SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION

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

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)

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)

ELASTIC ROCK PROPERTIES Shear wave velocity = Vs = (μ / ρ ) 0.5 where rigidity (shear modulus, μ), density (ρ)

ELASTIC ROCK PROPERTIES where σ is the Poisson ratio.

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).

SEISMIC VELOCITY AND POROSITY a high negative correlation between porosity and acoustic impedance has a physical basis

SEISMIC VELOCITY MAP AND WELL POROSITY

SEISMIC VELOCITY AND WELL POROSITY

Snap 3D Seismic to Grid Impedance (Stochastic Inversion) 3D Inversion Cube

Acoustic Impedance Model

PREDICTION METHODS Regression Geostatistics Neural networks

PREDICTION PROCESS Calibration Choice of seismic attribute(s) Cross-validation Management decisions

SEISMIC VELOCITY MAP AND WELL POROSITY

RISK MAP based on 100 conditional simulations showing the probability that porosity is ≥ 9%.

FALSE CORRELATIONS Possiblity of false correlations increases with: small number of well data many seismic attributes

GEOMETRICAL ATTRIBUTES depict patterns related to: faults fracture swarms depositional patterns channels

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

GEOMETRICAL ATTRIBUTES left - seismic amplitude data showing channel-like features center - conceptual model right - pixel-based realization

Data Integration Geology Geophysics Structural Map on Top Reservoir using G&G

Example Well Average Porosities Seismic Amplitude Map Average Porosity Maps Combined using Collocated Cokriging R = 0.75

Uncertainty Reduction Average Porosities - Wells Only Realization 1 Realization 2 Realization 3 Average Porosities - Wells plus Seismic Realization 1 Realization 2 Realization 3

Example 3D sGs constrained by 2D seismic surface Cokriged Por. Map Amplitude Map Cokriged Por. Map 3D Porosity Model Avg. of 3D Model

Reservoir Facies Using Seismic

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

Reservoir Geometry Using Seismic Channel geometries are locally controlled by the use of Seismic facies.

Bright Spot Vizualization

Bright Spots

Seismic attributes can help in characterizing and modeling: Reservoir Geometry Reservoir Facies Reservoir Property (Porosity) Bright spots