Property of interest Core data Log data

Porosity Crushed dry rock He porosimetry

Density (mostly)

TOC LECO or RockEval GR, density, resistivity

Water saturation As-received retort or Dean-Stark

Resistivity + kerogen corrected porosity

Mineralogy XRD, FTIR, XRF Density, neutron, Pe, ECS-type logs

Permeability Pulse decay on crushed rock

This is tough…………

Geomechanics Static moduli DTC, DTS, RHOB, & synthetic substitutes

Geochemistry Ro, S1-S2-S3, etc. Resistivity (sort of…)

Global stochastic Global deterministic Local deterministic

› aka direct calibration to core Each has distinct advantages &

disadvantages Each has its staunch defenders &

proponents None is clearly “the best” for all shales Industry has not settled on one approach or

best practices

Global/stochastic › Figure out or guess what components are

present in the shale (mineral, organic, fluid) › Characterize end member properties of

each › Invert the logs for properties that best fit the

observed log character › Implementations include ELAN, Multimin,

Statmin, etc.

Guess at mineral and fluid volumes

Compute log responses from a forward model

Compare computed log responses with actual log data

Satisfactory match?

DONE

Yes

No

Solutions are very Sensitive to the “total error” bars

Actual log curves vs. forward models – convergence is the criteria for a satisfactory model.

How do you know, or guess at, the end member points? › What are the kerogen endpoints (GR, RhoB,

Nphi, etc.)? › What is the inorganic grain density of the

clay components? › What are the error functions associated with

those end points AND the log data (that is, the total error bar)?

Global/deterministic › Characterize properties using broad

collections of shale samples, either globally or basin/play specific

› Schmoker 1979, 1981 equations › deltaLogR (Passey, 1990) › Schlumberger SpectroLith is a service

company specific method

TOC (v/v) = (ρgray sh – ρb) / 1.378 (1979 eqn) TOC (v/v) = (GRgray sh – GR)/(1.378 * A) (1981 eqn) TOC (v/v) = WTOC * RHOma/RHOTOC

0

2

4

6

8

10

12

14

16

18

2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

Bulk density (g/cm3)

tota

l org

anic

car

bon

(wt %

)

New Albany Shale, Illinois basin EGSP cores (1976-1979), all blue logs

Published Schmoker relation

Basically a sonic F overlay Two universal eqn’s published by Passey

et al. (1990, AAPG Bull) › ∆logR = log10 (R/Rbl) + 0.02 (∆T-∆Tbl) This defines deltaLogR

› TOC = ∆logR * 10^(2.297 – 0.1688 LOM) This relates deltaLogR to TOC (wt%)

Most of us are concerned about the LOM parameter, but the second two constants were empirically determined

Passey et al, 2010, SPE 131350

TOC (wt %) = ∆logR * 10^(2.297 – 0.1688*LOM) Passey et al, 1990, AAPG 74 (12) 1777-1794

Deterministic suite of regression eqn’s to compute mineral volumes and kerogen-free grain density

RhoMecs = a + b Si + c (Ca,Na) + d (Fe,Al )+ e S

Regional or global equation does not apply locally & you don’t know that

Variable selected does not really correlate very well with property of interest › e.g. gamma ray, because Vuranium is not a

simple function of TOC Co-linearities

› e.g. using density to predict both porosity and TOC

Local/deterministic › Calibrate our log models in restricted areas,

down to individual wells or single cores › Simple linear & non-linear regressions GR vs. TOC, RHOB vs. TOC

› Multiple linear regression methods › Multiple non-linear regression A common implementation are neural

networks

( * / 1)(1 / )

m b TOC m TOC TOCT

m fl TOC fl m TOC

W WW

ρ ρ ρ ρφρ ρ ρ ρ ρ− − +

=− + −

But, we don’t directly measure the inorganic grain density, nor the kerogen density.

Have to solve for these two properties by comparing model prediction to core measured TOC’s and porosity

SPE 131768

(1 ) (1 )b hc T wT w T wT m T TOC TOC TOCS S V Vρ ρ φ ρ φ ρ φ ρ= − + + − − +

0

2

4

6

8

10

12

14

16

18

2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

Bulk density (g/cm3)

tota

l org

anic

car

bon

(wt %

)

New Albany Shale, Illinois basin EGSP cores (1976-1979), all blue logs

0% porosity, RhoKer = 1.00, RhoMa inorganic = 2.72 line

Scatter results from variation in porosity & RhoMa from sample to sample

TOC from GR model

Not so different than stochastic method, except it minimizes error in POROSITY only

Colorado Niobrara example (too few core points!)

∆logR = log10 (R/Rbl) + 0.02 (∆T-∆Tbl) TOC = ∆logR * 10^(2.002 – 0.1749 LOM), for LOM = 9

Merge in the core gamma scan

Depth shift, honor the physical breaks

Don’t interpolate Convert core depths

to log depth, then import

Keep an audit trail! › L = C + x

Kill the outliers

Marcellus Sh., Vclay vs. XRD clay model

Everybody thinks they are right and have the best method….. › Of course, we’re all selling our services too!

Each method has its advantages in certain settings or types of wells › Rich suite of modern logs vs. old, public

domain log coverage? › Vertical vs. horizontal well bores? › Do you have core data for calibration?

1. Transparency › Some methods are basically black boxes &

are difficult or impossible to reproduce › Others are totally transparent, you can write

out a set of equations and you may agree or not, but there they are And if you get more information, they are easy

to tweak and re-apply to prior wells

2. Transportability › Some solutions require proprietary software,

or only one vendor can really run it. › Others require a very specific logging

measurement If you ran the “wrong color” logging company,

or didn’t run the right tool: you’re done. If logging conditions prevent running a

particular service: you’re done. If it’s not your well and you don’t have access

to that data: you’re done.

3. Cheap and easy! › This is self-explanatory, everyone likes cheap

and easy. › Some solutions are powerful and accurate,

but may not be cheap (when you consider everything required to implement) .

› Some solutions are difficult. They require a specialist and might be totally non-reproducible – even by the same person! Try a double blind test sometime and see what

you get back…………….

4. Fit for purpose accuracy › Getting water saturation to 3 decimal places

is probably not realistic – so why do we do it? › Sometimes close enough is good enough Log analysis on “the hood of a Chevy” vs. log

inversions on a supercomputer › What will impact your business decisions,

and how different does an answer have to be to make you do something differently?

May your acreage map be yellow, your balance sheet black, And your 2012 outlook rosy! Happy holidays from all of us at The Discovery Group.