True pay thickness determination of laminated sand and shale sequences using borehole resistivity image logs


Reid, R.R.; Enderlin, M.B.

SPWLA 39th Annual Logging Symposium, May 26-29,1998

1998


Once an exploration well has been logged, economic evaluation begins. True pay thickness from logs is an important input to that economic evaluation. Knowledge of the true pay thickness is of particular importance in sands and shales laminated at a scale below the resolution of the standard logging suit. Resistivity image logs provide information about the spatial distribution of shallow resistivity. Since resistivity is a function of both the rocks and included fluids, resistivity images can provide information about the spatial nature of the rocks and fluids. With proper processing (which includes data transformation from depth to the time domain, correction for tool acceleration, transformation back to the depth domain, and static normalization) the resistivity images can provide a quantitative measure of the shallow resistivity at a resolution of a few centimeters. A resistivity-to-pay sand cutoff operator is selected by the optical application of petrophysical reasoning. The resistivity-to-pay sand operator determines which sand layers are pay and their apparent thickness. Further processing can resolve the true dip of individual pay sand layers. By combining the local structural dip interpreted from true dip of the pay sand layers with borehole orientation data, the apparent thickness of each pay sand layer can be converted into a true pay sand layer thickness. Summing over all the true pay sand layer thickness yields the true pay thickness. Resistivity images from a Gulf of Mexico exploration well are used to illustrate a processing technique to achieve an understanding of the true pay thickness.

SPWLA
39th
Annual
Logging
Symposium,
May
26-29,
1998
TRUE
PAY
THICKNESS
DETERMINATION
OF
LAMINATED
SAND
AND
SHALE
SEQUENCES USING
BOREHOLE
RESISTIVITY
IMAGE
LOGS.
Ray
R.
Reid
and
Milton
B.
Enderlin
Phillips
Petroleum
Company
ABSTRACT
Once
an
exploration
well
has
been
logged,
economic
evaluation
begins.
True
pay
thickness
from
logs
is
an
important
input
to
that
economic
evaluation.
Knowledge
of
the
true
pay
thickness
is
of
particular
importance
in
sands
and
shales
laminated
at
a
scale
below
the
resolution
of
the
standard
logging
suit.
Resistivity
image
logs
provide
information
about
the
spatial
distribution
of
shallow
resistivity.
Since
resistivity
is
a
function
of
both
the
rocks
and
included
fluids,
resistivity
images
can
provide
information
about
the
spatial
nature
of
the
rocks
and
fluids.
With
proper
processing
(which
includes
data
transformation
from
depth
to
the
time
domain,
correction
for
tool
acceleration,
transformation
back
to
the
depth
domain,
and
static
normalization)
the
resistivity
images
can
provide
a
quantitative
measure
of
the
shallow
resistivity
at
a
resolution
of
a
few
centimeters.
A
resistivity-
to-pay
sand
cutoff
operator
is
selected
by
the
optical
application
of
petrophysical
reasoning.
The
resistivity-to-pay
sand
operator
determines
which
sand
layers
are
pay
and
their
apparent
thickness.
Further
processing
can
resolve
the
true
dip
of
individual
pay
sand
layers.
By
combining
the
local
structural
dip
interpreted
from
true
dip
of
the
pay
sand
layers
with
borehole
orientation
data,
the
apparent
thickness
of
each
pay
sand
layer
can
be
converted
into
a
true
pay
sand
layer
thickness.
Summing
over
all
the
true
pay
sand
layer
thickness
yields
the
true
pay
thickness.
Resistivity
images
from
a
Gulf
of
Mexico
exploration
well
are
used
to
illustrate
a
processing
technique
to
achieve
an
understanding
of
the
true
pay
thickness.
INTRODUCTION
"True
Pay
Thickness"
in
the
context
of
this
processing
technique
considers
"true"
as
in
True
Stratigraphic
Thickness
(TST)
of
sand.
TST
is
a
fundamental
characteristic
of
the
rocks
where
as
True
Vertical
Thickness
is
a
mixture
of
rock
characteristic
and
geometry.
The
intent
of
the
technique
is
to
characterize
the
rocks
along
the
borehole
wall
in
terms
of
TST
and
"Pay".
Perhaps
better
stated,
the
intent
of
the
processing
technique
presented
is
to
determine
the
True
Stratigraphic
Pay
Thickness.
When
the
structural
dip
and
borehole
orientation
are
understood,
then
the
True
Vertical
Pay
Thickness
can
be
calculated
from
the
True
Stratigraphic
Pay
Thickness.
"Pay"
in
this
analytical
technique
is
identified
through
petrophysical
analysis
that
is
linked
to
the
shallow
resistivity
and
then
to
the
microresistivity
response
by
a
visual
judgement.
The
premise
used
to
illustrate
the
"pay"
linking
processing
technique
is
that
the
shallow
resistivity
in
the
hydrocarbon
bearing
sand
is
higher
than
shallow
resistivity
in
shales
or
water
bearing
sands.
Of
course, this
"pay"
linking
premise
is
not
true
for
all
environments.
The
focus
of
this
paper
is
on
the
technique
for
obtaining
True
Stratigraphic
Pay
Thickness
along
borehole
wall
utilizing
all
of
the
data
sampling
capacity
of
the
borehole
resistivity
imaging
tools
and
not
the
petrophysics
required
to
determine
the
link
between
pay
and
the
microresistivity
response.
The
example
borehole
resistivity
image
data
set
is
14.0
measured
depth
feet
(4.26
meters)
of
gross
reservoir
interval
taken
from
a
Gulf
of
1
SPWLA
39th
Annual
Logging
Symposium,
May
26-29,
1998
Mexico
exploration
well
and
was
recorded
by
a
Fullbore
Formation
MicroImager
*
(FMI)
tool.
The
image
data
consists
of
192
microresistivity
samples
recorded
circumferentially
around
the
borehole
at
a
depth
increment
of
0.00833
feet
(0.00254
meters).
The
example
image
data
contains
over
320000
microresistivity
samples.
The
processing
technique,
as
outlined
here,
was
developed
to
take
advantage
of
the
high
sampling
capacity
of
the
borehole
resistivity
imaging
tools.
The
processing
technique
consists
of
five
steps,
which
are
summarized
here.
Step
1
processes
the
data
delivered
to
the
customer
into
a
usable
configuration
for
this
analysis.
Step
2
establishes
a
link
between
sand,
"pay",
and
the
microresistivity
response.
Step
3
quantifies
the
sand/pay
in
terms
of
percent
volume
and
cumulative
sand/pay
thickness.
Step
4
determines
the
structural
dip.
Step
5
removes
the
local
structural
dip
and
compresses
the
resultant
data
to
a
True
Stratigraphic
Thickness
Log
Depth,
then
calculates
the
True
Pay
Thickness.
The
steps
are
illustrated
in
the
following
five
figures.
FIGURE
1
Step
I
processes
the
"as
received"
data
delivered
to
the
customer
into
a
usable
configuration
for
analysis.
The
left
most
track
contains
the
borehole
orientation,
with
the
next
track
containing
gamma
ray
and
caliper
data.
The
following
three
image
tracks
display
FMI
data
in
a
format
of
8
image
traces
(4
pads
and
4
flaps)
with
the
darker
colors
indicating
lower
resistivity.
The
images
are
azimuthally
oriented
with
north
at
the
track's
edges.
The
first
image
track,
labeled
"FMI
[RAW]",
displays
the
"as
received"
image
portion
of
the
example
data
set
use
here.
The
"as
received"
image
data
undergoes
five
data
manipulations.
First
the
image
data
is
shifted
into
the
recording
time
domain
and
then
it
is
corrected
for
acceleration.
The
center
image
tract,
labeled
"FMI
[LOG
TIME
W/ACCEL]",
contains
the
example
image
data
set
after
the
first
two
manipulations
have
been
completed.
The
data
set
is
then
migrated
back
to
the
depth
domain.
Next
it
goes
through
a
static
Mark
of
Schlumberger
normalization
routine
and
finally
a
"pad
to
flap"
alignment
correction.
The
fmal
product
of
the
data
manipulations
is
the
"basic
input
image
data
set".
This
data
set
provides
the
basic
information
about
the
spatial
distribution
of
resistivity
and
a
quantitative
measure
of
the
microresistivity
along
the
borehole
wall.
The
"basic
input
image
data
set"
for
the
example
is
displayed
in
the
right
most
image
track,
labeled
"FMI
[PAD/FLAP
MATCH]".
Step
2
(Figure
2),
Step
3
(Figure
3)
and
Step
4
(Figure
4)
require
the
"basic
input
image
data
set"
configuration
as
input.
FIGURE
2
Step
2
establishes
a
link
between
sand,
"pay",
and
the
microresistivity
response.
The
upper
half
of
the
figure
shows
a
histogram
and
2
image
tracks.
The
histogram
consists
of
the
320000
microresistivity
sample
values
of
the
example
"basic
input
image
data
set"
("FMI
[PAD/FLAP
MATCH]")
that
are
mapped
on
the
ordinate
of
microresistivity
values ranging
from
100
to
700.
The
horizontal
line
at
440
is
the
sand/shale
cutoff
operator.
The
petrophysicist
and
geologist
determine
initial
position
of
the
sand/shale
cutoff
operator.
The
resultant
binary
image
data
reflecting
the
sand/shale
cutoff
operator
setting
of
440
is
displayed
in
the
left
image
track.
The
"FMI
[PAD/FLAP
MATCH]"
image
data
is
displayed
in
the
right
image
track
for
comparison.
The
lower
half
of
Figure
2
shows
the
same
images
from
above
plotted
with
the
gamma
ray
(or
any
other
log
responses)
and
shallow
resistivity
(A010
in
the
example).
By
making
fine
adjustments
to
the
sand/shale
cutoff
operator;
the
petrophysicist
establishes
a
visual
collaborative
association
between
sand,
"pay",
shallow
resistivity,
and
the
binary
image.
The
association
is
accomplished
by
using
an
optical
application
of
petrophysical
reasoning.
That
is,
the
petrophysicist
will
bring
to
bear
all
of
the
data/information,
expertise,
and
clairvoyance
available
to
make
the
visual
judgement.
A
successful
visual
collaborative
association
equates
sand
on
the
binary
image
with
"pay"
from
petrophysical
reasoning
thereby
establishing
a
sand/pay
ensemble.
The
final
setting
of
the
histogram
sand/shale
cutoff
operator
is
used
in
Step
3
(Figure
3)
and
Step
5
(Figure
5).
2
SPWLA
39th
Annual
Logging
Symposium,
May
26-29,
1998
FIGURE
3
Step
3
quantifies
the
sand/pay
ensemble
in
terms
of
percent
volume and
cumulative
sand/pay
ensemble
thickness.
The
left
three
tracks
contain,
from
left
to
right,
borehole
orientation,
gamma
ray,
caliper,
and
shallow
resistivity.
The
image
track
contains
the
"basic
input
image
data
set"
("FMI
[PAD/FLAP
MATCI-1]")
for
the
example
from
Step
I
(Figure
1)
now
labeled
"FMI".
The
histogram
sand/shale
cutoff
operator
setting
(440
in
this
case)
from
Step
2
(Figure
2)
is
applied
to
the
192
microresistivity
samples
recorded
circumferentially
around
the
borehole
at
each
depth
increment.
The
percent
of
the
total
microresistivity
samples
that
passed
the
cutoff
is
consider
to
be
percent
volume
of
sand/pay
ensemble
per
depth
increment
and
is
displayed
as
a
curve
in
track
5
labeled
"SAND
VOL".
The
percent
volume
of
sand/pay
ensemble
per
depth
increment
is
multiplied
by
the
depth
increment
thickness
(0.00833
feet
in
this
case)
to
obtain
a
net
sand/pay
ensemble
thickness
per
depth
increment.
The
cumulative
sand/pay
ensemble
thickness
per
depth
increment
over
the
total
measured
depth
interval
is
presented
as
a
curve
in
track
4
labeled
"SAND
NET".
At
the
bottom
of
the
14.0
measured
depth
feet
(4.26
meters)
of
gross
reservoir
interval
example,
the
"SAND
NET"
curve
has
accumulated
8.54
measured
feet
(2.60
meters)
of
sand/pay
ensemble.
FIGURE
4
Step
4
determines
the
local
structural
dip.
The
"basic
input
image
data
set"
for
the
example
"FMI
[PAD/FLAP
MATCH]"
from
Step
1
(Figure
1)
is
displayed,
(right
image
track
of
Figure
4),
in
an
interactive
dip
picking
mode
showing
the
correlation
of
bed
boundaries
selected
for
dip
determination.
The
left
track
shows
the
resulting
bedding
dip
values.
The
bedding
dips
are
interpreted
in
terms
of
relative
constant
local
structural
dip
domains
over
the
entire
gross
interval
under
study.
The
gross
interval
is
then
zoned
in
terms
of
the
domains
of
relative
constant
local
structural
dip.
The
zones
of
relative
constant
local
structural
dip
may
be
as
short
as
a
few
inches
(centimeters)
or
as
long
as
tens
of
feet
(meters)
as
interpreted
necessary
to
adequately
describe
the
geometry
of
bedding
dips.
A
single
structural
dip
value
is
interpreted
for
each
zone.
The
complexity
of
the
local
structures
and
the
length
of
gross
interval
being
studied
will
control
the
number
of
zones
required.
The
example
is
treated,
for
simplicity,
as
a
single
zone
with
an
interpreted
structural
dip
value
of
16°
magnitude
at
25°
azimuth.
The
structural
dip
value
along
with
the
borehole
orientation
data
per
zone
is
used
to
calculate,
in
the
conventional
manner,
a
True
Stratigraphic
Thickness
Log
Depth
Scale
for
that
zone.
Therefore,
every
zone
of
relative
constant
local
structural
dip
domain
has
an
associated
single
interpreted
structural
dip
value
and
a
calculated
True
Stratigraphic
Thickness
Log
Depth
Scale.
FIGURE
5
Step
5
removes
the
structural
dip
and
compresses
the
resultant
data
to
the
True
Stratigraphic
Thickness
Log
Depth
Scale,
then
calculates
the
True
Stratigraphic
Pay
Thickness.
For
each
zone
of
relative
constant
local
structural
dip
domain
that
was
determined
in
Step
4,
the
structural
dip
value
for
that
particular
zone
is
removed
from
the
"basic
input
image
data
set"
from
Step
1
over
that
zone
producing
a
"flattened"
image
data
set.
In
the
single
zone
example,
the
interpreted
structural
dip
value
of
16°
magnitude
at
25°
azimuth
was
removed
from
the
entire
"basic
input
image
data
set"
from
Step
1
(Figure
1)
"FMI
[PAD/FLAP
MATCH]".
For
each
zone,
the
"flattened"
image
data
is
then
compressed
to
conform
to
the
True
Stratigraphic
Thickness
Log
Depth
Scale
calculated
for
that
particular
zone
in
Step
4.
The
"flattened"
and
compressed
"basic
input
image
data
set"
is
viewed
as
the
TST
image
data
set.
For
the
single
zone
example,
the
TST
image
data
set
is
displayed
in
the
image
track
as
"FMI
[TST]".
Using
the
procedures
described
above,
a
gross
interval
of
interest
containing
many
zones
of
relative
constant
but
different
local
structural
dip
domains
would
produce
a
TST
image
data
set
consisting
of
a
stack
of
True
Stratigraphic
Thickness
Logs,
one
log
per
zone.
As
described
in
Step
3,
the
histogram
sand/shale
cutoff
operator
setting
from
Step
2
(440
in
this
case)
is
applied
to
the
192
microresistivity
samples
at
each
depth
increment
in
the
stack
of
True
Stratigraphic
Thickness
Logs.
For
the
single
zone
example,
the
percent
volume
of
sand/pay
ensemble
per
depth
increment
is
presented
as
a
3
SPWLA
39th
Annual
Logging
Symposium,
May
26-29,
1998
curve
in
track
2
labeled
"SAND
VOL".
The
cumulative
sand/pay
ensemble
thickness
per
depth
increment
for
the
True
Stratigraphic
Thickness
interval
is
recognized
to
be
the
True
Stratigraphic
Pay
Thickness
or
just
"True
Pay
Thickness".
For
the
example,
the
original
14.0
measured
depth
feet,
(now
compressed
to
13.6
TST
feet
(4.14
meters)),
of
gross
reservoir
interval,
the
True
Stratigraphic
Pay
Thickness
or
"True
Pay
Thickness"
is
presented
as
a
curve
in
track
1
labeled
"SAND
NET".
At
the
bottom
of
the
interval,
"SAND
NET"
has
accumulated
8.34
feet
(2.54
meters)
of
True
Pay
Thickness.
CONCLUSION
A
wide
diversity
of
expertise
is
required
to
determine
True
Pay
Thickness
by
the
method
presented.
Competencies
required
to
make
this
method
most
effective
include
geology,
petrophysics,
data
manipulation,
and
dip
interpretation
for
sedimentary
and
structural
information.
ACKNOWLEDGMENTS
The
authors
would
like
to
thank
Phillips
Petroleum
Company
for
making
the
resources
available
to
present
this
technique
and
Anadarko
for
releasing
the
data.
We
would
also
like
to
thank
Dave
McCoy
and
Richard
Lenzer
for
editing
assistance
and
Tom
Ives
for
assistance
with
the
graphics.
ABOUT
THE
AUTHORS
Ray
Reid
is
a
Senior
Geophysical
Technician
Specialist
in
the
Exploration
Support
Section
of
Americas
Exploration
Division
at
Phillips
Petroleum's
E&P
office,
Bellaire,
Texas.
He
has
nineteen
years
of
geophysical
and
petrophysical
experience
with
Phillips
Petroleum,
the
last
eleven
years
concentrating
on
the
processing
and
analysis
of
acoustic
waveforms,
dipmeters,
digital
core
photographs,
and
borehole
images.
Milt
Enderlin
is
a
Formation
Evaluation
Geologist
in
the
Petrophysics
Section
of
the
Reservoir
and
Production
Technology
Branch
at
Phillips
Petroleum's
Research
and
Development
Center
in
Bartlesville
Oklahoma.
Prior
to
joining
Phillips,
Milt
spent
fourteen
years
in
various
management
and
technical
positions
at
Gearhart
Industries
and
Halliburton
Logging
Service.
Recent
emphasis
has been
placed
on
the
estimation
of
reservoir
structural
framework
and
flow
unit
configuration
in
data
limited
(starved)
exploration
settings.
Milt
has
authored
or
co-
authored
seven
U.S.
patents
and
various
publications
on
dipmeter
analysis
and
log-core
integration.
He
holds
a
B.Sc.
in
Chemistry
and
a
B.Sc.
in
Geology
both
from
Sonoma
State
University
and
is
a
member
of
SPE,
AAPG,
SPWLA,
IDIS
and
SCA.
4
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1998
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