determination of formation organic carbon content … · the producible pore fluid are within the...

SPWLA 54th

Annual Logging Symposium, June 22-26, 2013

1

DETERMINATION OF FORMATION ORGANIC CARBON CONTENT

USING A NEW NEUTRON-INDUCED GAMMA RAY

SPECTROSCOPY SERVICE THAT DIRECTLY MEASURES CARBON

Jorge Gonzalez, Richard Lewis, James Hemingway, Jim Grau, Erik Rylander and Ryan Schmitt, Schlumberger

Copyright 2013, held jointly by the Society of Petrophysicists and Well Log

Analysts (SPWLA) and the submitting authors.

This paper was prepared for presentation at the SPWLA 54th Annual

Logging Symposium held in New Orleans, Louisiana June 22-26, 2013.

ABSTRACT

Over the past few decades, many techniques

have been developed for the log evaluation of

organic-rich rocks (ORR). More recently, ORR

have gained economic interest across the globe,

not only as the source rocks for conventional

reserves, but also as potential reservoirs

themselves. Quantifying total organic carbon

(TOC) and the complex mineralogy of these

rocks are two key components for a robust

characterization.

A number of well-log-derived TOC indicators

based on measurements such as gamma ray,

spectral gamma ray, resistivity, density, sonic,

neutron, and nuclear magnetic resonance (NMR)

have been developed over the years with

different levels of success depending on the

specific ORR and where it occurs within a basin.

An accurate TOC estimation faces two main

challenges: 1) the necessity for a correlation of

log data with core to utilize the most

representative model and 2) extensive log

interpretation. The process of an empirically

derived TOC has been hindered by the lack of a

consistent direct organic carbon measurement.

The introduction of a new neutron-induced

capture and inelastic gamma ray spectroscopy

tool using a very high-resolution scintillator and

a new type of pulsed neutron generator allows us

to measure new elements which, in turn,

increases our ability to properly interpret

lithologically complex formations. In addition to

measuring elements derived from capture

spectroscopy, this new service is capable of

measuring reliable inelastic yields, including

carbon, at reasonable logging speeds. This

carbon measurement allows the calculation of a

stand-alone quantitative TOC value that does not

require local calibrations or multitool

interpretation. This is possible because of the

successful combination of capture and inelastic

gamma ray spectra. Inorganic carbon in

carbonate is estimated by using other elements

from this logging tool (Ca, Mg, Mn, Fe) to

estimate total carbonate, and this value is

subtracted from the measured total carbon to

give TOC.

INTRODUCTION

For nearly the entire history of the logging industry,

fluid saturation calculations have been primarily

based on resistivity measurements which were

combined with porosity and lithology information. In

conventional reservoirs, the water saturation

calculations produced reasonable estimations of

actual water saturation and, consequently, oil and gas

recovery factors. However in cases where formation

water salinity is either too fresh or unknown the

resistivity calculations are unreliable. This created a

need for fluid saturation measurements that were

either independent of salinity or not nearly so reliant

upon knowing the exact formation water salinity.

Notable techniques are typically based on high-

frequency dielectric permittivity, nuclear magnetic

resonance, or carbon-oxygen ratio from neutron-

induced inelastic gamma spectroscopy.

This paper will focus on the application of inelastic

gamma spectroscopy in combination with capture

gamma spectroscopy, to measure the carbon content

of the formation to estimate its total organic carbon

(TOC) content.

Inelastic gamma spectroscopy has been used

with increasing success for well over 30 years

because of its ability to respond directly to

elemental carbon, which can then be related

directly to oil saturation using either a porosity

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log or elemental oxygen. Because of the nature

of this measurement, it works in either open or

cased wellbores. Historically, we have used the

term ―water saturation‖ when talking about fluid

saturations because we were measuring water

and assuming that what was not water was either

gas or oil. Carbon-based measurements such as

the (TOC) measurement described here are used

to calculate ―oil saturation‖ because we are

actually measuring the carbon rather than water.

The hydrocarbon volume is derived based on

knowledge of density of the hydrocarbon phase.

Neutron-induced gamma spectroscopy was

studied for potential use in the oil field in the

1960s (Tittman et al., 1960) and introduced as a

commercial service in the mid-1970s (Lock et

al., 1974; Ward 1974, Hertzog 1978). Gamma

rays resulting from thermal neutron capture are

used to identify and quantify elemental

concentrations of the major components making

up common sedimentary rocks (Figure 1).

Fig.1 When a neutron has slowed to a thermal

energy of, typically, 0.025 eV, by inelastic and

elastic interactions, it can be absorbed by the

nucleus. The excited nucleus will emit capture

gamma rays which will have an energy(s)

specific to the element that emitted the gamma

ray. Recording gamma ray count rate as well as

gamma ray energy allows one to estimate the

relative abundance of specific elements.

Weight percent of elements such as silicon,

calcium, iron, and sulfur can be used to quantify

quartz, carbonates, clay, evaporites, and heavy

minerals (Herron and Herron, 1996). These

measurements have been used in both open and

cased wellbores. By focusing on the gamma rays

generated by inelastic neutron interactions, we

can identify elements such as carbon and oxygen

(Figure 2).

Fig.2 When a high-energy neutron bounces off

a nucleus, it excites it into giving off inelastic

gamma rays. The energy(s) of these gamma rays

depends on the element emitting the gamma ray.

The unique gamma ray energy response for

carbon inspired the early attempts to calculate

oil saturation directly from the carbon

measurement. As seen in Figure 3, there are

several common elements that can be identified

based on their unique spectra. Since several

elements produce gamma rays in the same

manner as carbon, it is necessary to solve for

each element that contributes gamma rays to the

inelastic spectrum. Historically, it was the

carbon and oxygen yields which were used to

calculate oil saturation. The inelastic elemental

yields from the other elements were not used for

analytical purposes; they were simply calculated

to ensure that gamma rays generated from

elements other than carbon or oxygen did not

contribute to the carbon or oxygen yields. We

will demonstrate that with this new generation

tool we do use other inelastic elemental yields to

normalize the inelastic and capture gamma ray

spectra to quantify mineralogy and the carbon

content of the formation.

This latest generation tool, which uses a

lanthanum bromide scintillator, greatly increases

ones ability to resolve the details of a gamma ray

energy spectrum. The lanthanum bromide

scintillator offers many advantages over the

previous generation bismuth germinate (BGO)

detectors or sodium iodide, which were used

before the BGO detector. The spectral resolution

is more than twice as good as what could be

achieved with BGO, and the count rate handling

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capabilities are improved by a factor of nearly

10. Not only is it possible to measure elements

that were previously too difficult to detect, but

one can now accurately measure elemental

carbon at higher logging speeds over long

intervals.

It is this improved measurement of many

elements from both capture and inelastic

reactions that allows us to compute an absolute

formation carbon concentration, and in turn

calculate TOC in the formation within the region

of investigation of the measurement. At this

point it is important that we define what we are

actually measuring and how we derive TOC

from these basic measurements. It is also

important that we understand what TOC actually

means in this context. TOC in the context of this

measurement includes all forms of organic

carbon that are not part of the inorganic rock

matrix. This includes carbon associated with

kerogen, bitumen, oil, gas and coal. However, it

does not include carbon within the inorganic

minerals (e.g. carbonate).

Fig.3 By comparing the recorded gamma ray

spectrum to a set of elemental spectra standards

it is possible to determine the best combination

of standards that match the recorded spectra.

These calculated elemental yields are the best-fit

combination of the percentage yield of gamma

rays from each element. These elemental yields

can then be used to determine the relative

abundance of each element.

LITERATURE REVIEW

TOC is the weight percent of carbon that resides

within the organic portion of the rock.

Historically, TOC has been used to equate

directly to just the carbon in the kerogen fraction

of the rock, but, in practice in direct carbon

measurements, it also includes the portion of the

carbon that resides within bitumen and residual

oils (Rylander et al., 2013). Generally speaking,

the carbon density of gas is too low to be

detectable, although very high-pressure gas or

supercritical CO2 may be detectable in high-

porosity formations.

Quantification of TOC is critical to the

evaluation of organic shales. Organic shales are

typically defined by the quantity of TOC

present, which is usually from 1.5 to 2 wt%

(Herron, 1991). Thus, the quantification of TOC

is a valuable step in identifying potential organic

shale reservoirs, and the quantity of TOC can be

related to the reservoir quality of gas shales as

the producible pore fluid are within the organic

matter (Ambrose et al., 2010). Kerogen, the

organic matter where the TOC resides, has

petrophysical properties similar to pore fluids

(e.g., low bulk density, high neutron porosity,

low photoelectric factor), and it can be difficult

to differentiate from pore volume. An accurate

porosity estimation for an organic shale requires

an accurate TOC estimation.

Multiple models for the estimation of TOC from

logs are discussed below. The first four

measurements (gamma ray, uranium, density,

ΔLogR) are empirically derived and are based

on calibration to core measurements. They rely

on assumptions that matrix properties are

invariant through zones in the organic shale. The

last two measurements (NMR density,

geochemistry) are direct measurements.

Gamma ray. One petrophysical property for

which kerogen and pore fluids differ is their

gamma ray activity. Organic shales are

commonly associated with elevated gamma ray

activity. According to Schmoker (1981) factors

controlling this association include: 1) uranium

content of water at time of deposition; 2) type of

organic matter deposited; 3) water chemistry

near the water-sediment interface; and 4) rate of

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sediment deposition. The elevated gamma

activity is due to the concentration of uranium in

these sediments.

The gamma ray activity in some organic shales

can be high, with values in some Paleozoic

shales exceeding 1000 gAPI. Variations in gross

gamma activity primarily reflect differences in

uranium activity as the contribution from

potassium and thorium are relatively

insignificant in comparison. An empirical

correlation between gamma ray activity and

TOC content measured with core analysis must

be derived for each organic shale, and it may

need to be adjusted throughout a basin or field.

Some organic shales, especially those deposited

in the Mesozoic and Cenozoic eras and those

deposited in lacustrine environments, may have

moderate to low gamma ray activity. In these

cases, the use of gross gamma activity to

quantify TOC is not practical because of

relatively high contributions from the other two

radioactive elements, potassium and thorium.

A primary advantage of using the gamma ray

measurement for TOC quantification is that this

log is run in almost every well drilled. It can be

collected in practically any borehole

environment and is amenable to environmental

corrections. The primary disadvantage is that

gamma ray activity responds to the presence of

uranium, not kerogen, and the quantity of

uranium is also dependent upon differences in

kerogen type, water chemistry, and

sedimentation rate. In general, one can assume

that zones with higher gamma ray activity will

have higher TOC content, but this relationship is

commonly not linear, and it can vary abruptly

within one organic shale section. Finally,

differences in the quantity of clay or other

radioactive minerals can affect the potassium

and thorium content which, in turn, can lead to

changes in gross gamma activity that are not

related to changes in TOC content.

Uranium. Spectral gamma ray logs present the

advantage of direct uranium quantification (Fertl

and Rieke, 1980), negating the last issue

presented in the previous paragraph. An

empirical relationship between uranium content

and TOC still needs to be derived. And errors

can still arise due to the presence of other

uraniferous minerals like phosphates (Jacobi et

al., 2008) that could be misidentified as TOC.

The issues with sedimentation rate, etc. are still

valid.

Density. Gamma-gamma density logs acquired

over organic shales characteristically show a

lower value when compared to conventional

formations. A general rule would be that a gas

shale should have a bulk density less than 2.57

g/cm3 (equivalent to limestone porosity of 8 p.u.

with fluid density of 1 g/cm3) to be prospective.

Note that the actual porosity of the organic shale

will be less this estimate because a significant

portion of the anomaly is due to an incorrect

matrix density estimate for this calculation. This

is due to the low grain density of kerogen [1.1 to

1.4 gm/cm3 (Tissot and Welte, 1978)], in

contrast to a minimum of 2.65 g/cm3 for the

inorganic matrix. Algorithms have been

published that estimate TOC content based

solely on the bulk density of the formation. A

notable example is one published by Schmoker

and Hester (1983) that is based on core analysis

from the Bakken, Appalachian Devonian shales

and Woodford shales. This type of algorithm

implicitly assumes that any change in bulk

density is due to variations in kerogen content

and that the inorganic grain density is invariant.

The Schmoker equation assumes an inorganic

grain density of 2.69 g/cm3. This algorithm does

return a reasonable TOC estimate in clay-poor,

thermally mature organic shales—basically, the

shales used for this calibration—and a lithotype

that is common for organic shales. However, this

algorithm underestimates TOC content in clay-

or carbonate-rich shales (too low of a grain

density) and overestimates TOC in thermally

immature shales. Nevertheless, a density-based

estimate is simple to calculate; gamma-gamma

density logs are commonly acquired, and there is

limited need for environmental corrections. It

provides a reasonable first-pass estimate of TOC

content.

logR. The elevated apparent porosity values for

organic shale are not limited to gamma-gamma

density. Sonic and neutron logs commonly also

exhibit elevated apparent porosity in organic

shales. The compressional slowness of kerogen

is high, 109 µs/ft (Vernik et al., 1994), compared

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to an estimated 54 to 63 µs/ft for a shaly matrix,

parallel and perpendicular to bedding (Vernik et

al., 1994). The presence of kerogen will increase

the apparent sonic porosity if the matrix

slowness remains invariant. Neutron porosity

response in an organic shale is not as

straightforward as the responses for gamma-

gamma density and compressional sonic. The

neutron responds primarily to hydrogen in the

formation, and this element can reside in: a) pore

fluids (water, oil, or gas), b) clay-bound water,

c) hydroxyls within the clay mineral matrix, d)

bitumen and e) kerogen. The multiple sources of

hydrogen can lead to a complicated neutron

response in organic shales.

Compared to conventional shales, formation

resistivity is commonly elevated in organic

shales because of their low total water

saturations. This is a function of low effective

water saturation caused by the dominance of

hydrophobic kerogen-hosted pores (Rylander et

al., 2013), and a generally lower clay mineral

content, with associated lower clay-bound water,

in organic shales. Low-resistivity organic shales,

with resistivities less than 15 ohm-m and

neutron porosity greater than 30 p.u. (limestone

matrix), typically contain expandable clay

minerals.

Passey et al. (1990) developed an empirical

relationship to estimate TOC that is driven by

the elevated formation resistivity and elevated

apparent porosity typical in organic shales as

described above. A porosity curve, typically

sonic porosity, is overplotted with resistivity and

scaled so that they overlie, ―baseline,‖ in zones

with conventional shales above and/or below the

organic shale. The difference between these

curves, called LogR, is equated to TOC

content. An estimate of thermal maturity and

kerogen type is necessary to convert LogR to

TOC. This methodology is underlain by the

assumption that the baseline shale sections and

the organic shale have similar matrix properties

regarding resistivity and apparent porosity. This

resulting TOC estimate is very sensitive to the

thermal maturity estimate (Issler et al, 2002). If

there is a priori knowledge of the TOC content,

then the thermal maturity can usually be

adjusted to give a good TOC log representation.

Finally, the presence of expandable clays, with

low resistivities due to increased clay-bound

water, can lead to a significant underestimate of

TOC in zones where they are encountered.

LogR was later modified (Passey et al., 2010)

to estimate TOC for shales within the gas

window.

NMR density logs. The integration of NMR,

density, and geochemical logs can be used to

directly measure kerogen volume in an organic

shale. Simplistically, the low grain density of

kerogen leads to the density log identifying a

kerogen-rich zone as porous, yet the kerogen

will appear as matrix to an NMR log. The

difference in these two volumes can be equated

to kerogen volume (Figure 4). In practice, a

matrix density from a geochemical log is used to

calculate the density porosity because this

parameter does not include carbon in its

estimation (Herron and Herron, 2000). The

estimated density porosity will include the

kerogen volume.

Fig.4 Petrophysical representation of kerogen

volume estimate from integration of gamma-

gamma density, geochemical, and NMR logs.

The estimation is:

(1)

where

(2)

(3)

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and

= volume kerogen

= volume pore fluid

= apparent matrix density from geochemical

log (not corrected for organic carbon)

= kerogen density

= pore fluid density

= total NMR porosity

= hydrogen index of fluid

The conversion of kerogen volume to TOC, which

is in weight percent, is provided by Tissot and

Welte (1978):

(4)

= kerogen conversion factor (typically 1.2 to 1.4)

= bulk density

This methodology is generally robust. The

kerogen conversion factor is generally close to

1.2. It will decrease as the organic shale

thermally matures and the kerogen becomes

more carbon rich. Commensurately, the kerogen

grain density will increase as the shale thermally

matures. This methodology does require an

accurate total porosity from NMR, and some

organic shales do include fluids (e.g., clay-

bound water, bitumen) that relax faster than the

lowest T2 measured. An NMR logging tool

with a short echo spacing is advantageous (Hook

et al., 2011).

Geochemical logs.A geochemical log that uses

inelastic spectral measurements can directly

quantify the total carbon within the formation

plus mud (Herron, 1991; Herron et al., 2011;

Jacobi et al., 2008). The total carbon measured

with this technology (TC) needs to be

apportioned to estimate TOC:

Total Carbon = Inorganic Carbon + Organic

Carbon (5)

The inorganic carbon (TIC) can be quantified by

measuring calcium and magnesium with elastic

and inelastic spectral measurements and

calculating the amount of carbon that would be

bound into the appropriate inorganic minerals—

calcite and dolomite. The difference between

this value and the total carbon provides organic

carbon. In theory, this is an excellent way to

quantify organic carbon if the elemental

concentrations are accurate. There still are a few

issues to be aware of: 1) the calculated organic

carbon includes not only carbon in the kerogen,

but also carbon that resides in hydrocarbons in

the pore volume (e.g., gas, oil, bitumen) as

discussed previously and 2) some calcium and

magnesium may be in minerals other than

carbonates (e.g., clays) so the estimation of

inorganic carbon may not be straightforward.

The recently introduced neutron-induced capture

and inelastic gamma ray spectroscopy tool

allows the application of this last method to a

new level of accuracy and precision in the

industry. The combination of capture and

inelastic yields, including carbon, and required

borehole and formation corrections are described

below.

FROM SPECTRA TO TOC

While much of the basic tool analysis has been

described in Radtke, et al. (2012), we summarize the

more salient aspects here. The pulsed neutron source

used by the tool allows for a clean separation of

gamma rays produced by thermal neutron capture

from those produced by fast-neutron interactions

(mostly inelastic scattering). The spectra resulting

from the capture and inelastic reactions are analyzed

separately to produce two sets of elemental yields

(i.e., the fraction of the spectrum due to each

element). The capture set typically includes Si, Ca,

Mg, S, Al, Fe, Mn, Ti, K, Gd, Na, Ba, H, Cl, and tool

background. The inelastic set typically includes Si,

Ca, Mg, S, Fe, Ba, NaCl, C, O, and tool background.

Both sets of elemental yields are converted to

elemental weight fractions of the dry rock using a

simple transform:

(6)

where

= weight fraction of element i

= spectral yield of element i

= weight fraction sensitivity of element i

= yields-to-weights transform factor at depth d

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Although the transform equation has the same

form for both capture and inelastic elements, the

transform factor is governed by different

physics for the two sets of elements and must be

determined completely independently. For the

capture set we can robustly measure a sufficient

fraction of the commonly occurring matrix

elements to justify closure normalization. The

closure equation is expressed simply as:

(7)

where

= association factor of element i

The closure sum includes the elements Si, Ca,

Mg, S, Fe, Al, Ti, K, and Mn. The association

factors we use have been optimized from X-ray

fluorescence measurements on thousands of core

samples from around the world, resulting in an

accuracy of a few percent for the mineral

assemblages encountered in the oil field. The

beauty of closure normalization is that it

automatically accounts for the many

environmental parameters (e.g., borehole and

formation capture cross section, borehole size,

standoff, formation density, porosity) that alter

without needing to measure these parameters

or to quantify the perturbing effects of these

parameters using extensive calibration

measurements. However, acceptable accuracy

with closure normalization is achieved only with

accurate measurements of all of the elements

listed above. This is achieved for the capture

elements but not for the inelastic elements.

To determine the totally independent for the

inelastic elements one can simply insist on a

statistically weighted equivalence between the

elements measured robustly via both reactions.

This statistical weighting tends to be driven by

the common elements Si, Ca, and Mg. However,

once the inelastic is determined, it can be

applied to all of the elements measured via

inelastic, including, most importantly, carbon.

This process results in an extensive set of

elemental weight fractions, including carbon,

that all have a common reference, namely the

weight of the dry rock.

Corrections to carbon concentrations for OBM (oil in

the borehole). A complete description of the oil-

borehole correction is beyond the scope of this paper.

Suffice to say here that estimating the carbon

contribution from the borehole fluid is challenging

but the existing algorithm has been shown to be quite

accurate.

Corrections to carbon concentrations for inorganic

carbon. Assuming any carbon in the borehole has

been properly removed, the carbon concentration

measured represents total formation carbon, once

again relative to the total weight of the dry rock. To

get to TOC we must remove all of the inorganic

carbon found in the dry rock. The most common

minerals containing carbon in the oil field, together

with their diagnostic elements, include calcite (Ca),

dolomite (Ca, Mg), siderite (Fe), rhodochrosite (Mn),

ankerite (Ca, Mg, Mn, Fe), nahcolite (Na), and

dawsonite (Na, Al). With the extensive set of rock

matrix elements measured accurately by the new

spectroscopy tool (Si, Ca, Mg, Fe, Mn, Al, Na, S, Ti,

K), one can accurately estimate the concentrations of

the above carbon-bearing minerals, either from a

sequential process (Herron and Herron, 1996) or from

a more complete inversion. Since the carbon

concentration of these minerals is well known,

removing the inorganic carbon from the total carbon

is straightforward once the carbon-bearing mineral

fractions have been estimated.

CASE STUDY

In the case study, we present the TOC log of the new

spectroscopy tool of two wells from an

unconventional shale play in North America across

producible shale formations.

Figure 5 illustrates in a depth log the new carbon

outputs of the spectroscopy tool for an interval of

well 1. The improved measurement of carbon (TC)

combined with more robust estimation of mineralogy,

and therefore TIC, produces a new continuous TOC

log free from local calibrations and multitool

interpretations. TOC core from LECO analysis is also

displayed for comparison. The excellent match

between log and core TOC is generally quite good,

despite some local rapid TOC variations.

The entire section of wells 1 and 2 can be seen in

Figures 7 to 11 (tracks 1 and 2). Between the two

wells, a total of 68 sidewall cores have been collected

and TOC analyses performed. Crossplots (Figure 6)

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show a very good agreement with TOC core for both

wells.

Fig.5 Example of TOC determination technique

showing the new TC log and TIC log. In the left

track, TIC (in red) is subtracted from TC (in black) to

derive TOC. TOC from core using LECO analysis is

displayed with red dots.

Comparison with other models to estimate TOC.

Alternative models to quantify TOC described

earlier during the ―Literature Review‖ have been

calculated and their results compared to TOC

estimated with the new spectroscopy carbon

measurement presented in this paper. These

models are: density, uranium, LogR, and NMR-

density-geochemical. The comparisons for both wells

are illustrated in Figures 7 to 11 (depth log format)

and also in Figure 12 individually for each method

(crossplot format).

The common denominator of these alternative

methods is the need for calibration or extensive log

analyst interpretation. The exception to this is the

density model (Schmoker) and of course the

spectroscopy carbon measurement, the focus of this

paper. However, what we typically observe with the

density model is that depending on the environment

this is used in, it can lead to overestimation or

underestimation of TOC. In this case, we observed a

general overestimation of TOC for both wells (Figure

12-c).

Fig.6 Crossplot comparing core TOC and

geochemical spectroscopy, (a) well 1, (b) well 2. The

match core to log, is represented in the crossplot by

how close the trend line (in black) to the one to one

line (in red) is.

Although already mentioned, it is important to

reiterate that TOC quantification from uranium

(spectral gamma ray log) requires a local empirical

relationship to be derived. This is normally done with

core data. In this case, we have obtained relationship

with a priori knowledge (core) that while it seems to

provide reasonable results for well 1, it is clearly

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overestimating at the bottom of well 2 (Figure 11)

and missing the top peaks with high TOC content at

the top (Figure 12-b). The variability in this

comparison is typical for gamma ray/uranium.

LogR also requires a high input from the log

analyst. Setting the porosity-resistivity baseline is not

always straightforward and could, in some cases,

require an analyst with considerable experience using

the technique. Other important inputs to this

methodology are the thermal maturity (also known as

level of maturity, LOM) and the kerogen type. For

these two wells, the known LOM from core data

estimated TOC content (curve in pink of depth log

figures) that was much lower than core. In order to

match core a significantly lower LOM was input.

(curve in blue in depth log figures). The maturity

dependency of this method can also be seen in Figure

12-d.

The last model in these comparisons is the integration

of NMR, density, and geochemical logs. The

methodology is robust, as seen in both wells (Figure

12-d). The new spectroscopy tool contributes to this

method with an accurate and precise matrix density

as a result of improved mineralogy estimation. This

methodology also requires a priori knowledge of

kerogen maturity (conversion constant) and kerogen

density.

CONCLUSION

The application of a new neutron-induced gamma ray

spectroscopy measurement to estimate TOC in

organic shale rocks has been presented here.

From the examples shown in this paper, the proposed

method stands out as a reliable method to estimate

TOC without the need for local correlations and

extensive log interpretation. The alternative methods

to quantify TOC show a variable dependency of these

two factors. A noticeable approach is the integration

of NMR, density and geochemical logs which shows

reasonable good agreement for both wells.

The new pulsed neutron source in combination with

the also new lanthanum bromide scintillator allow for

accurately measure elemental capture and inelastic

yields including carbon. The improved carbon

measurement and mineralogy calculation provide a

better TOC quantification for, ultimately, a better

organic shale evaluation.

ACKNOWLEDGMENTS

The authors thank Energen Resources Corporation

and Bold Operating Ltd., for supporting field testing

of this tool in their wells and for allowing the use of

their well log data. We also want to thank the

Schlumberger development team in Sugar Land,

Texas, Princeton, New Jersey, and Cambridge,

Massachusetts, for bringing this tool from concept to

reality.

REFERENCES

Ambrose, R.J., Hartman, R.C., Diaz-Campos, M.,

Akkutla, I.Y., and Sondergeld, C.H., 2010, New

pore-scale considerations for shale gas in place

calculations: SPE 131772, SPE Unconventional Gas

Conference, 23–25 February, Pittsburgh,

Pennsylvania, USA,

Fertl, W.H., and Rieke, H.H, 1980, Gamma ray

spectral evaluation techniques identify fractured shale

reservoirs and source-rock characteristics: Journal of

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ABOUT THE AUTHORS

Jorge Gonzalez is a Petrophysicist currently

working in the in the Permian Basin in Midland as

an Associate Domain Champion for Schlumberger

Formation Evaluation, Wireline Services. Jorge has

been with Schlumberger for 7 years starting as a

Wireline Field Engineer for a short period and then

as a Log Analyst in the North Sea Data Services

Center. Jorge has received an MSc degree in

Mining Engineer from the Universidad Politecnica

in Madrid.

Richard Lewis is the Petrophysics Technical

Manager for the Schlumberger Unconventional

Resource Group, and he is located in Dallas. Rick

was a developer of the gas shale evaluation workflow

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that was initially fielded 10 years ago and which has

been applied to organic shales throughout North

America. In his current position, Rick manages a

group responsible for the continual improvement for

this workflow with application to lacustrine and

liquid-producing shales, and for its introduction and

application to the international market. He is also the

interface to the Schlumberger research and

engineering groups for the development of evaluation

technologies for organic shales. Prior to this

assignment, Rick was responsible for wireline

interpretation development for the central and eastern

United States. Rick has also worked for Shell Oil,

Battelle PNNL, and the U.S. Geological Survey. He

received a BS degree from UCLA and MS and PhD

degrees from Cal Tech, all in geology.

James Hemingway has been with Schlumberger

since January 1980, starting as a field engineer; since

then, he has held various log analyst and engineering

positions during his career. He is currently based in

Houston. Jim has authored many papers about pulsed

neutron logging and log interpretation. James

received a BS degree in chemistry from Emporia

State University in 1978 and a BS degree in chemical

engineering from Texas A&M University in 1979.

From 1982 to 1997 he held several assignments as a

log analyst, interpretation center manager, and

interpretation development engineer. Jim worked in

Ventura and Bakersfield from 1990 to 1997 as the

interpretation center manager. In 1997, he joined the

Formation Evaluation department at the

Schlumberger Sugar Land Product Center working on

the RSTPro* tool and "three-phase holdup"

interpretation techniques. He moved to Paris in 2001

as a new technology advisor responsible for teaching

the applications of new technology for formation

evaluation. In 2005, he was promoted to the position

of nuclear technology advisor. He has been based in

Houston since 2010 as a Petrophysics Advisor.

Jim Grau Jim Grau is a scientific advisor at

Schlumberger-Doll Research in Cambridge,

Massachusetts. He has been involved for over 30

years in all aspects of borehole elemental analysis

using nuclear spectroscopy techniques, including tool

design, data acquisition software, and spectral

analysis techniques. Jim received a BS in

engineering physics from the University of Toledo in

* Mark of Schlumberger

Toledo, Ohio, and an MS and PhD in experimental

nuclear physics from Purdue University in West

Lafayette, Indiana.

Erik Rylander is a Senior Log Analyst for the

Unconventional Resources Group of Oilfield

Services located in Dallas, TX. Erik is part of a

group responsible for the continual improvement of

shale analysis workflows and for their introduction

and application to the international market. Erik’s

current focus is the application of nuclear magnetic

resonance logs to liquid-bearing shale reservoirs.

Erik has been with Schlumberger for 18 years and

began as a wireline field engineer in West Africa and

the Gulf of Mexico. After an operations management

position, Erik entered into a product development

role at the Schlumberger product center in Clamart,

France, where he focused on the application of

dielectric logs. Eight years ago, Erik began focusing

on the petrophysical analysis of shale reservoirs in

Dallas, Texas. Erik has authored or coauthored seven

papers and holds two patents related to formation

testing. He received his BS degree from the

Colorado School of Mines in engineering.

Ryan Schmitt is a Segment Sales Engineer at

Schlumberger. He has spent the majority of his

career in Operations as a Field Engineer, gaining

operating proficiency in all of Schlumberger’s

reservoir evaluation logging tools. Ryan received a

BS in Electrical Engineering from Texas A&M

University in College Station, TX.

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Fig.7 Comparison of TOC measured by the new tool and comparison with core (track 2) in topmost interval of

well 1 of the unconventional shale play in North America. Also, comparison with alternative TOC estimation

models: NMR (track 3), uranium (track 4), density (track 5), and LogR (track 6). Two TOC ∆Log R

computations were done with different LOM; curve in pink represents TOC using LOM from core and curve in light

blue represent and adjusted LOM to match TOC content from core.

Fig.8 Comparison of TOC measured by the new tool and comparison with core (track 2) for second topmost

interval of well 1 of the unconventional shale play in North America. Also, comparison with alternative TOC

estimation models: NMR (track 3), uranium (track 4), density (track 5), and LogR (track 6). Two TOC ∆Log R



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Fig. 9 Comparison of TOC measured by the new tool and comparison with core (track 2) for second bottom-most

interval of well 1 of the unconventional shale play in North America. Also comparison with alternative TOC

estimation models: NMR (track 3), uranium (track 4), density (track 5), and LogR (track 6). Two TOC ∆Log R



Fig.10 Comparison of TOC measured by the new tool and comparison with core (track 2) for bottom-most

interval of well 1 of the unconventional shale play in North America. Also, comparison with alternative TOC

estimation models;: NMR (track 3), uranium (track 4), density (track 5), and ΔLogR (track 6). Two TOC ∆Log R


blue represent and adjusted LOM to match TOC content from core

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Fig.11 Comparison of TOC measured by the new tool and comparison with core (track 2) for well 2 of the

unconventional shale play in North America. Also, comparison with alternative TOC estimation models: NMR

(track 3), uranium (track 4), density (track 5), and LogR (track 6). Two TOC ∆Log R computations were done

with different LOM; curve in pink represents TOC using LOM from core and curve in light blue represent and

adjusted LOM to match TOC content from core.

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Fig.12 TOC for well 1 & 2 comparing core TOC versus; (a)NMR-spectroscopy, (b)uranium TOC, (c) density and (d)

∆Log R TOC models. Two TOC ∆Log R computations were done with different LOM; dots in pink represent TOC

using LOM from core and dots in light blue represent and adjusted LOM to match TOC content from core. For each

method, the match core to log, is represented in the crossplot by how close the trend line (in black) to the one to

one line (in red) is.

Well 1 –a) Well 1 –b)

Well 1 –c) Well 1 –d)

Well 2 –a) Well 2 –b)

Well 2 –c) Well 2 –d)

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determination of formation organic carbon content … · the producible pore fluid are within the...

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