ods-moscow2003
TRANSCRIPT
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FORWARD MODELLING FROM THE SIMULATOR
4D.inversion
Analysis and workflow
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FEASABLE TECHNOLOGY PUT INTO PLAY
AT THE RIGHT TIME
Well log analysis
Rock physicsdiagnostics
Seismicmodelling
Field studies ExploitationFeasibility
studies
Lithology &fluid prediction
4D timelapseReservoir
characterisationReservoirsimulation
SEISMIC INVERSION
Acoustic impedance
Poisson’sRatio
Density
ATTRIBUTE CALCULATION
NEURALNETWORK
3 WEEKS 6 WEEKS 1 YEAR
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Simulation Seismic
AI
PR
r
AI
PR
r
4D ISIS
MODELLED OBSERVED
Seismic data
Reservoir
Simulator
Production
data
Seismic
inversion
T
i
m
e
-
d
e
p
t
h
4
Static ModelBase Elastic Time/Depth Information
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4D Loop Seismic-2-Simulation-2-Seismic
3D Seismic
GeologicalModel
ReservoirModel
Seismic Acquisition
Time
4D Body Identification
New Data
DifferenceCube
Baseline
Time-lapse
Depth
GeologicalModelGeological
ModelGeological
ModelGeological
Model
ReservoirModelReservoir
ModelReservoir
ModelReservoir
Model
Predictions
Wells
Wells
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Understand acoustic vs physical propertiesin inverted seismic data. Apply rock physics in 4D modellingReservoir engineering aspectsMultiple wells with sonic and shear logs3 vintages of 3D seismic data (near and far offset)AVO inversion and lithology prediction
NELSON
4D AVO seismic
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The location of the Nelson Field
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Comparison of the conventional far-offset difference datato the inverted far offset difference data. In the lower section brightcolours indicate a positive impedance change. The difference signalis restricted to the lower Z3 reservoir interval.
Top Forties
Top Z3
Top Z2
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Oil sand prediction 1990 1997 2000Oil sand prediction 1990 1997 2000Oil sand prediction 1990 1997 2000
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Oil sand prediction 1990 1997 2000Oil sand prediction 1990 1997 2000Oil sand prediction 1990 1997 2000
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Crossplot of Poisson’s ratio versus acoustic impedance for well log data from the Nelson reservoir interval. Oil and water filled sands as well as shale fields can be clearly defined.
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Combined pressure and saturation response for a typical Nelson sandstone (Boyd–Gorst pers.comm.).
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Oil sand Brine sand
Oil sand Brine sand
1990
1997
4D Rock physics DiagnosticsDefining lithology and fluid fields from inversion data to carry out a probabilistic prediction of fluid and lithology volumes.
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A perspective view to the northeast of the Oil sand volumeprediction from the 1990 baseline acoustic impedance and Poisson’sratio data.
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Isometric perspective to view to the north of a volume detection of high oil sand probability (bright colours) masked by a detection of positive impedance change (2000-1990) in grey. Bright colours indicate potential unswept oil at the top of the reservoir at mid-2000.
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Oil Sand Probability Volume
Far Offset Impedance Difference
Positive impedance change (red) indicating sweep (2000)
SW NE
Oil Sands in Red (1990)
Cone around production well
Edge Drive
Basal Rise
4D Target
500m
Oil sand probability and far-offset impedance difference sectionsthrough the N30 target, between two nearby production wells. It canbe seen from the sweep pattern on the far-offset impedance sectionthat oil is not being swept effectively from the Z4 section between thetwo producers, in an area which the oil sand probability shows to begood reservoir.
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SIMULATOR PREDICTION – converted into AI changes including S/N filter
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Far offset impedance difference section intersecting the N30y production well. Bright colours show a high positive impedance contrast (sweep).
The pilot hole for the well encountered an 25 meters oil column before penetrating the moved OWC as prognosed by 4D.
Simulation had indicated that the area would be almost completely swept (orange horizon).
OWC from 4D
Seismic (FO Inv Diff) OWC from SimulatorTop Forties
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ABSTRACT: Integrated analysis of 4D seismic data and petrophysical data is used to produce probabilistic fluid and lithology volumes for monitoring reservoir performance on the Nelson Field.
Petrophysical analysis of log data shows distinct fields for oil sand, water sand, shale and heterolithic ‘lithologies’ in acoustic impedance - Poisson’s ratio space.
Elastic inversion techniques applied to conventional 4D AVO datasets convert the reflectivity data to acoustic impedance, shear impedance, Poisson’s ratio and angle impedances. The elastic inversion datasets are used to quantify oil water contact movements through volume sculpting techniques.
Well derived relationships are used to predict 3D volumes of oil sand probability from three different seismic survey vintages, 1990, 1997 and 2000. Changes in oil sand probability due to production are verified by comparison with repeat production logs.
Integrated volume interpretation of 4D far offset inversion difference (oil water contact movement) and oil sand probability show areas of unswept oil, highlighting infill opportunities. Early results from infill drilling have validated the method realising the potential economic benefits of 4D seismic technologies.
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TERN
4D full stack seismic
Reservoir Engineering aspectsPhysical properties linked to acoustic properties
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Gamma
Sonic
Quartz matrix
0 % 100
Bulk volume water
100 % 0
Porosity
100 % 0
Clay volume
0 % 100
Neutron
Density
Acoustic Impedance
Dep
th (m
)
WELL LOG FLUID SUBSTITUTION: 1980-1995
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4D DIFFERENCE VOLUME IN AI: 1995-1980
red = hardening
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4D INVERSION RESULTS
Acoustic impedance 1980
Acoustic impedance 1995
Acoustic impedance 2000
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SIMULATOR MODELING: WATER SATURATION
5 days
1307 days
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From change in Sw From change in pressureFrom combined effect of
change in Sw and pressure
ROCK PROPERTIES FROM SIMULATOR: 1980 - 1995
Changes in acoustic impedance
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AI 1983-1995 with alternative realistion of noise – Top Etive
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HUDSON
4D full stack seismic
QC of 4D potential in dataRe-processing required
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FLUID REPLACEMENT MODELLING
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1999 1990
ACOUSTIC IMPEDANCE
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SIMULATOR RESULTS 1999-1993
Relative change in Poisson’s ratioRelative change in Zp
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PRESSURE CHANGE 1999-1993
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Timelapse acoustic impedance Timelapse Poisson’s ratio
TIMELAPSE HORIZON 1999-1993
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KIMMERIDGE REPEATABILITY 1999 DATA
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4D - KIMMERIDGE REPEATABILITY
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NINIAN
4D seismic
Generated from 2D baseline and 3D time-lapse
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MAP OF SEISMIC
- 1981 origional 3D survey- 1995 three 2D seismic sections
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1981 ACOUSTIC IMPEDANCE
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1995 ACOUSTIC IMPEDANCE
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LINE 1: ACOUSTIC IMPEDANCE DIFFERENCE
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LINE 2: ACOUSTIC IMPEDANCE DIFFERENCE
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LINE 3: ACOUSTIC IMPEDANCE DIFFERENCE
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NINIAN CONCLUSION
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GULLFAKS
4D amplitude vs inverted seismic
3 vintages of 3D seismicInteraction rock Physics and reservoir model
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Elastic properties of Brent group sand (6 wells)
AI-PR-SWAI-PR-PHI
PHIT evaluation
AI-PR (for sandflag data only)Active Zone : 4:34/10-B-8 Z:2 Top Tarbert
3.
4.
5.
6.
7.
8.
9.
AI
10^6 (
kg
/m2s)
0.1 0.2 0.3 0.4 0.5PR
0.2
0.4PHIT
8730 points plotted out of 12340Well Zone Depths
34/10-B-8 (2) Top Tarbert 2616.M - 2768.M
34/10-B-8 (3) Top Ness 2768.M - 2919.M
34/10-B-8 (4) Top NER 2919.M - 3094.M
34/10-C-33 (2) Top Ness 2095.M - 2116.M
34/10-C-33 (3) Top NER 2116.M - 2231.5M
34/10-B-15 T2 (1) Top Tarbert 2476.M - 2579.M
34/10-B-15 T2 (2) Top Ness 2579.M - 2670.M
34/10-B-15 T2 (3) Top NER 2670.M - 2669.89551M
34/10-B-20 (1) Top Tarbert 1B 2910.M - 2967.M
34/10-B-20 (2) Top Ness 2967.M - 3208.M
34/10-B-20 (3) Top NER 3208.M - 3362.M
34/10-B-27 (2) Top Ness 2622.M - 2708.M
34/10-B-27 (3) Top NER 2708.M - 2887.M
34/10-C-33 (2) Top Ness 2095.M - 2116.M
34/10-C-33 (3) Top NER 2116.M - 2231.5M
34/10-C-36 (1) Top Tarbert 4644.M - 4857.M
34/10-C-36 (2) Top Ness 4857.M - 4861.M
34/10-C-36 (3) Top NER 4861.M - 4895.95947M
PHIT evaluation
AI-PR (for sandflag data only)Active Zones : W:4 Z:2, 3, 4 W:6 Z:3, 2 W:1 Z:2, 3 W:2 Z:2, 3, 1 W:3 Z:3, 2 W:6 Z:3, 2 W:7 Z:2, 1, 3
3.
4.
5.
6.
7.
8.
9.
AI
10^6 (
kg
/m2s)
0.1 0.2 0.3 0.4 0.5PR
0.
1.SW
8230 points plotted out of 11663Well Zone Depths
34/10-B-8 (2) Top Tarbert 2616.M - 2768.M
34/10-B-8 (3) Top Ness 2768.M - 2919.M
34/10-B-8 (4) Top NER 2919.M - 3094.M
34/10-C-33 (2) Top Ness 2095.M - 2116.M
34/10-C-33 (3) Top NER 2116.M - 2231.5M
34/10-B-15 T2 (2) Top Ness 2579.M - 2670.M
34/10-B-15 T2 (3) Top NER 2670.M - 2669.89551M
34/10-B-20 (1) Top Tarbert 1B 2910.M - 2967.M
34/10-B-20 (2) Top Ness 2967.M - 3208.M
34/10-B-20 (3) Top NER 3208.M - 3362.M
34/10-B-27 (2) Top Ness 2622.M - 2708.M
34/10-B-27 (3) Top NER 2708.M - 2887.M
34/10-C-33 (2) Top Ness 2095.M - 2116.M
34/10-C-33 (3) Top NER 2116.M - 2231.5M
34/10-C-36 (1) Top Tarbert 4644.M - 4857.M
34/10-C-36 (2) Top Ness 4857.M - 4861.M
34/10-C-36 (3) Top NER 4861.M - 4895.95947M
•PP and Sw of four generations are extracted from the reservoir model.
•The RP model transform changes in SW and PP into changes in elastic rock properties.
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Seismic data vintage 1- near and far
Significant AVO effect
Significant 4D effect on both stacks
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Vintage 1 - AI around well A
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Vintage 3- AI around well A
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OFFSHORE UK
MULTI ATTRIBUTE ANALYSIS
Integration of physical attributes in wells with acoustic attributes in seismic
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1. Generate suite of attributes from the seismic
2. Extract attributes at well locations
3. Investigate methods of relating well log rock properties to volume derived data.
4. Apply derived relationships to input volumes
5. Interpret the resultant volumes
WORKFLOW
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Absolute acoustic impedance (AI):
• Real rock physics property
• Contains low frequency information not present in seismic
• Good ties with well log derived acoustic impedance
• Hydrocarbon identification not possible on AI alone.
ACOUSTIC IMPEDANCE
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ATTRIBUTE CROSSPLOT – NEURAL ANALYSIS
Synth.seis Zp Attenuation Inst.amp Inst.freq Inst.phase Coherence Mean.freq
Sn
th.s
eis
Z
p
A
tte
n.
I
nst.
am
p In
st.
fre
q I
nst.
ph
as C
oh
e M
ea
n.f
req
Hydrocarbons
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• All the well data loaded into a GEOVIEW database.
• All the original and generated attribute volumes loaded.
• A target log specified – water saturation
• Attributes extracted along the well path
WELL LOG/ATTRIBUTE RELATIONSHIP
Sw synth Vp f fm Ia If phase dIa/dt filtseis IntIa
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• Neural networks used to generate non-linear transform between attributes and target logs
• Transform applied to the inputs at the well locations produces very good results
WELL LOG PREDICTIONS
WATER SATURATION
Well Prediction
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Comparison of actual and predicted water saturation
WATER SATURATION PREDICTION AND FORECASTING
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HIGH HYDROCARBON POTENTIAL BODIES
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We would like to thank the following contributors to the presentation:
STATOIL
NORSK HYDRO
CONCOPHILLIPS
AMERADA HESS
SHELL
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• Experience with this type of datasets
25 major 4D projects ‘under the belt’
Variety of PE Objectives Realised
Number of Different Geological Settings
Large group, Varied Disciplines
• Proprietary Technology
Simultaneous Inversion
4D ISIS
• Fast Project Start-up and Turnaround
• Cost Effective, High Quality
WHY USE INVERSION DATA 4D CAPABILITIES
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RA Geophysical Well Log Analysis
IP Rock Properties Modeling
OSIRIS Precise Seismic Modeling
EMERGE Neural Net
4D*ISIS Global Seismic Inversion
MAAT Seismic Attributes