Guang Yang, Amit Suman, Celine Scheidt, Jef Caers Annual Meeting 2013 Stanford Center for Reservoir Forecasting Generalized Sensitivity Analysis (GSA) applied to 4D seismic modeling -- Application to Norne Field Guang Yang, Amit Suman, Celine Scheidt, Jef Caers

Coalesced calcite concretions in Eocene Formation, Egypt Norne Field Coalesced calcite concretions in Eocene Formation, Egypt Antar, 2001 Heterogeneity induced by calcite concretions Thin cemented horizon

Norne Field After Hoffmann, 2005; Cheng, 2008

Previous Studies Hoffmann, 2005

Seismic Surveys on Norne Field 4 Q-marine survey: July 2001, Aug. 2003, Aug. 2004, Aug. 2006 i-23 Section Haven’t done the time to depth conversion. (avf for which software?? Maybe I can even do conversion in matlab?? Ask Tapan tomorrow). Petrel uses negative time value. Observed relative impedance change (2004-2001) layer 13

Objective of the Study Understand the impact of calcite-cemented concretion on time lapse seismic modeling Identify the influential parameters in 4D seismic modeling using GSA method

Design of Parameter Space Group important parameters from previous studies together: LHS (combinations of 8 parameters) Fault transmissibility Calcite proportions Calcite distribution Relative perm residual saturations Clay content Cement fraction Coordination number Fluid mixing Reservoir Simulation Hoffmann, 2005 Rock Physics Modeling Suman, 2012

Parameters Varied in Calcite Modeling Calcite proportion: triangular distribution [5% , 25%] Calcite vertical probability curve No trend Scenario1: distributed among few specific layers Scenario2: distributed among more layers Scenario3 Layer index Mention the scale issue

Representative Realizations Calcite concretion Modeled as zero transmissibility Model A (21.5%,Scenario 3) Model B (22.7%,Scenario 1) 46*112*22 cells

Calcite Cementation’s Impact on Drainage Pattern i-23 section of calcite model A Sw at year 2001 Sw at year 2003 Sw at year 2006 i-23 section of calcite model B Sw at year 2001 Sw at year 2003 Sw at year 2006

Pattern Correlation between ΔSw and ΔAI i-23 section of calcite model 1 ΔSw, 2004-2001 ΔAI, 2004-2001 i-23 section of calcite model 10 ΔSw, 2004-2001 ΔAI, 2004-2001

Pattern Correlation between ΔSw and ΔAI Layer 11 of calcite model 1 ΔSw, 2004-2001,layer 12 ΔAI, 2004-2001,layer 12 ΔSw, 2004-2001,layer 12 ΔAI, 2004-2001.layer 12 Layer 11 of calcite model 10

Which distance measure? GSA on ΔAI Model responses Distance Map ΔAI, 2004-2001,model 1 ? Which distance measure? … GSA ΔAI, 2004-2001,model 10 …

Distance Measure (An illustrative example) 3 Distance Measure (An illustrative example) ΔAI, 2004-2001,model 1 Reference Human judgment: A>D>C>B 4 A B … C D ΔAI, 2004-2001,model 10

Manhattan distance Manhattan: local variation (location, magnitudes) √ global pattern X Manhattan is not sensitive to the global pattern, (3 closer than 2, & 2 closer than 5), but sensitive to the location where it happens (local variations), and sensitive to the magnitudes. Distance to reference case: B>A>D>C

Distance of cluster-based histogram of patterns (CHP) CHP: global pattern √ local variation X CHP detects global pattern (5 is closer to 2 because 5 has the exact same pattern), not sensitive to location where the changes happened, and also not very sensitive to the magnitudes of the changes ( 2 should be closer to 1 than 5) Distance to reference case: C>D>A>B

Manhattan distance + CHP distance D= D1/││D1││∞ + D2/││D2││∞ D1: Manhattan distance D2: CHP distance Match to reference case: A>D>C>B

GSA Results using Manhattan Distance Fluid mixing: Upper and lower bound of mixed fluid bulk moduli Coordination number: Stiffness of the rock Clay content: Moduli of the mineral

GSA Results using CHP Distance Calcite proportions and VPC became sensitive in global patterns of ΔAI

Conclusion & Future Work P(Model, Interpretation|Data)= ∑P(M|Interpretation,D)P(Interpretation|D) Manhattan distance captures local variation; while CHP distance captures global pattern Local variation of ΔAI is sensitive to rock physics parameters; Global pattern of ΔAI is sensitive to the calcite model scenarios Future PhD work: Bayesian calcite model scenarios rejection for global pattern matching; Rock physics model tuning for local history matching