# Thin Is In: Here’s a Helpful Attribute

Contributors: Satinder Chopra, John Castagna, Yong Xu

And now, the rest of the story…

You may recall that a novel poststack inversion method was discussed in the May 2008 Geophysical Corner; the output from the method described in that article was a reflectivity series that had a resolution superior to that of the input data used to generate the reflectivity response.

Some applications of this inversion method were discussed in the 2008 article.

Here we illustrate another application of that 2008 reflectivity calculation that aids in quantifying numerous geological features – with the emphasis here being on thin beds.

Many flow units within reservoirs are thin layers that are below seismic resolution, because their thickness is less than one-eighth of the dominant wavelength of the illuminating wavefield, causing the unit to not be resolved seismically.

Determining the actual thicknesses of such thin layers is an important task for many geophysicists. We achieve this objective of quantifying thin-bed thickness by a two-step process:

• First, invert the seismic amplitudes into a reflectivity series using spectral inversion (the topic discussed in the May 2008 article).
• Second, transform this reflectivity series into relative impedance layers. This step is a trace-by-trace calculation process and can be computed quickly.

Impedance profiles can be represented as either absolute impedances, which have magnitudes equivalent to the magnitudes of log data measured across targeted intervals, or as relative impedances, which have arbitrary amplitudes that show depth-dependent variations equivalent to those exhibited by log data.

We emphasize here the option of calculating relative impedances.

When interpreting relative impedance profiles, the top and bottom reflection boundaries of a unit are not correlated with well log curves. Instead, the thicknesses of relative impedance layers are correlated with log curve shapes.

On figure 1 we illustrate how a 50-meter thick carbonate reef can be distinguished from the base platform carbonate unit that it rests on.

As indicated on figure 1(a), the frequency bandwidth of the prestack time-migrated (PSTM)seismic data does not distinguish the reef and the platform carbonate. In contrast, thin-bed reflectivity derived from the PSTM data and then converted into relative impedance data does distinguish between the two units (figure 1b).

The lateral extent of the reef is interpreted as 600 meters.

Two wells have penetrated this gas-producing reef, as indicated by the vertical black lines, and verify this interpretation.

Figure 2 shows a vertical section through thin-bed impedance data calculated across a Far East offshore area. This profile follows the trajectory of a horizontal oil producer labeled Well C, which targeted a seven-meter thick sand that was previously encountered in wells A and B.

This sand thickness is well below the tuning thickness of the seismic data. The seismic response is further complicated by the presence of coal units, one-meter to two meters thick, both above and below the target sand interval.

The horizontal oil producer, Well C, was positioned using the thin-bed impedance data, which showed indications of a higher quality pay sand toward the base of the low-impedance interval that is indicated.

The well encountered over 400 meters of good quality pay sand, with high net-to-gross, and stayed inside the seven-meter thick sand interval throughout its entire trajectory.

Our final example shows how relative impedance data helped to distinguish individual sands in a stacked sand sequence.

Figure 3 shows sections through:

(a) A prestack depth migrated volume (PSDM), also from a Far East offshore area.

(b) An absolute impedance inversion volume.

(c) A relative impedance inversion data volume.

The log curve is the gamma-ray response that shows an upper dirty sand A, a middle clean sand B and a reservoir in the basal part of sand C.

The poor frequency content of the seismic data (figure 3a) limits the vertical resolution of the stacked sand sequence and gives an erroneous interpretation of the upper reservoir, the B sand. The equivalent acoustic impedance section (figure 3b) appears to have done a better job of separating the upper sand from the lower reservoirs.

Relative acoustic impedances were calculated from the thin-bed reflectivity volume, and the equivalent section shown in figure 3c shows the separation of the upper dirty sand, the middle clean sand and the reservoir in the basal part of sand C.

The stratigraphic boundary corresponding to the basal part of the stacked sands is well defined and allows for a more accurate interpretation.

Relative acoustic impedance calculated from a thin-bed reflectivity series is a useful attribute for extracting thin-bed information from seismic data. We’ve demonstrated this principle by this column’s three examples, which show results that cannot be achieved with seismic amplitudes alone.

We recommend that relative impedance be calculated and used for both qualitative and quantitative reservoir characterization.

Finally, we thank two anonymous companies for permission to publish the examples shown here. The thin-bed reflectivity method mentioned here is commercially referred to as ThinManTM, a trademark owned by FusionGeo, Houston.

Rongfeng Zhang is a senior geoscientist with Geomodeling Technology Corp.

### Geophysical Corner

Satinder Chopra, award-winning chief geophysicist (reservoir), at Arcis Seismic Solutions, Calgary, Canada, and a past AAPG-SEG Joint Distinguished Lecturer began serving as the editor of the Geophysical Corner column in 2012.

Arthur Barnes, an AAPG member, is with Landmark Graphics Corp., Highlands Ranch, Colo. He can be contacted at Landmark .

Yong Xu are with Arcis Corp., Calgary, Canada

Alistair Brown, a consultant from Allen, Texas, is a former editor of the EXPLORER’s Geophysical Corner and in 2009 received an AAPG Distinguished Service award.

John P. Castagna is with the University of Houston/Fusion Geo Inc., Houston.

### Geophysical Corner

The Geophysical Corner is a regular column in the EXPLORER that features geophysical case studies, techniques and application to the petroleum industry.

The Marcellus Shale is considered to be the largest unconventional shale-gas resource in the United States. Two critical factors for unconventional shale reservoirs are the response of a unit to hydraulic fracture stimulation and gas content. The fracture attributes reflect the geomechanical properties of the rocks, which are partly related to rock mineralogy. The natural gas content of a shale reservoir rock is strongly linked to organic matter content, measured by total organic carbon (TOC). A mudstone lithofacies is a vertically and laterally continuous zone with similar mineral composition, rock geomechanical properties, and TOC content. Core, log, and seismic data were used to build a three-dimensional (3-D) mudrock lithofacies model from core to wells and, finally, to regional scale. An artificial neural network was used for lithofacies prediction. Eight petrophysical parameters derived from conventional logs were determined as critical inputs. Advanced logs, such as pulsed neutron spectroscopy, with log-determined mineral composition and TOC data were used to improve and confirm the quantitative relationship between conventional logs and lithofacies. Sequential indicator simulation performed well for 3-D modeling of Marcellus Shale lithofacies. The interplay of dilution by terrigenous detritus, organic matter productivity, and organic matter preservation and decomposition affected the distribution of Marcellus Shale lithofacies distribution, which may be attributed to water depth and the distance to shoreline. The trend of normalized average gas production rate from horizontal wells supported our approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for optimizing the design of horizontal well trajectories and hydraulic fracture stimulation strategies.

Seismic correlations and well data confirm that deep-water carbonate beds of Mesozoic age have been found above the shallow allochthonous salt canopy in the northern Gulf of Mexico. These rafts of carbonate strata often overlie equivalent age Mesozoic carbonates in their correct stratigraphic position below the salt canopy. The presence of displaced Mesozoic carbonate rafts above the canopy raises two important questions: 1) how did Mesozoic strata get to such a shallow level in the basin statigraphy? and 2) what effect do high velocity carbonates have on seismic imaging below shallow salt?

With advances in digital technology, use of integrated reservoir models has become commonplace for making informed reservoir development and management decisions. Yet even with faster computers, one of the biggest challenges associated with such modeling is to deliver a model appropriate for the business objective in a timeframe such that it can inform the relevant decision.