Leveraging the ‘Hidden Dimension’ 3-D Seismic

Adding value with azimuthal attributes

Many in the 3-D seismic business are familiar with the offset dimension in seismic data because they work with pre-stack offset gathers on a routine basis. Fewer, however, recognize the other important dimension located in these offset traces: the azimuthal dimension.

While the seismic industry has traditionally ignored the azimuthal dimension, it is now beginning to recognize this important consideration in seismic data processing.

Calling the azimuthal dimension the “hidden dimension” in 3-D seismic data, Bill McLain, vice president of Seismic Processing at Global Geophysical Services said that if recognized and properly processed for, far-angle, wide-azimuth data will respond with more accurate offset amplitudes and rock property attributes.

It can also provide new azimuthal attributes that serve as proxies for differential horizontal pressure, fractures and even fluid types. This improved set of offset and azimuthal attributes, when combined using a multi-variant statistical approach, can lead to improved production prediction models.

And, the ability to better predict production - especially sweet and sour spots - allows for strategic well placement and completion strategies, which will lower field development costs over time.

Operators are already adopting elements of this production prediction attribute approach by geo-steering through the production prediction volume and skipping certain stage completions, which has led to increased production at lower cost, McLain said.

He made his case for “squeezing more information from 3-D seismic data” at the Landmark Innovation Forum and Expo in Houston in August. In a presentation titled, “Leveraging 3-D Seismic for Lower Field Development Costs,” he took audience members down a highly technical path toward the proverbial pot of gold.

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Many in the 3-D seismic business are familiar with the offset dimension in seismic data because they work with pre-stack offset gathers on a routine basis. Fewer, however, recognize the other important dimension located in these offset traces: the azimuthal dimension.

While the seismic industry has traditionally ignored the azimuthal dimension, it is now beginning to recognize this important consideration in seismic data processing.

Calling the azimuthal dimension the “hidden dimension” in 3-D seismic data, Bill McLain, vice president of Seismic Processing at Global Geophysical Services said that if recognized and properly processed for, far-angle, wide-azimuth data will respond with more accurate offset amplitudes and rock property attributes.

It can also provide new azimuthal attributes that serve as proxies for differential horizontal pressure, fractures and even fluid types. This improved set of offset and azimuthal attributes, when combined using a multi-variant statistical approach, can lead to improved production prediction models.

And, the ability to better predict production - especially sweet and sour spots - allows for strategic well placement and completion strategies, which will lower field development costs over time.

Operators are already adopting elements of this production prediction attribute approach by geo-steering through the production prediction volume and skipping certain stage completions, which has led to increased production at lower cost, McLain said.

He made his case for “squeezing more information from 3-D seismic data” at the Landmark Innovation Forum and Expo in Houston in August. In a presentation titled, “Leveraging 3-D Seismic for Lower Field Development Costs,” he took audience members down a highly technical path toward the proverbial pot of gold.

“Everybody,” he said, “needs to start thinking about the hidden dimension in seismic data.”

Evolution of 3-D Seismic

For the past decade or more, 3-D seismic data has been used to locate sweet spots in unconventional reservoirs. Yet, how the data is acquired, processed and interpreted can have an enormous impact on success, McLain said.

The secret lies in acquiring far-angle, wide-azimuth data. This richer type of seismic data captures more information at each sub-surface image point than would otherwise be captured during seismic data acquisition.

“In the past, this type of data was difficult and costly to acquire because of the limitations of cabled recording systems,” McLain said. “Yet, today’s autonomous nodal recording systems are much more flexible and have nearly unlimited scalability - making wide-azimuth data much easier to acquire at little or no additional cost. “It has helped to generate better images and new attributes” he added, because it fundamentally contains more information than narrow-azimuth data typical of older cabled systems.”

Advances in seismic processing, including regularization strategies and solving for seismic anisotropy, also play a critical role in leveraging 3-D seismic data. So does the methodology in combining traditional rock properties, such as ductile-brittleness, with azimuthal variations in amplitude and velocity, which reveals valuable information about fractures, overpressure and fluids, and ultimately leads to a more accurate production prediction attribute and new field development strategies, McLain said.

Wide-azimuth data acquisition greatly enhances the predictive ability and accuracy of the resulting 3-D seismic data. Once one can accurately predict hydrocarbon production numbers, then the processes of well placement and stage completions become much more cost effective, versus pattern drilling and well completions, McLain said.

“This new strategy increases overall production of the well and lowers the cost of the well at the same time,” he said. “More production and lower costs mean improved efficiency and may allow certain field development economics to work even at these lower oil and gas prices.”

Even if oil hovers around $38 a barrel, the cost of acquiring wide-azimuth data remains beneficial to a field development operation, McLain said.

“The added cost for this additional data is quite small compared to the uplift in the accuracy of the 3-D seismic to predict production and how this accuracy impacts well placement and completion strategies,” he said.

Anatomy of Anisotropy

There are two types of anisotropy typically found in seismic data: vertical transverse Isotropy (VTI anisotropy), and horizontal transverse Isotropy (HTI anisotropy).

VTI anisotropy is caused by vertical versus horizontal velocity differences in the subsurface, and it distorts reflection arrival times in seismic data with earlier than expected times, especially in the far-angular offsets. This type of anisotropy is best seen on offset gathers and is usually referred to as the “hockey-stick” effect, McLain said.

HTI anisotropy is caused by horizontal variations in velocity in the subsurface and manifests itself as small timing distortions in 3-D seismic data, especially on the far-angular offsets. However, it is best seen on shot-receiver azimuthal image gathers, in which the reflected energy arrives in a sinusoidal pattern, with a fast direction (early arrival time) and a slow direction (delayed arrival time), he explained.

Correcting for both VTI and HTI anisotropy produces flat gathers in both offset and azimuth, resulting in sharper images even at far-angular offsets. Clearer seismic data produces more accurate traditional rock properties - ultimately resulting in better production models, which positively impact field development strategies and result in lower costs, he said.

Not solving for both VTI and HTI anisotropy essentially means that valuable pieces of information are being left on the table and that distortions will continue to skew seismic data.

“We have lots of anecdotal feedback from clients in the Marcellus, Eagle Ford and Niobrara unconventional plays where pattern drilling hits one of these HTI anisotropy anomalies, and production just spikes,” McLain said.

A Patented Approach

In 2005, Weinman Geoscience, which was acquired by Global Geophysical in 2008, became the first service company to incorporate HTI anisotropy into its imaging algorithm. After successfully solving a strong anisotropy imaging problem in Cook Inlet, Alaska this technology received a patent in 2007, McLain said.

The approach seeks to quantify at each image point (i.e., common mid-point, time sample) within the seismic volume the two attributes that describe root mean square (rms) HTI anisotropy: Vfast azimuth, or the direction of anisotropy, and the ellipticity factor, or the magnitude of anisotropy.

The algorithm measures HTI anisotropy by systematically imaging the data using different combinations of HTI parameters and then determining which azimuth/factor pair maximizes stack power at each output image point, McLain said.

Because ranges of likely HTI parameters are systematically scanned, the approach has become known as “Migration Scanning Analysis,” and it uniquely incorporates the anisotropy into the HTI analysis itself, he added.

“Several service providers are now creating reservoir anisotropy properties,” McLain added. “But Global is winning validation studies where we tie our interval anisotropy attributes to well control that has either FMI or cross-dipole sonic information, which gives us confidence that we are doing things correctly.”

Ultimately it is production prediction that validates the azimuthal attributes best, McLain added.

“When we add the HTI azimuthal attributes into the multi-variate statistical analysis to predict production, the correlation coefficient of actual production versus predicted production always goes up,” he said. “This tells us that azimuthal attributes derived from far-angle, wide-azimuth data are adding value to the process of production prediction, and that sweet and sour spots are more accurately being identified.”

Comments (1)

Leveraging the "hidden dimension" of 3D seismic.
In the subject article, the phrase production prediction from 3D seismic is implied at least twelve (12) times. 3D seismic responds to variations in lithology, rock properties, porosity and pore fluids. Multi-variant statistics targeting variations in these properties is perfectly sound and quite invaluable for reservoir modeling. 3D seismic does not respond to variations in hydrocarbon production per se. Multi-variant statistics targeting variations in production without a through foundational targeting / understanding of lithology, rock properties, porosity and pore fluids is fraught with peril. .
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11/9/2015 1:00:50 PM

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