The Depth-dependence can often be understood using Rutherford-Williams classification2 4 6Д«ayes TheoremBayes Theorem is used to calculate the probability that any new (I,G) point belongs to each of the classes (brine, oil, gas): Last Updated: April 2005 Authors: Dan Hampson, Brian 2000m The Results are Depth DependentBecause the trends are depth-dependent, so are the predicted 1400m Monte-Carlo AnalysisBy repeating this process many times, we get a probability distribution for each of the 3 sand fluids: KGAS GASG ILast Updated: April 2005 Authors: Dan Hampson, Brian Russell This creates 3 points on the I-G cross plot:

Using Biot-Gassmann SubstitutionStarting from the Brine Sand case, the corresponding Oil and Gas Sand models are generated using Biot-Gassmann substitution. 45o 0oД¬alculating a Single Model ResponseOn the synthetic traces, pick the event corresponding to the top of the sand layer:Note that these amplitudes include interference from the second interface.Д¬alculating a Single Model ResponseUsing these picks, calculate the Intercept and Gradient for this model: I G = P1 = (P2-P1)/sin2(45) Trend Analysis Castagnas Relationship with % error Trend AnalysisД¬alculated from sand trend analysis Trend Analysis Uniform Distribution from petrophysics Uniform Distribution from petrophysics Uniform Distribution13Д¬alculating a Single Model ResponseFrom a particular model instance, calculate two synthetic traces at different angles.

Practically, this is how we set up the distributions:Shale: Vp Vs Density Sand: Brine Modulus Brine Density Gas Modulus Gas Density Oil Modulus Oil Density Matrix Modulus Matrix density Dry Rock Modulus Porosity Shale Volume Water Saturation ThicknessLast Updated: April 2005 Authors: Dan Hampson, Brian Russell Trend AnalysisSome of the statistical distributions are determined from well log trend analyses:5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 0.4 0.9 1.4 1.9 DBSB (Km) 2.4 2.9 3.410Дetermining Distributions at Selected LocationsAssume a Normal distribution. The Basic ModelThe Sand is characterized by: Brine Modulus Brine Density Gas Modulus Gas Density Oil Modulus Oil Density Matrix Modulus Matrix density Porosity Shale Volume Water Saturation ThicknessД®ach of these has a probability distribution.Last Updated: April 2005 Authors: Dan Hampson, Brian Russell 9 The Basic ModelEach parameter has a probability distribution: We assume a 3-layer model with shale enclosing a sand (with various fluids). Monte Carlo Simulation: Creating many synthetics STOCHASTIC AVO MODEL FLUID PROBABILITY MAPSД¬onventional AVO Modeling: Creating 2 pre-stack syntheticsIN SITU =OIL IN SITU OIL ! GRADIENT ! INTERCEPT ! BURIAL DEPTH AVO ATTRIBUTE MAPS ISOCHRON MAPS In this talk we present a procedure for analyzing and quantifying AVO uncertainty.ДЄs a result, we will calculate probability maps for hydrocarbon detection.ДЄVO Uncertainty Analysis: The Basic Process But: all AVO attributes contain a great deal of uncertainty there is a wide range of lithologies which could account for any AVO response.

OverviewAVO Analysis is now routinely used for exploration and development.

Last Updated: April 2005 Authors: Dan Hampson, Brian Russell AFI (AVO Fluid Inversion)Uncertainty in AVO: How can we measure it?Dan Hampson, Brian Russell Hampson-Russell Software, Calgary

Hampson russell fluid substitution