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Published byMarianna Dean Modified over 8 years ago
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SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION
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SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION
Elastic Rock Properties Seismic Velocity inversely correlated with Porosity Rock Property Predictions from Seismic Attributes Geometrical Attributes – Faults, Sedimentary Features, Channels Bright Spot vizualization
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FOR FLOW SIMULATION MODEL THE RESERVOIR ENGINEER NEEDS
Porosity: amount and spatial distribution Permeability Nature of fluids and saturation Pressure Temperature Barriers to flow (faults, strat. barriers)
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ELASTIC ROCK PROPERTIES
For isotropic media, (Sheriff, 1992) Compressional wave velocity Vp = [λ + 2μ /ρ] 0.5 where Lamé’s constant λ, an elastic parameter sensitive to fluid content, is related to μ and κ by λ = κ - 2μ/3. rigidity (shear modulus, μ), Incompressibility (bulk modulus, κ) density (ρ)
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ELASTIC ROCK PROPERTIES
Shear wave velocity = Vs = (μ / ρ ) 0.5 where rigidity (shear modulus, μ), density (ρ)
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ELASTIC ROCK PROPERTIES
where σ is the Poisson ratio.
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SEISMIC VELOCITY AND POROSITY
where φ is the porosity, Vf is the velocity of the interstitial fluid, and Vm is the velocity of the rock matrix (Wyllie et al. 1956).
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SEISMIC VELOCITY AND POROSITY
a high negative correlation between porosity and acoustic impedance has a physical basis
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SEISMIC VELOCITY MAP AND WELL POROSITY
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SEISMIC VELOCITY AND WELL POROSITY
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Snap 3D Seismic to Grid Impedance (Stochastic Inversion)
3D Inversion Cube
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Acoustic Impedance Model
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PREDICTION METHODS Regression Geostatistics Neural networks
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PREDICTION PROCESS Calibration Choice of seismic attribute(s)
Cross-validation Management decisions
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SEISMIC VELOCITY MAP AND WELL POROSITY
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RISK MAP based on 100 conditional simulations showing the probability that porosity is ≥ 9%.
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FALSE CORRELATIONS Possiblity of false correlations increases with:
small number of well data many seismic attributes
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GEOMETRICAL ATTRIBUTES
depict patterns related to: faults fracture swarms depositional patterns channels
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GEOMETRICAL ATTRIBUTES
MULTIPLE-POINT STATISTICS allows geological patterns integration through pixel-based modeling (Journel, 1997, 2002; Caers, 2000; Strebelle, 2000). Training images depict the geological conceptual model then a sequential-based simulation algorithm is used to generate multiple realizations
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GEOMETRICAL ATTRIBUTES
left - seismic amplitude data showing channel-like features center - conceptual model right - pixel-based realization
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Data Integration Geology Geophysics
Structural Map on Top Reservoir using G&G
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Example Well Average Porosities Seismic Amplitude Map
Average Porosity Maps Combined using Collocated Cokriging R = 0.75
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Uncertainty Reduction
Average Porosities - Wells Only Realization 1 Realization 2 Realization 3 Average Porosities - Wells plus Seismic Realization 1 Realization 2 Realization 3
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Example 3D sGs constrained by 2D seismic surface Cokriged Por. Map
Amplitude Map Cokriged Por. Map 3D Porosity Model Avg. of 3D Model
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Reservoir Facies Using Seismic
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A series of equally probable high resolution impedance models
Impedance Logs 3D Seismic Data Deterministic Inversion Stochastic Inversion A series of equally probable high resolution impedance models
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Reservoir Geometry Using Seismic
Channel geometries are locally controlled by the use of Seismic facies.
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Bright Spot Vizualization
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Bright Spots
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Seismic attributes can help in characterizing and modeling:
Reservoir Geometry Reservoir Facies Reservoir Property (Porosity) Bright spots
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