Reducing Uncertainty through Multi-Measurement Integration: from Regional to Reservoir scale Efthimios Tartaras Data Processing & Modeling Manager, Integrated Electromagnetics CoE, Schlumberger Geosolutions Integrated EM Center of Excellence Multi-measurement Integrated Earth Model Dedicated to the integration of seismic, electromagnetic and potential field data to build a MT uncertainty FTG EM 2 Seismic less uncertain earth models combining all available independent information on the subsurface. From regional scale… Grav … to reservoir characterization CSEM-Seicmic Integration to Reduce Uncertainty Structural Imaging • Simultaneous Joint Inversion Prospect Ranking • Seismically-constrained CSEM inversion Reservoir • Petrophysical Joint Inversion Characterization CSEM-Seicmic Integration to Reduce Uncertainty Structural Imaging • Simultaneous Joint Inversion Prospect Ranking Reservoir Characterization Simultaneous Joint Inversion LINK Earth Model Space Velocity Model Space Seismic DATA CSEM/MT DATA Property Link Space Simultaneous Joint Inversion Resistivity Model Space Velocity Model Resistivity Model Sunshine Project: Integrated Seismic & EM (SLB + EMGS Multiclient data) ? 35 Blocks ? 35 Blocks ~ 800km2 WAZ Seismic data SALT CSEM & MMT data 360 Receivers 51Transmitters Resistivity Velocity Method: Simultaneous Joint Inversion Workflow EM EM-Modelling CSEM/MT CSEM/MT DATA Intermediate Resistivity Model EM-Based Interpretation Seismic DATA Vintage RTM Intermediate Velocity Model FINAL RTM SJI Final Resistivity Model Final Velocity Model CSEM Modeling – Inversion Evolution through Iterations Synthetic data, it 0 Ex Amplitude at 0.24Hz Original seismic image Resistivity at it 0 CSEM Modeling – Inversion Evolution through Iterations Synthetic data, it 97 Ex Amplitude at 0.24Hz Original seismic image Resistivity at it 97 EM-based Interpretation LOG(R) ? Poor imaging Complex salt interpretation Mismatch between velocity and resistivity New interpretation Structural geological restoration validation RTM LSI validation EM-based Interpretation – Structural restoration validation CSEM suggests a thicker salt CSEM Resistivity model Seismic Results before SJI with CSEM WAZ SEISMIC ONLY Seismic Results after SJI with CSEM WAZ SEISMIC + EM CSEM-Seicmic Integration to Reduce Uncertainty Structural Imaging Prospect Ranking • Seismically-constrained CSEM inversion Reservoir Characterization CSEM value: Resistivity vs. Hydrocarbon Saturation From Electrical Methods in Geophysical Prospecting by Keller & Frischknecht – 1966 CSEM responds well to high HC saturation / high resistivity targets Commercial Hydrocarbon? CSEM Anomaly Water in Oil AVO Anomaly Oil in Water Reduce Risk Important caveats Resistivity changes can be due to • • • • Lithology Porosity Saturation Type of pore fluid (HC or water) Not all resistors indicate H/C (volcanics, carbonates, tight sands, etc.) Must understand geology and incorporate all available G&G information But even then... 16 CSEM non-uniqueness 1D Transverse resistance – Inline - 0.1Hz Target ρ target Thickness T CSEM data are sensitive to the transverse resistance of a resistive layer target Transverse resistance = resistivity x thickness CSEM non-uniqueness Joint interpretation of seismic and CSEM data using well log constraints: an example from the Luva Field, First Break - May 2009 - Peter Harris, Zhijun Du, Lucy M. MacGregor, Wiebke Olsen, Rone Shu and Richard Cooper 18 18 Seismically-constrained 3D CSEM inversion Use available geological information (depth surfaces from seismic, logs, etc.) as apriori information in the inversion scheme: • • Regularization breaks (aka “tear surfaces”): forcing the inversion not to smooth across a certain surface. This is relevant if a significant resistivity change is expected at that discontinuity (e.g., Top Reservoir, Top Salt, Top Basalt, etc.) Locks: penalizing model changes in a certain region of the 3D model. This is mainly relevant to constrain rather homogeneous overburden sections above potential reservoirs or to constrain resistivity anomalies within seismically defined geobodies. Seismic Geobody-driven Constrained EM inversion Lovatini et al., EAGE 2012 Seismic Geobody-driven Constrained EM inversion Lovatini et al., EAGE 2012 CSEM-Seicmic Integration to Reduce Uncertainty Structural Imaging Prospect Ranking Reservoir • Petrophysical Joint Inversion Characterization Petrophysical Joint Inversion (PJI) Seismic Inversion CSEM Inversion PJI Petrophysical Properties EM for Reservoir Characterization Petrophysical Joint Inversion Acoustic Impedance Geophysical Shear Impedance properties Properties Resistivity 3D AVO Inversion 3D CSEM Inversion Bulk modulus Rock model Properties Shear modulus Density Water Saturation Geophysical Petrophysical Properties Porosity properties Volume of Shale Petrophysical Joint Inversion Rock model calibration PJI Case Study: Barents Sea - West Loppa Input data from the Barents sea: Here STUDY AREA Schlumberger Multiclient seismic library Emgs Multiclient CSEM library Scope of work: Phase 1 CSEM Inversion Phase 2 Seismic Inversion Phase 3 Petrophysical Joint Inversion 25 6/6/2 014 Barents Sea - Survey Map CSEM survey (white) Seismic survey (yellow) Bathymetry [m] 26 6/6/2 014 Petrophysical modeling - Porosity model - Water saturation model (biphase water/gas) Two procedures are tested: - PI - Petrophysical Inversion of AI & Density - PJI - Petrophysical Joint Inversion of AI & Resistivity Barents Sea – CSEM Inversion Workflow Starting Model Horizontal resistivity • CSEM input data preconditioning and qualitative imaging analysis • • 3D resistivity model building – Well log and seismic input – Anisotropic 1D inversions – Mesh and resistivity population 3D CSEM anisotropic Inversion & QC – Unconstrained – Seismically Constrained – Reliability Testing Starting Model Vertical resistivity Barents Sea – Seismic Inversion Workflow • Seismic input data analysis and preconditioning • • Well-based analysis – Wavelet extraction – Low-frequency model building – Inversion around the well Density Inversion on whole cube AI Tertiary Anomaly Petrophysical model of the Tertiary anomaly after: - Geological interpretation Reference: Guerra et al. 2013. A multi-measurement integration case study from West Loppa area in the Barents Sea. First Break Volume 31. - Geophysical evidences Seismic data co-rendered with: − Poisson’s ratio − Vertical resistivity (contour lines) 29 6/6/2 014 Petrophysical Joint Inversion – Data Input Cubes Acoustic Impedance Subvolume Geometry: Top (from sea level): -617 m Base: -1247m X width: 7428 m Ywidth: 4779 m 30 6/6/2 014 Density Resistivity The resistive anomaly is focused within the gas channel by applying the transverse resistive principle Reference: Constable S. 2010. Ten years of marine CSEM for hydrocarbon exploration. Geophysics, vol. 75, no. 5; P. 75A67–75A81 PJI Results Z SLICE Porosity and Water saturation model Porosity Water saturation Porosity Water saturation Petrophysical Inversion Single Meausurement Seismic Petrophysical Joint Inversion Multiple Meausurements Seismic and Electromagnetics Consistent results for porosity, but integrating multiple measurements the estimated water saturation model improves showing the role of the resistivity attribute to discriminate fluids Miotti et al. SEG 2013 Conclusions • CSEM limitations are now well understood • Data processing, modeling and visualization tools have improved significantly in recent years • Integration with seismic and geology allows to obtain maximum value out of CSEM data • This integration can take place at the basin, prospect or reservoir scale using appropriate technologies. 32 Acknlowledgements We thank EMGS and Schlumberger for access to their CSEM and seismic Multiclient libraries. 33
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