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Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Analysis of offshore Gulf of Mexico – Class 3 AVO setting

The case study highlighted below is an offshore oil/gas field in 470 feet of water on the continental shelf of the Gulf of Mexico. The eld has two producing wells that were drilled on the upthrown side of a normal fault and into an amplitude anomaly. The normally-pressured reservoir is approximately 100 feet thick. The hydrocarbon-filled sandstone reservoir has low impedance compared to the encasing shales, indicative of a Class 3 AVO environment.

Applying both traditional interpretation methods and the Self-Organizing Maps (SOM’s) capability within Paradise, Mr. Rocky Roden, a Sr. Geophysical Consultant with Geophysical Insights, conducted an analysis on the 3D volume over this field. A group of seismic attributes were selected that would best expose Direct Hydrocarbon Indicators (DHIs).

Using known conditions, including AVO Class, reservoir characteristics, well logs, and seismic response data, the Rose and Associates DHI Consortium has developed a methodology for quantifying the risk of DHI-related prospects. The Consortium has compiled a database of several hundred wells and has been able to identify the most important DHI Characteristics.

Data quality, bed resolution, or seismic attribute algorithm may or may not allow an interpreter to nd any of the following examples, even if they are physically present in an ideal natural setting. The existence and/or variance in any of these DHI characteristics, as expressed in the data, enable the extent of risk to be better understood, and more so in a group of prospects, to be far better appreciated and quantified. According to the DHI Consortium, some of the most important DHI characteristics are:

  1. Amplitude conformance to downdip structural closure
  2. Internal consistency of amplitude
  3. Phase, frequency, or character changes at edge of reservoir
  4. Flat spots
  5. Attenuation

A SOM probability display using flat spot attributes


Results – Reduced risk through greater insights

The key findings of the study are as follows:

  • The SOM analyses of the seismic attributes associated with Flat Spots helped define two levels of hydrocarbon contacts in this dataset.
  • The SOM analyses on Attributes for Attenuation amplified apparent attenuation features, especially within the reservoir sand.
  • The SOM analyses of the seismic attributes named Ten Attributes 10% was especially helpful in defining amplitude conformance to downdip closure, and provided confidence in the internal consistency of the reservoir.
  • All three combinations of seismic attributes analyzed by SOM analysis revealed phase and character changes near the edge of the hydrocarbon reservoir.

SOM Analysis proved to complement and enhance the conventional interpretation by providing a second, completely independent method of exposing DHIs. This application of the SOM method increased confidence that insightful DHI characteristics are truly evidenced in the appraisal area. The results of this case study demonstrate that applying the DHI methodology with the SOM analysis engine in Paradise on selected seismic attributes can dramatically reduce uncertainty in the interpretation, thereby decreasing exploration risks in this geological setting.

A SOM classification of attenuation attributes


Seismic data provided courtesy of Petroleum Geo-Services (PGS)

Using Self-Organizing Maps to Define Seismic Facies

Using Self-Organizing Maps to Define Seismic Facies

Offshore South America Seismic Facies Analysis

This analysis and application of Paradise involved the evaluation of a 3D volume offshore
South America. A well had been drilled and encountered unexpected high pressures which prevented drilling to the desired deeper target. Traditional methods to identify pressure did not readily reveal a high-pressure zone before drilling. The challenge was to identify the high-pressure zone employing Self-Organizing-Maps (SOMs).


Initial evaluation of the 3D seismic volume suggested there may be facies and stratigraphic variations in the high-pressure zone.

After an interpretation of the local geology and putting this into a regional context, the anomalous high-pressure area appeared to be associated with a slope facies as interpreted from the conventional stacked seismic data.

Therefore, five different combinations of seismic attributes were applied in a SOM analysis to help define the seismic facies in the zone of interest. One specific combination of six seismic attributes clearly defined the seismic slope facies and associated high-pressure region. With the use of 2D colorbars in the 3D Viewer in the Paradise software, the highlighting of specific neurons enabled the visualization of the high pressured seismic facies.


A SOM Classification that exposes the region of high pore pressure using the Paradise 2D neural color map (right)




Analysis Results – Exposing High Pore Pressure Region


  • Based on pressure readings from a single well, the increase in pressure seems to be associated with a hummocky, wavy, and at times chaotic seismic reflection character.
  • This reflection character is typically associated with a slope setting where there are rapid facies changes, discontinuous siltstone and mudstone beds and at times channelized sands with interchannel mudstones.
  • Dozens of seismic attributes were generated to help define this seismic facies associated with pressure in the well.
  • Five different sets of seismic attributes were selected for SOM analysis to define this pressure associated seismic facies.
  • All of the SOM Classification volumes and to some degree Probability volumes defined components of this seismic facies (e.g., top, bottom, internal seismic reflection character, reflection character above and below, etc.).
  • A specific set of seismic attributes effectively isolated the pressure zone through a SOM analysis


A SOM probability display showing the high pore pressure region as an anomaly in Paradise


Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast

Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast

Analysis of Middle Texas Gulf Coast 3D to explore for shallow Yegua Formation Potential

In this case study of the use of Self-Organizing-Maps (SOM analysis), the gathers were used to generate AVO volumes such as Far-Near (used on the angle stacks, where nears were 0-15 degrees and fars were 31-45 degrees), (Far-Near)xFar, Gradient (B), Intercept (A) x Gradient (B), ½(Intercept + Gradient) and Poissons’ Reflectivity (PR).

Conventional amplitude interpretation identified a potential area of hydrocarbon accumulation, downthrown on a down-to-coast fault. Figure 1 is the amplitude extraction from the PSTM-raw volume.

Using self-organizing maps to explore the yegua in the texas gulf coast


In addition to the created AVO attributes, volumes of Spectral Decomposition, curvature, similarity and other frequency-related attributes were created.  Conventional interpretation of the reservoir area indicated the anomaly covered approximately 70 acres.


Using SOMs to explore the Yegua

A SOM of the above mentioned AVO attributes, plus Sweetness and Average Energy was run to more closely identify the anomaly and the aerial extent. Figure 2 shows the results of this analysis.

A detailed engineering study of the production indicates that the results of the SOM analysis concur with the aerial extent of the sand deposition to be more in line with almost 400 acres of drainage rather than the initial 70 acres first identified. The SOM identified in this time slice shows a network of sand deposition not seen in conventional mapping.

Figure 3 shows an arbitrary line going through a second, upthrown Yegua anomaly identified by the SOM analysis, and now drilled, confirming the economic presence of hydrocarbons.

The conclusion drawn from this study is that SOM analysis proved to complement and enhance the conventional interpretation by providing a second, completely independent method of exposing direct hydrocarbon indicators.

Self-organizing maps to explore the Yegua


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


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.


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.


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.


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.


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.



Relating Seismic Interpretation to Reserve / Resource Calculations

Relating Seismic Interpretation to Reserve / Resource Calculations

By: Rocky Roden and Mike Forrest, Rose & Associates, Roger Holeywell, Marathon Oil Company
Published with permission: The Leading Edge
September 2012

In the process of quantifying resources/reserves, geoscientists attempt to employ all the available pertinent information to produce the most accurate results. The presence of direct hydrocarbon indicators (DHI) on seismic data can have a significant impact on the reserve/resource calculations not only for volumes, but also uncertainty levels. In 2001 a consortium of oil companies was organized in an attempt to understand seismic amplitude anomalies interpreted as DHIs and their impact on prospect risking and resource calculations (Roden et al., 2005; Forrest et al., 2010). The geologic setting, seismic and rock physics data quality, DHI characteristics, and calibration of drilling results are all incorporated into a database in a consistent and systematic process. From this process, the evaluation of 217 prospects and associated well results has enabled an in-depth understanding of the relevant key aspects of seismic amplitude anomalies and how they relate to drilling results.

DHI Consortium background

In the DHI Consortium, 41 oil companies have contributed to developing a seismic amplitude analysis process that systematically provides a probability of geologic success (Pg) values and associated implications for resource evaluations (Figure 1). The goals of the DHI consortium over the past 11 years have been and continue to be the following:

  1. Gain a better understanding of how seismic amplitudes impact prospect risking (i.e., predrill chance of geological success or Pg).
  2. Objectively characterize DHI observations using documented occurrences of recoverable hydrocarbons in the subsurface via prospect reviews and risk analysis discussions.
  3. Archive a statistically significant library of drilled prospect results.
  4. Use the prospect database to improve the predictability of Pg and range of uncertainty of hydrocarbon volumes.
  5. Improve the DHI work analysis process to help risk seismic amplitudes and provide an educational tool for interpreters.
  6. Discussion and review of pertinent technologies for amplitude interpretation.

These companies have supplied 217 prospects from around the world with approximately an equal number of successes, dry holes, and objective reservoirs ranging in age from Triassic to Pleistocene. The prospects range in size from less than 100 acres to greater than 10,000 acres and depths from 2000 to more than 20,000 ft. This database is predominantly made up of exploratory wells with only 14% coming from “development” or “extension to known reservoir” wells. The closure types include structural (59%), stratigraphic (26%), and combination structural/stratigraphic (15%) traps.

Critical in the evaluation of DHI characteristics is an understanding of the geologic setting. Because expected DHI responses vary depending on the geologic setting, the prospects in the database are categorized by AVO classes 1–4 as displayed in Figure 4 (Rutherford and Williams, 1989; Castagna et al., 1998). However, 76% of the prospects are class 3 and 22% are class 2, with only a few from class 1 and 4 settings.

A general description of the input information and quantified output for this database as is described in Table 1.

quanitifed workflow

Table 1. General input and quantified output from DHI analysis process. Note that the input parameters are not necessarily linked directly to the corresponding quantified output row.

Well locations for the DHI Consortium

Figure 2. Locations around the world of the 217 wells in DHI Consortium database

There are several ways the most important DHI characteristics can be determined from the well results. The following list is a result of when a DHI characteristic was rated to be a 4 or 5 on a scale of 1–5, with 1 being the worst and 5 the best. These results were then correlated with whether the wells were successful or dry holes. This approach eliminates any scaling or weighting effects and compares only whether the DHI characteristic is strongly correlated with the actual postdrill well results. The top five DHI characteristics have been determined for both class 2 and 3 AVO categories, which relate to different geologic settings, and for which there are a sufficient number of example prospects to be meaningful.

seismic interpretation prospect types 

Figure 3. Prospect types of wells in the DHI Consortium database.

A class 3 AVO setting relates to an unconsolidated gas sand with a gross interval velocity usually less than 8500 ft/s (2650 m/s). These reservoirs typically are low-impedance sands encased in relatively higher-impedance shales. Gas sands in this setting exhibit high amplitudes on the stack section as well as all offsets and angles. The seismic response from the top of these reservoirs produces high amplitudes with respect to background and increase (at times only slightly) with increasing offset/angle (Figure 4).

Top five class 3 DHI characteristics

    1. Amplitude downdip conformance (fit to closure) on stacked or far offset seismic data. This is the top-rated DHI characteristic in the database and describes how well the seismic amplitudes of the anomaly conform to the downdip structural contours of the reservoir. Key issues are whether the structural contours are in time or depth, potential velocity variations that can affect imaging, the accuracy of how the amplitudes were picked (top, bottom, or window) at the anomaly, and consideration of any stratigraphic variations. It is important to remember that this characteristic relates only to the downdip edge of the amplitude anomaly and how it compares to the interpreted structure. It does not relate to an amplitude’s conformance on the flanks of stratigraphic traps. This lateral conformance characteristic in stratigraphic traps depends on the geologic model and the strength of the independent evidence that supports that model.
    2. Phase or character change at the downdip edge of the anomaly. This DHI characteristic relates to a change in character such as phase or frequency at the downdip edge of the anomaly. As the model in Figure 6 displays, the transition from the hydrocarbon leg to the water leg in a sand can produce a seismic response indicative of the edge of a reservoir.
    3. Amplitude consistency in the mapped target area. The internal consistency of the seismic amplitude in a reservoir was found to be a significant DHI characteristic. This relates to the uniformity of the amplitude response within the mapped target area as interpreted from the stacked seismic data (Figure 7). When evaluating this characteristic, consideration should be given to possible faulting and stratigraphic changes that may modify internal consistency.

amplitude change with offset

Figure 4. (top) AVO classes based on amplitude change with offset from the top of gas sands (Rutherford and Williams, 1989; Castagna et al., 1998). (bottom) Angle gathers, zero-offset, and stack responses for typical class 2 and 3 AVO classes.

  1. Flat spots. This characteristic represents the seismic response at a hydrocarbon contact that presumably is relatively flat. This contact can be at the gas/oil contact, oil/ water contact, or gas/water contact. The gross reservoir thickness of the hydrocarbon unit must be greater than the tuning thickness (vertical resolution) to image a flat spot. Extreme care must be taken because the base or edge of channels, low-angle faults, diagenetic boundaries, or even processing artifacts are often misinterpreted as flat spots. Low-saturation gas in a reservoir can also produce a flat spot and this possibility is usually assessed by regional geologic studies.
  2. Excluding possible stacked pays, the AVO response is anomalous compared to events above and below. On prestack data, usually gathers, this characteristic refers to whether there are high-amplitude events above and below the targeted anomaly that look similar (Figure 8). The reasoning is that the anomaly is relatively unique suggesting a hydrocarbon-bearing reservoir.

A class 2 setting contains gas sands more consolidated than class 3 sands with gross interval velocities usually between 8500 ft/s and 12,000 ft/s (2650 m/s and 3650 m/s). The acoustic impedances of these gas sands and encasing shales are about equal. The intercept or near-offset amplitude can vary from a weak positive to a weak negative. The AVO effect can be strongly more negative with offset (large gradient) in these settings (Figure 4).

Top five class 2 DHI characteristics

  1. Amplitude downdip conformance (fit to closure) on far-offset seismic data. The principles behind this characteristic are the same as the top characteristic for class 3 settings, but for class 2 prospects this is usually evaluated on the far offset seismic data.
  2. Consistency in mapped target area (typically on gathers, faroffset/angle stacks, or windowed attributes). Internal consistency of the seismic amplitude identified on far-offset data was found to be an important DHI characteristic for class 2 rocks just as it is for class 3 rocks.
  3. AVO observations using gathers, far-offset/angle stacks, or windowed attributes. This characteristic relates to an interpreter’s confidence that the AVO response is proper for a class 2 setting. In other words, the near-offset is low in amplitude (small peak or trough) and the amplitude increases in negative amplitude with offset. (Figure 4) Noisy gathers, incorrect NMO corrections, multiples, insufficient offset during acquisition, and processing artifacts often complicate evaluation of this characteristic.
  4. The AVO event is anomalous compared to the same event outside the closure. This characteristic describes whether the class 2 AVO response is unique compared to the correlative event outside the closure. This characteristic is often interpreted from far-offset/angle stacks, as well as, intercept, gradient, intercept x-gradient, far-near, and (farnear) x-far displays.
  5. Change in AVO compared to model (wet versus hydrocarbon- filled). Modeling of the AVO response, usually applying Gassman’s equation, typically involves substitution modeling of gas, oil, and water responses. A comparison of the in-situ and modeled responses to actual gathers provides confidence that hydrocarbons are present or not.

amplitude conformance

Figure 5. Examples of amplitude conformance to the downdip structural contours from grade 1 (worst) to grade 5 (best).

Reasons for failure

reservoir hydrocarbon and water logs

Figure 6. Model of hydrocarbon and water legs of a reservoir and location of possible phase or frequency change at edge of fluid contact (courtesy Quantum Earth Corp).

The consortium defines successful wells as geologic successes (i.e., wells with flowable hydrocarbons). However, in this database all successes were determined to also be commercially successful except for a few wells. These exceptions contained from 50 to more than 100 ft of gas, but were determined to be noneconomic because of their location in the world and lack of infrastructure. Wells containing only low-saturation gas are considered by the consortium to be dry holes even though they may represent successful predictions of acoustic response.

Wet sands in the target interval accounted for 49% of the dry holes in the database. Nearly half of these wet sands were thick. In fact, these thick wet sands were greater than the predrill P10 estimates for reservoir thickness. Hard shale on top of a wet sand, blocky sands with higher than expected porosities, and tuning effects of wet sands all produced amplitude anomalies misinterpreted to be direct hydrocarbon effects.

Low-saturation gas (LSG) accounted for 23% for the failures in the database. This well known phenomenon is caused by a small percentage of residual gas in a reservoir (5–10%) producing an acoustic effect similar to commercial saturation and is usually associated with a breach or break in reservoir seal. All but one of the LSG dry holes were in class 3 rocks, with one in a class 2 setting. All but one of the LSG dry holes were in normally pressured sediment columns. Four of the LSG wells exhibited acoustic flat spots indicative of a paleo hydrocarbon contact. Low vertical effective stress caused by undercompaction accounted for several LSG results in deepwater settings, but was also found where a relatively young sediment column overlaid the amplitude anomaly. Even though not dry holes, several wells exhibited hydrocarbons, usually gas, overlying a LSG column in the sand.

No reservoir present was responsible for 17% of the dry holes. Amplitudes misinterpreted to be hydrocarbons included low-density shale, ash, coal beds, top of hard overpressures, seismic processing artifacts, mud volcano, and diagenetic boundary.

Tight reservoirs accounted for 11% of the dry holes in the database. Examples of these include oil-charged marl, gas-charged condensed section, low-impedance siltstone/mudstone and low-permeability reservoir.

DHI anomaly

Figure 7. Examples of amplitude consistency within a defined DHI anomaly from grade 1 (worst) to grade 5 (best).

Implications for resource calculations in exploration

The presence of direct hydrocarbon indicators can have a significant impact on methods to compute prospective resources, especially in exploration settings. How do DHIs change area determination for resources? How do DHIs impact thickness determinations in resource computations? How does a seismic-amplitude (DHI) defined area and thickness relate to a geologically defined area and thickness? The presence of true direct hydrocarbon indicators indicates that all geologic chance factors (e.g., source, migration, seal, reservoir, and trap) are working to form a petroleum system; therefore, the critical issue is determining the confidence level that the seismic is truly displaying a direct hydrocarbon indicator because not all anomalous amplitude events are DHIs.

AVO anomaly

Figure 8. An unmuted CDP gather displaying an AVO anomaly that appears prominent compared to the events above and below (Roden et al., 2005).The near vertical lines represent offset angles (purple = 10°, red = 20°, green = 30°).

DHI Index

Figure 9. Illustration of DHI Index method to define and weight geologic and amplitude areas for resource calculations

For area determinations, one approach is to consider two separate distribution estimates. One distribution is based on geologic evidence, independent of the amplitude as a DHI, while the other distribution is based on the amplitude-defined area (Figure 9). Each distribution can be defined by a P10 (largest reasonable) and a P90 (smallest reasonable) value. The variance or P10/P90 ratio of the area is often large for the geologic distribution, but much smaller for the amplitude-defined distribution because the anomaly usually has a sharp cutoff. From statistics based on the DHI consortium results, a “DHI Index” has been calculated that indicates the likelihood that the interpreted amplitude anomaly is truly a direct hydrocarbon indicator. This “DHI Index” can be used as a relative weighting factor for the two distribution estimates.

seismic interpretation software - DHI Index

Figure 10. DHI Index values color-coded by drilling outcomes. Note that above 20% almost all wells are successful. There are negative DHI Index values, usually associated with dry holes, where DHI characteristics should be present, but were absent.

The histograms of Figure 10 show that, when the DHI Index is more than 20%, essentially all wells are successful. To get a DHI Index of 20% or higher requires having numerous positive DHI characteristics. Therefore, using this logic, prospects with a DHI Index of 20% or larger employ the DHI-defined area, whereas, a DHI Index of 0% or less employ the geologically defined area (Figure 9). A DHI Index of 0% or less indicates there is no DHI element at all in the area determination. When a prospect has a DHI Index between 0% and 20%, the area is defined by a simple linear weighting (e.g., a DHI Index of 10% would have a 50/50 weighting of geology and amplitude-defined areas) between the two distribution estimates.

For thickness determinations, similar to the DHI Index approach for area, two thickness distributions can be estimated. One is defined geologically and one by the amplitude-de- fined thickness. The geologically defined distribution should be based on well control, sand studies, depositional trends, and usually involves constructing regional and local sand isopachs consistent with the depositional model in terms of reservoir shape and thickness. From the combination of sand isopachs, depositional environment determinations, models and stratigraphy studies, an estimate of the P10 and P90 average net pay thicknesses can be calculated.

For the amplitude-defined thickness, the interpreter first needs to determine whether the anomaly is above tuning, below tuning, or both. Above tuning, the thickness is a function of the time separation between the top and bottom of the anomaly. Gross pay maps (isochrons) can be generated for the P10 and P90 cases including any geometric factors. An estimated net-to-gross and interval velocity are applied to determine the final thickness distributions. Below tuning, in the ideal situation, the composite amplitude from the top and bottom of the bed decreases linearly with thickness starting from tuning thickness. The amplitude should be calibrated with well control and stratigraphy studies. Below tuning, a starting point P10 could be the tuning thickness and a P90 could be the detection limit (approximately 1/30 wavelength of the dominant frequency). Appropriate interval velocities may need to be applied for thickness conversions. Below tuning, the composite amplitude usually incorporates any net-to-gross effects. Once a geologically defined thickness distribution and a seismic amplitude-defined thickness are determined, the DHI Index can be used to determine the weighting of these two distributions. It can be more difficult to separate and differentiate between a geologically and amplitude-defined thickness as compared to interpreting distributions for the area. In this situation, it may be more appropriate to combine geologic and amplitude thickness information and use one distribution without weighting to compute resources. Spectral decomposition has been found to be helpful in certain situations to help determine thickness trends.

Employing the DHI Index method to determine area and thickness for resource calculations is one approach that gives credit to both geologically and amplitude defined distributions. However, it is not uncommon in strong amplitude driven plays to employ the amplitude-defined distributions only, especially for area. The logic here is that the presence of amplitudes is defining the reservoir and the geologic distributions do not apply. Care should be taken in that all amplitudes are not DHIs and not all geologic settings exhibit DHIs equally. In addition, even if there is evidence that the area can be narrowly estimated, there will often be significant remaining uncertainty in resource size because of the variance in average net pay thickness and recovery factor estimates.

Implications for reserve calculations

The U. S. Securities and Exchange Commission (SEC) defines “proved oil and gas reserves” in part as “those quantities of oil and gas, which, by analysis of geosciences and engineering data, can be estimated with reasonable certainty to be economically producible— from a given date forward, from known reservoirs, and under existing economic conditions, operating methods, and government regulations — prior to the time at which contracts providing the right to operate expire, unless evidence indicates that renewal is reasonably certain, regardless of whether deterministic or probabilistic methods are used for the estimation.” Unlike resource calculations in exploration, reserve calculations have had a well or wells drilled to confirm geologic conditions and to some degree seismic amplitude features. Therefore the interpretation of DHI features relates to the determination of “reliable technology” as defined by the SEC. As defined by the SEC, “reliable technology is a grouping of one or more technologies (including computational methods) that has been field tested and has been demonstrated to provide reasonably certain results with consistency and repeatability in the formation being evaluated or in an analogous formation.” Therefore for DHI characteristics to qualify as reliable technology requires the correlation of amplitude features with well control, reservoir engineering tests, known geologic trends and stratigraphy.

Even with well control and substantial geologic control of thickness and area extents, care should be taken in incorporating DHI information from seismic data. Considerations for amplitude anomalies and reserve determinations include:

  1. Strong amplitude is primarily at wedge where tuning occurs, weaker amplitude updip
  2. Stratigraphic trap where conventional downdip and updip limits do not apply
  3. Reservoir thins updip, bald structure
  4. Velocity tilt can affect conformance to closure interpretations
  5. Faults affecting amplitudes for area determinations
  6. Stratigraphic changes across field
  7. Masking of amplitudes from bright spots above prospect
  8. Amplitude effects from low-saturation gas downdip of proven pay in the same reservoir
  9. Beware of large column heights for gas reservoirs
  10. Data issues, such as prospect at edge of survey, complicate area estimates

In development, seismic technologies such as spectral decomposition, neural networks, statistical approaches, and advanced seismic inversion techniques are often applied to help determine reserves. The transition from exploration to development and the use of seismic inversion requires the incorporation of all available geologic, seismic, and engineering data in an attempt to discriminate specifically between lithology, porosity, and fluid effects and how these parameters relate to reserve calculations.


Over the past 11 years, 41 oil companies have contributed data for the evaluation of more than 200 drilled prospects (including assessment of the associated technical information and drilling results). The subsequent database developed from these assessments has enabled the identification of the pertinent aspects of direct hydrocarbon indicators and their impact on risking and resource/reserve determination. This database is composed primarily of exploratory wells (86%) and has direct relevance for computing resources in exploration. However, the lessons learned from this database are also pertinent for reserve assessment in development because an understanding of the most important DHI characteristics, reasons for failure, and potential pitfalls in DHI interpretation provide valuable information in the reserve assessment process. Critical in the evaluation of DHI characteristics is the pertinent technical approaches employed and their calibration to known drilling results. However, even in development settings with a certain amount of well control for calibration, DHI interpretation requires a consistent and systematic evaluation procedure, including recognition of potential pitfalls and an understanding of the cause of the DHIs and their significance in reserve assessment.


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Forrest, M., R. Roden, and R. Holeywell, 2010, Risking seismic amplitude anomaly prospects based on database trends: The Leading Edge, 29, 936–940.

Roden, R., M. Forrest, and R. Holeywell, 2005, The impact of seismic amplitudes on prospect risk analysis: The Leading Edge, 24, no. 7, 706–711.

Roden, R., J. Castagna, and G. Jones, 2005, The impact of prestack data phase on the AVO interpretation workflow-A case study: The Leading Edge, 24, 890–895.

Rutherford, S. E. and R. H. Williams, 1989, Amplitude versus offset variation in gas sands: Geophysics, 54, no. 6, 680–688.

U. S. Securities and Exchange Commission, 2008, Modernization of oil and gas reporting, December 31.

Acknowledgments: The authors thank the member companies of the DHI Consortium for providing invaluable information necessary to develop the resulting interpretation process and prospect database.