Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with seismic inversion or rock modeling.  The descriptions below differentiate SOM, which is a statistical classifier, from seismic inversion.

Seismic Inversion
The purpose of seismic inversion is to transform seismic reflection data into rock and fluid properties.  This is done by trying to convert reflectivity data (interface properties) to layer properties.  If elastic parameters are desired, then the reflectivity from AVO must be performed.  The most basic inversion calculates acoustic impedance (density X velocity) of layers from which predictions about lithology and porosity can be made.  The more advanced inversion methods attempt to discriminate specifically between lithology, porosity, and fluid effects.  Inversions can be grouped into categories: pre-stack vs. post-stack, deterministic vs. geostatistical, or relative vs. absolute.  Necessary for most inversions is the estimation of the wavelet and a calculation of the low frequency trend obtained from well control and velocity information.  Without an accurate calibration of these parameters, the inversion is non-unique.  Inversion requires a stringent set of data conditions from the well logs and seismic.  The accuracy of inversion results are directly related to significant good quality well control, usually requiring numerous wells in the same stratigraphic interval for reasonable results.

SOM Seismic Multi-Attribute Analysis
Self-Organizing Maps (SOM) is a non-linear mathematical approach that classifies data into patterns or clusters.  It is an artificial neural network that employs unsupervised learning.  SOM requires no previous information for training, but evaluates the natural patterns and clusters present in the data.  A seismic multi-attribute approach involves selecting several attributes that potentially reveal aspects of geology and evaluate how these data form natural organizational patterns with SOM.  The results from a SOM analysis are revealed by a 2D color map that identify the patterns present in the multi-attribute data set.  The data for SOM are any type of seismic attribute which is any measurable property of the seismic.  Any type of inversion is an attribute type that can be included in a SOM analysis.  A SOM analysis will reveal geologic features in the data, which is dictated by the type of seismic attributes employed. The SOM classification patterns can relate to defining stratigraphy, seismic facies, direct hydrocarbon indicators, thin beds, aspects of shale plays, such as fault/fracture trends and sweet spots, etc.  The primary considerations for SOM are the sample rate, seismic attributes employed, and seismic data quality.  SOM addresses the issues of evaluating dozens of seismic attribute volumes (Big Data) and understanding how these numerous volumes are inter-related.

Seismic inversion attempts to invert the seismic data into rock and fluid properties predicted by converting seismic data from interface properties into layers.  Numerous wells and good quality well information in the appropriate zone is necessary for successful inversion calculations, otherwise solutions are non-unique.  For successful inversions, wavelet effects must be removed and the low frequency trend must be accurate.

SOM identifies the natural organizational patterns in a multi-attribute classification approach.  Geologic features and geobodies exhibit natural patterns or clusters which can be corroborated with well control if present, but not necessary for the SOM analysis.  For successful SOM analysis the appropriate seismic attributes must be selected.

Rocky Roden

Sr. Consulting Geophysicist | Geophysical Insights

ROCKY R. RODEN has extensive knowledge of modern geoscience technical approaches (past Chairman-The Leading Edge Editorial Board).  As former 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 B.S. in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University.

Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

By: Rocky Roden, Geophysical Insights, and Deborah Sacrey, Auburn Energy
Published with permission: GeoExPro Magazine
December 2016

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including 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 terabytes of data. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM. This identifies the natural clustering and patterns in the data and has been beneficial in defining stratigraphy, seismic facies, DHI features, sweet spots for shale plays, and thin beds, to name just a few successes. Employing these approaches and visualizing SOM results utilizing 2D color maps reveal geologic features not previously identified or easily interpreted from conventional seismic data.

Steps 1 and 2: Defining Geologic Problems and Multiple Attributes

Seismic attributes are any measurable property of seismic data and are produced to help enhance or quantify features of interpretation interest. There are hundreds of types of seismic attributes and interpreters routinely wrestle with evaluating these volumes efficiently and strive to understand how they relate to each other.

The first step in a multi-attribute machine-learning interpretation workflow is the identification of the problem to resolve by the geoscientist. This is important because depending on the interpretation objective (facies, stratigraphy, bed thickness, DHIs, etc.), the appropriate set of attributes must be chosen. If it is unclear which attributes to select, a principal component analysis (PCA) may be beneficial. This is a linear mathematical technique to reduce a large set of variables (seismic attributes) to a smaller set that still contains most of the variation of independent information in the larger dataset. In other words, PCA helps determine the most meaningful seismic attributes.

seismic interpretation workflow

Figure 1: Multi-attribute machine learning interpretation workflow with principal component analysis (PCA) and self-organizing maps (SOM).

Figure 2 is a PCA analysis from Paradise® software by Geophysical Insights, where 12 instantaneous attributes were input over a window encompassing a reservoir of interest. The following figures also include images of results from Paradise. Each bar in Figure 2a denotes the highest eigenvalue on the inlines in this survey. An eigenvalue is a value showing how much variance there is in its associated eigenvector and an eigenvector is a direction showing a principal spread of attribute variance in the data. The PCA results from the selected red bar in Figure 2a are denoted in Figures 2b and 2c. Figure 2b shows the principal components from the selected inline over the zone of interest with the highest eigenvalue (first principal component) indicating the seismic attributes contributing to this largest variation in the data. The percentage contribution of each attribute to the first principal component is designated. In this case the top four seismic attributes represent over 94% of the variance of all the attributes employed. These four attributes are good candidates to be employed in a SOM analysis. Figure 2c displays the percentage contribution of the attributes for the second principal component. The top three attributes contribute over 68% to the second principal component. PCA is a measure of the variance of the data, but it is up to the interpreter to determine and evaluate how the results and associated contributing attributes relate to the geology and the problem to be resolved.

principal component analysis for seismic interpretation

Figure 2: Principal Component Analysis (PCA) results from 12 seismic attributes: (a) bar chart with each bar denoting the highest eigenvalue for its associated inline over the displayed portion of the seismic 3D volume. The red bar designates the inline with the results shown in 2b and c; (b) first principal component designated win orange and associated seismic attribute contribution to the right; and (c) second principal component in orange with the seismic contributions to the right. The highest contributing attributes for each principal component are possible candidates for a SOM analysis, depending on the interpretation goal.

Steps 3 and 4: SOM Analysis and Interpretation

The next step in the multi-attribute interpretation process 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, developed by Teuvo Kohonen in 1982, efficiently distill multiple seismic attributes 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, some of which can relate to desired geologic characteristics. The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure. SOM analysis identifies these natural organizational structures in the form of natural attribute clusters. These clusters reveal significant information about the classification structure of natural groups that is difficult to view any other way.

Figure 3 describes the SOM process used to identify geologic features in a multi-attribute machine-learning methodology. In this case, 10 attributes were selected to run in a SOM analysis over a specific 3D survey, which means that 10 volumes of different attributes are input into the process. All the values from every sample from the survey are input into attribute space where the values are normalized or standardized to the same scale. The interpreter selects the number of patterns or clusters to be delineated. In the example in Figure 3, 64 patterns are to be determined and are designated by 64 neurons. After the SOM analysis, the results are nonlinearly mapped to a 2D color map which shows 64 neurons.

SOM workflow process

Figure 3: How SOM works (10 seismic attributes)

At this point, the interpreter evaluates which neurons and associated patterns in 3D space define features of interest. Figure 4 displays the SOM results, where four neurons have highlighted not only a channel system but details within that channel. The next step is to refine the interpretation and perhaps use different combinations of attributes and/or use different neuron counts. For example, in Figure 4, to better define details in the channel system may require increasing the neuron count to 100 or more neurons to produce much more detail. The scale of the geologic feature of interest is related to the number of neurons employed; low neuron counts will reveal larger scale features, whereas a high neuron count defines much more detail.

SOM seismic interpretation analysis

Figure 4: SOM analysis interpretation of channel feature with 2D color map

Workflow Examples

Figure 5 shows the SOM classification from an offshore Class 3 AVO setting where direct hydrocarbon indicators (DHIs) should be prevalent. The four attributes listed for this SOM run were selected from the second principal component in a PCA analysis. This SOM analysis clearly identified flat spots associated with a gas/oil and an oil/water contact. Figure 5 displays a line through the middle of a field where the SOM classification identified these contacts, which were verified by well control. The upper profile indicates that 25 neurons were employed to identify 25 patterns in the data. The lower profile indicates that only two neurons are identifying the patterns associated with the hydrocarbon contacts (flat spots). These hydrocarbon contacts were difficult to interpret with conventional amplitude data.

SOM results defining hydrocarbon contacts

Figure 5: SOM results defining hydrocarbon contacts on a seismic line through a field. Attributes chosen for the identification of flat spots were 1. instantaneous frequency; 2. thin bed indicator; 3. acceleration of phase; 4. dominant frequency

The profile in Figure 6 displays a SOM classification where the colors represent individual neurons with a wiggle-trace variable area overlay of the conventional amplitude data. This play relates to a series of thin strandline sand deposits. These sands are located in a very weak trough on the conventional amplitude data and essentially have no amplitude expression. The SOM classification employed seven seismic attributes which were determined from the PCA analysis. A 10x10 matrix of neurons or 100 neurons were employed for this SOM classification. The downdip well produced gas from a 6’ thick sand that confirmed the anomaly associated with a dark brown neuron from the SOM analysis. The inset for this sand indicates that the SOM analysis has identified this thin sand down to a single sample size which is 1 ms (5’) for this data. The updip well on the profile in Figure 6 shows a thin oil sand (~6’ thick) that is associated with a lighter brown neuron with another possible strandline sand slightly downdip. This SOM classification defines very thin beds and employs several instantaneous seismic attributes that are measuring energy in time and space outside the realm of conventional amplitude data.

SOM results showing thin beds

Figure 6: SOM results showing thin beds in a strandline setting

Geology Defined

The implementation of a multi-attribute machine-learning analysis is not restricted to any geologic environment or setting. SOM classifications have been employed successfully both onshore and offshore, in hard rocks and soft rocks, in shales, sands, and carbonates, and as demonstrated above, for DHIs and thin beds. The major limitations are the seismic attributes selected and their inherent data quality. SOM is a non-linear classifier and takes advantage of finely sampled data and is not burdened by typical amplitude resolution limitations. This machine learning seismic interpretation approach has been very successful in distilling numerous attributes to identify geologic objectives and has provided the interpreter with a methodology to deal with Big Data.

Rocky RodenROCKY 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.
Deborah SacreyDEBORAH 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.

 

Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-Organizing Neural Nets for Automatic Anomaly Identification

By Tom Smith, Geophysical Insights and Sven Treitel, TriDekon

Self-organizing maps are a practical way to identify natural clusters in multi-attribute seismic data. Curvature measure identifies neurons that have found natural clusters from those that have not. Harvesting is a methodology for measuring consistency and delivering the most consistent classification. Those portions of the classification with low probability are an indicator of multi-attribute anomalies which warrant further investigation.

Introduction

Over the past several years, the growth in seismic data volumes has multiplied many times. Often a prospect is evaluated with a primary 3D survey along with 5 to 25 attributes which serve both general and unique purposes. These are well laid out by Chopra and Marfurt, 2007. Self-organizing maps (Kohonen, 2001), or SOM for short, are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data in an orderly fashion.

Multi-attributes and natural clusters

We organize a 3D seismic survey data volume regularly sampled in location X, Y and time T (or depth estimate Z). Each survey sample is represented by a number of attributes, f1, f2, …, fF. An individual sample is represented in bold as a vector with four subscripts. Together, they represent the survey space k , so the set of samples

with indices c, d, e and f represent time, trace, line number and attribute number, respectively. It is convenient to represent a sample fixed in space as a vector of F attributes in attribute space. Let this set of attribute samples {x1, x2,…xi,…, xI} be taken from k and range from 1 to I. The layout of survey space representing a 3D attribute space is illustrated in Figure 1. An attribute sample, marked as resting on the top of pins, consists of a vector of three attribute values at a fixed location.

3D Surveys

Figure 1: Three 3D surveys are bricked together in a survey space comprising 3 attributes. Marked in the survey labeled Attribute 1 is a blue-green data sample. Connected to it are samples in the other attributes at the same position in the survey.

Attribute Space

Figure 2: An example attribute space is marked here as Amplitude, Semblance and Frequency. The data sample in Figure 1 is located in the cluster of other blue-green data samples. Also shown are natural clusters of red samples (lower) and white samples (upper).

The sample of Figure 1 resides in attribute space as shown in Figure 2. Included in the illustration are other samples with similar properties. These natural clusters are regions of higher density which can constitute various seismic events with varying attribute characteristics. A natural cluster would register as a maximum in a probability distribution function. However, a large number of attributes entails a histogram of impractically high dimensionality.

Self-Organizing Map (SOM)

A SOM neuron lies in attribute space alongside the data samples. Therefore, a neuron is also an F-dimensional vector noted here as w in bold. The neuron w lies in a topology j called the neuron space. At this point in the discussion, the topology is unspecified so use a single subscript t as a place marker for any number of dimensions. Whereas data samples remain fixed in attribute space,

neurons are allowed to move freely in attribute space. They are then progressively drawn toward the data samples.

A neuron “learns” by adjusting its position within the attribute space as it is drawn toward nearby data points. Then let us define a self-organizing neural network as a collection of neurons {w1, w2,…, wi,…, wJ} with an index ranging from 1 through J. The neural network learns as its neurons adjust to natural clusters in attribute space. In general the problem is to discover and identify an unknown number of natural clusters distributed in attribute space, given the following information: I data samples in survey space; F attributes in attribute space; and J neurons in neuron space. The SOM was invented by T. Kohonen and discussed in Kohonen, T., 2001. It addresses such issues as a classic problem in statistical classification.

Neural Network Analysis - Attribute Spacep class=”wp-caption-text”>Figure 3:The winning neuron is the one which is closest to the selected data point.

In Figure 3 we place three neurons at arbitrary locations in attribute space from Figure 2. A simple process of learning proceeds as follows. Given the first sample, one computes the distance from the data sample to each of the 3 neurons and selects the closest one. We choose the Euclidean distance as our measure of distance.

The winning neuron with subscript k is defined

where j ranges over all neurons. In Figure 3, the neuron on the left is identified as the winning neuron. The winning neuron advances toward the data sample along a vector which is a fraction of the distance from the data sample. Then the second sample is selected and the process is repeated. In this example, the neuron marked as the winning neuron may end up in the leftmost cluster of the Figure. The lowermost neuron may end up near the center of the lowermost cluster on the right and the third neuron might end up in the cluster in the upper right of the Figure. This type of learning is called competitive because only the winning neuron moves toward the data.

A key point to note is that after one complete pass through the data samples, although not every neuron may have moved toward any data points, nevertheless every sample has one and only one winning neuron. A complete pass through the data is called an epoch. Many epochs may be required before the neurons have completed their clustering task.

The process just described is the basis of the SOM. A SOM neuron adjusts itself by the following recursion.

where wj(n) is the attribute position of neuron j at time step n and k is the winning neuron number. The recursion proceeds from time step n to step n + 1. The update is in the direction toward x along the “error” direction xwj(n). The amount of displacement is controlled by the learning control parameters, η and h, which are both scalars.

The η term grows smaller with each time step, so large neuron adjustments during early epochs smoothly taper to smaller adjustments later.

The h term embodies still another type of learning and which is also part of the SOM learning process.

Here d is the Euclidean distance between neurons in the neuron space introduced in equation (2)

And

Neuron Network Topologies

Figure 4:An assortment of neuron network topologies is shown here. Let the neuron position be r(p) with the distance between a neuron and its nearest neighbor set to 1 unit.

In equation (7), y is a positional vector in the neuron topology. Several options for neuron topology in neuron space are shown in Figure 4.

From equation (6) we observe that not only is the winning neuron moving toward a data point, other neurons around the winning neuron are moving as well. These neurons constitute the winning neuron neighborhood.

In the hexagonal topology of Figure 4, note that the marked neuron has 6 nearest neighbors. If this neuron is selected as a winning neuron, equations (6) and (7) indicate that the 6 nearest neurons move toward the data sample by a like amount. More distant neurons from the winning neuron move a lesser amount.

Neighborhoods of neuron movement constitute cooperative learning. For a 2D neuron space, hexagonal topology offers the maximum number of similarly distant neurons. Here we have chosen a hexagonal neural network because it maximizes cooperative learning in 2D. The SOM embodies both competitive and cooperative learning rules (Haykin, 2009).

Curvature measure

To search for natural clusters and to avoid the curse of dimensionality (Bishop, 2007), we allow the SOM to find them for us. However, there is no assurance that at the end of such a SOM analysis the neurons have come to rest at or near the centers of natural clusters. To address this issue, we turn to the simple definition of a maximum. A natural cluster is by definition a denser region of attribute space. It is identified as a maximum in a probability distribution function through analysis of a histogram. In 1D the histogram has a maximum; in 2D the histogram is a maximum in 2 orthogonal directions and so on.

In F-dimensional attribute space, a natural cluster is revealed by a peak in the probability distribution function of all F attributes. Recall that at the end of an epoch there is a one-to-one relationship between a data sample and its winning neuron. That implies that to every winning neuron there corresponds a set of one or more data samples.

Then for some winning neuron with index k, there exists a set of data samples x for which

and where k N include the samples drawn from k for that winning neuron. Some winning neurons in equation (9) have a small collection of x samples while others will have a larger collection.

In the set x for a winning neuron wk, for each of the f attributes [1 to F] we can determine a histogram. If there is a peak in the histogram we have found a higher magnitude in the probability distribution in that particular dimension and so score this attribute as a success. We count all attributes in this way (1 for success or 0 for failure) and divide the result by the number of attributes. Curvature measures density and lies in the range [0,1]. Each neuron and each attribute has a curvature measure.

Harvesting and consistency

A harvesting process consists of three steps. First, unsupervised neural network analyses are run on independent sets of data that have been drawn from a 2D or

3D survey. A rule is then used to decide which candidate is the best solution. Finally, the set of neurons of the best solution are used to classify the entire survey.

We have conducted a series of SOM analysis and classification steps for the Stratton Field 3D Seismic Survey (provided to us by courtesy of the Bureau of Economic Geology and the University of Texas). A time window of 1.2 to 1.8s was selected for SOM analysis with an 8 x 8 hexagonal network and 100 learning epochs. We measured performance by standard deviation of error between data samples and their winning neurons. Standard deviation error reduction was typically 35%. A separate SOM analysis was conducted on each of the 100 lines in order to assess consistency of results. The SOM results were highly consistent with a variation of final standard deviation of error of only 1.5% of the mean. The rule used here is to select the SOM solution for the line which best fits its data through smallest error of fit.

BEG Line 53 Full Classification - CopyFigure 5: SOM classification of Line 53. Timing lines at 1.3 and 1.6s are marked by arrows.

Honeycomb DefaultFigure 6: Hexagonal colorbar for Figure 5.

The SOM results form a new attribute volume of winning neuron classifications. Every sample in this new volume is the index of the winning neuron number for a given data sample. The SOM analysis was derived from 50 attributes which included basic trace attributes, geometric attributes and spectral decompositions. A solution line is shown in Figure 5.

First observe that the SOM results more or less track geology as shown by flat reflections near 1.3s. At the well are shown SP and GR well log curves with lithologic shading, formation top picks as well as a synthetic seismogram (white WTVA trace along the borehole). A second reflector above 1.6s is a second key marker. Also notice the green patches near 1.5s. These were identified as patches with lateral consistency. We have no geologic interpretation at this time. In Figure 6 we show the colorbar patterned to the neuron topology and colored to assist in identification of regions of neuron clusters.

salt_dome_preSOM_analysis

Figure 7:Vertical section through a 3D survey of a Gulf of Mexico
salt dome.

SOM Classification

Figure 8: SOM classification with p > .1 corresponding to the same
slice as Figure 7.

Time slice

Figure 9: Time slice for Figure 8.

Curvature measure (CM) of the neurons fell in the range .72 to .9 except for one neuron which had a curvature measure of .26. It was found that this winning neuron resulted from only 8 samples while others had 17 to 139.

We have investigated the CM attribute measure and found that there were 3 poor performing attributes (CM < .2); 4 attributes which we consider questionable (CM ≈ .5) and 43 strong attributes (CM > .8).

Automatic anomaly identification

Equation 9 is the basis on which we classify the quality of classification samples. For any winning neuron, we select samples with probability p which exceed threshold pmin

based on the distance between the winning neuron and its samples, shown here as equation 10. Those samples that are near their winning neuron have higher probability.
From Figure 7, the classification of Figure 8 results from a SOM analysis with an 8 x 8 neuron network, designed on the basis of F=13 attributes and 100 epochs. Those samples whose probabilities lie below pmin are anomalous and assigned a white color. The rest of the classification uses the colorbar of Figure 6.

The red lines of Figures 8 and 9 register their views so the white area anomaly on the right side of the salt in Figure 8 has an areal extent that appears to be bounded by the salt in Figure 9. We are not suggesting that all anomalies are of geologic interest. However, those of sufficient size are worthy of investigation for geologic merit.

Conclusion

This presentation investigates the areas within a 3D survey where multiple attributes are of unusual character. Self-organizing maps assist with an automatic identification process. While these results are encouraging, it is readily apparent that additional investigations must be made into appropriate learning controls. Various structural and stratigraphic questions which might be posed to SOM analysis will require careful selection of appropriate attributes. It is also clear that calibration of SOM analyses with borehole information offers an attractive area of investigation.

Acknowledgment

Tury Taner was our inspiration and pioneer in this area.

THOMAS A. SMITH received BS and MS 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 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.

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 RodenROCKY 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 SacreyDEBORAH 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.

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 RodenROCKY 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.