It has been known for some time now that very useful amplitude and velocity information resides in the 30 to 50 degree incidence angles of 1-C, 3D land surface seismic data. Including these far-angular offset amplitudes in pre-stack inversions helps reduce uncertainty in the acoustic impedance, elastic impedance, and density estimates of the various geologic layers. Further, quantifying how these far-offset amplitudes and velocities vary with azimuth may also tell us something about rock fractures. But historically this type of data has been expensive to acquire in a wide-azimuth fashion due to limited channel count availability typical of cabled recording systems. Recent 3D land seismic acquisition innovations such as slip-sweep and simultaneous recording techniques, and autonomous nodal recording systems have increased crew productivity and raised available channel count, now enabling the cost-effective acquisition of wide-azimuth, long-offset 3D land seismic data. Also, compute power continues to follow Moore’s law, allowing processing to keep up with the higher fold data and greater data volumes.

Since these far-angular offsets of 30-50 degrees and higher is also where seismic anisotropy presents itself, the seismic processing industry must now better understand and confront seismic anisotropy, in order to fully leverage this more complete type of data acquisition. Figure 1 shows the two types of anisotropy typically found in wide- azimuth, far-offset PP seismic data: 1) Vertical Transverse Isotropy, or VTI anisotropy, and 2) Horizontal Transverse Isotropy, or HTI anisotropy.

VTI anisotropy is best seen in far-angle offset gathers, and the VTI effect is often referred to as the ‘hockey stick’ effect. VTI anisotropy describes velocity variations in the vertical plane containing the trace shot and receiver locations. Eta is the attribute that characterizes VTI anisotropy and allows removal of this hockey stick effect. A common source of VTI anisotropy is fine layering found in dewatered shales. Figure 2 shows some example offset trace gathers, with and without correcting for VTI anisotropy during pre-stack time migration. The VTI correction removes the reflector offset variation in reflected energy arrival times and is important for flatness of offset gathers at higher angular offsets.

Detailed analysis of 3D surface seismic data shows a subtle velocity (or reflector arrival time) dependency on the shot-receiver azimuth of

Advances in Surface Seismic Attribute Fidelity: Solving Anisotropy

Bill McLain, Global Geophysical

Figure 1. Types of anisotropy commonly seen in wide-azimuth, far offset seismic data, which causes distortion in arrival times of the seismic energy. A popular geologic interpretation of azimuthal arrival time variations is the presence of vertical fracturing.

1

Types of Anisotropy in Seismic Data

Azi

mut

h

Fast Fast

Slow

Offse

t

VTI Vertical Transverse Isotropy

Polar Anisotropy Epsilon ,Delta => Eta

HTI Horizontal Transverse Isotropy

Azimuthal Anisotropy Vfast Azimuth, Magnitude

S S R R

S

S

R

R

Velocity variation in vertical plane Velocity variation in horizontal plane

Figure 2. Three cmp offset gathers from Wyoming, USA, showing isotropic pstm results (left) versus vti-only anisotropic pstm results (right). Including the eta correction in the migration helps produce flat gathers at 30 degrees of angular offset and beyond. Wyoming, USA.

April 2014

each seismic trace. This velocity versus azimuth (or VVAz) variation is a form of seismic anisotropy known as horizontal transverse isotropy, or HTI. On wide-azimuth PP surface seismic data, HTI anisotropy is best observed on far-angle azimuthal gathers and manifests itself as velocity variations as a function of trace shot-receiver azimuth. Figure 3 shows a set of azimuthal trace gathers output from our proprietary pre-stack time migration module with and without including the HTI correction. The HTI correction removes the reflector azimuthal variation in reflected energy arrival times and is important for flatness of azimuthal gathers, especially at higher angular offsets.

A popular geologic interpretation of the source of HTI anisotropy is vertical fracturing. In this case, a seismic trace recorded on the earth’s surface with a shot and receiver pair at an azimuth such that the

seismic energy crosses the fractures will see a slightly lower velocity, and hence experience a small increase in reflector two-way time at this azimuth. Quite often accompanying this delay in reflection arrival time is a noticeable decrease in amplitude and frequency content of the reflected energy, which is attributed to increased inelastic attenuation of the energy moving across fractures. Conversely, seismic energy that moves in the same direction as (or parallel to) the fractures will exhibit no delay in reflection arrival times and no or little attenuation in amplitude and frequency content. Characterizing both the amplitude and velocity variation vs. azimuth produces interesting attributes potentially related to fracture density and direction. Figure 4 shows an azimuthal trace gather exhibiting both velocity variations and amplitude variations as a function of azimuth.

2

Figure 3. Four cmp azimuthal gathers showing vti-only pstm results (left) versus the same migration but also including the HTI correction within the pstm. Note that the sinusoidal travel time distortions have been removed in the vti + hti pre-stack time migration. Pennsylvania, USA.

Figure 4. Azimuth gather example showing both velocity and amplitude variation as a function of azimuth. The fast velocity and max amplitude axis seen on the reflected event at TWT 950ms is 75 – 255 degrees from geo-graphic north and is interpreted as seismic energy that traverses parallel to the rock fractures. Pennsylvania, USA.

April 2014

Global Geophysical has a novel (and patented) approach to characterize rms HTI anisotropy using far-angle, wide-azimuth PP surface seismic data. The approach seeks to quantify at each cdp/time point within the seismic volume the two attributes which describes HTI anisotropy: Vfast azimuth (or direction of anisotropy) and the Ellipticity Factor (or magnitude of anisot-ropy.) The algorithm measures HTI anisotropy by systematically pre-stack time imaging the data using different combinations of the HTI parameters and then determines which azimuth/factor pair maximizes stack power at each output image point. Since ranges of likely HTI parameters are scanned using pre-stack time migration, the approach has become known as Migration Scanning Analysis and uniquely incorporates HTI anisotropy into the HTI analysis itself. Figure 5 shows a 2-dimensional cross plot of stack power vs. scanned Vfast azimuth and Ellipticity Factor, at just one output image location. Such cross plots and corresponding pick of azimuth/factor pair is made at each output image point for the entire survey, producing high-resolution volumes of Vfast Azimuth and Ellipticity Factors.

These parameter volumes are considered rms HTI values as they are measured from the earth’s surface and show cumulative overburden effects from the surface down to each impedance boundaries. These RMS HTI values are required to flatten the azimuthal gathers in all space and time. In fact, the basic QC of the RMS HTI values are how

well they flatten the azimuthal gathers, similar to the display shown in figure 3. In this manner, the QC of these parameters using azimuthal gathers is analogous to using offset gathers to QC velocity and eta fields: flatness of gathers.

Global Geophysical incorporates both the rms VTI and HTI parameters into their proprietary (and patented) pstm algorithm which in turn produces more accurately positioned steep dip and fault planes, and helps preserve accurate offset amplitude information, especially at the crucial far-angle offsets. Having accurate amplitude information at the far offset angles is critically important in estimating rock properties, especially the density term, using elastic inversion techniques. Figure 6 shows the impact on far-angle amplitudes in an offset cmp gather

Figure 5. 2D cross plot of “stack power” vs. az/factor pairs used in the scanning migration, for a single cmp/time image point. The azimuth/factor pair that maximizes stack power is chosen as the rms HTI characterization for that one image point. In this manner, a high-density HTI parameter field is created for all cmp’s and all time within the analysis window.

3

Figure 6. Real data example showing a moveout corrected offset gather imaged using 1) vti-only correction (left), 2) vti + hti correction, and 3) shows the difference in amplitudes. The amount of rms hti anisotropy is about (plus/minus) 1% at this cmp. Note that the hti correction becomes important for offset angles greater than 30 degrees, even when only mild hti anisotropy is present. Permian Basin, West Texas, USA.

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

220 2420 4620 6820 9020 11220 13420 220 2420 4620 6820 9020 11220 13420 220 2420 4620 6820 9020 11220 13420

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

155

0

OFFSET

Time

(ms)

Time

(ms)

30˚ 50˚ 30˚ 50˚ 30˚ 50˚

DIFFERENCEVTI + HTIVTI ONLY

April 2014

with just a mild (plus/minus) 1% of velocity variation with azimuth. The name we give to offset-azimuth gathers that are flat in both offset domain and azimuth domain, all while preserving true amplitude is “inversion ready gathers”.

Once the RMS HTI parameters are deemed of high quality, the RMS parameters are inverted using vector equations to produce interval attributes more closely associated with individual geologic layers. Figure 7 shows example interval HTI parameters with overburden effects removed from a recent West Texas project.

The interval HTI attributes contribute two independent seismic measures (amplitude and velocity) of reservoir properties that azimuthally influence seismic velocity (such as fractures, differential stress, overpressure, etc...). Figure 8 shows how such HTI seismic attributes may be co-rendered and visually related to well productivity. HTI attributes may also be integrated with a full-suite of post-stack and pre-stack seismic attributes to find relationships to reservoir properties and engineering & production date via multi-variant attribute analysis to create 3D predictive models. Reliable well productivity predictions should equate to better well planning and enhanced field productivity; helping to reduce risk and overall field development costs, while optimizing production rates and recovery.

With these technologies, expertise, and workflows, Global Geophysical has become an industry leader in detecting, quantifying, and removing anisotropy effects in PP surface seismic data using pre-stack time migration. Validation of the derived HTI attributes from hydrocarbon basins around the world using both well FMI log information and production data is now occurring with encouraging results. Real data examples show the improved uplift of incorporating HTI anisotropy corrections in the seismic data and seismic attribute work.

Figure 8. Map view of HTI Magnitude (colors) and Vfast Azimuth (black lines) over an 80 square mile area of the Eagle Ford in South Texas. Various Eagle Ford Gas producing wells are shown with the red dots representing maximum monthly gas production and the white lines representing the lateral sections of the horizontal wells. HTI attributes are frequently an influential component of the suite of seismic attributes that are related to production.

4

Bill McLain is VP of Applied Technology at Global Geophysical. His current interest is in building best practices in the processing of wide-azimuth, far-angle data. He has over 30 years of seismic industry experience involving advanced processing techniques used in land, transition zone, and marine environments. Bill spent nearly 23 years with ARCO (then later BP) in hands-on processing, quality assurance, management, and advisory positions. He is also an expert at multi-component (2C/4C/9C) processing, and represented BP as the technical chairperson for SMAART JV, a consortia of oil companies that developed deep water sub-salt imaging strategies such as WAZ acquisition design, illumination studies, 3D SRME, complex model building, and PSDM.

Figure 7. Time slice near zone of interest showing Interval HTI Parameters Vfast Azimuth (direction) and Magnitude of anisotropy. Permian Basin, West Texas, USA.

Direction of Anisotropy

Magnitude of Anisotropy

April 2014