends north through central
South Texas (green). Several E&P companies are evaluating the Eagle Ford
for both oil and gas production.
Austin Chalk
M
X,475
the Litho Scanner tool’s carbon measurements
consists of all forms of organic carbon, including
kerogen, bitumen, coal and oil. Magnetic
resonance measurements are sensitive only to
liquids. The integration of fluid property measurements from the CMR-Plus tool with TOC data
from the Litho Scanner tool allows geologists to
distinguish between solid and liquid hydrocarbons and quantify oil potential for the reservoir.
Spring 2014
X,525
X,550
X,575
X,600
Maness Shale
. Optimizing liquids production from the Eagle Ford Formation. Operators
developing the Eagle Ford (blue shaded interval) have found that oil can be
produced economically. Based on the SpectroLith mineralogy data (Track 1),
the Eagle Ford Formation is rich in calcite (light blue), unlike the clay-rich
(tan shading) Maness Shale that lies below it; the Austin Chalk that lies
above it is almost pure calcite. The calcite in the Eagle Ford facilitates
hydraulic stimulation treatments. As seen in the ELANPlus mineralogy data
(Track 2), the Eagle Ford has significant TOC content (Track 2, maroon
shading)—the source of its oil; the TOC is composed of both oil and
kerogen—the nonproductive solid hydrocarbon portion. Petrophysicists
used the results from a combination of tools to determine the optimal
interval for landing the lateral, locating the well in the better rock type for
stimulation while also taking advantage of the liquids-rich section. For
example, the clay component of the Eagle Ford is composed of varying
amounts of montmorillonite, kaolinite and illite (Track 2); illite may be less
ductile than other clay types and thus a target for fracture stimulation.
Engineers also landed the lateral in the intervals with stiffer rocks such as
the calcite-rich sections. To determine oil-bearing intervals, the density
porosity was first corrected for matrix density from Litho Scanner
mineralogy. This porosity (Track 3) is the sum of the volumes of all liquids
and the solid hydrocarbons (kerogen). The CMR-Plus total porosity (Track 3,
thick black curve) is the sum of all liquid volumes—clay-bound water
(light blue), free water (blue) and oil (green). The difference between the
CMR-Plus total porosity and the mineralogy-corrected density porosity is
the unproductive kerogen portion of the TOC volume (Track 3, maroon
shading). The remaining TOC volume, not associated with kerogen, must be
the liquid oil volume.
Eagle Ford
X,500
X,625
Operators can use this information to plan the
placement of lateral sections and make completion decisions.
For the formation evaluation program, BHP
conventionally cored the Eagle Ford section; sample plugs were taken at 1- to 5-ft [0.3- to 1.5-m]
intervals and analyzed for TOC weight % using a
LECO carbon analyzer. The wireline logging
program included a traditional Platform Express
triple combo suite along with the CMR-Plus and
Litho Scanner tools.
Litho Scanner mineralogy data clearly differentiate the compositions of the Maness Shale and
the Eagle Ford Formation (above). Compared with
that of the Eagle Ford, the Maness section has a
large volume of illite and smectite, which are
ductile clays not suitable for hydraulic fracture
stimulation. However, the most telling difference
47
between the two formations is the high TOC
volume in the Eagle Ford, which is absent in the
Maness. High organic carbon volume in the Eagle
Ford Formation makes it a target for exploration.
In the Eagle Ford Formation, TOC weight %
from core analysis and from the Litho Scanner processed data ranges from 2 to 7 weight %. Organic
carbon may be associated with both kerogen and
oil in the formation; therefore, without more information about the composition of the TOC, fully
evaluating the resource potential of this reservoir would be difficult. Nuclear magnetic resonance (NMR) data from the CMR-Plus tool
helped resolve this uncertainty.
NMR tools respond to liquids in formation
rocks. If the pore space is filled with oil or water,
the NMR porosity should replicate the porosity
determined from the Litho-Density tool. Because
gas has a low density and kerogen is a solid, the
NMR porosity measured in rocks containing
these substances will be lower than that computed from density tools.
Pores in unconventional reservoirs such as
the Eagle Ford Formation are small, and therefore most NMR tools are incapable of properly
measuring the total liquid volume. The CMR-Plus
tool has the shortest echo spacing in the industry,
which translates into the ability to resolve small
pores and compute a more accurate liquid volume than other tools can in similar environments, especially when the tool is operating in
the enhanced precision mode. The NMR porosity
measurement includes water—both free and
bound—and oil. In clay-rich rocks, most of the
water measured by the CMR-Plus tool is bound
water associated with the clays.
For liquids-rich unconventional resource
plays such as the Eagle Ford, petrophysicists can
compare fluid volumes computed from CMR-Plus
data with the Litho Scanner TOC volume and derive
a volumetric oil component. Reservoir engineers
can then use this information to determine the
Nafud Basin Stratigraphic Column
Chronostratigraphy
Lithostratigraphy
Epoch
Wenlockian
Stage
Homerian
Silurian
Sheinwoodian
Telychian
Llandoverian
Aeronian
Qalibah Formation
Period
Rhuddanian
Zarqa facies
Sandbian
Llandeilian
Darriwilian
Llanvirnian
Quwarah
member
Qasim Formation
Ordovician
Katian
Ra’an
member
Kahfah
member
Hanadir
member
Dapingian
Floian
Tremadocian
Hot
shale
Sarah Formation
Himantian
Arenigian
Qusaiba
member
Hot shale
Ashgillian
Caradocian
Sharawra
member
Saq Formation
Tremadocian
> Nafud basin stratigraphic column. Geologists consider the organicrich Qusaiba Shale member of the Silurian-period Qalibah Formation the
hydrocarbon source rock for many Middle East oil and gas fields. Because
the gamma ray logs from the Qusaiba Shale have very high counts, the shale
is considered a “hot shale.” High gamma ray counts indicate organic-rich
shales, and geologists target these formations for exploration. (Adapted
from Al-Salim et al, reference 27.)
48
volume of oil in place, estimate the oil production
potential and make better informed decisions on
where to land the lateral.
Saudi Arabia Unconventional Reservoir
Saudi Aramco utilized the Litho Scanner tool to
evaluate formations in the Nafud basin and determine their potential as unconventional resource
plays.27 The basin is characterized by a thick
sequence of Paleozoic rocks from the Cambrian
through Devonian periods. The Silurian-aged
Qusaiba Shale—the target for these wells—is a
member of the Qalibah Formation (below left). The
organic-rich Qusaiba Shale is a prolific source of
hydrocarbons, generating an estimated 90% of
the Paleozoic light oil and gas found in the Middle
East, and it is the source rock for many major oil
and gas fields.
The Qusaiba Shale is characterized by high
gamma ray readings, which result from precipitated uranium in the reducing environment
where the shale was deposited. The deepest
shale intervals are Rhuddanian stage and typically have 8 to 9 weight % TOC on average.
Younger Aeronian- and Telychian-aged intervals
have lower TOC values.
To evaluate the Litho Scanner tool’s ability
to characterize the mineralogy of the formation
and quantify TOC, Saudi Aramco ran the tool
in two wells, one drilled with 10-lbm/galUS
[1,200-kg/m3] OBM and another with 9.2-lbm/galUS
[1,100-kg/m3] WBM. Saudi Aramco did not cut a
conventional core in the first well drilled with
OBM because LECO TOC core data were available from a well located about a mile away.
These data compared favorably with TOC from
the Litho Scanner tool run in the new OBM well.
For a more direct comparison between log
data and core measurements, the operator ran
the Litho Scanner tool in a second well and cut
core over the kerogen-rich zone of interest. The
target formation in this case was the Rhuddanian
hot shale. The operator conducted a special study
on core samples. To minimize the effects of rock
heterogeneity on core measurements and obtain
measurements more representative of the volume probed by the spectroscopy tool, technicians
took 1 ft [0.3 m] long trim core slabs. These samples were then crushed into a homogenized powder for analysis.
Technicians used XRF to analyze a portion of
the powder for elemental concentrations and a
LECO total carbon analyzer to determine TOC.28
Schmoker TOC was computed from the formation
Oilfield Review
TOC from Core
Mineralogy
0
Pyrite
Dolomite
Depth,
ft
Calcite
Quartz+Feldspar+Mica
Clay
0
Caliper
6
in. 16 0
Al from Core
Si from Core
Fe from Core
S from Core
%
%
%
%
20 0
50 0
20 0
10 0
Ca from Core
Mg from Core
Na from Core
%
%
%
20 0
10 0
Litho Scanner
Dry Weight Al
Litho Scanner
Dry Weight Si
Litho Scanner
Dry Weight Fe
Litho Scanner
Dry Weight S
Litho Scanner
Dry Weight Ca
Litho Scanner
Dry Weight Mg
Litho Scanner
Dry Weight Na
%
%
%
%
%
%
%
20 0
50 0
20 0
10 0
20 0
10 0
K from Core
5 0
%
Litho Scanner
Dry Weight K
5 0
%
%
20
Litho Scanner
TOC
5
0
%
20
Schmoker TOC
5 0
%
20
X,600
X,650
X,700
X,750
> Dry weight yields and TOC from a Middle East well. To confirm the quality of downhole spectroscopy data, Saudi Aramco petrophysicists compared
core-derived elemental yields from XRF measurements (Tracks 2 to 9, black dots) with Litho Scanner dry weight yields (red curves). The elemental
concentrations show good agreement, except around X,600 ft, where there are high concentrations of pyrite (Track 1, orange) and TOC (Track 10).
The recovered core from that zone was fractured and fragmented, possibly causing some depth mismatch when the core was analyzed. TOC computed
from the Litho Scanner data (Track 10, red) was compared with core TOC (black dots) and TOC computed from the Schmoker technique (blue);
Litho Scanner TOC matched core results better than did the Schmoker technique. (Adapted from Al-Salim et al, reference 27.)
density log as a third source for comparison.29 The
results of the core laboratory measurements
compared favorably with the Litho Scanner elemental dry weights and TOC data (above).
Petrophysicists combined the Litho Scanner dry
weight data with other logging data and computed the reservoir mineralogy; these data were
then compared with dual range Fourier transform infrared (FTIR) spectroscopy measurements from the core (next page). The mineralogy
analysis is a model-dependent computation, and
application of the appropriate model is crucial
for correct results.
Engineers with Saudi Aramco and
Schlumberger made several findings from the
analysis of data from the Litho Scanner tool. The
Litho Scanner TOC data closely matched core
TOC data without empirical calibration. Using
core-derived TOC values as the baseline for comparison, they determined that the Schmoker
Spring 2014
technique TOC was not as accurate as the TOC
computed from the Litho Scanner carbon output.
Because the Schmoker technique was developed
specifically for Appalachian Devonian shales and
the Bakken Formation, whose characterizations
differ from those of the Nafud basin, the results
are not surprising. Further refinement or calibration is necessary to apply this technique in formations other than the ones for which it was
developed.
The Litho Scanner tool provides reliable
information for developing or refining petrophysical models in formations with complex lithologies. The improved accuracy in measuring certain
elements allows petrophysicists to include more
minerals in formation evaluation models to
describe the reservoir rocks and better understand depositional environments. Correctly characterized mineralogy translates into more
accurate matrix properties and, consequently,
more accurate porosity and water saturation
computations. These benefits may be achieved in
a fraction of the time and at a fraction of the cost
of cutting and analyzing whole core. This information is especially important during exploration and early development stages when core
data may be scarce or cover a limited area of a
new prospect.
27. Al-Salim A, Meridji Y, Musharfi N, Al-Waheed H,
Saldungaray P, Herron S and Polyakov M: “Using a New
Spectroscopy Tool to Quantify Elemental Concentrations
and TOC in an Unconventional Shale Gas Reservoir:
Case Studies from Saudi Arabia,” paper SPE-SAS-312,
presented at the SPE Annual Technical Symposium and
Exhibition, Al-Khobar, Saudi Arabia, April 21–24, 2014.
28. X-ray fluorescence is a measurement technique that
bombards materials with X-rays to ionize the atoms.
Ionization results in the emission of characteristic
fluorescent radiation in a manner similar to the
element-specific gamma ray emissions from neutron
capture. Individual elements in complex mixtures
can be accurately measured in the laboratory using
this technique.
29. Schmoker, reference 13.
49
Depth,
ft
XRD-Derived
Mineralogy
Litho Scanner
Mineralogy
Biotite
Ca-Feldspar
Orthoclase
Siderite
Pyrite
Muscovite
Dolomite
Calcite
Ankerite
Na-Feldspar
Quartz
Smectite
Kaolinite
Illite
Chlorite
Siderite
Pyrite
Muscovite
Dolomite
Calcite
Ankerite
Na-Feldspar
Quartz
Smectite
Kaolinite
Illite
Chlorite
Illite
from Core
0
%
Kaolinite
from Core
100 0
Litho Scanner
Dry Weight Illite
0
%
100 0
%
Na-Feldspar
from Core
100 0
%
Muscovite
from Core
50 0
%
Siderite
from Core
50 0
%
Pyrite
from Core
20 0
%
Dolomite
from Core
25 0
%
50
Litho Scanner
Litho Scanner
Litho Scanner
Litho Scanner
Litho Scanner
Litho Scanner
Litho Scanner
Dry Weight Kaolinite Dry Weight Quartz Dry Weight Feldspar Dry Weight Muscovite Dry Weight Siderite Dry Weight Pyrite Dry Weight Dolomite
100 0
Illite
%
Quartz
from Core
%
Kaolinite
100 0
%
Quartz
100 0
%
Na-Feldspar
50 0
%
50 0
Muscovite
%
Siderite
20 0
%
Pyrite
25 0
%
50
Dolomite
X,000
X,100
X,200
> Mineralogy comparison. Scientists at the Schlumberger-Doll Research Center performed FTIR spectroscopy analysis on cores from a well drilled with
WBM and compared the XRD-derived core mineralogy (Track 1) with the mineralogy computed from Litho Scanner data and other log inputs (Track 2).
Accurate mineralogy data are crucial for computing many petrophysical properties such as porosity and fluid saturations. In this well, mineralogy data
helped petrophysicists make proper analyses; for instance, high K levels in sands can be attributed to orthoclase (K-feldspar) or muscovite (K-mica)
(Track 7). The matrix density values of these minerals are 2.57 g/cm3 and 2.76 g/cm3, respectively. In this case, geologists have local knowledge of the
rock types, and all the K was attributed to muscovite. The correct mineralogy results in a more accurate matrix density and, consequently, more accurate
density porosity and water saturation computations. In addition, a better quality Na measurement from the Litho Scanner tool can be used to quantify
concentrations of Na-bearing minerals such as albite—Na-plagioclase feldspar (Track 6)—with less uncertainty. (Adapted from Al-Salim et al, reference 27.)
Ultimate Answers
Downhole spectroscopy is just one method petrophysicists use to determine the complex nature
of reservoir rocks. Spectroscopy tools provide
bulk measurements but are not able to determine rock fabric. For instance, the Litho Scanner
tool can identify zones with pyrite but cannot
determine how the mineral is dispersed.
Similarly, the percentage of clay in one zone may
be identical to that in another, but the tool cannot determine spatial distribution of the clay
50
particles, specifically whether they are structural, laminated or pore filling. Certain questions
about mineral composition can be answered only
from core analysis. Many mineralogic and lithologic conditions affect log responses, especially
those of resistivity and nuclear tools. In this age
of unconventional reservoir development, petrophysicists must rely on the integration of multiple data sources to understand the rock
composition and fabric.
In earlier times, simple models sufficed to
identify hydrocarbon productive zones and quantify production potential. Wells in which simple
conditions prevail are becoming more uncommon. To characterize hydrocarbons in complex
rocks and reservoirs, petrophysicists now have
more and better tools and techniques at their disposal. Geologists and petrophysicists are using
these new tools and techniques to help operators
find and produce more oil and gas from increasingly complex plays.
—TS
Oilfield Review