3035_Rock-Properties-for-Success-in-Shales-WhitePaper.pdf

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www.cgg.com CGG. All Rights Reserved.

Introduction

A shift in drilling economics has made it increasingly

attractive for operators to explore and produce

unconventional plays. By using new techniques

in horizontal drilling and hydraulic fracturing,

operators now access resources that were never

before considered viable. Rising commodity prices

and worldwide demand reward these operators for

their efforts to free tight oil and shale gas.

Unlike conventional plays, shale plays have very low

permeability and are both the trap and seal. These

resources, until recently considered only the sourcerock for hydrocarbon reservoirs, are now recognized

in their own right for their huge potential for both oil

and gas production.

As with conventional plays, the economic case for

developing and producing a eld is based on how

much hydrocarbon resource exists, whether it is

primarily a gas or oil opportunity, and how much can

be extracted at what cost. The answers in shale plays

lie in the volume and maturity of the total organic

carbon (TOC) and the ability to create an effective

fracture network that will conduct the hydrocarbons

to each borehole. This in turn requires an

understanding of mineralogy, lithology, relative rock

brittleness, natural fracturing and the directionality

of in situ rock stresses.

This paper provides a rock properties-based

workow for shale plays and discusses the inuence

of local variation on the specic analysis performed.

The goal of such an analysis is to gather enough

intelligence to dene drilling locations, well bore

placement and orientation, plus provide valuable

input for developing the completion and stimulation

program.

Rock Properties for Success in Shales

Ted Holden, John Pendrel, Fred Jenson, and Peter Mesdag


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Field-Related Data Included in Workow

Gamma ray logs: indicate overall clay

and uranium content, which has a known

association with organic richness and is

useful in differentiating shales from other

lithologies.

Resistivity logs: record high readings

for hydrocarbon uids and lower readings

for high clay or pyrite presence.2 Highly

mature reservoirs can have a resistivity

signicantly lower than the same

formation at lower thermal maturities.

Density logs: used to build proxies

for TOC when there are no large local

variations in other parameters that

would affect bulk density. These can be

very useful when combined with highresolution resistivity logs to differentiate

subtle and closely spaced vertical

variation in TOC.3

Compressional and shear sonic logs:

calibrated to TOC content due to low

p-wave velocity or organic matter when

there is no signicant local variation

in parameters such as porosity and

mineralogy.

Borehole image logs: useful for

identifying closely spaced verticalvariation in resistivity and detecting both

open and healed fractures and fracture

orientation.

Core data: provides matrix

permeability, bulk mineral density,

kerogen, grain density, total porosity,

and gas-lled porosity (both free and

adsorbed gas). Cores provide ground

truthing for well log and seismic data.4

3D Seismic data: adds valuableperspective on the areas beyond well

control. Seismic data enables better

characterization of structural and

stratigraphic complexities, reveals

fracture orientation and shows

preferential stress direction based on

azimuthal anisotropy.

Shale Play Workow

Shale play sweet spots are typically characterized by

mid to high kerogen content, lower clay volumes, higher

effective porosity, low water saturation, high Youngs

Modulus and low Poissons Ratio. Using these properties

as a guide, reservoir engineers can dene a drilling

program that focuses on the best targets in the eld andoptimizes the recovery from each well.

Petrophysical analysis is the starting point, combining

laboratory measurements, core data and well logs.

Rock physics then establishes the relationship between

petrophysical and elastic properties of the formation and

enables the creation of synthetics for missing and bad

log data from drilling and invasion effects. Seismic data

analysis moves the analysis beyond well control to the

whole eld.

High level workow steps are:

1. Determine TOC and mineralogy including porosity

and water saturation, using petrophysical and rock

properties analysis. Determine bulk density for

each mineral, calculate TOC weight percentage, and

convert this measure to bulk volume kerogen.1

2. Extend analysis beyond well control to visualize

the entire area of interest by combining well log

and seismic data. Characterize structural and

stratigraphic complexities to identify high value

intervals and potential hazards like water conduits.

3. Evaluate relative brittleness and ductility from well

logs and seismic inversion to identify areas prone to

fracturing.

4. Analyze rock stresses, natural fracture networks,

and fracture directionality by examining image

logs, directional borehole acoustics and azimuthal

seismic inversion data to determine optimal

horizontal well direction and fracturing strategy.

5. Plan the well bore trajectory.

At the conclusion of the workow there should be

sufcient information about the reservoir character to

select optimal drilling locations, as well as orientation

and placement of horizontal wells for the most effective

production program.


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TOC and Mineralogy

Determining total organic volume and mineral

composition within the zone of interest is a critical

rst step in unconventional formation evaluation

(Figure 1). The relative quantity and distribution

of minerals and TOC are key to understanding the

formation and optimizing production from it.

5

Forexample, certain minerals such as quartz are more

prone to fracture, while clay tends to ll and close

fractures when they occur (Figure 2). Pyrite is

commonly present and decreases measured resistivity

if volume is sufcient. Kerogen type and maturity

determine the oil/gas ratio, and volume establishes

whether there is sufcient economic potential to

continue the analysis.

The highly laminated nature of most shales presents

a challenge for traditional analysis. These ne grain

sand formations harbor consolidated and compactedparasequences of shallow marine sediment, clay,

quartz, feldspar, and heavy minerals.6They exhibit

ultra-to-low inter-particle permeability, low-to-

moderate porosity, and complex pore connectivity.7

A stochastic or statistical model is used to estimate

relative volume and distribution of TOC and

minerals. First, the presence and volume of some

constituents are determined directly from core

and well log measurements, such as shale volume

from gamma ray or natural gamma ray logs and

dry clay bulk density from crossplots of porosity

and resistivity. Then these constituents are used

as input to the model to estimate relative volume

and distribution of TOC and minerals. If mineral

composition is well understood, a deterministic

approach can be taken instead.

Core data is the optimum control mechanism to

validate the model. Uranium can also be a quality

check, as its presence is a strong indicator of TOC.

Passey and Modied Passey methods can also be

used as a quality control check on the volume of

total organic carbon. The methods work best in

shale sections where there is high clay content and

no permeability. If the reservoir is self-sourcing

and self-sealing, TOC is directly proportional to the

kerogen volume, which can be determined in an area

by calibrating log responses to core data for at least

one well in that area.8

Figure 1: Petrophysical analysis usingPowerLogyields

initial estimates of clay volume, kerogen volume and

porosity. These values can be used as input to stochastic

modeling in Statminto estimate TOC and mineral volume

and distribution.

Figure 2: Log plot displaying a quartz-rich zone bounded by

two clay-rich zones identied inFaciesID. The quartz-

rich sweet spot in this log plot is characterized by relatively

higher porosity and higher brittleness.

Lithologies


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Density plays an important role in the analysis,

given the disparity between various constituents

(e.g., pyrite is high density and has a smaller volume

percent; kerogen has a larger volume percent than

indicated by weight percent).9Core-XRD mineralogy

provides bulk-rock mineral weight percent, but

excludes porosity and kerogen, whereas volume

percent includes all minerals plus kerogen.

After the model is built and validated against well

and core data, it can be applied to other wells in the

eld within the same general lithology. Geoscientists

can then compare water saturation, porosity, and

mineralogy with condence.10

Field Level Lithology

Once well and core data are interpreted they

are combined with seismic data to extend the

understanding of rock properties to the spacebetween wells (Figure 3). This allows a better

understanding of lithological detail across the eld

and leads to identication of the most attractive

facies.

Shales present several challenges to seismic

interpretation:

Laminations cause polar anisotropy that distorts

seismic data and therefore must be corrected

during seismic processing or inversion.

Laminations are below seismic data resolution,

so special averaging must be performed to

accurately reect the composition of the

formation.

A tie must be interpolated between well and

seismic data, such that data at any wellbore can

be recreated by the seismic. This well tie is whatenables characterization and modeling of the

eld away from well control (Figure 4).

Simultaneous AVO inversion produces a

deterministic set of rock properties that can be

QCd against core and well log data (Figure 5). The

inversion process accounts for AVO anomalies and

reduces tuning and interference effects that can be

problematic in simple seismic data analysis. Because

laminations are below the seismic data resolution,

Backus averaging is employed to transform

laminations to the seismic scale. Detail is addedthrough a low frequency model generated as par t of

the inversion workow.

Geostatistical inversion provides additional layer

detail necessary to simulate ow. It simultaneously

inverts impedance and lithology, producing more

objective and geologically plausible models than

obtained with other methods. The models are

accurate both near and away from wells and have

realistic detail, often beyond the seismic band. They

also include uncertainty estimates (Figure 6).

Integrating 3D seismic into

geostatistical modeling can

be challenging. The physical

relationship between petrophysical

properties and seismic

measurement must be specied

directly or by analyzing well log

data in conjunction with rock

physics modeling. This software-

based analysis establishes a

proper multivariate statistical

relationship between elastic and

petrophysical properties of interest

(e.g., impedance and porosity) that

accounts for uncertainty.

Petrophysical properties of interest

are simulated by constraining them

to the relationship (specied or

Figure 3: Cross plot of Youngs Modulus vs Poissons Ratio, colored by Sw.

Cross plots such as this are used to dene key identiable reservoir facies. The

data points within the polygon are highlighted (white) in the log plots.


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statistical) and inverted together with the elastic

properties. This method simultaneously produces

detailed volumes of petrophysical properties,

elastic properties and lithology. Alternatively,

combined with the volumes of elastic parameters

and lithology from geostatistical inversion,

cosimulation yields highly detailed models of

lithology-dependent petrophysical properties.

Following seismic inversion and analysis, there

should be sufcient detail about the distribution

of TOC and minerals across the eld to make

a preliminary assessment of the distribution of

the reservoir facies for production. Potential

well bore trajectories can be dened and rened

with brittleness, rock stress and directionality

information.

Brittleness and Ductility

Once TOC, mineralogy and lithology are

understood, the formation can be evaluated for

relative fracability. Brittleness is a key factor,

indicating the likeliness to fracture under stress.

Ductile shale naturally heals, while brittle silty

shale with a quartz fraction is more likely to

fracture and remain open.11Geomechanical

properties aid in determining relative brittleness

or ductility of rock, providing valuable input into

completion and fracture stimulation design.

A combination of static and dynamic testingtriaxial compression for the former and ultrasonic

velocity for the latterestablish a relative

brittleness measure that is generally accepted in

the industry.12Zones with high Youngs Modulus

(ability to maintain a fracture) and low Poissons

Ratio (propensity to resist failure under stress)13

will be more brittle and have higher reservoir

quality (TOC and porosity are both higher)

(Figure 7). High Poissons Ratio and low Youngs

Modulus rock is ductile.

Calculating Poissons Ratio from seismic datais straightforward given that it depends strictly

on P-impedance and S-impedance. Youngs

Modulus requires a measure of density, which

is usually unavailable due to the limited range

of angles in the seismic data. In this case, it is

necessary to evaluate several different potential

proxies for density to determine the best one

Figure 4: Well log and seismic data are tied by identifying a

matching wavelet using Well Tie. Once the well tie is made,

simultaneous seismic data inversion is performed using

RockTraceto obtain an initial eld wide estimate of lithology.

Figure 5: Poissons Ratio was computed from P Impedance and

S Impedance usingRockTracedeterministic simultaneous AVOinversion. The plot is overlain with Poissons Ratio from logs.

The white arrow indicates the reservoir level in the lower Barnett.

Low Poissons Ratio rocks are more brittle.

Figure 6: The volume of quartz obtained from the mean of ten

realizations using cosimulation usingRockModgeostatistical

inversion. Smoothed Vquartz logs are overlaid. The interval

shown is from the Top Barnett to the Top Viola.


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for the particular geology. The starting point is

P-impedance, although this is rarely sufcient. Other

potential proxies involve S-impedance plus Poissons

Ratio, or Poissons Ratio plus S-impedance and a

regression of P-impedance. The method chosen for

inferring density depends on the specic lithology.

Core analysis can then be used as a real world

conrmation at each well location.

Note that brittleness is a relative not an absolutemeasure. It is estimated based on a combination of

core and sonic data (well log and/or seismic) and

assumes that fractures open and remain open better

in brittle rock. Shale formations are quite distinct

from each other and vary in quality internally.14

Facies can be identied from deterministic inversions

following a Bayesian scheme (Figure 8). The inputs

to the process are the inversion outcomes and PDFs

representing the facies to be determined. These can

be estimated from log data or analogues. The outputs

of the process are probability volumes for each facies

and a most-probable facies volume. An example of

the most probable facies is shown in Figure 9.

Fracture Directionality

Following brittleness analysis, the areas most prone

to fracturing should be well understood. The next

step is to determine the best well bore direction for

optimized conductivity and production.

Productivity is a function of fracture direction,

induced fracture extent, network intensity, propensity

to sustain fractures15, effective conductivity and

matrix permeability.16

These properties are governedby mineralogydiscussed earlierand rock

stresses, which can be evaluated from seismic.

Determining these properties improves sweet spot

identication, reserve estimation, well placement,

completion design, stimulation effectiveness, and

production enhancement.17

Fractures occur when the rock is stressed naturally or

with stimulation. Induced fractures run perpendicular

to the direction of minimum rock stress, and open

fractures created perpendicular to the well bore

provide the best opportunity to drain the areaaround the well bore. These fractures are typically

vertical. If the formation is incorrectly fraced, the

fractures may close again, extend into water areas,

or be ineffective in conducting hydrocarbons to the

wellbore.

The three principal components of rock stress allow

estimation of how rocks are likely to fracture under

stress during fracture stimulation.18The vertical

stress component is the overburden pressure of the

rock on top of the reservoir. Differential horizontal

stress components (minimum and maximum) areconsequences of tectonics.

The effects of rock stress can be seen on borehole

images (Figure 10), where natural fractures are quite

apparent. Differential effective stress squeezes the

borehole causing breakouts in the direction of the

Figure 7: Log crossplots of Youngs Modulus vs. Poissons

Ratio colored by Brittleness from Logs (upper) and

Brittleness from Inversion (lower). The arrow shows the

direction of increase in Brittleness and also Vquartz.

Youngs Modulus vs. Poissons Ratio

Colored by Brittleness from Logs

Colored by Brittleness from Inversion


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Figure 8: Pdfs (Probability Density Functions) of predicted volume of quartz vs. predicted brittleness displayed inFacies

and Fluids Probabilities. The ve numbered zones each enclose similarly colored clusters of data points that indicate

the different lithotypes.

Figure 9: Cross section of the most likely lithology correlated across all of the wells from the Top Barnett to the

Top Viola computed usingFacies and Fluids Probabilities.


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minimum stress effect. The structural

model from seismic also shows how the

rock is stressed, and seismic structural

attributes can be used in some situations to

indicate fractures below accepted seismic

resolution. The coherence attribute, used

to detect faults or discontinuous features,

can pick up swarms of parallel fractures.

Directional stress is determined using azimuthal

anisotropy analysis.19The distinct layers of organic

material in laminated shales exhibit electrical

anisotropyelectrical conductivity in one direction

that is different from another. Sand-bearing

hydrocarbon assets have high resistivity (low

conductivity) whereas shales have lower resistivity.

Anisotropy has a rst-order inuence on shear and

mode-converted PS-waves, which split into fast

and slow modes with orthogonal polarizations.20

Because fractures and faults are mostly in the

vertical direction and aligned along the direction of

maximum horizontal stress, the result is azimuthal

anisotropy (HTI).

An azimuthal map can show the direction of the fast

component, its magnitude and

a measure of the difference

between the maximum and

minimum velocity (Figure

11). Together, these data help

determine the drilling direction,

well positioning and fracturing

strategy.

The differential horizontal

stress ratio can be calculated

from seismic parameters

without any knowledge of the

stress state of the reservoir.21

Wide angle, wide azimuth 3D

seismic is best suited for this.

Greater differential stress and/

or higher fracture density

results in greater anisotropy. By

mapping the anisotropy at the

reservoir level geoscientists can

see the direction, magnitude and

difference between the maximum

and minimum. A combination of

Youngs Modulus and differential

horizontal stress indicates high

potential areas for creating fracture networks,

optimal drilling locations and best well bore

orientation.22

Azimuthal anisotropy is typically caused by

near-vertical systems of aligned fractures and

microcracks23, pinpointing higher potential producing

areas.Anisotropic analysis identies both higher

differential stress and natural fracturing, but the

difference between them is dependent on the play and

cannot be separated mathematically.

Through these analyses, geoscientists can nd

natural fractures and areas with low anisotropy that

are prone to fracturing. Where there are many faults,

many fracs may be required. Where anisotropy is

high, fracture networks may already exist and fewer

Figure 11: Map of Interval Velocity Anisotropy from the Fayetteville shale.

Color indicates magnitude of anisotropy; arrow length indicates magnitude of

fast velocity. Arrow directions represent azimuths. Initial production from

Well Y was three times that from Well X (Courtesy Southwestern Energy).

Figure 10: Borehole image log showing faults and fractures.


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fracs are needed. Effective fracturing determines

the production rate and drainage area recovery.24

Complex fractures appear to be preferable to long,

planar fractures25, and drainage is very efcient

when a high-relative-conductivity primary fracture

is present compared to a uniform-conductivity

network.26

Planning the Well Trajectory

By this nal step in the workow, there should be

sufcient information to determine the details of well

placement and fracture stimulation. Planning each

well trajectory is an important element of success,

given the heterogeneity common in shales.

Target facies are identied through petrophysical

and lithological analyses and rened with brittleness

analysis. These targets should consist of mid to

high kerogen content, low clay and brittle rock (e.g.,quartz, carbonate). Because of the laminated nature

of shale, these targets may vary in height of pay

interval and proximity to each other (laterally and/

or vertically), requiring adjustments to the well bore

trajectory to optimize contact.

Results from rock stress analysis identify the optimal

well bore direction for fractures that will remain

open and provide effective conductivity to the well

bore, with the assistance of proppants close to the

bore hole and perhaps beyond. Placement of the

vertical segment of each well can then be guided bythe optimal horizontals in combination with surface

considerations.

Each well can be placed to remain in the optimum

stratigraphy throughout its entire length and

simultaneously avoid water and ductile zones.

Known fracture conductivity barriers can be used to

separate the well bore from water zones. Stimulation

can be managed to keep fractures small and avoid

communications with adjacent water bearing zones.

High clay zones can be mapped so that frac jobs are

not wasted, with oil and gas trapped in permeabilityjail27because the fractures do not remain open.

Fractures must remain open to be conductive,

which may require propping or partial propping.

To maximize fracture complexity, operators may

utilize closer spacing of perforation clusters with

more fracture treatments, small proppants at higher

injection rates, closer spacing between laterals,

and simultaneously alternate fracture treatments in

offsetting wells to focus stimulation energy. Success

of these strategies depends on a strong understanding

of the rock properties and rock stresses unique to

each eld and well.

Conclusion

Shale plays require special analysis to consistently

obtain optimum results from each well drilled. By

combining all the well and eld dataincluding

cores, well logs and pre-stack 3D seismic data

geoscientists can understand key characteristics that

enable them to estimate reserves, place well bores in

the most appropriate trajectory and dene the overall

drilling completion and fracture stimulation program.

Combining well log and core analysis, seismicattribute analysis, and seismic inversion is the best

practice for success in shales. Petrophysical analysis,

rock physics and stochastic modeling determine

the distribution of TOC and minerals. Seismic data

extends this understanding from individual well

bores to the eld level, creating a 3D lithological

model. Analysis of the formation propensity to

fracture and ability to remain open requires an

understanding of formation density. Given that

density generally cannot be extracted directly from

seismic, a method of inferring density must be

carefully evaluated and employed specically for

each elds geology.

With a proxy for density, brittleness and ductility can

be evaluated and combined with previous TOC and

mineral distribution data to determine the sweet spots

for both hydrocarbon content and fracability. Finally,

individual bore hole trajectories can be plotted based

on azimuthal data.

All of this analysis is enhanced and accelerated by

specialized reservoir characterization software for

methodical analysis of total organic carbon, minerals,natural fractures, rock stresses, fracture orientation,

brittleness and other aspects of the play. Using these

tools and methods, geoscientists can make better

decisions about where to drill and how to frac, and

can better predict economic outcomes.


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About GeoSoftware

CGG GeoSoftware delivers innovative software

products that help clients identify and produce

hydrocarbon deposits by integrating information

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software applications make it possible to integrate

geological, geophysical, geostatistical, petrophysicaland rock physics information into a single consistent

model of the earth.

Applying Jason technology substantially improves

E&P investment return by adding invaluable

reservoir model information to reduce the risks, costs

and cycle-times associated with exploration, appraisal

and eld development and production.

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Abrstract.


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2Q.R. Passey, K.M. Bohacs, W.L. Esch, R. Klimentidis,

and S. Sinha. From Oil-Prone Source Rock to Gas-

Producing Shale ReservoirGeologic and Petrophysical

Characterization of Unconventional Shale-Gas Reservoirs.

SPE 131350. Society of Petroleum Engineers, 2010.

3Passey, ibid.

4Themig. New Technologies Enhance Efciency of

Horizontal, Multistage Fracturing. Journal of Petroleum

Technology, April 2011.

5Haege, S. Maxwell,L. Sonneland, and M. Norton.

Integration of Passive Seismic and 3D Reection Seismic

in an Unconventional Shale Gas Play: Relationship

Between Rock Fabric and Seismic Moment of Microseismic

Events. SEG 2012 Abstract.

6Haege, ibid.

7Themig. op. cit.

8F. Jenson, H. Rael. Stochastic Modeling & Petrophysical

Analysis of Unconventional Shales: Spraberry-Wolfcamp

Example. 2012.

9Haege, op. cit.

10A.N. Tutuncu. Incorporating Stress Anisotropy in

Determining Time-Dependent Rock Properties During

Production Optimization and Environmental Monitoring in

Shale Reservoirs. SEG 2012 Abstract.

11K. Bandyopadhyay, R. Sain, E. Liu, C. Harris, A. Martinez,

M. Payne, and Y. Zhu. Rock Property Inversion in

Organic-Rich Shale: Uncertainties, Ambiguities, and

Pitfalls. SEG 2012 Abstract.

12L.K. Britt and J. Schoefer. The Geomechanics of a Shale

Play: What Makes a Shale Prospective! SPE Paper 125525,

2009.

13R. Varga, R. Lotti, A. Pachos, T. Holden, I. Marini, E.

Spadaford, and J. Pendrel. Seismic inversion in the Barnett

Shale successfully pinpoints sweet spots to optimize well-

bore placement and reduce drilling r isks. SEG 2012.

14L.K. Britt, op. cit.

15K.Bandyopadhyay, op. cit.

16D. Denney. Highlights from Whole Core vs. Plugs:

Integrating Log and Core Data to Decrease Uncertainty in

Petrophysical Interpretation and Oil-In-Place Calculations.

Journal of Petroleum Technology, August 2011.

17Passey, op. cit.

18R. Varga, R. Lotti, A. Pachos, T. Holden, I. Marini, E.

Spadaford, and J. Pendrel. Seismic Inversion in the Barnett

Shale Successfully Pinpoints Sweet Spots to Optimize

Well-bore Placement and Reduce Drilling Risks. SEG

2012.

19C.L. Cipolla. Modeling Production and Evaluating

Fracture Performance in Unconventional Gas Reservoirs.

Journal of Petroleum Technology, September 2009.

20I. Tsvankin, J. Gaiser, V. Grechka, M. van der Baan, and

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27Britt, op. cit.

3035_Rock-Properties-for-Success-in-Shales-WhitePaper.pdf

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