1. 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, Austria Lisa Stright and Anne Bernhardt Geological and Environmental Sciences STANFORD UNIVERSITY Rohöl-Aufsuchungs Aktiengesellschaft

2. Stright and Bernhardt – MA-MS Calibration AAPG 2010 MA-MS (multi-attribute, multi-scale) calibration Fact #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

3. 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

4. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Multi-attribute, Multi-scale (MA-MS) calibration

5. Stright and Bernhardt – MA-MS Calibration AAPG 2010 ? Using the calibration ? ? ???

6. 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

7. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Seismic reflectivity profiles: where is the gas? N BB’ 1000 m 100 m 1000 m 100 m A A’ A B B’

8. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Rock properties validated with core observations Bierbaum 1 AI (g/cm 3 m/s) 5000 13000 10km 17km 2 issues: 1)biased sampling 2)poor resolution of seismic

9. 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?

10. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Calibrated proportions

11. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Depositional model for the Puchkirchen reservoir Bernhardt et al. (2008)

12. 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

13. Stright and Bernhardt – MA-MS Calibration AAPG 2010 Modeling workflow Rock Properties Fine scale facies patterns Combine and filter to seismic scale Combine and filter to seismic scale Assign fine scale patterns to seismic volume Assign fine scale patterns to seismic volume Analyze and interpret results Analyze and interpret results 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

14. 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

15. 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 Advisors Tapan Mukerji Alexandre Boucher Steve Hubbard