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DATA QUALITY, EDITING,DATA QUALITY, EDITING,
AND RECONSTRUCTIONAND RECONSTRUCTION
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WIRE.GR_8
GAPI0 200
WIRE.CALI_2
MM150 400
WIRE.CALS_1MM150 400
2350
2400
2450
2300.0
DEPTH
METRES
WIRE.DT_1US/M700 100
WIRE.RHOB_1K/M31950 2950
WHY DO WE EDIT LOG DATA?WHY DO WE EDIT LOG DATA?
WIRE.DT_1US/M700 100
WIRE.DT_7US/M700 100
WIRE.RHOB_1K/M31950 2950
WIRE.RHOB_13K/M31950 2950
Sonic Density
DONT THE LOGS
MEASURE WHATTHEYRE DESIGNEDTO MEASURE?
2500
2550
2600
2650
2700
2750
2800
28502875.0
RCHD
TGLU
RCHD
TGLU
RCHD
TGLU
RCHD
TGLU
For best results, it requires some
knowledge experience
common sense, and ALL CURVES !
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BASIC EDITING WORKFLOWBASIC EDITING WORKFLOW
LOAD ALL DATA
UNDERSTAND WHAT YOUVE GOT
MERGE LOGGING RUNS (CREATE COMPOSITE LOGS)
ENVIRONMENTAL CORRECTIONS (if necessary)
NORMALIZATION (if necessary)
ENVIRONMENTAL CORRECTIONS (if necessary)
NORMALIZATION (if necessary)
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BASIC EDITING WORKFLOWBASIC EDITING WORKFLOW
IDENTIFY AND FLAG BAD DATA
DEPTH SHIFTS
ADD
ATA
GENERATE PSEUDO DATA
REPLACE BAD DATA WITH PSEUDO DATA
MULTIWELL TREND PLOTS AND HISTOGRAMS TO QC
IDENTIFY
ANDR
EPAIR
B
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UNDERSTAND WHAT YOUVE GOT
SOME KEY QUESTIONS:
HOW MANY LOGGING RUNS?
UP-LOGS OR DOWN LOGS?
MUD LOGS AVAILABLE?
CORE DATA AVAILABLE?
CASING POINTS
AVAILABLE CURVES UNITS
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Some common LAS naming practices:
MP, MAINMAIN PASS
UNDERSTAND WHAT YOUVE GOT LAS FILE NAMES
,
TVD, TV TOTAL VERTICAL DEPTHHR, H HIGH RESOLUTION DATAAIT, DT TOOL STRINGS OR LOGS
Load everything in measured depthand convert to TVD using the directionalsurvey data.
Stay away from files measured in TVD; often mislabeled in LAS
NOTE THE FOLLOWING:
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Much of what we need to know for a petrophysicalanalysis can be found in the log header
information. Key pieces of information include KBelevation, mud type, Rmf, and casing points.
When working with neutron log data, we need to
know, prior to our analysis, the type of neutron tooland the matrix on which the data was recorded.Sometimes this is clearly indicated in themnemonic (e.g., NPLS, NCNPL), but more often
than not, we need to look in the LAS file to find thisinformation. Oftentimes we actually need to refer
to the hard copy of the log to find this information.
Density is also frequently presented as a porositycurve, in which case we need to know the matrixand fluid densities used to calculate the density
porosity.
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LOAD ALL DATA
Load all data from all logging runs
Either organize the data in a coherentdatabase, or name the curves in such a
run
Load repeat data!
EXAMPLE
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LOAD ALL DATA
Exercise!!!
CLASS PROJECT
EVALUATE LAS FILES
LOAD BOTH LOGGING RUNS TOPOWERLOG/HRS???
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LOAD ALL DATA
DEPTHM
A_GRGAPI0 150
B_GR
GAPI0 150
B_C13
MM0 500
A_C13
MM0 500
A_RDOHMM0.2 200
B_M2R9
OHMM0.2 200
A_DT24QSUS/M500 0
B_DT24QS
US/M500 0
B_PORZSSPU40 -10
B_CNCSS
PU40 -10
100
200300
400
500
600
700
800
900
QUESTIONS:
1) WHERE ARE THE PROBLEM DATA?2) WHY ARE THERE NEGATIVE POROSITIES?
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
21002200
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2400
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2800
2900
3000
3100
3200
3300
3400
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Merge log runs to create single curves to be used
In multiwell projects, compare curves well-to-wellto check for differences in logging contractors, tool
MERGE LOGGING RUNS (CREATE COMPOSIT LOGS)
ca rat on, too type v ntage, an ot ermeasurement differences.
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RUN1.GR_1
GAPI0 200
RUN2.GR_1
GAPI0 200
SP
MV-160 40
RUN2SONIC.GR_1
GAPI0 200
RUN1.RHOB_1
K/M31950 2950
RUN2.RHOB_1
K/M31950 2950
RUN2.PE_1
B/E0 10
RUN1.PE_1
B/E0 10
SP
MV-160 40
RUN1.CAL2_2MM150 400
RUN2.CAL2_1MM150 400
RUN1.CALI_1MM150 400
RUN2.CALI_1
MM150 400
SP
MV-160 40
RUN1.DT_1
US/M500 100
RUN2.DT
US/M500 100
RUN2SONIC.DT_1
US/M500 100
SP
MV-160 40
3375
3350.0
DEPTHMETRES
MERGE LOGGING RUNS (CREATE COMPOSIT LOGS)
3400
3425
3450
3475
3500.0
When merging curves, it is usually best to take theuppermost curve down as far as reasonable. Havingbeen logged earlier in time, it is less likely to beimpacted by deteriorating borehole conditions andadditional invasion.
Merge at a point where logs are reading same,whenever possible.
Casing point
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EVALUATE AND DISCUSS CASING POINT (see nextslide)
MERGE LOGGING RUNS (CREATE COMPOSIT LOGS)
MERGE LOGGING RUNS
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MERGE LOGGING RUNS (CREATE COMPOSITE LOGS)
DEPTH
M
A_GR
GAPI0 150
B_GRGAPI0 150
B_C13MM0 500
A_C13MM0 500
A_RD
OHMM0.2 200
B_M2R9OHMM0.2 200
A_DT24QS
US/M400 100
B_DT24QSUS/M400 100
B_PORZSS
PU40 -10
B_CNCSSPU40 -10
SPL_GR
API0 150
1525
1550
GAMMA RAY MERGE
- WHAT IS WRONG WITH THIS?
MERGE POINT
1575
1600
1625
1650
1675
IS THE DATA ACROSSTHIS ZONE USEFUL?WHY OR WHY NOT?
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MERGE THE REMAINDER OF THE CURVES
MERGE LOGGING RUNS (CREATE COMPOSITE LOGS)
As with anything in petrophysics, I like to be very consistent (if possible) with mynaming conventions. When I splice curves together, I usually prefix the newcurve with an SPL_*. If I do multiple splices during the editing process, I add a
version number to the splice. For instance, if I do further splice work on the GRcurve, the new curve will become SPL2_GR. If you are reasonably consistent, itwill be relatively easy to remember what you did at a later date.
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ENVIRONMENTAL CORRECTIONS (if necessary)
CONTRACTOR SPECIFIC CORRECTIONS
CORRECT FOR BOREHOLE CONDITIONS
Most of us dont typically apply environmental corrections. There are numerous reasons for this, but ingeneral it is because the magnitude of the corrections are small, and the uncertainties in how they areapplied are large (most frequently, we dont even have the requisite data to apply the corrections).
GR is about the only log I will sometimes correct, simply because the changes can be large andimportant. Note, however, that the corrections to the GR can often be too large!
HAVE SIMPLY DIGITIZED THE PUBLISHED
CHARTS
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DEPTHFT
DCALIN-5 5
DCAL 0
GRGAPI0 150
CORE_GR
API0 150
GRCapi0 150
DSCOREGR
API0 150
10700
10710
10720
10730
ENVIRONMENTALCORRECTIONS TO GR
MINOR DEPTH SHIFT OFCORE DATA
ENVIRONMENTAL CORRECTIONS (if necessary)
10740
10750
10760
10770
10780
10790
10800
10810 RAW DATA CORRECTED GR
AND DEPTH-
SHIFTED CORE
DATA
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DEPTH SHIFTS
DEPTH
M
B_GR
GAPI0 150
B_C13MM0 500
( )0 500
B_M2R9
OHMM0.2 200
B_DT24QS
US/M400 100
B_PORZSS
PU40 -10
B_CNCSSPU40 -10
2520
2530
2540
WHICH CURVE IS OFF DEPTH?HOW DO YOU KNOW?
2550
2560
2570
2580
2590
2600
2610
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For effective depth shifting, pick a reference curve you believe (usuallyshallow resistivity or first-run GR: has good character and vertical
resolution)
Be careful of too many shifts up and down. Fewer is better. As withseismic data, you can get off a cycle if youre not careful.
DEPTH SHIFTS
NOTE: DEPTH-SHIFTING CANNOT CURRENTLY BE DONE IN H-R
CLASS EXCERSIZE: WE WILL SCROLL DOWN THROUGH THELOG, AND INTERACTIVELY DEPTH SHIFT THE SONIC LOG
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IDENTIFY AND FLAG BAD DATA
BADD
ATA
IDENTIFICATION AND REPAIR OFIDENTIFICATION AND REPAIR OF
BAD DATABAD DATA
GENERATE PSEUDO DATA
REPLACE BAD DATA WITH PSEUDO DATAIDENTIFY
ANDR
EP
AI
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KEY POINT: tool readings do not reflect the properties of the formation
Although the terms bad data and badhole are not particularly scientific, nor
perhaps grammatically correct, they are the common terminology among log
analysts. Be aware when conversing with log analysts, however, as they will oftendifferentiate between truly bad data (i.e., tool failure), vs. valid tool readings in poor
borehole conditions. As seismic petrophysicists, we want to edit the log data suchthat it represents what we believe to be the true formation properties.
WHAT IS BAD DATA?WHAT IS BAD DATA?
We will look at some examples and discuss:
Causes some typical reasons for bad data- (obvious things you should always check first)
Recognition practices to identify poor quality data- (some ways to recognize and isolate bad data)
Edits and Reconstruction useful methods to repair curves
We will focus on the two slowness curves and density
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12700
12800
12900
13000
13100
Type G sand
Logrun break clearly indicated by
bitsize change, caliper, straightlined curves
Resistivity and neutron-densityseparation suggest no sand here
THE EASIEST: CASING POINT PROBLEMSTHE EASIEST: CASING POINT PROBLEMS
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13300
13400
13500
13600
13700
13800
13900
ALWAYS REFER TO LOGHEADER INFORMATION!
BEWARE OF CASING POINT
PROSPECTS!
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COMMON LOG DATA PROBLEMSCOMMON LOG DATA PROBLEMS
BS_1MM150 400
CALI_2
MM150 400
GR_2
GAPI0 200
SP_1
MV-160 40
3125
3115.0
DEPTHMETRES
DT_2
US/M500 100
NPHI_1
V/V0.45 -0.15
RHOB_2
K/M31950 2950
PEFZ_1
B/E0 10
SFL
OHMM0.2 2000
ILM
OHMM0.2 2000
AF90_1
OHMM0.2 2000
LLD
OHMM0.2 2000
Tool problems (dead)
Log run breaks
Hole conditions
Cycle skips
3150
3175
3200
3215.0
Tool pulls Casing
Mud and mud cake
Digitizing errors
Depth shifts
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BSMM150 400
CALI_1MM150 400
GR_1
GAPI0 150
800
775.0
DEPTHMETRES
DT_1
US/M500 100
DTSM
US/M1200 200
WIRE.DEN_1
K/M31950 2950 .
.
.
Tool problems
Log run breaks
Hole conditions
Tails
Cycle skips
Tool pulls
Poor hole
conditions
COMMON LOG DATA PROBLEMSCOMMON LOG DATA PROBLEMS
825
850
875
900.0
as ng
Mud and mud cake
Digitizing errors
Depth shifts
Cycle Skips
In this example, we see poor holeconditions affecting the density logs. Thesame hole conditions are causing cycleskipping on this sonic data.
WHAT IMPORTANT QUESTIONSHOULD WE BE ASKING ABOUT THE
REASONS FOR THE BOREHOLEPROBLEMS?
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BS_1MM150 400
CAL2_2
MM50 300
CALI_2
MM50 300
GR_2
GAPI0 200
VSH_GR_1V/V0 1
3665.0
DEPTHMETRES
NPHI_2V/V0.45 -0.15
RHOB_2
K/M31950 2950
DRHO_2
K/M3-400 100
PE_2
B/E0 10
Tool problems
Log run breaks
Hole conditions
Tails
Cycle skips
COMMON LOG DATA PROBLEMSCOMMON LOG DATA PROBLEMS
3700
CasingMud and mud cake
Digitizing errors
Depth shiftsBarite mud filling fractures (or rugosityalong the borehole wall) can result in
very high RHOB and PEF readings.
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Recommendations for badhole identification:Recommendations for badhole identification:
Borehole and measurement quality indicators
Log header information for log run breaks Reasonable log values and frequency content
Comparison to other data using crossplots andhistograms
RECOGNIZING BAD DATARECOGNIZING BAD DATA
. .,
Trend plots and cross-plots Empirical curves or regression equations
Also look for consistency amongst specific lithologies(i.e. do the curves make sense collectively!)
Our ability to recognize poor quality log data will depend
on our understanding of the following:
local geology
tool responses, and the borehole environment
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1600
300
400
500
600
700
800
900
UNDERSTANDING THE GEOLOGY WILL HELP TOUNDERSTAND QUALITY vs. BAD LOG DATA.
ALWAYS LOOK AT LOGS USING A VARIETY OF VERTICAL SCALES!ALWAYS LOOK AT LOGS USING A VARIETY OF VERTICAL SCALES!
1700
1000
1100
1200
1300
1400
1500
1600
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1800
How much of this spiky data is bad?
Closer examination of the data shows that much of thespikiness may be due to geology. Editing out thesespikes would be inappropriate!
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DEPTH
FT
A_GR
GAPI0 200
A_HCAL
IN6 16
A_HCGR
GAPI0 200
A_RLA5
OHMM0.1 10000
A_RLA3
OHMM0.1 10000
A_RLA1
OHMM0.1 10000
RAW_RHOB
g/cc1.95 2.95
A_NPHI
CFCF0.45 -0.15
A_PEFZ
0 10
RAW_VP
ft/s10000 25000
500
1000
1500
2000
2500
VALID DATA?VALID DATA?
3000
3500
4000
4500
5000
5500
6000
6500
7000
NOTE:
Washout on caliperAnomalously low density valuesVelocity drop
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NOTICE THAT DENSITY IS NOTNECESSARILY BAD EVERYWHERETHERE IS WASHOUT OR LARGE
DENSITY CORRECTIONS. BE
BE CAREFUL WHEN AUTOMATINGTHE PROCESS OF CREATING A
BADHOLE FLAG!
CALIPERDRHO
HOLE QUALITYHOLE QUALITY
Be careful when Drho > 100 kg/m3
(+) correction when mud and large holesize cause density to read too low(common)
() correction when heavy mud or smallborehole diameter
Sonic appears tobe largely
unaffected by thewashouts
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DEPTH
FT
GR
GAPI0 150
DCAL
IN-5 5
AT90
OHMM0.2 2000
AT30
OHMM0.2 2000
AT10OHMM0.2 2000
RAW_RHOB
g/cc1.95 2.95
NPHI
V/V0.45 -0.15
PEFZ----0 20
HDRA
G/C3-1 1
RAW_VP
km/s2 7
1000
1500
2000
2500
3000
3500
4000
4500
5000
BADHOLE from automated process
Hand-picked BADHOLE
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COMMON PROBLEMS
Digitizing errors
Differences in tool type / contractor / vintage
REPAIR TECHNIQUES
OTHER SOURCES OF BAD DATAOTHER SOURCES OF BAD DATA
IF POSSIBLE, USE MULTIPLE WELLS IN AN AREA TO DETERMINE WHETHER OR NOTIF POSSIBLE, USE MULTIPLE WELLS IN AN AREA TO DETERMINE WHETHER OR NOTYOUR LOG CURVES ARE RECORDING REASONABLE VALUES.YOUR LOG CURVES ARE RECORDING REASONABLE VALUES.
Re-digitize
Normalization
Rescaling
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2450
2500
2550
2600
2650
2700
2750
2400.0
TGLU
DT LOOKS NORMAL
100
160
220
280
340
400
460
520
580
640
700
0 0
500 500
1000 1000
1500 1500
2000 2000
2500 2500
DEPTH vs. WIRE_INT5.DT_SMT CrossplotWell: 7 Wells
Range: All of WellFilter:
(METRES)
4354
4354
0
0
0 0
Beware can be hard to catch.DIGITIZING ERRORSDIGITIZING ERRORS
2800
2850
2900
2950
3000
3050
3100
3150
3200
3250
3312.5
100
160
220
280
340
400
460
520
580
640
700
3000 3000
3500 3500
4000 4000
4500 4500
5000 5000
DE
PTH
WIRE_INT5.DT_SMT (US/M)
Well Legend:300C426930134450 300D556930134450300F436930134450 300G336930134450300H066930135000 300H546930134450300P036930135000
Functions:
ri_ne_opress : No description given.
xtrend_ri_ne : No description given.
Sometimes log data looks normal in terms of frequency content and correlation
to other logs but can be much different when compared to offset well data.
Digitizing errors can be difficult to identify using only the log display.
Always go back to the hardcopy logs for verification !
LOOK FOR SCALE CHANGES THAT THE DIGITIZER MAY HAVE
MISSED!
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500
1500
2500
3500
4500
5500
6500
7500
500 500
1000 1000
1500 1500
2000 2000
DEPTH vs. 1000000/ESSO_INT.DT CrossplotWell: 7 Wells
Range: All of WellFilter:
S)
7001
7277
275
0
0 1 These erroneous logs can hang around fora long time particularly with the multipledata sources available to geoscientists
Heres a number of velocity logs still beingused today although they were drilled in
VELOCITYVELOCITY--DEPTH CALIBRATIONDEPTH CALIBRATION
500
1500
2500
3500
4500
5500
6500
7500
2500 2500
3000 3000
3500 3500
4000 4000
4500 4500
5000 5000
DEPTH
(M
ETR
1000000/ESSO_INT.DT (US/M)
Well Legend:300C426930134450 300D436930134450300D556930134450 300G336930134450300H066930135000 300H546930134450300P036930135000
t e m s nto t e . c ag u e
in the Mackenzie Delta
SHOULD DATA BE HUNG
STRUCTURALLY ORSTRATIGRAPHICALLY?
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As we have seen, many logs measure
similar properties of the formation (e.g.matrix, porosity), and we expect there tobe reasonable correlation between them.Log analysts use the collective behavior
CALI_RES_1MM100 350
GR_2
GAPI0 200
SP_5MV-25 75
780
785
790
775772.0
DEPTHMETRES
NPHI_2
V/V0.6 0
RHOB_2
K/M31650 2650
PE_3B/E0 10
DT_2
US/M500 100
DTSM_1
US/M2400 200
SFL_1
OHMM0.2 200
ILM_1
OHMM0.2 200
ILD_1OHMM0.2 200
PATTERN MATCHINGPATTERN MATCHING
evaluation as will we. Qualitativeinterpretations help to identify log datathat just doesnt fit our expectations.
795
805
810
815
820
830
835
800
825
839.5
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IDENTIFY AND FLAG BAD DATA
BADD
ATA
IDENTIFICATION AND REPAIR OF BAD DATAIDENTIFICATION AND REPAIR OF BAD DATA
GENERATE PSEUDO DATA
REPLACE BAD DATA WITH PSEUDO DATAIDENTIFY
ANDR
E
PAI
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GENERATING PSEUDO DATAGENERATING PSEUDO DATA
When repairing bad data, we often times use simple hand-patches. However, this approach becomesunwieldy when trying to process thousands of feet of data. Therefore, it is frequently useful to generatepseudo data. This is generally done via application of an empirical transform, or via application of multilinearregression or neural network analysis. Empirical relationships typically use one log to compute an unknown.For example, Gardners famous relationship uses P-wave velocities to estimate density. Although the
empirical transforms are often very useful, I prefer to use multilinear regression when generating pseudo
data. This allows the user to include more than one curve type when estimating an unknown.
In this section we will focus on practical (meaning time-efficient) methods of editing sonic and density data.We will cover the empirical relationships, but also application of multilinear regression and neural net analysis.
We will also explore the various options that Hampson-Russell gives the user for generating pseudo data.
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EMPIRICAL RELATIONSHIPS
Compressional Velocity
Faust: Dt = 513.3 / (Depth*Rt)**(1/6)
Density
Gardner: Rhob = 0.23 * Vp**(1/4) (Vp in ft/s; rhob in g/cc)Gardner-Castagna: Rhob = fcn(Vp,Vsh)
Shear VelocityGreenburg-Castagna coefficients
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affected by local stresses and formation properties
also affected by invasion and near-wellbore alteration
these issues will be discussed later today and tomorrow morning
USED TO ACQUIRE FORMATION VELOCITIES
SONIC LOGSSONIC LOGS
Schematic of Schlumbergers long
spaced sonic tool. Taken from
Bassiouni.
Note that sonic tools are run centered
(ostensibly) in the wellbore.
MONOPOLE (primarily P-wave velocities) ANDDIPOLE SOURCES (P- and S-wave velocities)
NOTE: sonic logs can be thought of as refraction devices. Thus, you need to have
a critical angle to receive a signal (we will discuss critical angle later in the class).
What does this mean for the lower limit of velocities we can detect with sonic tools?
transmitters emit a pulse, which is recorded by an array of receivers
records transit time between receivers
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TWO METHODS FOR MEASURING SONIC TRANSIT TIMES:
First arrival picking (older tools like BHC, LSS)
Full waveform capture entire waveform from which various arrivals are picked.
Compressional wavestravel parallel to the direction of particle motion. They readthe rock matrix, fluid in the rock, and elements of pore structure.
Shear wavestravel perpendicular to the direction of particle motion. They measure
SONIC LOGSSONIC LOGS
the rigidity of the formation and are influenced by the rock matrix and elements of
pore structure.
Tube waves(or Stonely waves) travel along the wellbore
FIRST ARRIVAL PICKING
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SONIC LOGSSONIC LOGS
From Tang and Cheng, 2004
ADVANTAGES OF ARRAY PROCESSING:
- different moveouts for different modes
- better processing and results
FULL WAVEFORM ARRAY PROCESSING
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f
=
= wavelength
= formation velocityf= frequency
DEPTH OF INVESTIGATIONIS RELATED WAVELENGTH, WHICH IS
RELATED TO THE FREQUENCY OF THE MEASUREMENT AND
THE FORMATION VELOCITY
SONIC LOGSSONIC LOGS
2000.0 4000.0 6000.0 8000.0 10000.0 15000.0 20000.0
6000.0 3.0 1.5 1.0 0.8 0.6 0.4 0.3
8000.0 4.0 2.0 1.3 1.0 0.8 0.5 0.4
10000.0 5.0 2.5 1.7 1.3 1.0 0.7 0.5
12000.0 6.0 3.0 2.0 1.5 1.2 0.8 0.614000.0 7.0 3.5 2.3 1.8 1.4 0.9 0.7VEL
OCITY
(ft/s)
FREQUENCY (Hz)
Commonly encountered ranges for the DWGOM
If low frequency data can be acquired, the sonic data will be less affected by invasion.
While sonic data is less susceptible to well-bore washout than density data,
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it still frequently requires considerable editing. Editing of sonic data usuallytakes the form of correction of cycle skips, and de-spiking noisy data.SONIC LOGSSONIC LOGS
WHAT DOES BAD SONIC DATA LOOK LIKE? abundant spikes (fast and slow) very noisy (abnormally high frequency)
too fast for the area
CYCLE SKIPS (common in older logs) acoustic wave is attenuated below the threshold of the receive abnormally long travel times (records Vp that is too slow)
WHERE ARE CYCLE SKIPS LIKELY TO OCCUR? thin beds with large velocity contrasts gas sands gas-cut mud
poorly consolidated formations fractured formations
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SONIC LOGSSONIC LOGS
FAST SPIKES (noise) receivers record early noise arrivals results in apparent fast rock
EDITING CYCLE SKIPS AND NOISE simple approach is to simply filter the data generate pseudo-sonic data
neural networks and multilinear regression hand edits
WHEN ARE SPIKES NOT CYCLE SKIPS OR NOISE? must be careful to not edit lithology
coal
hard streaks (e.g., carbonate interbeds) look for lithology indicators compare behavior of resistivity, neutron, and density to sonic data.
Do they move in the same directions?
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Cycle skipsNoise
Note the noise in the resistivity and the density data. Some of the
spikes in the sonic data may be reflecting true lithology effects
across this zone.
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SIMPLE SMOOTHING
RAW DATARAW DATA
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DEPTHM
TVDM
GRGAPI0 150
HCALMM100 300
LCALMM100 300
AHT90OHMM0.2 2000
AHT30OHMM0.2 2000
AHT20OHMM0.2 2000
VP_RAWft/s10000 20000
DPHI_RAWv/v0.4 -0.1
NPHI_SSv/v0.4 -0.1
2750
2760
2770
2780
2710
2720
2730
RAW DATARAW DATA
SIDERITE
My general approach is to generate
pseudo-sonic data for editing purposes.
Usually I do this via multilinear
regression. The best answers I usually
get involve resistivity, neutron, depth, and
some lithology indicator.
CADOTTE
2790
2800
2810
2820
2830
2840
2850
2860
2870
2880
2740
2750
2760
2770
2780
2790
2800
2810
2820
SONIC LOGSSONIC LOGS
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DEPTHM
TVDM
GRGAPI0 150
HCALMM100 300
LCALMM100 300
AHT90OHMM0.2 2000
AHT30OHMM0.2 2000
AHT20OHMM0.2 2000
VP_RAWft/s10000 20000
VP_PSE10000 20000
1400
1500
1600
1700
1400
1500
1600
1700
Multilinear regression
Typical data used for Vp
estimation:
oResistivity
oNeutron porosity
SONIC LOGSSONIC LOGS
DUNVEGAN
SHAFTSBURY
CADOTTE
1800
1900
2000
2100
2200
2300
2400
2500
2600
2700
2800
1800
1900
2000
2100
2200
2300
2400
2500
2600
2700
o N-D difference
oVshale
SONIC EDITSSONIC EDITS
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19000
00
16000
170
00
18000
1.2
0.8
1
SONIC EDITSSONIC EDITS
BAD SONIC DATA
VP_PSE2
9000 1900010000 11000 12000 13000 14000 15000 16000 17000 18000
VP_
RA
W
9000
10000
11000
12000
13000
14000
15
VCLGR
0
0.2
0.4
0.6
R2 = 0.88
EDITED SONIC DATAEDITED SONIC DATA
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EDITED SONIC DATAEDITED SONIC DATA
DEPTH
M
TVD
M
GR
GAPI0 150
RDEEP
ohmm0.2 2000
RMEDohmm0.2 2000
RSHAL
ohmm0.2 2000
VP_RAW
ft/s10000 20000
VP_FINALft/s10000 20000
2780
2790
2800
2740
2750
There are multiple reasons why the sonicdata in the Cadotte might not be valid in thiswell. However, from multiwell analysis, weknow that the recorded velocity across theCadotte is invalid. The velocity estimatedusing multilinear regression is within the
expected range of values, as determinedfrom analysis of the other wells in the field.
2810
2820
2830
2840
2850
2860
2870
2880
2760
2770
2780
2790
2800
2810
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NOTE BAD DATA AT CASING POINT.
DO THE OTHER LOGS SUPPORT THIS VELOCITY?
LOOK FOR CONSISTENCY WITH OTHER CURVESLOOK FOR CONSISTENCY WITH OTHER CURVES
SONIC LOGSSONIC LOGS EMPIRICAL TRANSFORMSEMPIRICAL TRANSFORMS
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SONIC LOGSSONIC LOGS -- EMPIRICAL TRANSFORMSEMPIRICAL TRANSFORMS
Besides transforms such as Faust, various relationships exist that relate
velocities to porosity. The most famous, of course, is Wileys time-average equation, which treats the velocity as a volume weightedaverage of its components. All of these empirical porosity-velocitytransforms have some uses, but by and large they do a poor job ofpredicting velocities.
As we will see when we discuss seismic rock properties, the reasonsthese transforms typically dont work well are numerous. Nonetheless,we present them here, as they locally may help you create pseudo sonicdata.
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
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Wyllie (1956, 1958, 1963)( )11
+=
The most commonly used relationships are either
heuristic or empirical, and are often times based
on limited data sets. Note that these models are
essentially a linear mass balance of the velocities
of each constituent in the rock.
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
matrixVpfluidVpVp ,,
Raymer et al., 1980
fluidVpmatrixVp1Vp 2 ,,)( += - for porosities < 37%
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
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Han, 1986
068086Vp .. = 286064Vs .. =
- clean sandstones at 40 MPa (~5800 psi)
- shaley sandstones at 40 MPa (~5800 psi)
NOTE: velocities are in km/s
and porosities and clay content
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
...
C891914523Vs ... =
Eberhart-Phillips, 1989
( )eP716e 01P4460C731946775Vp.
.....
+=
are fractional values
NOTE: velocities are in km/s
and porosities and clay content
are fractional values. Pressure
is in kilobars
( )eP716
e 01P3610C571944703Vs
.
.....
+=
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
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Castagna et al., 1985
C212429815Vp ... =
- shaley sandstones of the Frio Formation (log-based)
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
C042077893Vs ... =
NOTE: other Vp-porosity relationships have also been published. In addition, Vp-porosity relationships can also be derived
from theoretical models. Model-based results, however, often predict velocities which are much faster than the measureddata.
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
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20000
25000
Vshale = 0
Vshale = 0.2Eberhart-Phillips
/s)
Porosity decrease due to cementation
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
0
5000
10000
0 0.05 0.1 0.15 0.2 0.25 0.3
POROSITY
VELOCITY(
ft
Porosity decrease due to increasing clay content
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
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20000
25000
Vshale = 0
Vshale = 0.25
Vshale = .5
MARLIN A5 PAYCastagna, Vsh = 0
Castagna, Vsh = 0.5
s)
VELOCITY SYSTEMATICSVELOCITY SYSTEMATICS
Note that E-H and Castagna are similar in form.
However, each model yields different results, especially
when porosities are greater than approximately 10%. This
highlights the need to locally calibrate whenever possible!
Note the difference in porosity values for a
velocit of 10 000 ft/s. These t es of
0
5000
10000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
POROSITY
VELOCIT
Y(
ft
SHALE
uncertainties are one reason why empirical
porosity-velocity models are difficult to use.
What is the dominant control on porosity reduction?
SONIC LOGSSONIC LOGS
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Invasion-corrected P-wave velocity
SONIC LOGSSONIC LOGS
Sonic data can be affected by
invasion, especially when thereservoir is charged withhydrocarbons. Unfortunately, theeffects of invasion on sonic dataare difficult to detect and quantify.Correcting sonic data for invasion is
also complex, and loaded withassumptions. We will discuss sonicinvasion corrections in ourdiscussion on fluid substitutionpitfalls.
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DENSITY LOGSDENSITY LOGS
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PAD TYPE DEVICE strongly affected by well-bore washout also affected by invasion
EDITS ARE ALMOST ALWAYS REQUIRED! fortunately, density is easier to edit and estimate than sonic data ALWAYS closely evaluate the density you are using for rock
proper y wor pay close attention to the caliper
NOTE: Not all wellbore wash-out will result in bad density data is dependent upon the rugosity of the well-bore. if the washout is large, yet the well-bore is smooth, it is possible to
get good density readings.
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EDITING DENSITY LOGSEDITING DENSITY LOGS
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Typical approaches include the following:
- Gardners relationship (Gardner et al., 1974)- Castagnas modification of Gardner (Castagna et al., 1993)
- neural networks- multilinear regression- cross-plotting of local data
,
Neither method works particularly well in poorly consolidated rocks
25.23.0 VPb =
Gardner et al., 1974
VP is in feet/second
25
b VP7411..= VP is in km/second
DENSITY LOGSDENSITY LOGS
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LITHOLOGY a b c
Shale -0.0261 0.373 1.458
Sandstone -0.0115 0.261 1.515
Coefficients for the equation rhob = aVP^2 + bVP + c
CASTAGNA et al., 1993:
- . . .
Dolomite -0.0235 0.390 1.242
Anhydrite -0.0203 0.321 1.732
LITHOLOGY d f
Shale 1.75 0.265
Sandstone 1.66 0.261
Limestone 1.500 0.225
Dolomite 1.74 0.252
Anhydrite 2.19 0.160
Coefficients for the equation rhob = dVP^f
NOTE: it is my experience that the sand and
shale coefficients generally do a good job in
more consolidated clastic basins.
VELOCITYVELOCITY--DENSITYDENSITY
It is important to note that there is no unique relationshipbetween velocity and density. The are various reasons forthis, many of which we will cover during the course of thisclass. You should always be aware, however, that no singlerelationship between velocity and density may be applicable
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relationship between velocity and density may be applicable.
DEPTHFT
VSHALEv/v0 1
PHIE_FINv/v1 0
( CORE_POR )
100 0
RDEEPohmm0.2 20
FIN_RHOBg/cc1.65 2.65
NPHI_SS0.6 0
FIN_RHOB NPHI_SS
FINAL_VPkm/s2 6
FINAL_VSkm/s1 3
FINAL_SWv/v0 2
PHIE_FINv/v0.4 0
BVWv/v0.4 0
14200
14300
14400
GUERRA SANDGUERRA SAND
Guerra Sd base
14600
14700
14800
14900
15000
15100
15200
15300
15400
VELOCITYVELOCITY--DENSITYDENSITY
It is important to note that there is no unique relationshipbetween velocity and density. The are various reasons forthis, many of which we will cover during the course of thisclass. You should always be aware, however, that no singlerelationship between velocity and density may be applicable
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2.8
2.5
2.6
2.7
1
0.6
0.8
GUERRA 20GUERRA 72GUERRA 21GUERRA 36GUERRA 104GUERRA_109GUERRA 20
GUERRA 72GUERRA 21GUERRA 36GUERRA 104GUERRA_109
relationship between velocity and density may be applicable.
9 Jan 2007 @ 7:42
VP (km/s)
2 52.5 3 3.5 4 4.5
RHO
B(g/cc)
2
2.1
2.2
2.3
2.4
VSHALE
0
0.2
0.4
Density calculatedusing both Gardnerand Castagna.
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In the absence ofother wells to use forcalibration it can bedifficult to know what
density values areappropriate for theshale.
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EDITING DENSITY LOGSEDITING DENSITY LOGS
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CROSS-PLOTTING- cross-plot local data and do regressions on the valid data points
R^2 = 0.68
CAUTION!
Velocity-density (or porosity)relationships are extremely complex.Indeed, it is often difficult to find ameaningful relationship between the
VP (ft/s)
RHOB(g
/cc)
R^2 = 0.87
.
cracks in the rock matrix, grainboundary effects, pore-geometryeffects, complex mineralogy, fluiddistributions, and other microscopicflaws in the rock matrix.
1) regress valid sand and
shale data points
2) linearly mix the two
equations using Vsh (or
some other lithology
curve)
Note that the localdensity estimationworks best.
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EDITING DENSITY LOGSEDITING DENSITY LOGS
As with sonic data, multilinearregression is often an effective tool forediting density data. Key inputs tend tobe velocity and Vshale (or GR)
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DEPTH
FT
GR_2
API0 200
CALIN6 16
RAW_RHOB
g/cc1.8 2.8
FIN_RHOBg/cc1.8 2.8
RAW_VP
km/s2 7
FINAL_VPkm/s2 7
RAW_VS
km/s0 4
FINAL_VSkm/s0 4
RAW_PR
v/v0 0.5
PRv/v0 0.5
7500
8000
8500
9000
NOTE: LARGE UNCERTAINTY WITH REGARDS TO DENSITY EDITS.
9500
10000
10500
11000
11500
12000
12500
13000
DEPTH
FT
GR
GAPI0 150
SPMV50 250
AT90
OHMM0.2 2000
AT30OHMM0.2 2000
RAW_RHOB
g/cc2 3
PRED_RBg/cc2 3
RAW_VP
km/s2 7
PRED_VPkm/s2 7
DENSITY EDITS: WHAT IS THE GROUND TRUTH?
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DCAL
IN-5 5
AT10
OHMM0.2 2000
PRED3_RB
g/cc2 3
4100
4200
4300
4400
4500
DENSITY:
GREEN = RAW DATARED = 1st-PASS REGRESSIONBLUE = REFINED REGRESSION
MBFL
UBRNTSH
ORD_UNC
4600
4700
4800
4900
5000
5100
5200
5300
VELOCITY:
BLUE = RAW VP
RED = REGRESSION
DENSITY LOGSDENSITY LOGS
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RECOMMENDATIONS:
- generate pseudo-density for all wells- locally calibrate if possible- top to bottom of data
- use pseudo-data where raw data is bad
If no density data is available for calibration, I recommend usingCastagnas modification of Gardners relationship.
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EDITING DENSITYEDITING DENSITY
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Creating Pseudo Densities
Gardner coefficients (eLog)Emerge (H-R)
Editing Density based on new logs andgeologic understanding
EDITING DENSITY LOGSEDITING DENSITY LOGS -- INVASIONINVASION
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porosity fluid density
During drilling, the near-wellbore environment is invaded to some degree with mudfiltrate. Thus, in reality the bulk density is responding to the rock matrix and thedensity of the mud filtrate. While this may not be particularly problematic for brine-filled sands, it can cause problems for gas sands. We hope to demonstrate this withthe following examples.
n
flgB += )1(
Measured bulk density
grain density
ra n ens y,
=i
iig
1
=
=n
i
iifl x1
Fluid density,
PROBLEM SETPROBLEM SET
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PROBLEM 1a:
A gas sand has a bulk density of 2.05 g/cc and a grain density of 2.65 g/cc.
- calculate the porosity assuming a fluid density of 1.0 g/cc- ca cu ate t e poros ty assum ng xo = . ; assume a gas ens ty o .
g/cc and a brine density of 1.0 g/cc
- correct the bulk density for the effects of invasion; assume an Sw of 0.18 (18%)- calculate the percent difference between the measured and corrected bulk
density.
PROBLEM SETPROBLEM SET
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BULK DENSITY 2.05 Fluid density Fluid density
Grain density 2.65 Gas 0.25 Gas 0.25
Filtrate 1 Brine 1
Sxo 0.55 Sw 0.18
PART 1 FLUID DENSITY AT Sxo 0.6625 Fluid density at Sw 0.385
porosity with fluid density = 1 0.363636
PART 2
porosity with 55% invasion 0.301887
PART 3
Corrected bulk density 1.966226
% differen 4.086516
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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As seismic petrophysicists, most of our work isdependent upon reliable shear measurements orestimates
Dipole measurements not available prior to about 1990
Need to frequently estimate shear velocities, especially
for older wells
It is also desirable to estimate shear velocity as a guidefor shear log editing
It is ALWAYS good practice to purchase and evaluate theraw wave-forms and evaluate the vendors shear picks!
DIPOLE SONICDIPOLE SONIC
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Needed for Vp and Vs
Modern dipole tools utilize amonopole source and twoorthogonally-arranged dipole
sources Two orthogonally-arranged
receiver arrays
Diagram of Schlumberger's DSI tool (visit theirWEB page for additional information)
Why dipole?
Older monopole tools could record shear
waves, as long as the velocity of thewellbore fluid was slower than the shearwave velocity of the formation (that is,fast rocks). Dipole technology can
avoid this limitation.
and slow shear waves when
run in crossed-dipole mode Ideally the tool is centered in
the wellbore
Less susceptible to wash-outs than density
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SHEAR ESTIMATIONSHEAR ESTIMATION -- comparisoncomparison
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10000
12000
14000
SHALE
ft/s)
0
2000
4000
6000
5000 7000 9000 11000 13000 15000 17000 19000
G-C
WILLIAMS
VP (ft/s)
VS
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SHEAR ESTIMATIONSHEAR ESTIMATION -- comparisoncomparison
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6000
8000
10000
12000
14000
SHALE - G-C
SAND - G-C
14000
0
2000
4000
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
SHALE - WILLIAMS
SAND - WILLIAMS
WHAT ARE THE IMPORANTDIFFERENCES?
WHY MIGHT THIS BE IMPORTANT IN
ROCK PROPERTIES WORK?
SHEAR ESTIMATIONSHEAR ESTIMATION
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)
6000
9000
8000
7000
1
.6
0.8
Many wells in the DWGOM have lowvelocity contrast between sands andshales. If we were to apply the G-C
coefficients to these wells, the shearvelocities would be too fast (note,however, that both Williams and G-Cwould predict similar shear velocities forshale). Later in the class we will discussthe importance of this for the AVOresponse.
20 Oct 2005 @ 11:11
VS (ft/s)
0 8000800 1600 2400 3200 4000 4800 5600 6400 7200
DEPTH(BML;feet
14000
13000
12000
11000
10
000
Vs
hale
0
0
.2
0.4
0
7 1
Note that sands are much faster than theshales in this well. This is an indicatorthat G-C coefficients might be moreappropriate for shear estimation. Use of
the Williams estimator may result in Vp/Vsratios that are too high
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P(km
/s)
5
6
Vs
ha
le
0.6
0.8
ratios that are too high.
Please note that this is a rule of thumb,and may not be globally applicable. Localcalibration is ALWAYS desirable!
11 Apr 2007 @ 9:32
DEPTH (feet)
500 55001000 1500 2000 2500 3000 3500 4000 4500 5000
2
3
4
0
0.2
0.4
BARNETTSHALE
THE IMPORTANCE OF COMPOSITIONTHE IMPORTANCE OF COMPOSITION
DEPTHM
GRGAPI0 200
RTOHMM0 2 200
RHOBG/C32 3
VS_RAWft/5000 10000
PR_RAW/0 0 5
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M GAPI0 200
DCALin-5 5
0 DCAL
OHMM0.2 200
RXOOHMM0.2 200
MSFLOHMM0.2 200
G/C32 3
VP_FINALft/s9000 19000
PERC_DIF%-100 100
ft/s5000 10000
VS_CASTft/s5000 10000
v/v0 0.5
PR_CASTv/v0 0.5
4400
Vp
13,000 ft/s (3.96 km/s)
Vs
7,500 ft/s (2.29 km/s)
Note the positivecontrast in PRbetween shale and
sand
4500
4600
DIFFERENCE BETWEEN PREDICTED AND
MEASURED VS LESS THAN 10%
COMPOSITIONCOMPOSITION
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IMPORTANT DIFFERENCES IN
RESULTANT AVO RESPONSE
CLASS IV vs. CLASS III
IMPLICATIONS FORPROSPECTING?
FLUIDSUB TO GAS USING
IN-SITU SHEAR LOG
FLUIDSUB TO GAS USING
G-C COEFFICIENTS
UPPER SAND
-0.1
-0.05
0
0.05
0.1
0 10 20 30 40 50 60
Incident angle
Rpp
()
AVO response calculated
using the measured
properties
AVO response calculated
using G-C Coefficients
Measured = positive AVO gradient
Predicted = negative AVO gradient
Note the clay rich matrix and that the
COMPOSITIONCOMPOSITION
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33.6
11.8
41.1
Detrital Quartz
Total Feldspar
Total Lithics
Total Diagenetics
Note the clay-rich matrix, and that the
quartz and feldspar grains are not in
contact with each other.
K = 1.24 md; = 6.4%
13.5
0.01.6 4.0
5.6
85.6
0.0
0.0
0.8
2.4 0.0
Clay coatings
TOTAL SMECTITE
TOTAL CHLORITE
TOTAL QUARTZ AND FELDSPAR
TOTAL CARBONTATE (CC, DOLO, SID)
HALITE
ILLITE
TOTAL TiO2TOTAL PYRITE
Bitumen
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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KEY INDICATORS OF BAD SHEAR DATA:
1) anomalous (or unexpected) Vp/Vs ratios
2) negative (or anomalously low) Poissons ratios
3) shear values that are significantly different from estimated values
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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NEGATIVE POISSON RATIOS
It is not uncommon to encounter negative Poisson Ratios when doing rock property work.There are a couple of reasons for this, but it is necessary to correct this problem.
, ,to calculate PR and look for negative, or anomalously low, values.
NEGATIVE POISSON RATIOS: BED
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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NEGATIVE POISSON RATIOS: BEDBOUNDARY EFFECTS
- Mostly a problem in Class I and Class II rocks- Solutions:
1) stretch/squeeze log data (Vp and Vs),2) estimate shear and splice in pseudo shear
across the bad data zone
appears to affect
NEGATIVE POISSON RATIOS: P-wave ATTENUATION? CRACKS?
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appears to affectfairly tight rock
data can beproperlyprocessed, andstill yield thisresult.
if shear data
looks reliable,generatepseudo-VP
NOTE: note that the shear data mimics the geometry
of the sand better than the P-wave velocity. This is an
indicator that we should probably correct the VP, and
not the shear.
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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1) Simple, but reasonably accurate
2) All of these are derived for shale and brine-filled sands
- a lication of an em irical shear estimator to a as sand will
EMPIRICAL SHEAR ESTIMATORS
result in a shear velocity that is too slow.
3) Need to locally calibrate to known dipole and/or coremeasurements
4) In the absence of local calibration, I use contrasts in P-wavevelocity as a guide for selecting the appropriate shear velocityestimator.
NOTE:
- estimated shear velocity accurately
predicts measured shear velocity
- shear velocity for gas sand is not
predicted
- however, note that cross-plottingestimated vs measured shear may
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Gas Sandestimated vs measured shear may
provide a means of identifying gas
sands!
R^2 = 0.91
*Data are from East Anstey (MC607)
1) ll f th l d h ti t f h l d b i fill d
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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1) all of the commonly used shear estimators are for shale and brine-filled
sands.
2) estimation of shear velocities in pay is problematic
- Greenburg and Castagna, 1992; an iterative approach
- P-wave modulus approximation
3) Use the P-wave modulus (VP2) to take the gas P-wave velocity back tobrine (Mavko et al., 1998). Then we apply one of the empirical shear
.
00
2
0
1
1
M
M
MM
MM
MMdry
fl
dry
drysat
+
+=
Msat = VP2 ; the saturated P-wave modulus (measured from the log data)Mdry = the P-wave modulus of the dry frame
M0 = the P-wave modulus of the mineral matrix
Mfl = the P-wave modulus of the pore-filling fluid
= porosity
Since we are calculating the P-wave modulus for two fluids, we can algebraically eliminate the
SHEAR EDITS AND ESTIMATIONSHEAR EDITS AND ESTIMATION
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)20(
2
20
2
)10(
1
10
1
MflM
Mfl
MsatM
Msat
MflM
Mfl
MsatM
Msat
=
Since we are calculating the P wave modulus for two fluids, we can algebraically eliminate the
need to calculate Mdry:
We can solve forMsat2 and calculate the compressional velocity for the brine case:
2
2
satMVP=
Note that the bulk density must also be fluid substituted back to brine!
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( )( )2
222
22
/ matrixfluidmatrix
fluidsituin
situin
VSVPVP
VPVPVS
=
Kriefs model for the rock frame (we will discuss this later in the class) can becombined with Gassmanns equation to determine the shear velocity across a pay
sand. This technique is an easier approach than via the P-wave modulus
approximation.
VSHv/v0 1
DEPTHFT
RDEEPohmm0 2 200
VSft/s0 10000
SWv/v0 2
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v/v0 1
PHIEv/v1 0
0 VSH
FT ohmm0.2 200 ft/s0 10000
VS_DWGOMft/s0 10000
XVS_INSIft/s0 10000
v/v0 2
PHIEv/v0.5 0
BVWv/v0.5 0
12200
Shear estimate without
Measured shear velocity
12300
12400
12500
gas correct on
Shear estimate with
gas correction
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0.05
0.1
0.15
0.2
oefficient
-0.2
-0.15
-0.1
-0.05
0 10 20 30 40 50 60
INSITU RESPONSE
GAS CORRECTION
NO GAS CORRECTION
Refle
ction
Offset (degrees)
- note phase reversal and brightening at the far offsets for the insitu gas case
- these characteristics are lost if shear is not properly estimated.
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P-wave MODEL
COMPARISON STUDYCOMPARISON STUDY
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DEPTH
FT
VCL_FIN
v/v0 1
PHIE_FINv/v1 0
0 VCL_FIN
AO90
OHMM0.2 200
AO30OHMM0.2 200
AO10
OHMM0.2 200
RHOB_FIN
g/cc1.65 2.65
NPHI_SSv/v0.6 0
RHOB_FIN NPHI_SS
VS_RAW
ft/s3000 8000
VS_PWAVEft/s3000 8000
PR_RAW
v/v0 0.5
PR_PWAVEv/v0 0.5
10300
10400
10500
10600Use of the P-wave shear estimator is based on Gassmann. It
also requires that the user select one of the empirical shear
estimators. In this case, I used the G-C coefficients for sand
and shale. Note the quality of the match in the shales and
brine sand. Whether using Krief or P-wave, it is useful to
calibrate the estimated shear to either shales and/or brine
sands.
P-wave MODELKRIEF MODEL
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8000
6000
6500
7000
7500
1.2
0.8
1
R2 = 0.87
8000
6000
6500
7000
7500
1.2
0.8
1
R2 = 0.82
WELL: East Cameron 131 #1ZONE: 5410.500 - 10819.000 FTDATE: 14 Oct 2005 @ 9:10 PHIE_FIN > .04
VS_KRIEF
3000 80003500 4000 4500 5000 5500 6000 6500 7000 7500
VS_
RAW
3000
3500
4000
4500
5000
5500
VSHALE
0
0.2
0.4
0.6
WELL: East Cameron 131 #1ZONE: 5410.500 - 10819.000 FTDATE: 14 Oct 2005 @ 9:09 PHIE_FIN > .04
VS_PWAVE
3000 80003500 4000 4500 5000 5500 6000 6500 7000 7500
VS_
RAW
3000
3500
4000
4500
5000
5500
VSHALE
0
0.2
0.4
0.6
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CAUTION!
If you are using estimated shear velocities that have not been gas-corrected, the AVO response you get will be wrong. Even in relativelyti ht and fast rock incorrect shear estimation for a can make asubtle difference on the calculated AVO response.
It is not uncommon to see people do this!
VCL_FIN
v/v0 1
PHIE_FIN
v/v1 0
GR
API0 150
DEPTH
FT
RDEEP
ohmm0.2 2000
RMED
ohmm0.2 2000
RHOB_FIN
g/cc1.65 2.65
NPHI_SS
v/v0.6 0
VS_EST
ft/s4000 10000
VS_FINAL
ft/s4000 10000
PR
v/v0 0.5
PR_MEAS
v/v0 0.5
SW_FINAL
v/v0 2
PHIE_FIN
v/v0.5 0
A BLIND TESTA BLIND TEST
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0 VCL_FIN
RSHAL
ohmm0.2 2000 RHOB_FINNPHI_SS
BVW
v/v0.5 0
7600
7700
LIMESTONE LAYER
7900
8000
8100
8200
8300
8400
11000
100
00
1
0.8
1: VS_EST-VS_FINAL-VCL_FIN
Correlation Coefficient:
r = 0.9074 r-square = 0.8234
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URED
VS(ft/s
)
8000
9000
Vs
ha
le
0.6
25 Jan 2004 @ 19:55
ESTIMATED VS (ft/s)
4000 110005000 6000 7000 8000 9000 10000
MEAS
4000
5000
6000
7000
0
0.2
0.4
NOTE: the estimated shear velocity
predicts values which typically are slightly
faster than the measured values. However,
the estimated shear velocity in this case
was calculated using Castagnas
coefficients for sand and shale. If local
coefficients were developed, a better fitwould probably be observed. You will
note on the following page, however, that
these small differences make no
appreciable difference in the AVO models.
0.1
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0
0.05
0 10 20 30 40 50 60
-0.2
-0.15
-0.1
-0.05
RED = estimated shear
BLUE = measured shear
CASE NAME: DIPOLE CASE NAME: ESTIMATED SHEAR
SHALE SAND SHALE SAND
VP 11772 13491.26 VP 11772 13491.26
VS 6069 8488.129 TRUE VS 6231 8731.205 TRUE
RHOB 2.64 2.318 RHOB 2.64 2.318
Display Results Display Results
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SOME OBSERVATIONS:
If velocities are greater than approximately 9,000 ft/s, Castagnas coefficients will result inrealistic frame properties. Shear estimators designed for soft rock will result in Vp/Vsratios which are too high.
, ,frame properties which are too stiff.
As always, locally calibrate when possible, and build shear coefficients which areappropriate for your area.
PREDICT SHEAR VELOCITIES (USING G-C) AS A QC TOOL
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Look for large discrepancies (>15%) Possible data quality issues Smaller differences (< 10%?) may be due to unexpected
composition
INCORRECT SHEAR VELOCITIES CAN ESTABLISH THE
AVO models
Geomechanical analysis Registering of multicomponent datasets
COLLECT RAW WAVEFORMS
stacked semblance is not enough! evaluate STC plots at key depth intervals (especially across the
shales)
IF NECESSARY, REPROCESS
TWO FINAL EDITING STRATEGIESTWO FINAL EDITING STRATEGIES
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PATCHES
RESCALING OTHER CURVES
EXAMPLEEXAMPLEDEPTH
M
A_GR
GAPI0 150
B_GRGAPI0 150
B_C13
A_RD
OHMM0.2 200
B_M2R9OHMM0.2 200
A_DT24QS
US/M400 100
B_DT24QSUS/M400 100
B_PORZSS
PU40 -10
B_CNCSSPU40 -10
SPL_GR
API0 150
Lets repair the data across the casing point.Remember, we want to generate final curves that more-or-less represent what we think is in the earth.
Since the deep resistivity penetrates deepest into thereservoir, we will start by making a few minor edits toour merged RT
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MM0 500
A_C13MM0 500
1525
1550
1575
1600
1625
1650
1675
SIMPLE HANDSIMPLE HAND--PATCH ON RTPATCH ON RT
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ARGUMENTS FOR OR AGAINST MY PATCH?
RESCALED SONICRESCALED SONIC
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The correlation between the sonic and resistivityacross the casing point indicates that theSPL_RT may not require extensive editing.
RESCALED SONICRESCALED SONIC
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Rescaled sonic data to mimic RT. Notice thatthis more closely mimics the resistivity than mysimple hand-patch.
What does the resistivity profile suggest aboutlithology?
What are the potential pitfalls in our model?
WHAT ABOUT GR?
RESCALED GR?RESCALED GR?
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COMPARE RAW vs. EDITED DATACOMPARE RAW vs. EDITED DATA
DEPTHFT
RAW_RHOBg/cc2 3
FIN_RHOBg/cc2 3
QUAL RB
RAW_VPkm/s2 7
FINAL_VPkm/s2 7
QUAL VP
RAW_VSkm/s1 4
FINAL_VSkm/s1 4
QUAL VS
RAW_PRv/v0 0.5
PRv/v0 0.5
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QUAL_RBflag0 20
QUAL_VPflag0 20
QUAL_VSflag0 20 RAW_PR PR
1500
2000
2500
MBFLUBRNTSHLBRNT
ORD_UNC
3000
3500
4000
4500
5000
5500
6000
6500
SUMMARY: HANDLING BADHOLE DATASUMMARY: HANDLING BADHOLE DATA
Recognition of bad data
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g
Look for consistency between various log measurements!
Log plots Histograms Trend plot Cross-plots
Look for deviations from expected behavior
Look for badhole indicators such as caliper (washouts) anddensity correction
Various tools available for reconstructing bad data(regressions, empirical equations, patches, rescaling)
EDITING STRATEGYEDITING STRATEGY
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Load all data check it carefully
Display the raw data Identify all possible rhob, dt, dts curves; compare and select Merge, depthshift, clip tails, compare to nearby wells Spend some time looking at log display at variety of scales, cross-
plotting curves, histograms, trend plots
Study the well. Look at the curve patterns in light of the geology
Always create a display illustrating the raw data and theedited data
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