1. Stanford Center for Reservoir Forecasting The Stanford VI-E Reservoir: A Synthetic Data Set for Joint Seismic-EM Time- lapse Monitoring Algorithms Jaehoon Lee, Tapan Mukerji Stanford University

2. Stanford Center for Reservoir Forecasting Why an updated synthetic data set? 2 Time-lapse (4D) seismic methods and electromagnetic (EM) imaging techniques have been used for reservoir monitoring. Joint time-lapse monitoring using seismic and EM data can provide a powerful means of reservoir management. Stanford V & VI are not enough for testing joint seismic-EM time- lapse monitoring algorithms. Stanford V (Mao & Journel, 1999)Stanford VI (Castro et al., 2005)

3. Stanford Center for Reservoir Forecasting Workflow to Create the Stanford VI-E 3 Stanford VI (Castro et al., 2005) - Structure - Stratigraphy (facies) - Porosity Petrophysical Properties - Density - P-wave velocity - S-wave velocity Permeability Elastic Attributes - Acoustic impedance - S-wave impedance - Elastic impedance - Lame’s parameters - Poisson’s ratio Flow Simulation - Saturation Electrical Resistivity Time-lapse Electrical Resistivity Time-lapse Petrophysical Properties & Elastic Attributes

4. Stanford Center for Reservoir Forecasting Stanford VI- Structure & Stratigraphy Synthetic reservoir data set (Castro et al., 2005) – A 3-layer fluvial channel system – Asymmetric anticline with axis N15°E – 4 facies – 150×200×200 cells –  x =  y = 25 m,  z =1 m Layer 1: Sinuous channelsLayer 2: Meandering channelsLayer 3: Deltaic deposits Boundary Channel Point bar Floodplain x( m ) y( m )

5. Stanford Center for Reservoir Forecasting Update - Clay Mineralogy 5 Elastic moduli of shale cross Hashin-Shtrikman bounds. The properties and fraction of clay are changed. Porosity Old bounds New bounds New data Old data

6. Stanford Center for Reservoir Forecasting Sand facies: Constant cement model (Avseth, 2000) Shale facies: Gardner’s empirical relation (Gardner et al., 1974) Update - P-wave Velocity 6 Porosity Constant cement model Contact cement model Hashin-Shtrickman bound

7. Stanford Center for Reservoir Forecasting Update - P-wave Velocity 7 Porosity Brine sandstone Oil sandstone Shale x( m ) y( m ) z( m ) Initial State

8. Stanford Center for Reservoir Forecasting Update - S-wave Velocity 8 Shale Brine sandstone Oil sandstone x( m ) y( m ) z( m ) Porosity Initial State

9. Stanford Center for Reservoir Forecasting Update - Elastic Attributes 9 Acoustic ImpedanceS-wave ImpedanceElastic Impedance (30°) Lame’s Parameter Lame’s Parameter  Poisson’s Ratio

10. Stanford Center for Reservoir Forecasting Addition - Electrical Resistivity 10 Sand facies: Archie’s method (1942) ‒ R t : Electrical resistivity of rock ‒ R w : Electrical resistivity of water (=0.25  · m) ‒ S w : Water saturation ‒  : Porosity ‒ F : Formation factor Shale facies: Waxman-Smits model (1968) ‒ CEC : Cation exchange capacity Rock mineral Water Clay mineral Water Cation + ‒ ‒ + + + ‒‒‒‒‒‒‒‒‒‒‒‒‒‒

11. Stanford Center for Reservoir Forecasting Addition - Electrical Resistivity 11 Porosity Brine sandstone Oil sandstone Shale x( m ) y( m ) z( m ) Initial State

12. Stanford Center for Reservoir Forecasting Update – Permeability & Flow Simulation 12 Shale permeability is reduced by the factor of 100. Sand facies (point bar and channel) - oil saturated (S brine = 0.15). Shale facies (floodplain and boundary) - brine saturated (S brine = 1). Net to gross (NTG) of pore volume is introduced; 0.05 and 1 are assigned to shale and sand facies. Stanford VIStanford VI-E

13. Stanford Center for Reservoir Forecasting Update – Time-lapse Elastic Attributes 13 Elastic Impedance (30°) x( m ) y( m ) z( m ) x( m ) y( m ) z( m )

14. Stanford Center for Reservoir Forecasting Addition – Time-lapse Electrical Resistivity 14 x( m ) y( m ) z( m ) x( m ) y( m ) z( m )

15. Stanford Center for Reservoir Forecasting Ongoing Research – Statistical Integration 15 Acoustic impedanceElastic impedance (30°) Reservoir - Oil Saturation Well Data Electrical Resistivity

16. Stanford Center for Reservoir Forecasting Ongoing Research – Statistical Integration 16 Elastic Impedance ( km/s·g/cc ) Acoustic Impedance ( km/s·g/cc ) Assess the probability of hydrocarbon occurrence based on the conditional probability of facies given data. Prob.(Facies = 0.5|Data) Brine sandstone Oil sandstone Shale Prob.(Oil Sandstone|Data)

17. Stanford Center for Reservoir Forecasting Conclusions 17 Large-scale data set (6 million cells), Stanford VI-E reservoir generated for testing algorithms. Improved rock physics models in the Stanford VI-E reservoir. Generated electrical resistivity for sand and shale facies, will allow testing EM time-lapse algorithms. Joint seismic and EM time-lapse monitoring, is currently being studied. (Michael J. Tompkins,SLB) Value of information of seismic and EM data for reservoir monitoring.

18. Stanford Center for Reservoir Forecasting Stanford VI Reservoir - P-wave Velocity 18 Sandstone (brine) New data Old data Contact cement model Constant cement model Porosity

19. Stanford Center for Reservoir Forecasting Shale facies ( V p ): Gardner’s empirical relation (Gardner et al., 1974) ‒  : Bulk density of the rock ( g/cc ) ‒ V p : P-wave velocity of the rock ( km/s ) ‒ d = 1.75 and f = 0.265; typical values for shale (Castagna et al., 1993) Sand facies ( V s ): empirical relation for brine-saturated sandstone (Castagna et al., 1993) Shale facies ( V s ): mudrock line (Castagna et al., 1985) P-wave and S-wave Velocity 19

20. Stanford Center for Reservoir Forecasting Addition - Electrical Resistivity 20 Shale facies: Waxman-Smits model (1968) – B : Equivalent conductance of sodium clay exchange cation – Q v : Cation exchange capacity per unit pore volume – CEC : Cation exchange capacity –  m : Matrix density

21. Stanford Center for Reservoir Forecasting Addition - Electrical Resistivity 21 Sand facies: Archie’s method (1942) ‒ R t : Electrical resistivity of rock ‒ R w : Electrical resistivity of water (=0.25  · m) ‒ F : Formation factor ‒  : Porosity ‒ S w : Water saturation ‒ a : Tortuosity constant (=1.0) ‒ m : Cementation exponent (=1.8) ‒ n : Saturation exponent (=2.0)

22. Stanford Center for Reservoir Forecasting Improvement - Permeability 22 Shale facies in the Stanford VI significantly contributes to flow rather than works as a flow barrier. Shale permeability is reduced by the factor of 100 in the Stanford VI- E reservoir. Porosity (SGSIM)Permeability (COSGSIM) Permeability (Stanford VI-E) Flow simulation (Stanford VI)

23. Stanford Center for Reservoir Forecasting Stanford VI - Flow Simulation 23 A fully-implicit, 3D black oil simulator (ECLIPSE) with two phases (brine and oil). Active constant flux aquifer with inflow rate 31,000 STB/day. 30 year simulation with 31 oil producers and 15 injectors. y( m ) x( m ) (Castro et al., 2005) 0 1875 3750 x( m ) 0 2500 5000 Tops( m ) -2500 -2920 ProducersInjectors

24. Stanford Center for Reservoir Forecasting Downscaling Saturation 24

25. Stanford Center for Reservoir Forecasting Ongoing Research – Statistical Integration 25 MPS Realizations Elastic Impedance ( km/s·g/cc ) Acoustic Impedance ( km/s·g/cc ) Oil Saturation Prob.(Data|Shale)Prob.(Data|Brind Sandstone) Prob.(Data|Oil Sandstone) Acoustic ImpedanceElastic Impedance (30°)Electrical Resistivity

26. Stanford Center for Reservoir Forecasting Ongoing Research – Statistical Integration Acoustic ImpedanceElastic Impedance (30°)Electrical Resistivity Forward Modeling / Filtering