The following abstract has been submitted to the SEG for a talk at the 2009 convention.  If accepted, you can hear the whole talk/presentation there:



Using Horizon Binning and 2D GrAZ for horizon based fluid contact detection and anomaly assessment

Michael J. Padgett,* Quantum Earth Corporation





Horizon Binning and 2D GrAZ are horizon based methods for assessing the quality of an HCI and for determining the location of a fluid contact, if any.  Horizon Binning is a technique for accumulating statistics as a function of structure, beginning in the water leg of a prospective sand and working “up structure.”  A fluid contact is inferred when at a given time (or depth) there is a consistent change from a water leg response to a “not-water” response.  This change should be approximately a step function.  An attribute that can be used with Horizon Binning to further enhance a hydrocarbon effect is 2D GrAZ.  2D GrAZ is a horizon attribute computed as a dot product of the gradient of the structure with the gradient of an attribute (e.g., amplitude).  After horizon binning, a 2D GrAZ anomaly will appear as a peak, above background, at the time (depth) of the fluid contact.  In the water leg the 2D GrAZ signal should be near zero, indicating isotropy of the seismic response with respect to structure.




After identifying an amplitude, AVO or other attribute anomaly, it is normal to ask the question, “Does it fit structure?”  Many anomalies can be judged to “fit structure,” by inspection.  In other cases, the fit might be more tenuous, more debatable or simple corrupted by noise.  In the less straight-forward cases, it is helpful to have a set of tools to make simple reproducible measurements. 


The purpose of both Horizon Binning and 2D GrAZ are to assist in measurements around the question, “Does it fit structure?”  In both cases the inference is made by comparing a down-dip water leg response to a response either up-dip or at the fluid contact.  These methods might best be thought of water-leg analysis tools, with the existence hydrocarbons being inferred only at the confidence level of asserting a “not water-leg” response.


Horizon Binning calculation


Horizon Binning is a method of accumulating statistics as a function of structure, either time or depth (the “Z” value).  This is a horizon based calculation that requires a fully interpreted horizon that contains both Z values and an extracted horizon based attribute.  Typical horizon based attributes might be the maximum instantaneous amplitude near the picked event, RMS over a short window or simply maximum negative trough amplitude.  In the area of an anomaly of interest, an enclosing polygon must be selected that encompasses both the anomaly and a corresponding down-dip water-leg region.  A sequence of bins is constructed that covers the Z-range of the data.  Within each Z-bin, corresponding attribute values are collected.  For each bin, the mean and standard deviation are computed and then graphed (see Figure 1).  In the graph of Figure 1, down-structure is to the right (increasing Z).  The transition from the water-leg to the hydrocarbon-leg is seen as a step function, in this example plot.



Figure 1:  In a Horizon Binning plot, the X-axis is time or depth and increases to the right.  The vertical axis is the horizon-attribute.

  The mean is plotted as a solid black line.  The +/- 1 standard deviations are plotted as error-bars with the dotted red lines.


2D GrAZ calculation


2D GrAZ is a method of determining whether changes in the derived horizon attribute change abruptly in the up-dip direction, at a specific Z value and, hopefully, due to a fluid contact.  To make this determination, the gradients of both the interpreted Z values and of the extracted attributes are computed in 2D.  This produces 2 gradient vectors at each horizon grid location.  A dot product is formed using both vectors. The dot product can be put through a Horizon Binning process to make a graph like the one seen in Figure 2, or can be mapped directly, as shown in the example of Figure 3.


2D GrAZ relies on 2 assumptions.  First, changes in the water-leg response must be isotropic in map view, with respect to structural dip.  This means that locally the dot product with a structural gradient vector should average to zero.  Second, the assumption is made that a change in the up-dip direction that occurs at a specific time or depth is associated with a flat event, e.g. a fluid contact.  This is the origin of the peak of Figure 2.


Figure 2:  In an 2D GrAZ plot after Horizon Binning, the X-axis is time or depth and increases to the right.  The vertical axis is the binned dot product  of the gradient of the structure map with the gradient of the seismic attribute.  In the water-leg, isotropy is inferred by a very small dot-product values to the right, down structure..




Figure 3:  A 2D GrAZ dot product can be mapped.  Shown is a 2D GrAZ calculation with contours over-lain.  The location of the anomaly (roughly north-sourth) corresponds with an inferred fluid contact.  Down-dip is to the north-east.  Note that the anomaly appears to break at likely fault locations.


Water-leg and size of the polygon area


Both Horizon Binning and 2D GrAZ require enough points over a long enough Z-range to accumulate statistics on a bin-by-bin basis.  For instance, for 12 ms bins and with at least 10 bins in the water-leg and 10 bins in the hydrocarbon-leg, the mapped horizon must span 240 ms.  Within each bin, one would prefer at least 35 data points, for decent statistics.  If the 3D bins are 110 ft x 110 ft, this means 9.7 ac per bin or about 100 acres for both the water-leg and for the hydrocarbon-leg.  The need for sufficient bins for statistics over a Z range limits the utility of these tools for studying channel sands, low relief structures and smaller fault-blocks.




Horizon Binning and 2D GrAZ are simple measurement techniques aimed at the question of “Does the anomaly fit structure?” The calculations are simple, fast and intellectually trivia.  They do, however, introduce elements of rigor and reproducibility in the anomaly analysis process.  These techniques are not “silver bullets,” but can logically augment a normal exploration and/or development work-flow in many settings around the world.




The author would like to thank OpenSpirit Corporation for permission to use the data analyzed in Figure 3.