Stright and Bernhardt – MA-MS Calibration AAPG 2010

Sub-seismic scale lithology prediction for enhanced reservoir-quality interpretation from seismic attributes, Puchkirchen Field, Molasse Basin, AustriaLisa Stright and Anne BernhardtGeological and Environmental SciencesSTANFORD UNIVERSITY

Rohöl-Aufsuchungs Aktiengesellschaft

Stright and Bernhardt – MA-MS Calibration AAPG 2010

MA-MS (multi-attribute, multi-scale) calibrationFact #1: Seismic data and well logs sample different

volumes of the reservoir

Fact #2: Combining these data generally compromises information from the finer scale (well logs)

New methodology to obtain proportions from well to seismic calibration– MA-MS (multi-attribute, multi-scale) calibration– VFP calibrated to seismic attributes– Tie fine-scale facies in wells to coarse scale seismic

attributes

Proportion volumes used for interpreting sedimentology

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Reconciling scale differences

Options:1) Lump facies together to

seismic facies

2) Apply relationships observed at log scale to seismic scale

3) Turn seismic attributes into probabilities

4) A new approach… “What proportions of each facies creates that reflector?”

~10m

~12-25m

~15cm

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Multi-attribute, Multi-scale (MA-MS) calibration

Stright and Bernhardt – MA-MS Calibration AAPG 2010

?

Using the calibration

?

??

?

?

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Late Oligocene Puchkirchen Formation, Molasse Basin, Austria

after Bernhardt et al., 2008; Hubbard and deRuig, 2008

A’

A

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Seismic reflectivity profiles: where is the gas?

N

B B’1000 m

100

m

1000 m

100

m

A A’

A

A’

B

B’

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Rock properties validated with core observations

Bierbaum 1

AI (g/cm3m/s)

5000 1300010km 17km

2 issues:1) biased sampling2) poor resolution of seismic

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Rock type prediction from seismic attributes

• What sub-seismic scale facies generate high/low amplitudes?

• How can we combine these multiple scales to make accurate predictions?

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Calibrated proportions

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Depositional model for the Puchkirchen reservoir

Bernhardt et al. (2008)

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Using proportion models to validate sedimentological hypothesis

5 km

0 m

20 m

40 m

60 m

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Modeling workflow

RockProperties

RockProperties

Fine scale facies patterns

Fine scale facies patterns

Combine andfilter to seismic scale

Combine andfilter to seismic scale

Assign fine scale patternsto seismic volume

Assign fine scale patternsto seismic volume

Analyze and interpretresults

Analyze and interpretresults

Underlying “Model” of patterns

1) 1-D Patterns from • logs interpretations• synthetic patterns from Markov Chain

2) 2-D and 3-D patterns from • numerical models• outcrop sections• experimental results• conceptual model (training images)• interpretation from 3D proportion models

New Approach

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Conclusions • Important to understand volume support of the input data as it relates to the desired

prediction volume support

• Probabilities account for the approximate relationship between facies descriptions and seismic attributes

– may camouflage issues between assumed calibrating well and seismic data– poor way of handling scale differences– are conceptual constructions and nonphysical measurements – proportions are more intuitive, scale-based and directly link rock properties

• Multi-attribute, multi-scale calibration (MA-MS) for proportion prediction:– data-driven observations of subseismic-scale features – direct relate to seismic-scale attributes– consistency between geologic concept, rock properties and data

• Understanding rock physics is critical in using seismic attributes as soft data in modeling.– how they will inform the geologic model, and– at what scale

• Training image generation is interpretive and iterative

• Facies Proportion Models help to validate sedimentological interpretations in the subsurface

• Validated sedimentological interpretations form the basis for the development of training images

• Accurate interpretations of the depositional history of the channel-fill are key to reduced exploration risk and efficient production

Stright and Bernhardt – MA-MS Calibration AAPG 2010

Acknowledgements

Industry Sponsor:Richard Derksen and Ralph Hinsch (RAG)

SPODDS Students:Julie Fosdick, Anne Bernhardt,

Zane Jobe, Katie Maier, Jon Rotzien, Larisa Masalimova,Glenn Sharman, Blair Burgreen

Lizzy Trower

Advising Committee:Stephen Graham, Andre Journel,

Gary Mavko, Don Lowe

Other AdvisorsTapan Mukerji

Alexandre BoucherSteve Hubbard