ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010

1. ENI-MITEI Annual Meeting, S. Donato M., 29 June 2010 Multiscale Reservoir Science for Enhanced Oil Recovery: Technology Development and Field Applications Rob van der Hilst, Steve Brown, Dan Burns, Michael Fehler, Brad Hager, Tom Herring, Ruben Juanes, Dennis McLaughlin Earth Resources Laboratory MIT

2. Overall Motivation: To meet demand: • New fields (e.g., deep off-shore, near/beneath complex structures, arctic region) • Enhanced Oil Recovery (EOR) from existing fields (global average < 40%) • Unconventional oil/gas (heavy oils, tar sands, tight gas reservoirs, hydrates)

3. Integration of geophysical description with reservoir models  more reliable prediction of performance Challenge: Increase production from reservoirs that are complex and strongly heterogeneous (both for new and existing fields) Reservoir management: Predict reservoir performance to enable optimal operation: • Maximize reservoir sweep • Best well placement and completion design

4. For example: fractured reservoirs deformation during passage of a compressional wave Carbonate cliffs

5. Water Front ? Oil Production Water Injection Oil geology/geophysics ↔flow modeling ↔ enhanced production Seismic Data Willis et al (2006)

6. What do we want to know? • Where are the fractures? • What are the fracture orientations? • What are the fluid-flow properties of fractures (that is, how do fluids flow through them)? Approach: • Joint analysis of geophysical response (e.g., scattering from fractures and heterogeneity, deformation) and flow

7. Using Geophysics to Constrain Flow Model Geophysics-constrainedreservoir description Geophysics-constrainedpermeability model Kfrac Reservoir description from geophysics Response (e.g. well rate) model Qwell Model updated with new data data time

8. INTEGRATED RESERVOIR SCIENCE • 1: • Reservoir Structure and Response • Fracture Characterization (e.g., seismics) • Flow Simulation • Data assimilation & real-time control • Quantitative integration

9. INTEGRATED RESERVOIR SCIENCE • 1: • Reservoir Structure and Response 2: Reservoir Evolution and Performance • Surface deformation (GPS & InSAR) • Coupled geomechanical/reservoir modeling

10. INTEGRATED RESERVOIR SCIENCE • 1: • Reservoir Structure and Response 2: Reservoir Evolution and Performance 3: Application of New Concepts (Field Case Study) Integration of Geophysics & Reservoir performance modeling

11. Surface seismic • Fracture characterization Data Geophysical interpretation Model CTRW-RTT joint inversion methodology 3-way data assimilation methodology Different Levels of Integration • Surface deformation • - tiltmeters • - InSAR, GPS • Wellbore breakouts • Induced seismicity • Production data • Well logs • Analogue reservoirs • 3D seismic Geomechanical modeling Flow models coupled Clearly insufficient • Main outcomes: • Better forecasts • Optimal production to maximize recovery while controlling subsidence

12. Data Model 3-way data assimilation methodology Different Levels of Integration • Surface deformation • - tiltmeters • - InSAR, GPS • Wellbore breakouts • Induced seismicity • Production data • Well logs • Analogue reservoirs • 3D seismic • Surface seismic • Fracture characterization Geophysical interpretation Geomechanical modeling Flow models coupled coupled • Main outcomes: • Better forecasts • Optimal production to maximize recovery while controlling subsidence

13. Data Model 3-way data assimilation methodology Different Levels of Integration • Surface deformation • - tiltmeters • - InSAR, GPS • Wellbore breakouts • Induced seismicity • Production data • Well logs • Analogue reservoirs • 3D seismic • Surface seismic • Fracture characterization Geophysical interpretation Geomechanical modeling Flow models coupled coupled CTRW-RTT joint inversion methodology 3-way data assimilation methodology • Main outcomes: • Better forecasts • Optimal production to maximize recovery while controlling subsidence

14. Data Model Different Levels of Integration • Surface deformation • - tiltmeters • - InSAR, GPS • Wellbore breakouts • Induced seismicity • Production data • Well logs • Analogue reservoirs • 3D seismic • Surface seismic • Fracture characterization Geophysical interpretation Geomechanical modeling Flow models coupled coupled CTRW-RTT joint inversion methodology 3-way data assimilation methodology • Main outcomes: • Better forecasts • Optimal production to maximize recovery while controlling subsidence

15. Numerical and Laboratory Modeling of Scattering from Fractures • Understand seismic response of fractures and fracture systems • Develop new field-data analysis approaches • Platform/data for testing & evaluation of new methods • Develop models to test relationships between fracture compliance, roughness, permeability, and seismic scattering

16. Numerical and Laboratory Modeling of Scattering from Fractures • Seismic response • Numerical • Single and multiple fractures • 2D and 3D • P-to-P and P-to-S scattering • Finite difference; semi-analytical; boundary element • Static models to estimate compliance • Experimental • Multiple fracture model • Incorporate flowing fractures

17. wave length fracture seismic response (1) homogeneous anisotropy zone (2) (3) Focus Area

18. Linear-slip Fracture Model (Schoenberg, 1980) Fracture Compliance fracture u1 u2 displacement compliance traction length/stress [m/Pa] “zero” thickness

19. Numerical Model 1 2D P-to-P Fracture Response Function (FRF) single fracture (NB we can do this also in 3D) P-wave P scattered waves

20. Numerical Model 2 Multiple (parallel) Fractures) Numerical Model Multiple Fractures Fracture Spacing 50 m Aperture 5 m Fracture Zone 50 m thick

21. 900 900 900 800 800 800 700 700 700 Transverse component shows strong amplitude near 45 degrees 600 600 600 500 500 500 400 400 400 300 300 300 2 New approach to analyzing scattering in field data? 200 200 200 100 100 100 00 00 00

22. Laboratory Experiments: Current Status • Seismic acquisition geometries • Iso-Offset acquisition at different azimuths • Common source gathers at different azimuths • CDP gathers at different azimuths • Comparison with numerical models • Move towards joint seismic-flow experiments 30 cm

23. Laboratory Experiments: Acquisition Geometry 900 100 00 Offset = 6 cm P Wave Source P, S Receiver

24. 90 80 70 60 50 40 30 20 10 0 PP 2nd interface P-S Converted SS Fracture Tip PP Fracture Tip Transverse component shows strong amplitude near 45 degrees (similar to numerical result)

25. Conclusions Modeling • The amplitude of scattered-waves scales with compliance (Z) • Radiation patterns depend mostly on ratio of normal to tangential compliance (ZN/ZT) • On the transverse component, P-S Converted wave shows maximum amplitude at about 40-500 possible new orientation attribute • On the inline component, P-S Converted wave shows systematic increase in amplitude towards 900 (not shown) possible new orientation attribute • Stacking enhances signal in a direction parallel to fracture orientation (consistent with Scattering Index - Willis et al., 2006) The insight thus obtained can be used to infer fracture compliance from seismic field data

26. Compliance (e.g., from seismics)  Permeability • Elastic compliance is a key parameter influencing seismic scattering in fractured rocks. • We want to know more about compliance values, scaling, and relation to permeability • We are conducting numerical studies based on realistic fracture roughness statistics Fehler, Burns, Brown

27. Compliance (e.g., from seismics)  Permeability Empirical Relationship (from fracture modeling) Relative Permeability 1/compliance (relative) Brown

28. Compliance (e.g., from seismics)  Permeability • Elastic compliance is a key parameter influencing seismic scattering in fractured rocks. • We want to know more about compliance values, scaling, and relation to permeability • We are conducting numerical studies based on realistic fracture roughness statistics • We find: • Large fractures have much larger compliance • Clear relationships between permeability, compliance, and stress Brown

29. Fracture Response Function (FRF) • Can be obtained directly from (multi-component) seismic data • Methodology validated with numerical and laboratory data • Provides information about fracture orientation, spacing, and relative compliance (& permeability) Now: Preliminary Application to data from Emilio Field Fehler, Burns, Brown

30. Seismic profile across Emilio Field Emilio Field

31. Geometry of the top of reservoir & wells Vp~4km/s Fehler, Burns, Brown

32. Fracture Orientation Confidence Confidence Fehler, Burns, Brown

33. Fracture Spacing Fracture Response Function Fehler, Burns, Brown

34. Relative Compliance scattering strength ~ fracture compliance x fracture density With constraints from geodetic data (below) and with (empirical) scaling relationships from modeling  this can be used to estimate permeability (and flow) ? Relative Compliance Fehler, Burns, Brown

35. Geophysical monitoringof sub-surface reservoirs (Hager, Herring) • (sub)Surface deformation (GPS, InSAR) • Fault (re-)activation • Induced seismicity seismic activity and subsidence • Surface subsidence due to reservoir pumping observed by GPS monitoring • effect on wells/production • impact of fault activation • potential seismic risk

36. Geodetic Characterization of Fractures:fractures change surface deformation resulting from pressure changes at depth Isotropic porosity NW-SE oriented vertical fracture Vertical (color) and horizontal (vectors, max = 3) surface displacements for the same point source volume change at unit depth. For the fracture, the maximum horizontal displacement is greater than the vertical displacement. Hager and Herring

37. Example of Observed Fracture Response: In Salah CO2 Injection Isotropic δv/v ~ 0.5% Fracture opening ~ 7 cm Observations (Onuma & Ohkawa, 2009) Model (Vasco et al., 2010)

38. Sensitivity to fracture properties b • Geodesy • Assume n cracks with width change δb • Displacement ~ nδb • Only the product is resolvable • Assume δb ~ b • Displacement is then proportional to nb • Flow studies ( permeability k) • k ~ nb3  Joint inversion of displacement and flow data can resolve n and b b+δb Hager and Juanes

39. Facies Identification in Petroleum Reservoirs Objective: Develop efficient and robust framework for the reconstruction of geologic facies from reservoir data. Problem Statement: Given production data from wells, we are interested in the following inverse problem: find the region Ω (the facies) corresponding to the high permeability of the reservoir. McLaughlin group

40. Synthetic Experiment: Initial guess 1 Identification of Absolute Permeability given production data from wells Data: Flow rates from 9 production wells and 4 injection wells. Initial guess 1 (with known facies at the well locations) Reference McLaughlin group

41. Synthetic Experiment: Initial guess 1 Identification of Absolute Permeability given production data from wells Reconstruction Gradient-based (180 iterations) Reference McLaughlin group

42. Flow Modeling – Research thrusts Viscous fingering in a Hele-Shaw cell  • Coupled flow and geomechanics • Computational aspects: discretization, staggered solution • Reservoir modeling: response of fractures / faults • Direct numerical simulation of flow in fractured reservoirs • Continuous-time random walk (CTRW) modeling of flow in fractures • Inversion / data assimilation • Towards joint seismic-flow inversion: joint CTRW-RTT paradigm • Towards 3-way inversion: flow, seismic, geomechanics  Juanes

43. Flow in fractured media – why a stochastic approach? • A deterministic multiscale approach is not attractive for inversion, optimization, and control: • Amount of data is insufficient to obtain a well-posed problem • Resolution of data is insufficient to locate individual fractures • Need a stochastic multiscale approach and, in particular: • Parsimonious flow model (fewer parameters) • Capture anomalous (non-Gaussian) behavior of transport • Allows assessment of predictability Juanes (Photograph by Jon Olson)

44. A simple fracture network – particle tracking • Two sets of fractures • (constant orientation and density) • Power-law distribution of velocities • (uncorrelated) • Develop model of expected transport (mean) and its confidence (variance) Juanes

45. A simple fracture network – effective model • The mean behavior is exactly • described by CTRW • The variance is exactly described • by a novel two-particle CTRW Juanes

46. “Continuous time random walk” and fractured reservoirs • CTRW can model fast paths (fractures) and their directionality along with slow paths (background matrix) • Parameters for y(s,t) can be related to fracture orientation, spacing, connectivity and transmissivity Juanes, Fehler, Burns, Brown

47. Concluding Remarks • Progress in several areas • Fracture modeling and laboratory experiments are catalysts for development of new field data analysis methods • Seismic-to-permeability is helping to bridge transition to reservoir modeling • Numerical simulation and laboratory experiments

48. Concluding Remarks • Inversion methodologies will be used to combine geophysical and reservoir modeling approaches • Reservoir analysis developing on many fronts • Attempt to find approach that makes best overlap with geophysics