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Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

This Application Brief is a companion to a series of four such Briefs. Please see Paradise® Application Brief EF-1 for an introduction to the project.

In Figure 1a on the left, a NW-SE seismic section across the location of Well #6 is comprised of a conventional amplitude seismic display with red/white/black 1D color scheme. The Figure shows the Austin Chalk – Eagle Ford Group – Buda stratigraphic interval resolved in roughly 3 peak/trough cycles. This sample was provided by the client. On it, amplitudes appear “boosted”, which in the early days, say prior to the early 1980’s, passed for continuity enhancement. Formations appear “continuous”, yet any details are obscured rather than resolved and occur in the amplitude domain, where tuning, absorption, and other vertical “influence” effects are a legitimate concern.

seismic interpretation in the eagle ford

The graphic on the right displays the results of a Self-Organizing Map (SOM) of multiple Instantaneous attributes colored by neurons of up to 64 classes (see Paradise Application Brief EF-1). A seismic interval from 10ms below the Buda to 100ms above the Buda or near to the top of the Austin Chalk was chosen for the SOM run. Shown clearly is the resolution improvement provided in Paradise when the interpretation interval is reduced to just the area of interest or to a few depositional sequences.

The results shown in Figure 1b reveal non-layer cake facies bands that include details in the Eagle Ford’s basal clay rich shale, High Resistivity and Low Resistivity Eagle Ford shale objectives, the Eagle Ford ash, and the Upper Eagle Ford marl, which are
overlain disconformably by the Austin Chalk.

The Basal Clay Shale (BCS) (Figure 1b) is distinctly resolved on top of the Buda (whose 10 ms are hidden by the shaded horizon as background) generally by the #1 neuron in dark gray. Its lithologic and neural uniqueness is concomitant with its distal detrital or pelagic emplacement after deposition of the underlying Buda carbonates. The BCS is distinct also from the overlying downlap of two red neurons (black arrow) and subsequent transgressive (white arrow) greyish-gold Eagle Ford High Resistivity organic-rich facies.

A previously unknown, encapsulated, discontinuous core of rust-colored facies within the gold section of the Eagle Ford is also well resolved. This zone can be localized and its distribution understood by moving the top Eagle Ford horizon down to intersect the geobody shallow (Figure 2a) where its fairway is wide, and (Figure 2b) deeper where its fairway is narrower. Note that a change in the 2D Colormap helped facilitate the geobody’s extraction. This geobody can also be discriminated (Figure 2c) by selection of only its three neurons in the Paradise 2D Colormap. Calibration of this zone has proven it to be of the High Resistivity reservoir type.

The Eagle Ford Ash (Figure 1b) lies above the gold and comprises a different red facies than the downlap; it is resolved as discontinuous and concave or low-seeking fill at the top of the Eagle Ford shale.

Lastly, the Upper Eagle Ford marl, in purple and magenta, Figure 1b, rather than showing simple layers, exhibits updip and downdip facies changes and features that may signify faults.

seismic interpretation in the eagle ford

seismic interpretation in the eagle ford

Figure 3a shows another geobody, which is highlighted by brown and yellow neurons, in the vicinity of the 11V calibration well and the cluster of associated horizontal wells. Note the concave upward shapes of the elements of the geobody. This geobody is  stratigraphically above the Rust zone. Only one of the lateral boreholes appears to sample it as most of these wells targeted the Upper Eagle Ford marl.

Figure 3b is a time slice taken at 2.722 seconds and on the right is the SOM classification. Stratigraphic “up”, the top of the Eagle Ford Group, is to the right, and “down” to the Buda is to the left. Note that the Red lapout (black arrow) and the Rust geobody onlap (white arrow) both lie below Geobody 2. The left view shows the same slice with the 10% probability filter on. This white overlay indicates that all of the Eagle Ford updip is rare or anomalous and is likely hydrocarbon rich. Note also the indication of a fault trace highlighted by the probability overlay as well as an offset of the two major pods of this geobody.

Figure 3c is the extraction of the brown part of the geobody for a view of its distribution. The yellow was not shown as it is non-unique neural facies that also occurs in the Austin Chalk. A major fault is also clearly shown in this extraction.

Later studies indicate that this geobody also comprises a unique neuron class #1 when its
geological and petrophysical characteristics are classified against other wells in the area.

seismic interpretation in the eagle ford

All Seismic data owned and provided courtesy of Seitel, Inc.

Resolution of Faults in the Eagle Ford, Part 3

Resolution of Faults in the Eagle Ford, Part 3

Prior to the analysis described in this and the related three Application Briefs (EF-1, EF-2, and EF-4), the client believed that the faults were not well resolved within the Eagle Ford on the base “amplitude” seismic survey or on views of a single similarity attribute. This brief demonstrates that faults can be readily resolved via analysis of multiple attributes simultaneously.

Principal Component Analysis in Paradise® was used initially to reduce an initial set of 25 geometric attributes to ten from the first two Eigenvectors. Five similarity attributes, which included Chaotic Reflection, Dip of maximum Similarity, Similarity, Smoothed Dip of Maximum Similarity and Smoothed Similarity, contributed the greatest variance to the result set according to the first Eigenvector. Curvature in the Dip and Strike Directions, Maximum Curvature, Mean Curvature, and Shape Index contributed the most variance within the second Eigenvector. For setting up the input to the Self Organizing Map (SOM) process, a recommended work process uses the most prominent 2-5 attributes that contribute the greatest variance among each of the largest Eigenvectors which may number 1-4 or 5. The combined contribution for the select set of attributes should comprise at least a cumulative 60% of the variance within the region of the PCA analysis.

A result illustrated in Figure 1 is from a geometric SOM made from these ten attributes visualized as a “ghost” on the Top Eagle Ford horizon, which has then been pushed down into the High Resistivity Eagle Ford shale objective. The multiplicity of faults that can now be seen defied expectations. Well 3H’s borehole encounter of six faults while drilling, which can be individually seen here, could not have been anticipated from the use of a single similarity attribute display such as Figures 2a and 2b.

seismic interpretation in the eagle ford 01

seismic interpretation in the eagle ford 02

Figure 3 shows results from the same Instantaneous SOM with the 2D Colormap formerly seen in Figure 2 in Paradise Application Brief EF-2 (PAB EF2). The figure is from an inline that transects the downdip portion of the geobody also shown there. The patterns seen in the neuron textures now reveal details in the structure of that body in the vertical section. Numerous faults, including many that are compressional in nature, can be interpreted that were not evident at all in the original seismic data.

seismic interpretation with SOMs in the eagle ford 03

Figure 4 shows a close-up of a single compressional fault with remarkable detail in the offset in the Eagle Ford shale. For this view, the favored 2D Colormap for an 8x8 topology, i.e., 64 neurons, called Map Shade Dark, is in use (see also Figure 1b in PAB EF2). Onlap of high resistivity greyish-gold facies are connoted by white left arrows. An underlying double red downlap is located by the black right arrow. Note that the former is evident as fill in the parting of the latter at the fault in the Eagle Ford. Offset is actually most subtle here in the Upper Eagle Ford marl.

seismic interpretation with SOMs in the eagle ford 04

All Seismic data owned and provided courtesy of Seitel, Inc.

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Spectral Decomposition (SD) is regarded as a useful tool for below-resolution seismic interpretation, reservoir thickness interpretation, and depositional structure enhancement. Amplitude components
using Normalized Instantaneous attributes help quantify thickness variability more reliably.
Phase components detect lateral discontinuities both stratigraphic and structural and also contribute to the segregation of various facies tracts. However, going beyond the visualization of one, two, or even three attributes at a time, this Application Brief describes the simultaneous analysis of multiple SD attributes using machine learning processes in Paradise®.

Initial steps were to take 20 sub bands from 8 to 85Hz. Run over the time interval of 1.5 to 3.2 seconds, the first three Eigenvectors yielded relatively low values for sub-bands 48.5 to 68.8Hz, moderate values for sub–bands 24.2 through 32.3Hz, and higher values for sub-bands 8 to 16.1Hz respectively. These results suggested a further look at the Linear/Octave Trace/envelope sub-bands from 12-50Hz. From these analyses, the Linear sub-band 24.7Hz and the Octave sub-band 26.5Hz stood out (Figure 1). The selections were based on the best resolution of the disconformity between the lower Austin Chalk and the Eagle Ford.

seismic interpretation in the eagle ford - 01

Instantaneous Principal Component Analysis (PCAs) and Self-Organizing Maps (SOMs) were applied using each of the two selected linear sub-bands as the base survey. When the data is delimited by area and by horizons (see Paradise Application Brief EF2), only one Eigenvector is dominant and the top two sub-bands, 24.2 and 28.3Hz, are those that encompass the aforementioned result. The SOM results from Linear 24.7Hz (Figure 2a) and 26.5Hz (Figure 2b) were then ghosted onto the Austin Chalk top for comparison. A subtle SW–NE trending fault encountered in the #2 well, which had not been seen using traditional methods, is resolved in Figure 2a; yet is a bit more subtle in Figure 2b.

seismic interpretation in the eagle ford 02

In Figure 3, the Instantaneous SOM result for the Linear 24.7Hz is displayed in SW to NE crosslines through two neighbor wells (see inset). It can be seen that stratal variations are rapid and subtle. In the Eagle Ford, turning off green neurons 1 and 2 blank out continuous bands in the upper Eagle Ford at Well 3, and at Well 4 only a smattering of pixels are gone. Also in the right view, two additional semi-continuous greens 9 and 17 in the upper part of the Eagle Ford shale are present. Both views share the basal green bands of neuron 25 and 26.

seismic interpretation in the eagle ford -03

seismic interpretation in the eagle ford - 04

The purple band in these views is the unique lithology of the Basal Clay shale (BCS), a presumed pelagic deposit. In the underlying Buda, scour shapes in neuron 57 and 59 (red) on the left contrast starkly with the continuous bands of both facies in the vicinity of Well 4. Neuron “facies” 51 and 58 at well 3, not present on the line over Well 4, have been turned off to enhance the appearance of the scours. The overall thickness of the Buda shown is only 10ms.

A time slice (Figure 4) in the area just downdip of the last figure shows detailed stacking variations across the upper Buda along its northern edge. Yellow neuron 49 facies come in above red 59 and underneath orange 58 of last figure, before the latter then the former laps out to the NE. A compressional fault is distinct in the time slice and is apparent throughout the vertical section in nearby crossline (circle). Probable karst features are apparent to the SW and NE in the uppermost Austin Chalk in both views.

At the dip position of wells 6 and 8 on the Instantaneous Spectral Decomp (Figure 5), the
Upper Eagle Ford marl varies little in neuron sequence. With neurons 54, 62, 63, and 64 turned off across the #6 boreholes, the scour at the base of the Austin Chalk outlined by a white dashed line can be seen to carve into marl neuron facies 46 and 54. In this dip position, the Basal Clay Shale (BCS) is lowest olive color.

seismic interpretation in the eagle ford - 05

Similar features of the angular unconformity at the base of the Austin Chalk and phenomenal karsts can be seen on the Instantaneous SOM result for the Linear 26.5Hz result (Figure 6a, b, c) and are enhanced by the use of transparency. Corresponding neurons are turned off in the 2D Colormap in the upper left for the Upper Eagle Ford above the Eagle Ford shale and in the upper right for measures below the Eagle Ford shale. Note the absence of faults or any of the key stratigraphic features on conventional seismic display.

seismic interpretation in the eagle ford - 06seismic interpretation in the eagle ford 07

All Seismic data owned and provided courtesy of Seitel, Inc.

Attribute Analysis in Unconventional Resource Plays Using Unsupervised Neural Networks

Attribute Analysis in Unconventional Resource Plays Using Unsupervised Neural Networks

 

Analysis of Unconventional Resource using Inversion attributes and seismic attributes

 

Key elements in understanding unconventional resource plays encompass the following categories:

  • Reservoir Geology: thickness, lateral extent, stratigraphy, mineralogy, porosity and permeability
  • Geochemistry: Total Organic Content (TOC), maturity (Ro-heat), and kerogen% (richness)
  • Geomechanics: acoustic impedance inversion, Young’s modulus, Poisson’s ratio (Vp/Vs) and pressures
  • Faults, Fractures, and Stress Regimes: coherency (similarity), curvature, fault volumes, velocity anisotropy (azimuthal distribution) and stress maps.

This case study involved a newly acquired 3D seismic volume in a fringe area of the Eagle Ford Shale Trend. The 3D is approximately 10 square miles and four wells had been drilled to date on 2D data previously interpreted. Two wells targeted the Eagle Ford Shale Formation, and another two wells were drilled for the Austin Chalk and the Buda Lime Formations. All four wells were drilled in normal-pressured reservoirs with mixed results when it came to quality shows and commercial production. After processing the 3D volume and the initial interpretation was completed, well results and logs were incorporated by the client to create critical inversion attributes known to assist in the assessment of the shale’s productivity. Attributes contributed by the client to the analysis were: Final Density, Lambda Rho, MuRho, Poisson’s Brittleness, Poisson’s Ratio, Shear Impedance, Brittleness Coefficient, and P-impedance. Additional attributes run for the analysis were: Spectral Decomposition volumes, curvature and similarity volumes, Instantaneous attributes and Amplitude-related volumes (Average Energy and sweetness). The zone of study was confined to roughly the Top Austin Chalk to the Top of the Edwards Lime, encompassing the Austin Chalk, Eagle Ford Shale and Buda Limestone, which was approximately from 1.2 to 1.6 seconds. In addition to the PSTM volume, the generated plus client-provided attributes used to highlight sweet spots included:

  • Attenuation
  • Bandwidth
  • Envelope Slope
  • Instantaneous Q
  • MuRho
  • S-Impedance
  • Trace Envelope
  • Young’s Brittleness

A 12 x 6 topology was used for the analysis, so there were 72 neurons training on the attribute information. Figure 1 is a time slice showing the interpreted “sweet spots” in the Eagle Ford Shale on the 3D from the SOM Analysis. multi attribute analysis for unconventionals Two wells had been drilled, targeting the Eagle Ford Shale Formation. One was drilled prior to the acquisition of the data, and had few shows. It was plugged as a non-commercial well. The second well had good shows in the horizontal section of the hole, but encountered mechanical difficulties during drilling and had to be temporarily plugged. Figure 2 is an arbitrary seismic line through the deviated borehole of the second well showing the anomalous zone in both the Eagle Ford and Buda Formations and the points at which the well encountered the shows. seismic attribute analysis for unconventionals In conclusion, SOM analysis proved to be complementary to the interpretation of the data. The company who owns this 3D is now planning on targeting the area with five additional wells in the coming year. The application of using SOM analysis using selected seismic attributes can dramatically reduce uncertainty and thus decrease exploration risk in unconventional reservoirs.

Distillation of Seismic Attributes to Geologic Significance

Distillation of Seismic Attributes to Geologic Significance

By: Rocky Roden, Geophysical Insights
Published with permission: Offshore Technology Conference
May 2015

Abstract

The generation of seismic attributes has enabled geoscientists to better understand certain geologic features in their seismic data. Seismic attributes are a measurable property of seismic data, such as amplitude, dip, frequency, phase and polarity. 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 the seismic data. Commonly employed categories of seismic attributes include instantaneous, AVO, spectral decomposition, inversion, geometric and amplitude accentuating. However, the industry abounds with dozens, if not hundreds, of seismic attributes that at times are difficult to understand and not all have interpretive significance. Over the last few years there have been efforts to distill these numerous seismic attributes into volumes that can be easily evaluated to determine their geologic significance and improve seismic interpretations. With increased computer power and research that has determined appropriate parameters, self-organizing maps (SOM), a form of unsupervised neural networks, has proven to be an excellent method to take many of these seismic attributes and produce meaningful and easily interpretable results. SOM analysis reveals the natural clustering and patterns in the data and has been beneficial in defining stratigraphy, seismic facies (pressure), DHI features, and sweet spots for shale plays. Recent work utilizing SOM, along with principal component analysis (PCA), has revealed geologic features not identified or easily interpreted previously from the data. The ultimate goal in this multiattribute analysis is to enable the geoscientist to produce a more accurate interpretation and reduce exploration and development risk.

Introduction

The object of seismic interpretation is to extract all the geological information possible from the data as it relates to structure, stratigraphy, rock properties, and perhaps reservoir fluid changes in space and time (Liner, 1999). Over the last two decades the industry has seen significant advancements in interpretation capabilities, strongly driven by increased computer power and associated visualization technology. Advanced picking and tracking algorithms for horizons and faults, integration of pre-stack and post-stack seismic data, detailed mapping capabilities, integration of well data, development of geological models, seismic analysis and fluid modeling, and generation of seismic attributes are all part of the seismic interpreter’s toolkit. What is the next advancement in seismic interpretation?

A significant issue in today’s interpretation environment is the enormous amount of data that is employed and generated in and for our workstations. Seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce quantities of data in the terabytes. The ability for the interpreter to make meaningful interpretations from these huge projects can be difficult and at times quite inefficient. Is the next step in the advancement of interpretation the ability to interpret large quantities of seismic data more effectively and potentially derive more meaningful information from the data?

This paper describes the methodologies to analyze combinations of seismic attributes for meaningful patterns that correspond to geological features. A seismic attribute is any measurable property of seismic data, such as amplitude, dip, phase, frequency, and polarity and can be measured at one instant in time/depth over a time/depth window, on a single trace, on a set of traces, or on a surface interpreted from the seismic data (Schlumberger Oil Field Dictionary). Seismic attributes reveal features, relationships, and patterns in the seismic data that otherwise might not be noticed (Chopra and Marfurt, 2007). Therefore, it is only logical to deduce that a multi-attribute approach with the proper input parameters can produce even more meaningful results and help reduce risk in prospects and projects. Principal Component Analysis (PCA) and Self-Organizing Maps (SOM) provide multi-attribute analyses that have proven to be an excellent pattern recognition approach in the seismic interpretation workflow.

Seismic Attributes

Balch (1971) and Anstey at Seiscom-Delta in the early 1970’s are credited with producing some of the first generation of seismic attributes and stimulated the industry to rethink standard methodology when these results were presented in color. Further development was advanced with the publications by Taner and Sheriff (1977) and Taner et al. (1979) who presented complex trace attributes to display aspects of seismic data in color not seen before, at least in the interpretation community. The primary complex trace attributes including reflection strength (envelope), instantaneous phase, and instantaneous frequency inspired several generations of new seismic attributes that evolved as our visualization and computer power improved. Since the 1970’s there has been an explosion of seismic attributes to such an extent that there is not a standard approach to categorize these attributes. Table 1 is a composite list of seismic attributes and associated categories routinely employed in seismic interpretation today. There are of course many more seismic attributes and combinations of seismic attributes than listed in Table 1, but as Barnes (2006) suggests, if you don’t know what an attribute means or is used for, discard it. Barnes prefers attributes with geological or geophysical significance and avoids attributes with purely mathematical meaning.

In an effort to improve interpretation of seismic attributes, interpreters began to co-blend two and three attributes together to better visualize features of interest. Even the generation of attributes on attributes has been employed. Abele and Roden (2012) describe an example of this where dip of maximum similarity, a type of coherency, was generated for two spectral decomposition volumes (high and low bands) which displayed high energy at certain frequencies in the Eagle Ford Shale interval of South Texas. The similarity results at the Eagle Ford from the high frequency data showed more detail of fault and fracture trends than the similarity volume of the full frequency data. Even the low frequency similarity results displayed better regional trends than the original full frequency data. From the evolution of ever more seismic attributes that multiply the information to interpret, we investigate principal component analysis and self-organizing maps to derive more useful information from multi-attribute data in the search for oil and gas.

Seismic Attributes Categories and Types

Table 1— Typical seismic attribute categories and types and their associated interpretive uses

Principal Component Analysis

The first step in a seismic multi-attribute analysis is to determine which seismic attributes to select for the SOM. Interpreters familiar with seismic attributes and what they reveal (see Table 1) 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.

Principal Compment Analysis PCA in Paradise

Figure 1 —Principal Component Analysis (PCA) results displayed in Paradise® with top histograms displaying highest eigenvalues for 3D inlines and bottom portion displaying the highest eigenvalue at the red histogram location above. The bottom right display indicates the percentage contribution of the attributes in the first principal component.

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 the value showing how much 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 over 95% of the highest eigenvalue. This suggests 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.

Self-Organizing Maps

The next level of interpretation requires pattern recognition and classification of this 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 (SOMs) (Kohonen, 2001) efficiently distills multiple seismic attributes into classification and probability volumes (Smith and Taner, 2010). 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 as listed in Table 1. Seismic data contains huge amounts of data samples, is highly continuous, greatly redundant, and significantly noisy (Coleou et al., 2003). 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. 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.

Seismic Attributes for SOM Analysis

Figure 2—Classification map at the Yegua sand level and Classification line through the successful well. OTC-25718-MS 5 Source: Images courtesy of Deborah Sacrey of Auburn Energy.

Specific Clusters in 2D Colormpa in paradise

Figure 3—Volume rendered displays at the Yegua sand with 2D colormaps in Paradise®. Specific clusters are identified by the 2D colormaps. Source: Images courtesy of Deborah Sacrey of Auburn Energy.

Case Study Examples

Once a set or perhaps several sets of seismic attributes are selected, often from a PCA evaluation, 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 onshore Texas geologic setting exploring for prospective Yegua sands. Hydrocarbon Yegua sands in this area typically produce Class 2 AVO seismic responses and the AVO seismic attributes employed in the SOM analysis are listed in Figure 2. The SOM classification map shows an anomalous area downthrown to a northeast-southwest trending fault which was drilled and found to be productive. The line displays the SOM anomaly through the field. Figure 3 displays volume rendered results of the SOM analysis where specific clusters or patterns are identified by associated 2D colormaps. An additional successful well was drilled north of the original well where a similar SOM anomaly was identified. The 2D colormaps are unique visualization approaches to identify geologic features and anomalous areas from SOM classification volumes.

Seismic Attributes for Flat Spots

Figure 4—SOM classification line employing seismic attributes specifically for flat spots. This line clearly identifies hydrocarbon contacts in the reservoir.

Seismic Attributes for Attenuation

Figure 5—SOM classification line employing seismic attributes to define hydrocarbon attenuation. The attenuation effects in the reservoir are prominent. OTC-25718-MS 7 Seismic data provided courtesy of Petroleum Geo-Services (PGS).

In a shallow water offshore Gulf of Mexico setting, anomalous seismic amplitudes were evaluated for DHI characteristics such as possible hydrocarbon contacts (flat spots) and attenuation with various SOM analyses. With input from PCA evaluation, Figure 4 lists the seismic attributes employed in an effort to identify flat spots. The SOM analyses for flat spots clearly denotes not only a gas/oil contact, but also an oil/water contact which was corroborated by two wells in the field. These hydrocarbon contacts were not clearly defined or identified from the conventional seismic data alone. To further evaluate this anomaly, a series of seismic attributes were selected to define attenuation, an important DHI characteristic and indicative of the presence of hydrocarbons. Figure 5 lists the seismic attributes employed in this SOM analysis. As the SOM classification line of Figure 5 displays, the anomalous attenuation effects in the hydrocarbon sand reservoir are very prominent. Figures 4 and 5 indicate with the appropriate selection of seismic attributes and SOM parameters, DHI characteristics such as flat spots and attenuation can be more easily identified with SOM analyses and ultimately decrease the risk in prospective targets for this geologic setting.

Conclusions

Seismic attributes help identify numerous geologic features in conventional seismic data. The application of Principal Component Analysis (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 multi-attribute analysis using Self-Organizing Maps (SOMs). Applying current computing technology, visualization techniques, and understanding of appropriate parameters for SOM, enable 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. The application of SOM to bring out geologic features and anomalies of significance may indicate this approach represents the next generation of advanced interpretation.

Acknowledgements

The author would like to thank the staff of Geophysical Insights for the research and development of the PCA and SOM applications. Thanks also to Deborah Sacrey for providing the information for the Yegua case study.

References

Abele, S. and R. Roden, 2012, Fracture detection interpretation beyond conventional seismic approaches: Poster AAPG-ICE, Milan.

Balch, A. H., 1971, Color sonograms: a new dimension in seismic data interpretation: Geophysics, 36, 1074–1098.

Barnes, A., 2006, Too many seismic attributes? CSEG Recorder, March, 41–45. Chopra, S. and K. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization: SEG Geophysical Development Series No. 11.

Coleou, T., M. Poupon, and A. Kostia, 2003, Unsupervised seismic facies classification: A review and comparison of techniques and implementation: The Leading Edge, 22, 942–953.

Kohonen, T., 2001, Self Organizing Maps: third extended addition, Springer Series in Information Services, Vol. 30.

Liner, C., 1999, Elements of 3-D Seismology: PennWell.

Schlumberger Oilfield Glossary, online reference.

Smith, T. and M. T. Taner, 2010, Natural Clusters in Multi-Attribute Seismics Found With Self-Organizing Maps: Extended Abstracts, Robinson-Treitel Spring Symposium by GSH/SEG, March 10-11, 2010, Houston, Tx.

Taner, M. T., F. Koehler, and R. E. Sheriff, 1979, Complex seismic trace analysis: Geophysics, 44, 1041–1063.

Taner, M. T., and R. E. Sheriff, 1977, Application of amplitude, frequency, and other attributes, to stratigraphic and hydrocarbon determination, in C. E. Payton, ed., Applications to hydrocarbonexploration: AAPG Memoir 26, 301–327.

Taner, M.T., S. Treitel, and T. Smith, 2009, Self-Organizing Maps of Multi-Attribute 3D Seismic Reflection Surveys: SEG 2009 Workshop on “What’s New In Seismic Interpretation?,” Houston, Tx.

 

Rocky Roden ROCKY RODEN owns his own consulting company, Rocky Ridge Resources Inc., and works with several oil companies on technical and prospect evaluation issues. He also is a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. He is a proven oil finder (36 years in the industry) with extensive knowledge of modern geoscience technical approaches (past Chairman – The Leading Edge Editorial Board). As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. He holds a BS in Oceanographic Technology-Geology from Lamar University and a MS in Geological and Geophysical Oceanography from Texas A&M University. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES.