ods-moscow2003

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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