NORSAR Seismic Modelling

1. NORSAR Seismic Modelling NORSAR-3D NORSAR-2D VelRock

2. NORSAR offices in Norway

3. NORSAR vs. UiB • Agreement on cooperation between NORSAR and the University of Bergen. • NORSAR was providing Institute of Solid Earth Physics with software for use in research projects, theses and education, • includes commercial tools such as NORSAR-2D, NORSAR-3D, VelRock, HybriSeis, and • prototype software for special studies. • NORSAR is willing to extend this to include the newly formed Department of Earth Science and the CIPR.

4. NORSAR-2D: Ray Modelling • Two-point tracing • Interactive • Robust • User-friendly

5. NORSAR-3D: Ray Modelling • Wavefront Construction (*) • Open models • Fits for PSDM • Network of computers (*) NORSAR is the developer of Wavefront Construction!

6. NORSAR-3D: Illumination • Various Maps • Simulated Migration Amplitudes • Projects • On-going research

7. Rock Physics Modelling Analysing and predicting how different reservoirproperties of porous rocks affect the seismic properties. VP = 2.6 km/s VS = 1.4 km/s  = 2.2 g/cm3 Rock Physics VP: P-velocity VS: S-velocity lithology fluids porosity permeability 

8. VelRock and N2D/N3D VelRock rock models can be used in N2D/N3D

9. HybriSeis Modelling of Local Target

10. Ray methods (standard software) 2D/3D Normal incidence ray tracing (zero offset unmigrated) 2D/3D Image ray tracing (zero offset migrated) 2D/3D Common shot (offset HSP/ VSP/OBC) 2D/3D Green’s functions 2D Anisotropic Ray Mapping Special methods (internal software) 1D Reflectivity (offset) 2D/3D Eikonal Method 2D Finite Difference 2D/3D Migrated Amplitudes 2D/3D HybriSeis Seismic Wave Simulation Tools

11. Typical tasks Model based analysis of well log data. Model based analysis of velocities measured on cores. Structural and velocity macromodel building. Geophysical reservoir model building. Modelling of seismic responses. Model based processing and analysis of seismic data. Special tasks Time-lapse feasibility studies. AVO modelling. Survey planning. Identification of multiples and peg-legs. Greens functions for PSDM. Amplitude and illumination maps. Reflector-oriented amplitude recovery (ROAR). Applications of NORSAR Software

12. NORSAR-3D NORSAR-3D:Structural and Velocity Model Building3D Wavefront construction

13. Possible Input Data • Interpreted horizons (time, depth). • Pre-calculated interval velocities (const., grid, cube). • Stacking velocities, migration velocities. • Checkshots. • Well log data (P-velocity, S-velocity, density). • Petromarkers.

14. Shot  Receivers   Salt diapir   Targets (gas pockets) Example: Undershooting of Salt Dome

15. Raypaths Ray Paths and Reflection Points Number of arrivals/receiver Reflection Points

16. Example: Illumination of Gas Caps

17. Marine Survey

18.  Wavefronts  Raypaths Wavefront Construction

19. Bulk Modelling • Depth model built from depth grids and velocity grids. • Ray modelling done by wavefront construction. • 4011 shots simulated in parallel on 10 workstations in a few hours. • A total of about 24 million events for PP reflections from the target horizon (Top Reservoir) was found.

20. {Event counts}/{nominal fold(30)} Illumination Density Map

21. Min. Incidence Angle in CRP

22. Max. Incidence Angle in CRP

23. Amplitude along Target Horizon

24. Real Model Example The example to follow was presented by Børge Rosland (Seispro) and Geir Drivenes (Enterprise Oil) in the paper“Large Scale 3D Seismic Modelling in Exploration”,paper no. C-42, EAGE meeting, Glasgow, 2000.

25. The first 5 reflectors are relatively flat. The BCU reflector and pre-BCU reflectors are complex. The P-velocity is shown on the left. Geometry Model

26. Target Horizon Every 2nd shot lines Receivers Model and Marine Survey

27. Modelled shot records, ~ 40 million traces. P-P reflection data from two reflectors: one smooth Cretaceous reflector. one complex pre-BCU reflector. Bulk Modelling Single Shot Footprint Pre-BCU Reflector

28. Highly-curved parts of the reflector are strongly illuminated Illumination Density Map About 25 000 000 reflected rays Illumination Density Map: Number of reflection points in bin cells (50x25m) on the target horizon

29. Migrated Modelled Data Reflection Amplitude Migrated Seismic Data Reflection Amplitude Real vs Modelled Amplitudes

30. Normal Incidence Reflection Coefficient Migrated Modelled Data Reflection Amplitude Modelled Amplitudes

31. Integrated Modelling Based Reservoir Analysis NORSAR tools for rock physics and advanced seismic wave simulation

32. Gullfaks Time Lapse Data -1985 and 1996 OWC Top Res. OWC Top Res. Courtesy of Statoil

33. Seismic data Well log data Fluid simulations Lab. data Geological model Mineralogy Integrated rock physics and seismic modelling Seismic response Integrated Rock Physics and Seismic Modelling

34. Rock Physics Models Analysing and predicting how different reservoir properties of porous rocks influence on the seismic properties. VP = 2.6 km/s VS = 1.4 km/s  = 2.2 g/cm3 Rock Physics VP: P-velocity VS: S-velocity lithology fluids porosity permeability 

35. Factors Influencing on Seismic Velocities brine shaley sand oil velocity velocity velocity gas sand shale pore fluid porosity lithology velocity velocity velocity pore pressure confining pressure effective pressure T 3 T velocity velocity velocity 2 T 1 age / depth temperature cementation

36. Seismic Response of Reservoir Properties • Rock physics is the key for understanding how geological properties are manifested in seismic observations, through bright spots, AVO characteristics etc. • Rock physics modelling allows for predicting seismic rock properties at other physical conditions than observed in wells or by laboratory measurements. • Improved understanding about how rock properties influence on seismic properties and seismic response is vital to the feasibility of seismic monitoring of hydrocarbon reservoirs. • Integrated rock physics and seismic modelling provide physically consistent tools for identifying and quantifying the important reservoir properties and predict their sensitivity to changes.

37. rfl=1.0 rfl=1.0 Multi-well Log Data Sorting and Quality Control

38. Rock Model Calibration Rock model variables: Porosity, Lithology, Pore fluid.

39. Model Based Analysis of Well Log Data • Use calibrated rock physics models for processing well log data, and predicting log properties. • Possible to combine with AVO analysis of modelled effects. Types of analyses: • Prediction of S-wave velocity. • Prediction of impedances, elastic moduli etc. • Prediction of geophysical properties assuming other saturating fluids (fluid substitution). • Corrections for drilling mud invasion. • Lithology and fluid sensitivity analysis. • Property cross-plotting analyses (QC and diagnosis). • Pressure effect sensitivity analysis (using core measurements). • Integration of fluid simulation results. • Upscaling and homogenization.

40. Log data Rock model Castagna Prediction of S-wave Velocity Logs

41. Model Derived from Interpolation Between Wells

42. Grid of reservoir properties

43. Integration with Fluid Simulations T0 T1 Sgas Sgas DvP DZP DSg

44. 0.25 km/s 0.1 g/cm3 Rock Physics Modelling of Fluid Effects

45. AVO Modelling of Fluid Effects Brine Saturated Gas Saturated

46. Reservoir properties and amplitude variations Lateral change in fluid composition and shale contents. Modelled seismic response from NIP tracer

47. Effect of Offset, Lithology and Saturating Fluid Homogeneous reservoir properties Vertical trend in lithology GOC GOC Zero Offset Section OWC 2km Offset Section

48. Gullfaks 2D Reservoir Models Scenario 1 : SOil=0.9 Scenario 2 : SOil=0.2 HybriSeis PSDM All offsets

49. Seismic data Inversion Seismic modelling Geological model Rock physics Fluid flow simulations Schematic View of a Time-lapse Modelling Loop

50. Reservoir Target Model Building