Delighting in Geophysics

Delighting in Geophysics

Q&A with Dr. Thomas A. Smith
Published with permission: GeoExPro September 2014, Volume 11, No 4

Why did you launch Geophysical Insights after the sale of SMT? Wasn’t it time for a break?

The work at SMT was thoroughly enjoyable, particularly generating new ideas and developing new technology, so after the sale of SMT, it seemed quite natural to continue. I jumped into geophysical research with delight. Geophysical Insights was launched to develop the next generation of interpretation technologies, a result of some of that research. We recognized that there was an opportunity to make a contribution to the industry. Response has been good, with a substantial number of people expressing a great interest in these new ways to conduct seismic interpretation. Here’s why: today we have more data, a greater variety of play concepts, and often less time for interpreters to analyze prospects. In particular, the number of seismic attributes available is now in the hundreds. Witnessing this growing body of information, several years ago M. Turhan Tanner (see GEO ExPro Vol. 3, No. 4), Sven Treitel and I began collaborating on the premise that greater insight may be extracted from the seismic response by analyzing multiple attributes simultaneously. We recognized that advanced pattern recognition methods were being used in many applications outside geoscience that could be adopted to address what we saw as an opportunity to advance the geoscience for exploration and production. Our thoughts on the opportunity were put forward at a 2009 SEG workshop entitled ‘What’s New in Seismic Interpretation’ in a presentation called ‘Self Organizing Maps of Multiple Attribute 3D Seismic Reflections’.

Tell us about the advanced geoscience analysis software platform, Paradise.

Paradise is an off-the-shelf analysis platform that enables interpreters to use advanced pattern recognition methods like Self-Organizing Maps and Principal Component Analysis through guided workflows. In 2009 we organized a team of interpretation software specialists, geoscientists, and marketing professionals to develop an advanced geoscience platform that would take full advantage of modern computing architecture, including large-scale parallel processing. Today, Paradise distills a variety of information from many attributes simultaneously at full seismic resolution, i.e. operating on every piece of data in a volume. This is one of the many differences in the application of machine learning and pattern recognition methods available in Paradise.

What is your perspective on the interpretation needs of unconventional compared to conventional resources? 

Both types of plays have their respective challenges, of course. Our work at Geophysical Insights is evenly divided between conventional and unconventional resources; however, there is growth in the use of seismic among E&P companies in unconventional plays. Systematic drilling programs are now being augmented more often by seismic interpretation, which is reducing field development costs by optimizing drilling and development. There is also growing recognition of what is termed ‘complex conventionals’, like carbonates – a geologic setting that requires advanced analysis for the characterization of carbonate reservoir rocks.

Where do you see the next big advances in geophysics? 

While traditional interpretation tools have made extensive use of improvements in interpretation imagery, their analysis has been largely qualitative – an interpretation of visual imagery on a screen. Certainly, qualitative interpretation is important and will always have a place in the interpretation process. We see the next generation of technologies producing quantitative results that will guide and inform an interpretation, thereby complementing qualitative analysis. Improvements in quantitative analysis will help interpretation add forecasting to prediction.

Do you think these advances will come from industry or academia?

Bright people and ideas are everywhere, and we must be open to solutions from a variety of sources. Technology breakthroughs are often an application of existing concepts from multiple disciplines applied in a whole new way. I believe that fluid, inter-disciplinary teams, enabled by advanced technology, offer an excellent organizational model for addressing the complex challenges of monetizing hydrocarbons in challenging geologic settings.

Where will these advances originate?

While the U.S. has emerged as a leader in O&G production due in large part to the development of unconventional resources and the application of new technologies, regions outside of the U.S. are beginning to develop these too. It is reasonable to expect that universities and companies in these regions will generate many new technologies, which will be essential to supply the growing demand for hydrocarbons worldwide. I applaud the next generation of geoscientists and hope that they enjoy the work of our industry as much as we do.


Seismic Pattern Recognition in Shale Resource Plays

Seismic Pattern Recognition in Shale Resource Plays

The application of PCA can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting and help determine which attributes to use in a multiattribute analysis using SOMs. Applying current computing technology, visualization techniques and understanding of appropriate parameters for PCA and SOM enables interpreters to take multiple seismic attributes and identify the natural organizational patterns in the data.

By: Rocky Roden, Geophysical Insights, and Deborah Sacrey, Auburn Energy
Published with permission: E&P Magazine
January 2015

Various approaches have been developed for workflows to exploit unconventional resource plays. For example, Slatt et al. (2008) describe a workflow that includes characterization of multiscale sedimentology and sequence stratigraphy, relating stratigraphy to log response, seismic response, petrophysical and geomechanical properties, and organic geochemistry. Newsham and Rushing (2001) tie together geology, petrophysics and reservoir engineering with geomechanics. Britt and Schoeffler (2009) describe a shale play in terms of mineralogy, rock mechanics and geomechanics and how these approaches can be used to optimally complete and fracture stimulate any unconventional reservoir.

The essential elements of unconventional shale resource plays are described as:

  1. Reservoir geology: thickness, lateral extent, stratigraphy, mineralogy, porosity, and permeability;
  2. Geochemistry: total organic carbon, maturity and percentage of kerogen (richness);
  3. Geomechanics: acoustic impedance inversion, Young’s modulus, Poisson’s ratio (Vp/Vs) and pressures; and
  4. Faults, fractures and stress regimes: coherency (similarity), curvature, fault volumes, velocity anisotropy (azimuthal distribution) and stress maps.

There is, of course, overlap in these various categories, and how these various elements are interrelated also depends on the objective, which might be to define sweet spots to drill, optimize drilling locations, define completion operations or even determine economic viability.

seismic interpretation software - PCA

FIGURE 1. PCA in the Paradise software displays highest eigenvalues for 3-D inlines in the upper portion with selected largest eigenvector (red); then all eigenvalues for the inline are shown in the lower left from largest (yellow) to smallest. The lower right portion shows the attributes and their proportion for the eigenvector corresponding to the largest eigenvalue. (Source: Geophysical Insights)

Seismic Attributes

In shale resource plays, conventional seismic data are one of the few tools geoscientists have at their disposal to interpret regional trends and guide locations and orientation of infill wells. In shale resource plays the interpretation of seismic data can be quite challenging because of resolution issues and anisotropy, and even though shales make up 70% of sediments, knowledge of shales as reservoirs is limited. Seismic attributes are often generated to help interpret the seismic properties of shale resource plays, which, of course, are a valuable guide to understanding the geology. Seismic attributes such as amplitude, dip, frequency, phase and polarity are measurable properties of seismic data. Attributes can be measured at one instant in time/depth or over a time/depth window and may be measured on a single trace, on a set of traces or on a surface interpreted from seismic data. Seismic attributes reveal features, relationships and patterns in the seismic data that otherwise might not be noticed (Chopra and Marfurt, 2007).

There are literally hundreds of seismic attributes in dozens of categories. In shale resource plays some of the most commonly employed seismic attributes are listed in Table 1. Often in shale resource plays seismic attributes are calibrated with well logs, microseismic results, production data, and completion information.

categories of seismic attributes

TABLE 1. These are typical seismic attribute categories and types employed in shale resource
plays and their associated interpretive uses.

(Source: Geophysical Insights).

Self-Organizing Maps (SOM)

The next level of interpretation requires pattern recognition and classification of subtle information embedded in the seismic attributes. Taking advantage of today’s computing technology, visualization techniques and understanding of appropriate parameters, self-organizing maps (SOMs, Kohonen, 2001) efficiently distill multiple seismic attributes into classification and probability volumes (Smith and Taner, 2010). SOM is a powerful nonlinear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data that can relate to desired geologic characteristics as listed in Table 1. Seismic data contain huge amounts of data samples and are highly continuous, greatly redundant and significantly noisy (Coleou et al., 2003).

seismic interpretation software - SOM

FIGURE 2. (Top) SOM classification from the Paradise software shows the Eagle Ford interval displaying dry hole Well A and good Well B; (bottom) vertical seismic display through Well B indicates shows as the well entered the Eagle Ford interval. (Source: Geophysical Insights)

The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure in the midst of noise (Taner, Treitel and Smith, 2009). SOM analysis identifies these natural organizational structures in the form of clusters. These clusters reveal significant information about the classification structure of natural groups that is difficult to view any other way.

Principal Component Analysis (PCA)

The first step in a seismic multiattribute analysis is to determine which seismic attributes to select for the SOM. Interpreters familiar with seismic attributes and what they reveal in their geologic setting may select a group of attributes and run a SOM. If it is unclear which attributes to select, a principal component analysis (PCA) may be beneficial. PCA is a linear mathematical technique to reduce a large set of variables (seismic attributes) to a small set that still contains most of the variation in the large set, in other words, to find the most meaningful seismic attributes. Figure 1 displays a PCA analysis where the blue histograms on top show the highest eigenvalues for every inline in that seismic survey. An eigenvalue is a value showing how ah variance there is in its associated eigenvector, and an eigenvector is the direction showing the spread in the data. An interpreter is looking for what seismic attributes make up the highest eigenvalues to determine appropriate seismic attributes to input into a SOM run.

The selected eigenvalue (in red) on the top of Figure 1 is expanded by showing all eigenvalues (largest to smallest left to right) on the lower leftmost portion of the figure. Seismic attributes for the largest eigenvector show their contribution to the largest variance in the data. In this example S impedance, MuRho and Young’s brittleness make up more than 95% of the highest eigenvalue. This suggests that these three attributes show significant variance in the overall set of nine attributes employed in this PCA analysis and may be important attributes to employ in a SOM analysis. Several highest ranking attributes of the highest and perhaps the second-highest eigenvalues are evaluated to determine the consistency in the seismic attributes contributing to the PCA. This process enables the interpreter to determine appropriate seismic attributes for the SOM evaluation.

Eagle Ford Shale Evaluation

Once a set or perhaps several sets of seismic attributes are selected, these sets of seismic attributes are input into separate SOM analyses. The SOM setup allows the interpreter to select the number of clusters, window size and various training parameters for a SOM evaluation. Figure 2 displays the classification results from an evaluation of the Eagle Ford Shale. The seismic attributes employed in the SOM analysis are a combination of attributes from prestack simultaneous inversion, instantaneous attributes and a curvature attribute. The westernmost well A had few shows and no production in the Eagle Ford interval. Well B to the east was drilled into a cluster identified from the SOM analysis as the region in red. This well encountered good shows in the Eagle Ford. The vertical seismic display through Well B in Figure 2 shows how the well encountered good shows as it entered into the Eagle Ford interval. Therefore, the cluster associated with the red areas in Figure 2 is defining apparent sweet spots or optimal productive zones in the Eagle Ford.