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Approach Aids Multiattribute Analysis

Approach Aids Multiattribute Analysis

By: Rocky Roden, Geophysical Insights, and Deborah Sacrey, Auburn Energy
Published with permission: American Oil and Gas Reporter
September 2015

Seismic attributes, which are any measurable properties of seismic data, aid interpreters in identifying geologic features that are not understood clearly in the original data. However, the enormous amount of information generated from seismic attributes and the difficulty in understanding how these attributes when combined define geology, requires another approach in the interpretation workflow.

To address these issues, “machine learning” to evaluate seismic attributes has evolved over the last few years. Machine learning uses computer algorithms that learn iteratively from the data and adapt independently to produce reliable, repeatable results. Applying current computing technology and visualization techniques, machine learning addresses two significant issues in seismic interpretation:

• The big data problem of trying to interpret dozens, if not hundreds, of volumes of data; and

• The fact that humans cannot understand the relationship of several types of data all at once.

Principal component analysis (PCA) and self-organizing maps (SOMs) are machine learning approaches that when applied to seismic multiattribute analysis are producing results that reveal geologic features not previously identified or easily interpreted. Applying principal component analysis can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting, which helps determine which attributes to use in a multiattribute analysis using self-organizing maps. SOM analysis enables interpreters to identify the natural organizational patterns in the data from multiple seismic attributes.

Multiple-attribute analyses are beneficial when single attributes are indistinct. These natural patterns or clusters represent geologic information embedded in the data and can help identify geologic features, geobodies, and aspects of geology that often cannot be interpreted by any other means. SOM evaluations have proven to be beneficial in essentially all geologic settings, including unconventional resource plays, moderately compacted onshore regions, and offshore unconsolidated sediments.

This indicates the appropriate seismic attributes to employ in any SOM evaluation should be based on the interpretation problem to be solved and the associated geologic setting. Applying PCA and SOM can not only identify geologic patterns not seen previously in the seismic data, it also can increase or decrease confidence in features already interpreted. In other words, this multiattribute approach provides a methodology to produce a more accurate risk assessment of a geoscientist’s interpretation and may represent the next generation of advanced interpretation.

Seismic Attributes

A seismic attribute can be defined as any measure of the data that helps to visually enhance or quantify features of interpretation interest. There are hundreds of types of attributes, but Table 1 shows a composite list of seismic attributes and associated categories routinely employed in seismic interpretation. Interpreters wrestle continuously with evaluating the numerous seismic attribute volumes, including visually co-blending two or three attributes and even generating attributes from other attributes in an effort to better interpret their data.

This is where machine learning approaches such as PCA and SOM can help interpreters evaluate their data more efficiently, and help them understand the relationships between numerous seismic attributes to produce more accurate results.

Principal Component Analysis

Principal component analysis is a linear mathematical technique for reducing a large set of seismic attributes to a small set that still contains most of the variation in the large set. In other words, PCA is a good approach for identifying the combination of the most meaningful seismic attributes generated from an original volume.

principal component analysis for multiattribute analysis

Results from Principal Component Analysis in Paradise® utilizing 18 instantaneous seismic attributes are shown here. 1A shows histograms of the highest eigenvalues for in-lines in the seismic 3-D volume, with red histograms representing eigenvalues over the field. 1B shows the average of eigenvalues over the field (red), with the first principal component in orange and associated seismic attribute contributions to the right. 1C shows the second principal component over the field with the seismic attribute contributions to the right. The top five attributes in 1B were run in SOM A and the top four attributes in 1C were run in SOM B.

The first principal component accounts for as much of the variability in the data as possible, and each succeeding component (orthogonal to each preceding component) accounts for as much of the remaining variability. Given a set of seismic attributes generated from the same original volume, PCA can identify the attributes producing the largest variability in the data, suggesting these combinations of attributes will better identify specific geologic features of interest.

Even though the first principal component represents the largest linear attribute combinations best representing the variability of the bulk of the data, it may not identify specific features of interest. The interpreter should evaluate succeeding principal components also because they may be associated with other important aspects of the data and geologic features not identified with the first principal component.

In other words, PCA is a tool that, when employed in an interpretation workflow, can give direction to meaningful seismic attributes and improve interpretation results. It is logical, therefore, that a PCA evaluation may provide important information on appropriate seismic attributes to take into generating a self-organizing map.

Self-Organizing Maps

The next level of interpretation requires pattern recognition and classification of the often subtle information embedded in the seismic attributes. Taking advantage of today’s computing technology, visualization techniques and understanding of appropriate parameters, self-organizing maps distill multiple seismic attributes efficiently into classification and probability volumes. SOM is a powerful non- linear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data that can relate to desired geologic characteristics such as those listed in Table 1.

Seismic data contain huge amounts of data samples and are highly continuous, greatly redundant and significantly noisy. The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure in the midst of noise. 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. The natural groups and patterns in the data identified by clusters reveal the geology and aspects of the data that are difficult to interpret otherwise.

Offshore Case Study

Offshore Case Study 01

This shows SOM A results from Paradise on a north-south inline through the field. 1A shows the original stacked amplitude. 2B shows SOM results with the associated five-by-five color map displaying all 25 neurons. 2C shows SOM results with four neurons elected that isolate attenuation effects.

Offshore Case Study 02

SOM B results from Paradise are shown on the same in-line as Figure 2. 3A is the original stacked amplitude. 3B shows SOM results with the associated five-by-five color map. 3C is the SOM results with a color map showing two neurons that highlight flat spots in the data.

 

A case study is provided by a lease located in the Gulf of Mexico offshore Louisiana in 470 feet of water. This shallow field (approximately 3,900 feet) has two producing wells that were drilled on the upthrown side of an east-west trending normal fault and into an amplitude anomaly identified on the available 3-D seismic data. The normally pressured reservoir is approximately 100 feet thick and is located in a typical “bright spot” setting, i.e. a Class 3 AVO geologic setting (Rutherford and Williams, 1989).

The goal of this multiattribute analysis is to more clearly identify possible direct hydrocarbon indicator characteristics such as flat spots (hydrocarbon contacts) and attenuation effects and to better understand the reservoir and provide important approaches for decreasing the risk of future exploration in the area.

Initially, 18 instantaneous seismic attributes were generated from the 3-D data in the area. These were put into a PCA evaluation to determine which produced the largest variation in the data and the most meaningful attributes for SOM analysis.

The PCA was computed in a window 20 milliseconds above and 150 milliseconds below the mapped top of the reservoir over the entire survey, which encompassed approximately 10 square miles. Each bar in Figure 1A represents the highest eigenvalue on its associated in-line over the portion of the survey displayed.

An eigenvalue shows how much variance there is in its associated eigenvector, and an eigenvector is a direction showing the spread in the data. The red bars in Figure 1A specifically denote the in-lines that cover the areal extent of the amplitude feature, and the average of their eigenvalue results are displayed in Figures 1B and 1C.

Figure 1B displays the principal components from the selected in-lines over the anomalous feature with the highest eigenvalue (first principal component), indicating the percentage of seismic attributes contributing to this largest variation in the data. In this first principal component, the top seismic attributes include trace envelope, envelope modulated phase, envelope second derivative, sweetness and average energy, all of which account for more than 63 percent of the variance of all the instantaneous attributes in this PCA evaluation.

Figure 1C displays the PCA results, but this time the second highest eigenvalue was selected and produced a different set of seismic attributes. The top seismic attributes from the second principal component include instantaneous frequency, thin bed indicator, acceleration of phase, and dominant frequency, which total almost 70 percent of the variance of the 18 instantaneous seismic attributes analyzed. These results suggest that when applied to a SOM analysis, perhaps the two sets of seismic attributes for the first and second principal components will help define different types of anomalous features or different characteristics of the same feature.

The first SOM analysis (SOM A) incorporates the seismic attributes defined by the PCA with the highest variation in the data, i.e., the five highest percentage contributing attributes in Figure 1B.

Several neuron counts for SOM analyses were run on the data, and lower count matrices revealed broad, discrete features, while the higher counts displayed more detail and less variation. The SOM results from a five-by-five matrix of neurons (25) were selected for this article.

 

Detecting Attenuation

The north-south line through the field in Figures 2 and 3 show the original stacked amplitude data and classification results from the SOM analyses. In Figure 2B, the color map associated with the SOM classification results indicates all 25 neurons are displayed. Figure 2C shows results with four interpreted neurons highlighted.

Based on the location of the hydrocarbons determined from well control, it is interpreted from the SOM results that attenuation in the reservoir is very pronounced. As Figures 2B and 2C reveal, there is apparent absorption banding in the reservoir above the known hydrocarbon contacts defined by the wells in the field. This makes sense because the seismic attributes employed are sensitive to relatively low-frequency, broad variations in the seismic signal often associated with attenuation effects.

This combination of seismic attributes employed in the SOM analysis generates a more pronounced and clearer picture of attenuation in the reservoir than any of the seismic attributes or the original amplitude volume individually. Downdip of the field is another undrilled anomaly that also reveals apparent attenuation effects.

The second SOM evaluation (SOM B) includes the seismic attributes with the highest percentages from the second principal component, based on the PCA (see Figure 1). It is important to note that these attributes are different from the attributes determined from the first principal component. With a five-by-five neuron matrix, Figure 3 shows the classification results from this SOM evaluation on the same north-south line as Figure 2, and it identifies clearly several hydrocarbon contacts in the form of flat spots. These hydrocarbon contacts are confirmed by the well control.

Figure 3B defines three apparent flat spots that are further isolated in Figure 3C, which displays these features with two neurons. The gas/oil contact in the field was very difficult to see in the original seismic data, but is well defined and can be mapped from this SOM analysis.

The oil/water contact in the field is represented by a flat spot that defines the overall base of the hydrocarbon reservoir. Hints of this oil/water contact were interpreted from the original amplitude data, but the second SOM classification provides important information to clearly define the areal extent of reservoir.

Downdip of the field is another apparent flat spot event that is undrilled and is similar to the flat spots identified in the field. Based on SOM evaluations A and B in the field, which reveal similar known attenuation and flat spot results, respectively, there is a high probability this undrilled feature contains hydrocarbons.

West Texas Case Study

Unlike the Gulf of Mexico case study, attribute analyses on the Fasken Ranch in the Permian Basin involved using a “recipe” of seismic attributes, based on their ability to sort out fluid properties, porosity trends and hydrocarbon sensitivities. Rather than use principal component analysis to see which attributes had the greatest variation in the data, targeted use of specific attributes helped solve an issue regarding conventional porosity zones within an unconventional depositional environment in the Spraberry and Wolfcamp formations.

The Fasken Ranch is located in portions of Andrews, Ector, Martin and Midland counties, Tx. The approximately 165,000-acre property, which consists of surface and mineral rights, is held privately. This case study shows the SOM analysis results for one well, the Fasken Oil and Ranch No. 303 FEE BI, which was drilled as a straight hole to a depth of 11,195 feet. The well was drilled through the Spraberry and Wolfcamp formations and encountered a porosity zone from 8,245 to 8,270 feet measured depth.

This enabled the well to produce more than four times the normal cumulative production found in a typical vertical Spraberry well. The problem was being able to find that zone using conventional attribute analysis in the seismic data. Figure 4A depicts cross-line 516, which trends north-south and shows the intersection with well 303. The porosity zone is highlighted with a red circle.

water oil contact

4A is bandwidth extension amplitude volume, highlighting the No. 303 well and porosity zone. Wiggle trace overlay is from amplitude volume. 4B is SOM classification volume, highlighting the No. 303 well and porosity zone. Topology was 10-by-10 neurons with a 30-millisecond window above and below the zone of interest. Wiggle trace overlay is from amplitude volume.

Seven attributes were used in the neural analysis: attenuation, BE14-100 (amplitude volume), average energy, envelope time derivative, density (derived through prestack inversion), spectral decomposition envelop sub-band at 67.3 hertz, and sweetness.

Figure 4B is the same cross-line 516, showing the results of classifying the seven attributes referenced. The red ellipse shows the pattern in the data that best represents the actual porosity zone encountered in the well, but could not be identified readily by conventional attribute analysis.

Figure 5 is a 3-D view of the cluster of neurons that best represent porosity. The ability to isolate specific neurons enables one to more easily visualize specific stratigraphic events in the data.

neural cluster with colormap

This SOM classification volume in 3-D view shows the combination of a neural “cluster” that represents the porosity zone seen in the No. 303 well, but not seen in surrounding wells.

 

 

Conclusions

Seismic attributes help identify numerous geologic features in conventional seismic data. Applying principal component analysis can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting, and help them determine which attributes to use in a multiattribute analysis using self-organizing maps. Applying current computing technology, visualization techniques, and understanding of appropriate parameters for SOM enables interpreters to take multiple seismic attributes and identify the natural organizational patterns in the data.

Multiple-attribute analyses are beneficial when single attributes are indistinct. These natural patterns or clusters represent geologic information embedded in the data and can help identify geologic features that often cannot be interpreted by any other means. Applying SOM to bring out geologic features and anomalies of significance may indicate this approach represents the next generation of advanced interpretation.

 

Editor’s Note

The authors wish to thank the staff of Geophysical Insights for researching and developing the applications used in this article. The seismic data for the Gulf of Mexico case study is courtesy of Petroleum Geo-Services. Thanks to T. Englehart for insight into the Gulf of Mexico case study. The authors also would like to acknowledge Glenn Winters and Dexter Harmon of Fasken Ranch for the use of the Midland Merge 3-D seismic survey in the West Texas case study.

Rocky Roden ROCKY RODEN runs his own consulting company, Rocky Ridge Resources Inc., and works with oil companies around the world on interpretation technical issues, prospect generation, risk analysis evaluations, and reserve/resource calculations. He is a senior consulting geophysicist with Houston-based Geophysical
Insights, helping develop advanced geophysical technology for interpretation.
He also is a principal in the Rose and Associates DHI Risk Analysis Consortium,
which is developing a seismic amplitude risk analysis program and worldwide
prospect database. Roden also has worked with Seismic Microtechnology
and Rock Solid Images on integrating advanced geophysical software applications.
He holds a B.S. in oceanographic technology-geology from Lamar University
and a M.S. in geological and geophysical oceanography from Texas A&M University.
Deborah Sacrey DEBORAH KING SACREY is a geologist/geophysicist with 39 years of oil and gas exploration experience in the Texas and Louisiana Gulf Coast, and Mid-Continent areas. For the past three years, she has been part of a Geophysical Insights team working to bring the power of multiattribute neural analysis of seismic data to the geoscience public. Sacrey received a degree in geology from the University of Oklahoma in 1976, and immediately started working for Gulf Oil. She started her own company, Auburn Energy, in 1990, and built her first geophysical workstation using
Kingdom software in 1995. She specializes in 2-D and 3-D interpretation
for clients in the United States and internationally. Sacrey is a DPA certified
petroleum geologist and DPA certified petroleum geophysicist.

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.

Innovative Solutions Pave Way Forward

Innovative Solutions Pave Way Forward

By: Kari Johnson, Special Correspondent

Reproduced with permission: The American Oil and Gas Reporter
November 2013

Special Report: Oil & Gas Computing

Not long ago, the playbook in unconventional operations called for drilling horizontal wells about anywhere in a “blanket” formation, so long as the wellbore stayed in zone to allow stimulation at regular intervals spaced along the lateral. The name of the game was breaking rock. While well productivity remains a function of creating fractures in low-permeability rock, oil and gas producers have come to appreciate the importance of how and where laterals are placed to ensure access to quality rock.

Advanced Attribute Analysis
Techniques that concentrate on a few key seismic attributes have proven highly effective in finding anomalies in subsurface datasets. But Tom Smith, chief executive officer of Geophysical Insights, says technological advances are making it possible to use seismic attributes in almost infinite combinations to delineate anomalies in unconventional plays.

There are hundreds of potential attributes of interest in seismic data, he notes. Reviewing them all to find the best attributes for analysis, and then using them to find sweet spots is a demanding task. “We set out to apply automated, unbiased analysis to this problem,” Smith says. “We developed Paradise™, an advanced geosciences
analytic software platform, to enable interpreters to apply these advanced pattern recognition methods to address this problem.”

Smith says Paradise provides workflows that guide geoscientists through the application of unsupervised neural networks (UNNs) and principal component analysis (PCA). “Paradise also takes full advantage of high-power, multicore processing using large-scale parallelism to accelerate the performance of these advanced techniques,” he says.

Unconventional reservoirs have introduced a new suite of rock mechanics properties, and Smith says the industry is still learning which ones provide the most valuable insights. UNNs have the advantage of running uninterrupted and unbiased by human assumptions.

“UNNs offer the advantage of operating on seismic data alone without the need for well logs. Where well logs are available, those data can be included in the UNN analysis, as can data from hydraulic fracturing,” Smith states. “The more data provided to the system, the more information we can discern from the results. We view this process as advantageous because we make no assumptions about linear or statistical combinations.”

Smith points out that even the most detailed well logs represent a tiny sampling of the subsurface. “It is not that obvious how to sort the properties,” he observes. “Running a supervised neural network is problematic in unconventional plays because the rock properties known at the borehole are an extremely limited sample set. Better tools are needed to lower exploration risk in unconventional plays. By applying both UNN and PCA on the seismic response, greater insights can be realized about the geology and sweet spots identified.”

UNNs look at the natural properties and find natural clusters that are not artificially biased in any way. “We are working in n-dimensional space, where n is the number of attributes,” Smith details. “Attributes can vary in data type and some parameters are predetermined.”

In unconventional formations, interpreters typically search for overpressured zones, sweet spots, AVO and fracture networks. They also look for anomalies and anything that is out of the ordinary. “Neural networks can scan large volumes to find areas of interest for further analysis,” Smith explains. “This capability enables interpreters to focus more effectively and efficiently.”

While the results of attribute analysis are presented in a 3-D cube, Smith says his team has built a 2-D color bar in the Paradise software to more effectively analyze and interact with the volume. The user selects a few neurons on the 2-D color bar, and the 3-D representation highlights only the regions in the volume that correspond to those neurons, enabling isolation of the classification results.

pattern recognition for seismic attributes

Geophysical Insights’ Paradise™ analytic software platform applies automated pattern recognition to analyze seismic attributes in almost infinite combinations to delineate anomalies in unconventional plays. The workflows use unsupervised neural networks and principal component analysis while taking advantage of high-power multicore processing using large-scale parallelism to accelerate performance. Shown here are attribute analysis results presented in a 3-D viewer with a 2-D color bar for interacting with the data volume.

From Insights to Foresights

From Insights to Foresights

By: Ashok Dutta
Published with permission: New Technology Magazine
October 2011

Risks and rewards are evenly poised in the hydrocarbons industry, and along with oil and gas companies, probably no one understands the nuances better than Tom Smith, founder and president of Geophysical Insights.

earth to earth model

 

In its first stage, the Houston-based software technology firm has developed a new technology based on unsupervised neural networks (UNN). Smith and his team are pioneering the application of UNN to the interpretation of 3-D seismic response data. Currently going to market as a service, the UNN technology has been applied successfully by medium-to-large capital energy companies.

And looking ahead, Geophysical Insights plans to launch a full-scale commercial software product suite in 2012, which will be scalable to fit the needs of small independents to oil and gas majors.

“In the E&P [exploration and production] industry, we have to analyze data in large volumes and it comes in different formats,” says Smith. “Today, seismic interpretation involves six to 100 attributes of data, each of which constitute a 3-D data. [For a 3-D seismic survey] three kinds of images emerge along with subtle combinations of data in higher dimensionality. The key to success lies in categorization and interpretation of all attributes simultaneously. That is the initial problem we are addressing using UNN technology.”

“For any seismic survey, there may be many seismic attribute volumes. Our initial step is to use principal component analysis to focus on the seismic attributes which are relevant for the study,” says Felix Balderas, information and system architect consultant with Geophysical Insights. “Then we process the selected volumes through the UNN. It’s effectively a quantization technique that reduces multi-dimensional data, which can be incomprehensible, into something that is understandable by humans. The process creates a new volume of data and identifies specific areas of interest within the survey. We call the resulting areas of interest ‘anomalies.’ UNN’s ability to identify anomalies results in time savings by focusing the interpreter’s attention on potential plays. It reduces risk for oil and gas companies if they avoid areas where there are no anomalies.”

The results are too obvious to ignore for any seismic data interpreter. With fossil fuels being a finite resource, time and money are of the essence for energy firms as they come under increasing pressure to make new discoveries. Be it Organization of Petroleum Exporting Countries (OPEC) or non-OPEC member states, with oil prices remaining north of US$80 per barrel, a buzzword in the industry is scouting and sniffing for new reserves.

Undoubtedly for them, a major challenge they have to overcome is an insightful interpretation of seismic data. “This is an issue of yesterday, today and tomorrow. But with UNN there are certain advantages as the technology is focused on isolating the anomalies in any survey and presenting a clearer picture,” says Smith.

According to him, the neural network becomes, in essence, a “learning machine” adapting to the characteristics of the data and resulting in what is called self-organizing maps. The input data are unclassified and the learning process is unattended. That is, no form of well log or reference data is required to calibrate the UNN at specific geographic locations.

“We introduce neurons in random coordinates and they are ‘attracted’ to data points via a mathematical methodology. The neurons will go through a learning process and then assign to each data point a winning neuron,” Smith says. “In the ‘learning’ stage, neurons are attracted to data samples in the clusters in a recursive process. Ultimately, after neuron movement has finished, the neurons reveal subtle combinations of attributes that may highlight the presence and type of hydrocarbons.”

Upon completion of the learning process, the neurons will identify anomalies in the data—materials that are different than their surroundings because of the variations in the seismic attributes at those locations. This supports and facilitates the exploration process by identifying the shapes and sizes of anomalies, which then suggest locations deserving additional study.

Unsupervised Neural Network

NEURAL NETWORK Geophysical Insights applies unsupervised neural networks to the interpretation of 3-D seismic data. The new volume of data created by the process identifies specific areas of interest within a survey. A neuron “learns” by adjusting its position within the attribute space as it is drawn toward nearby data points. The winning neuron is the one that is closest to the selected data point.

The company’s current pricing model is based on the size of the appraisal area. It is currently analyzing acreages ranging from 50 to 800 square miles, both onshore and offshore.

“The technology is now being offered as a service and we are gearing to launch a suite of software products next year,” says Hal Green, the acting director of business development. “The software pricing model is being worked out and will be available with the introduction of the product suite in 2012.”

Green elaborated on the “service” offer for oil and gas companies, stating that a prospective client typically selects a geographical appraisal area that is captured in a single SEG-Y file (a file format developed by the Society of Exploration Geophysicists).

“Without an initial investment on the part of the client, we perform an analysis on that entire region/file based on an initial set of 13 attributes using our technology. At that point, we request the client to identify a sample area representing not more than 10 per cent of the whole appraisal area,” he says.

As a next step, Geophysical Insights then presents the detailed results/findings of the UNN analysis on the selected sample area and a summary of the findings across the whole appraisal area. If the client finds those results promising, they enter into a mutually acceptable technical and commercial relationship that provides the oil and gas company the complete details of the analysis across the entire appraisal area, including supporting conclusions in written form from Smith.

Irrespective of what the price may be, Smith is sanguine about a growing future demand of the product. A growing shortage of geoscientists in the industry— primarily due to retirements— implies oil and gas companies will rely on the incremental use of technology to interpret seismic data and better understand the geology.

“We see the use of UNN not just in seismic surveys, but also in downhole measurements, monitoring production processes and more,” he says.

PPDM

Despite its initial success, Geophysical Insights is not resting on its laurels and is pursuing efforts to scale new heights in game-changing, “disruptive” technologies.

In a white paper presented early this year at an industry conference in Houston, Ken Cooley, a consultant to Geophysical Insights, highlighted the benefits of plans underway to implement their seismic data storage in the Professional Petroleum Data Management (PPDM) Association model.

Calgary-based PPDM is a global, not-for-profit standards organization that works collaboratively with industry to create and publish data management standards for the petroleum industry. Cooley’s paper described a pattern of “populating” seismic survey meta-data to unambiguously store and access the data in a PPDM database through standard methods.

“PPDM has a dedicated model that stores data, with post stack being already stored in large binary files,” he says. “We use this model to store post-stack meta-data and keep the binary files separately on disk. Currently, 130 different tables are being used and the number is growing. In the future, we can extend PPDM to support new data.”

The verdict will be much awaited, but in the meanwhile Smith has high expectations from UNN.


REDUCING RISK

Geophysical Insights’ seismic interpretation technology’s success lies in categorization and interpretation of all attributes of data—of which there may be six to 100— simultaneously. The company plans to implement its seismic data storage in the Professional Petroleum Data Management (PPDM) Association model. It has outlined a pattern of “populating” seismic survey meta-data to unambiguously store and access the data in a PPDM database through standard methods.

NEURAL NETWORK

Geophysical Insights applies unsupervised neural networks to the interpretation of 3-D seismic data. The new volume of data created by the process identifies specific areas of interest within a survey. A neuron “learns” by adjusting its position within the attribute space as it is drawn toward nearby data points. The winning neuron is the one that is closest to the selected data point.

Seismic Attribute Analysis Can Benefit From Unsupervised Neural Network

Seismic Attribute Analysis Can Benefit From Unsupervised Neural Network

Process identifies anomalies from original data without bias

By: Tom Smith, Ph.D. Geophysical Insights and Deborah Sacrey, Auburn Energy
Published with permission: Offshore Magazine
September 2011

The primary task facing a seismic interpreter is to recognize and attribute a geologic significance to observable patterns in the seismic response. The most apparent patterns are found in seismic reflections. In recent years, the industry is using more subtle patterns and connecting them to such attributes as porosity, lithology, and fluid content, as well as underground structure.

The separation of such patterns and their use as potential identifiers of subsurface characteristics comprises attribute analysis, a customary instrument in the geoscientist’s toolkit. Over the years, seismic data volumes have increased in terms of geographic area covered, depth of interest, and the number of attributes. New and potentially disruptive technologies have developed to take advantage of all the attributes available in the seismic data.

One new technology, based on unsupervised neural networks (UNN), reveals deeper insights into the seismic response and thereby reduces exploration risk. Unsupervised neural network technology can help interpreters recognize seismic anomalies that may indicate the presence of hydrocarbons, often when conventional techniques fall short. This new technology may also find application in the prediction of lithologies and fluid properties, as well as in estimating the size of reservoirs.

The self-organizing map (SOM), a form of UNN and a powerful pattern recognition method, was initially developed by Prof. Teuvo Kohonen of Finland during the 1970s-80s. Based on this approach, UNNs assist the interpreter in prospect evaluation by:

  • Enabling the rapid comparison of large sets of seismic attributes
  • Identifying combinations of attributes that reveal seismic anomalies
  • Distilling the interpretation process to identify hydrocarbons with greater speed and accuracy.

Supervised vs. unsupervised UNNs

For several years there have been a few commercial tools in the upstream industry that are based on “supervised” neural networks. A supervised neural network (SNN) operates on data that has been classified, i.e., “ground truth” is known in specific locations, providing reference points for calibration of the network. In terms of seismic data, for example, a segment of a seismic survey at each logged well is employed to calibrate the SNN. Supervised neural networks unite the seismic data at the well to the known conditions from the well, while inferring geophysical properties away from wells.

But what happens if there is no well log or other forms of ground truth available, such as in a greenfield exploration?

This is the advantage of unsupervised neural networks, which do not need an “answer” beforehand and cannot, therefore, be biased.

seismic interpretation of a salt dome

UNNs can be applied to “unclassified” data, which operates on the seismic data response alone.  Hence, the technology can be used to identify where even exploratory wells should be drilled by identifying geobodies that are different than their surroundings.

UNN analysis - offshore West Africa

Offshore examples

The edge of salt has been interpreted in this example along with several channel systems working through the data. Operating on this same data using a UNN produces results where the same edge of the salt outline can be seen, but “anomalous” areas (in white) stand out.

A few geobodies are associated with the previously interpreted channels in the original amplitude data.  The red horizontal line across marks the time slice which displays a horizontal view of the same salt dome.  In this time slice, the edge of the salt body has been interpreted along with a few of the major faults created by the salt uplift.

The white regions have been identified by the UNN as “anomalies” that have distinctly different properties than their surroundings, potentially indicating the presence of hydrocarbons.  These would be regions that deserve further analysis by the interpreter.

The UNN analysis also yields a classification volume in which the interpreter can see in greater details stratigraphic and structural elements that might not have been interpreted in the original amplitude volume.

The UNN Process

The following four tasks comprise a thorough analysis using an unsupervised neural network:

  • Carry out an assessment that reveals the right choice of seismic attributes
  • Perform an appropriate interpretation of attributes for the geologic trends of interest
  • Select the well information, where available, and calibrate the data
  • Generate new attribute volumes – The UNN classification and classification reliability.

One key to an effective analysis and interpretation is the selection of the best seismic attributes, exposed by a systematic assessment of the data.  Using Eigenvalue and Principal Component Analysis (PCA), it is practical to ascertain the relative contribution for each of the attributes to help guide the selection process.  Running multiple UNN analyses using different sets of attributes also may help understand their impact and if the results change with different sets of attributes.

A major transformation is required to take full advantage of the explosion of data in the oil field. Unsupervised neural network technology facilitates greater insights into all forms of data, but perhaps the greatest value to the oil and gas industry is derived from its application to seismic interpretation.  The technology is proving to reveal new insights into interpretation and increasing the reliability of the results.  Unsupervised neural networks can also be used to correlate well information with well log data while improving the value of reservoir simulation.  This new technology has the potential to be “disruptive” to the industry by providing a tool that comes close to the direct detection of hydrocarbons.

 
Dr. Thomas Smith

THOMAS A. SMITH

Tom Smith received B.S. and M.S. degrees in Geology from Iowa State University. In 1971, he joined Chevron Geophysical as a processing geophysicist. In 1980 he left to pursue doctoral studies in Geophysics at the University of Houston. Dr. Smith founded Seismic Micro-Technology in 1984 and there led the development of the KINGDOM software suite for seismic interpretation.  In 2007, he sold the majority position in the company but retained a position on the Board of DIrectors.  SMT is in the process of being acquired by IHS. On completion, the SMT Board will be dissolved. IN 2008, he founded Geophysical Insights (www.geoinsights.com) where he and several other geophysicists are developing advanced technologies for fundamental geophysical problems.

The SEG awarded Tom the SEG Enterprise Award in 2000, and in 2010, GSH awarded him the Honorary Membership Award.  Iowa State University awarded him Distinguished Alumnus Lecturer Aware in 1996 and Citation of Merit for National and International Recognition in 2002. Seismic Micro-Technology received a GSH Corporate Star Award in 2005.  In 2008, he founded Geophysical Insights to develop advanced technologies to address fundamental geophysical problems. Dr. Smith has been a member of the SEG since 1967 and is also a member of the HGS, EAGE, SIPES, AAPG, GSH, Sigma XI, SSA, and AGU.

Deborah Sacrey DEBORAH SACREY is the Frank and Henrietta Schultz Chair and Professor of Geophysics in the ConocoPhillips School of Geology & Geophysics at the University of Oklahoma. He has devoted his career to seismic processing, seismic interpretation and reservoir characterization, including attribute analysis, multicomponent 3-D, coherence and spectral decomposition. Marfurt began his career at Amoco in 1981. After 18 years of service in geophysical research, he became director of the University of Houston’s Center for Applied Geosciences & Energy. He joined the University of Oklahoma in 2007. Marfurt holds an M.S. and a Ph.D. in applied geophysics from Columbia University.

Neural network notices anomalies in seismic data

Q&A with Dr. Tom Smith

By Gene Kliewer

Tom Smith and Geophysical Insights are bringing an advanced mathematical analysis method to bear on seismic interpretation. The concept of unsupervised neural networks is being extended to seismic interpretation to speed interpretation and direct the interpreter’s attention to the key areas in the data that represent the anomalies being sought as indicators of possible hydrocarbons-bearing zones.

To get a better understanding of the process, Offshore magazine visited with Smith to peek behind the curtain to see how a  self-organizing map can benefit exploration.

Offshore: When you say neural network, what do you mean? How does it work?

Smith: The inspiration for the fellow who had this breakthrough was inspired by the brain and the visual cortex in particular. The big breakthrough was to think about a learning process which would be incorporated into a two-dimensional map of neurons – computer “neurons” in this case.

Here we’re thinking about a neuron as a little depository of information. That repository is based upon what gets to it. The neuron network is presented a set of information and the neurons adapt themselves to the information presented to them.

The neurons adjust to the characteristics of the data following a set of straightforward rules.  This results in what are called self-organizing maps (SOMs).

The input data can be completely unclassified (without reference “ground truth”) and the learning progression is unattended.

Large volumes of data can be evaluated by an unsupervised neural network quickly,  making their use reasonably practical. They can also be programmed to operate unattended and report by exception when anomalies are detect.

The unsupervised neural network works without any supervision at all.  Wells are not required for this process.

Offshore: What data do you start with?

Smith: Normally these days we might start with four or five cubes of data, each a seismic attribute.  The old standard is the seismic amplitudes.  Today, interpreters work commonly with at least five and as many as 30 different attributes, all derived from a single amplitude.  Those are then all input into this unsupervised neural network process.

There are two parts to this process. The first part is the learning.  That literally is taking the attribute cubes and presenting those to the neurons. You end up with a SOM. After this self-training, the second step is then to apply the results of the two dimensional map.

How we apply it is to go back to the data cube and take every single data point from those original data cubes and compare them to the neural network result.  We find the neuron that is the closest… that learned it was the best match to that particular sample.  We keep this process up. We go through all the samples in the original data, not just the amplitude but all the attributes and we do a comparison.  This is the classification process.  Once we’re done with that we will have taken every piece of our input data and found it nearest neighbor … the neuron that is the closest match.

Offshore: What about the math involved in the program?

Smith: If you think about a particular point in a 3D survey, if you’ve got five attributes, you’ve got five numbers associated with that point in the image.  Mathematically, that just a little column vector.  So, if it’s five attributes, the neuron is also a column vector that has place for five numbers.

We have done a good deal of design and development of the technology. Today, we are delivering the results of our analysis to clients on a turn-key basis.  We also have been making substantial investments in research and development.  Going forward, Geophysical Insights will announce a dramatic, commercial-grade software framework and applications that use unsupervised neural network technology.

So, our work in geobodies has demonstrated the ability to automatically identify areas that don’t fit, where the material is substantially different than its surroundings, and differentiate between noise and valuable information. We find those areas, the dimensions, and rank them as to size and shape, and deliver those. Once we have a geobody, we go back and post it in the original volume.  If this is a geobody with some shape, that’s what makes it an anomaly.

Offshore: Why is your technology being called “disruptive?”

Smith: We have good evidence that it will substantially reduce the risk and time associated with the interpretive process. This new technology has great potential to “disrupt” the industry by providing a tool that comes close to the direct detection of hydrocarbons.

neural network process