Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data
Presented by Deborah Sacrey, Owner of Auburn Energy
Challenges addressed in this webinar include:
Reducing risk in drilling marginal or dry holes
Interpretation of thin bedded reservoirs far below conventional seismic tuning
How to better understand reservoir characteristics
Interpretation of reservoirs in deep, pressured environments
Using the classification process to help with correlations in difficult stratigraphic or structural environments
The webinar is open to those interested in learning more about how the application of machine learning is key to seismic interpretation.
Deborah Sacrey is a geologist/geophysicist with 41 years of oil and gas exploration experience in the Texas, Louisiana Gulf Coast, and Mid-Continent areas of the US. Deborah specializes in 2D and 3D interpretation for clients in the US and internationally.
She received her degree in Geology from the University of Oklahoma in 1976 and began her career with Gulf Oil in Oklahoma City. She started Auburn Energy in 1990 and built her first geophysical workstation using the Kingdom software in 1996. Deborah then worked closely with SMT (now part of IHS) for 18 years developing and testing Kingdom. For the past eight years, she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience community, guided by Dr. Tom Smith, founder of SMT. Deborah has become an expert in the use of the Paradise® software and has over five discoveries for clients using the technology.
Deborah is very active in the geological community. She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is currently the incoming President of the Gulf Coast Association of Geological Societies (GCAGS) and is a member of the GCAGS representation on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She is active in the Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).
In March of 2018, a study was conducted of the Niobrara using machine learning for multi-attribute analysis. Geophysical Insights obtained 100 square miles of seismic data covering the Niobrara in northeast Colorado from the Geophysical Pursuit and Geokinetics multi-client seismic library. Using Paradise®, a multi-attribute learning application, geoscientists generated and classified seismic volumes, the results revealing thin beds below seismic tuning and anomaly resolution not practical with traditional interpretation methods. In total, the results of that classification demonstrated dramatically improved stratigraphic resolution and anomaly isolation within the Niobrara Formation and associated reservoir and source rock units.
Figure 1: Outline of Niobrara Phase 5. 100 square miles within this survey were selected for analysis.
Seismic Volume Classification Results
The result set, or classification in multi-colors, shows the continuity of facies and the continuity/discontinuity of anomalies in the greater Niobrara section. Each voxel represents 1 millisecond in the volume or approximately 15 feet. The white dashed correlation line shows anomalies on the B bench and the brackets indicate anomalies at the Codell level. Two horizontal boreholes are shown, however, neither seems an optimal penetration. The results demonstrate that machine learning in Paradise enables a sample-based thin bed analysis.
Figure 2a: Classification based on eight Instantaneous attributes.
Figure 2b: The original amplitude data.
Figure 3:Self-Organizing Map (SOM) classification with low probability (<10%) anomalies (white) in the greater Niobrara section. The results may indicate concentrations of hydrocarbons in organic-rich shales. Anomalies also may show migration up section along faults from the Niobrara into the Sharon Springs. Niobrara B anomalies are best developed in elevated areas. Additional well data can corroborate anomaly interpretations.
Figure 4: Interactive 2D Colormaps use transparency (left and see inset) to highlight and extract anomalous behavior or particular facies. Note that the wellbore intersects a fault (white arrow) and misses the bracketed anomaly.
Principal Component Analysis (PCA) was used to identify and quantify the key attributes in the seismic volumes. The SOM process was then applied on these attributes to learn and classify the data. The neural topology, shown in the Paradise 2D Colormap, establishes the number of classes in the resulting seismic volume. The classification volumes show geologic features and anomalies that can aid in well location and development planning.
Figure 5: Map of structural features identified in this Niobrara volume. Near vertical graben-style faulting predominates in the upper Niobrara..
The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir characterization information than is available from traditional interpretation methods. Two consequences of applying machine learning with several appropriately chosen seismic attributes include the discrimination of thin beds that are below conventional seismic tuning and the identification of Direct Hydrocarbon Indicators (DHIs). These capabilities enable a higher resolution interpretation of reservoirs and stratigraphy. An explanation of the machine learning methodology and its application to thin beds and DHIs is described briefly in this paper.
Machine Learning Methodology
Taking advantage of today’s computing technology, visualization techniques, and an understanding of machine learning on seismic data, Self-Organizing Maps (SOMs) (Kohonen, 2001), efficiently distills multiple seismic attributes into classification and probability volumes (Smith and Taner, 2010). When applied on a multi-attribute seismic sample basis, SOM is a powerful nonlinear cluster analysis and pattern recognition machine learning approach that helps interpreters identify patterns in their data that can relate to inherent geologic characteristics and different aspects of their data. SOM analysis, which is an unsupervised neural network application, when properly applied has been able to reveal both thin beds and DHIs in appropriate geologic settings. Figure 1 illustrates a single seismic amplitude trace and seven different seismic attributes computed from the amplitude data. All of these traces are displayed in a wiggle-trace variable area format. This display represents 100 ms vertically and each horizontal scale line represents a sample (4 ms). Each of these attributes are at different scales and in some cases vastly different scales. It is evident from this Figure that each of the attributes measures a different component of the total acoustic energy at every sample. SOM identifies clusters where different combinations of attributes congregate to reveal significant information about the natural groupings that are difficult to view any other way. The self-organizing property of SOM identifies and classifies natural clusters.
The SOM machine learning process is graphically presented in Figure 2. How large an area to select is dependent on the size of the geologic feature to be interpreted. For thin beds and DHIs, usually, a relatively thin zone of 50-250 ms around the anomalies is selected over a reasonable areal extent to provide sufficient data points for the SOM analysis. The selection of the seismic attributes is usually based on principal component analysis (PCA) and an interpreter’s knowledge of appropriate attributes for the area. Experience with SOM analysis has indicated that six to ten instantaneous seismic attributes are usually selected for thin beds and DHIs, depending on the geologic setting and data quality. In Figure 2 ten attributes are employed and all the data points from every sample from these attributes in the zone to be analyzed are placed in attribute space where they are normalized to put on the same scale. The SOM process employs cooperative and competitive learning techniques to identify the natural patterns or clusters in the data. Each pattern is identified by a neuron that sorts through the data in attribute space during the SOM training process of self-organization. In Figure 2 after training is completed, 64 winning neurons have identified 64 patterns or clusters in attribute space with an 8X8 neuron network. The SOM results are nonlinearly mapped back to a neuron topology map (2D colormap) where interpreters can select the winning neurons from the 2D colormap and identify in the 3D volume where the patterns and clusters occur for thin beds and DHIs.
Figure 1. Wiggle-trace variable area display format of a 100 ms window of seismic data with the amplitude trace and seven associated traces of attributes. Each attribute trace is at a different scale and each horizontal scale line is separated by the sample interval of 4 ms. If all these traces were employed in a SOM analysis, each red circle along a timing line indicates samples that would be input as a multi-attribute sample.
Figure 2. Display of SOM workflow where selected volume and data points from ten associated seismic attributes are input into Attribute Space. These data points are scaled and analyzed by the SOM process to identify 64 patterns by associated winning neurons. These neurons are nonlinearly mapped back to a 2D colormap where interpreters identify neurons and visually view the location of the patterns in the 3D survey.
In addition to the resultant classification volume, a probability volume is also generated which is a measure of the Euclidean distance from a data point to its associated winning neuron in attribute space (Roden et al., 2015). The winning neuron identifies a specific cluster or pattern. It has been discovered that a low classification probability corresponds to areas that are quite anomalous as opposed to high probability zones that relate to regional and common events in the data. Low probability anomalies identified by the SOM process are quite often associated with DHI characteristics.
Discriminating Thin Beds
The conventionally accepted definition of the tuning thickness (vertical resolution) is a bed that is ¼ wavelength in thickness, for which reflections from its upper and lower surfaces interfere and interference is constructive where the interface contrasts are of opposite polarity, often resulting in an exceptionally strong reflection (Sheriff, 2002). Several authors have described approaches to measure below tuning or thin beds usually employing various scaling techniques of amplitude or inversion data (MeckelandNath, 1977; Neidell and Poggiagliolmi, 1977; Schramm et al., 1977; Brown et al., 1986; and Connolly, 2007). However, these various techniques to determine thin beds have limitations and require assumptions that may not be met consistently (Simm, 2009). The application of SOM machine learning utilizing a multi-attribute classification has enabled the identification of thin beds and stratigraphy below tuning in a systematic and consistent manner as represented in the following case study.
The Eagle Ford Shale is a well-known unconventional resource play in Texas. Operators in this play must account for changing stratigraphy and facies to properly locate horizontal wells for optimum perforation intervals. The Eagle Ford stratigraphy is often associated with thin beds and facies well below conventional seismic resolution that change both vertically and laterally. This Eagle Ford case study contains 216 mi² (560 km²) of enhanced 3D PSTM data processed at a 2 ms sample interval. Conventional vertical resolution (tuning thickness) is 100-150 feet (30-45 meters) depending on the location within the Eagle Ford unit. In this study, over 300 wells were available for correlation including 23 type logs, 249 horizontal borehole montages, 9 vertical calibration wells with tops, logs, and time-depth corrections. Also available were five cores for which X-ray diffraction and saturation information was available. Well information was incorporated to corroborate the SOM results.
Figure 3. Resolution comparison between conventional seismic display and a Paradise® multi-attribute Self Organizing Map (SOM) classification: (a) Seismic amplitude profile through the 6 Well; and (b) the SOM results of the same profile identifying the Eagle Ford Group that comprises 26 sample based neuron clusters which are calibrated to facies and systems tracts. The 2D colormap displays the associated winning neuron cluster colors.
Ten instantaneous seismic attributes prominent in a Principal Component Analysis were selected for SOM. SOM training was conducted on a set of trial harvest lines and the successful line was then used to classify the entire survey. Figure 3a is a seismic amplitude line in color raster and wiggle-trace variable area formats across the location of Well 6 (V and 1H). The Figure shows the Austin Chalk-Eagle Ford Group-Buda stratigraphic interval represented by roughly 2.5 peak/trough cycles of a seismic trace. In the Eagle Ford, the amplitude appears continuous, yet any details are obscured because of the resolution limitations of the amplitude data where conventional tuning is 100-150 feet (30-45 meters). Figure 3b displays the equivalent line showing results of the SOM analysis. Sixty-four neurons were employed to identify 64 patterns in the data as seen on the associated 2D colormap. A seismic interval from 10 ms below the Buda to 100 ms above the Buda or near the top of the Austin Chalk was chosen for the SOM analysis. Shown clearly comparing Figures 3a and 3b is the resolution improvement provided by the SOM analysis over the seismic amplitude. The results reveal non-layer cake facies bands that include details in the Eagle Ford’s basal clay-rich shale, high resistivity and low resistivity Eagle Ford Shale objectives, the Eagle Ford ash, and the Upper Eagle Ford marl, which are overlain disconformably by the Austin Chalk (disconformity is a break in a sedimentary sequence that does not involve a difference in bedding angles). This interval of roughly 28 ms (or 14 samples) amounts to some 26 of the 64 SOM winning neurons to illuminate the various systems tracts within the Eagle Ford Group for this survey.
Adjacent to the SOM results at Well 6 are similar results at a nearby well. Figure 4a displays a zoomed-in vertical line display of the SOM results through Well 8(V) with the winning neurons identified. Figure 4b denotes the associated well log curves from well 8 and the correlative neuron associations. Winning neurons 63 and 64 are associated with the low resistivity Eagle Ford shale unit and neurons 53, 54, and 60 denote the high resistivity and more desirable Eagle Ford unit. The expanded display of the SOM results in Figure 4a denotes a low resistivity gold thin bed that is identified by a single neuron (#55) and is only one sample thick (2 ms). Shown here is clear evidence of consistent results between Wells 6 and 8 that lends itself to stratigraphic facies interpretation.
Figure 4. View of SOM results correlated with Well 8 logs: (a) Expanded view of SOM profile through Well 8; and (b) Well 8 logs with Eagle Ford units correlated with the SOM results of (a). Note that the resolution is down to the sample level of 10-12 feet and is illustrated by winning neuron 55 in (a). The dashed white line in (a) represents the top of the Eagle Ford and base of the Austin Chalk.
Over this survey area, 16 different winning neurons represent the various facies present in the Eagle Ford Shale over a 14 ms window (70-84 feet/21-26 meters). The facies of the entire Eagle Ford Group which includes the Basal Clay shale, Eagle Ford Shale, and Eagle Ford Marl, are defined by 26 different winning neurons over 28 ms (210-252 feet/64-77 meters). Individual facies units are as thin as one sample interval of 2 ms (10-12 feet/3-4 meters). These results of a single SOM classification are corroborated at multiple wells across the survey area.
Revealing Direct Hydrocarbon Indicators (DHIs)
The accurate interpretation of seismic DHI characteristics has proven to significantly improve the drilling success rates in the appropriate geologic setting where there is also adequate seismic data quality (Roden et al., 2012; Rudolph and Goulding, 2017). Specifically, DHIs are anomalies due to the presence of hydrocarbons induced by changes in rock physics properties (P and S wave velocities, and density). Typically, anomalies stand out as the difference in a hydrocarbon-filled reservoir in relation to the encasing rock or the brine portion of the reservoir. DHI characteristics are usually associated with anomalous seismic responses in a trapping configuration, such as structural traps, stratigraphic traps, or a combination of both. These include bright spots, flat spots, amplitude conformance to structure, etc. DHI anomalies are often compared to models, similar events, background trends, proven productive anomalies, and geologic features. DHI indicators can also be located below presumed trapped hydrocarbons where shadow zones or velocity pull-down effects may be present. These DHI effects can even be present and dispersed in the sediment column in the form of gas chimneys or clouds.
As described above there are numerous DHI characteristics and all of which should be evaluated in a consistent and systematic approach for any prospect or project. Forrest et al. (2010) and Roden et al. (2012) have identified the top four DHI characteristics, as related to commercially successful wells in a Class 3 AVO setting, based on an industry-wide database of almost 300 wells. These DHI characteristics include:
Anomaly conformance to structure
Phase or character change at the downdip edge of the anomaly
Anomaly consistency in the mapped target area
Figure 5. From the top of the producing reservoir: a) time structure map in contours with an amplitude overlay in color and b) SOM classification with low probability less than 1% denoted by white areas. The yellow line in b) represents the downdip edge of the high amplitude zone picked from a).
These DHI characteristics will be identified in the following case study by a multi-attribute SOM analysis. The case study is an offshore oil/gas field in 470 feet (145 meters) of water on the Louisiana continental shelf of the Gulf of Mexico. The field 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 (30 meters) thick and contains oil and gas. The hydrocarbon filled sandstone reservoir has low impedance compared to the encasing shales, indicative of a Class 3 AVO environment. The SOM analyzed a 170 ms window surrounding the reservoir. Applying the (SOM) multi-attribute analysis, a group of eight seismic attributes was selected based on Principal Component Analysis that would best expose Direct Hydrocarbon Indicators (DHIs). A neuron network of 5X5 (25 neurons) was employed. Figure 5a displays a time structure map as denoted by the contours with an amplitude overlay (color) from the mapped top of the reservoir in this field. The horizon at the top of the reservoir was picked on a trough (low impedance) on zero phase seismic data (SEG normal polarity). Figure 5a indicates there is a relatively good amplitude conformance to structure based on the amplitude as noted by the general agreement of the time contour and the red/green amplitude break (see color bar insert). Figure 5b is a display of classification probability from the SOM analysis at the top of the reservoir at the same scale as Figure 5a. This indicates that the top of the reservoir exhibits an anomalous response from the SOM analysis. Ordinary classifications such as those of green and yellow-greens are shown around the reservoir (see colormap insert). However, within the reservoir and in several other areas, anomalous classification of low probability below 1% are colored white. In comparing Figure 5a and 5b it is apparent that the low probability area corresponds closely to the amplitude conformance to structure as denoted by the yellow outline in Figure 5b. This confirms the identification of the productive area with low probability and proves the efficacy of this SOM approach. The consistency of the low probability SOM response in the field is another positive DHI indicator. In fact, the probabilities as low as .01% still produce a consistent response over the field indicating how significant evaluating low probability anomalies is critical in the interpretation of DHI characteristics.
Figure 6. North-south vertical profile 9411 through the middle of the field: a) stacked seismic amplitude display with the field location marked, b) SOM classification with 25 neurons (5X5) indicated by the 2D colormap over a 170 ms window, and c) three neurons highlighting the reservoir above the oil/water and gas/oil contacts and the hydrocarbon contacts (flat spots) as marked by winning neurons 15, 20, and 25. The expanded insets in the right column denote the details from the SOM results at the downdip edge of the field (see dashed black boxes on left).
This field contains an oil phase with a gas cap and before drilling, there were hints of possible flat spots suggesting hydrocarbon contacts on the seismic data, but the evidence was inconsistent and not definitive. Figure 6 displays a north-south vertical inline seismic profile through the middle of the field with its location denoted in Figure 5. Figure 6a exhibits the initial stacked amplitude data with the location of the field marked. Figure 6b denotes the SOM analysis results of this same vertical inline which incorporates the eight instantaneous attributes listed with the 5X5 neuron matrix in Figure 5. The associated 2D colormap in Figure 6b denotes the 25 natural patterns or clusters identified from the SOM process (see colormap insert). It is apparent in Figure 6b that the reservoir and portions of both the gas/oil contact and the oil/water contact are easily identified as opposed to the conventional seismic data shown in Figure 6a. This is more easily seen in Figure 6c where the 2D colormap indicates that the neurons highlighted in gray (winning neurons 20 and 25) are defining the hydrocarbon-bearing portions of the reservoir above the hydrocarbon contacts and the flat spots interpreted as hydrocarbon contacts are designated by the rust-colored winning neuron 15. The location of the reservoir and hydrocarbon contacts are corroborated by well control (not shown). Note that the southern edge of the reservoir is revealed in the enlargements in the column on the right (from the dashed black box on left). A character change at the downdip edge of the anomaly where the oil contact thins out is easily identified compared to the line of amplitude change. Downdip of the field is another undrilled anomaly defined by the SOM analysis that exhibits similar DHI characteristics identified by the same neurons.
A seismic multi-attribute analysis utilizing Self-Organizing-Maps is a machine learning approach that distills information from numerous attributes on a sample-by-sample basis to provide a more accurate assessment of thin beds and DHI characteristics than conventional methods. We have shown in these cases that the results readily correlate to conventional well log data. The SOM classification process takes advantage of natural patterns in multiple seismic attributes space that is not restricted to the resolution limits of conventional amplitude data. This process enables interpreters to produce higher resolution interpretations of reservoirs and stratigraphy. Another advantage of a SOM analysis is the generation of classification probability where low probability anomalies are often associated with DHIs. SOM analyses in appropriate geologic settings can improve confidence in interpreting DHI characteristics and more clearly define reservoir edges and thin bed components.
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.
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.
Patricia Santogrossi is a Consultant to Geophysical Insights, producer of the Paradise multi-attribute analysis software platform. Formerly, she was a Leading Reservoir Geoscientist and Non-operated Projects Manager with Statoil USA E & P. In this role Ms. Santogrossi was engaged for nearly nine years in Gulf of Mexico business development, corporate integration, prospect maturation, and multiple appraisal projects in the deep and ultra-deepwater Gulf of Mexico. Ms. Santogrossi has previously worked with domestic and international Shell Companies, Marathon Oil Company, and Arco/Vastar Resources in research, exploration, leasehold and field appraisal as well as staff development. She has also been Chief Geologist for Chroma Energy, who possessed proprietary 3D voxel multi-attribute visualization technology, and for Knowledge Reservoir, a reservoir characterization and simulation firm that specialized in Deepwater project evaluations. She is a longtime member of SEPM, AAPG, GCSSEPM, HGS and SEG and has held various elected and appointed positions in many industry organizations. Ms. Santogrossi holds an M.S. in Geology from the University of Illinois, Champaign-Urbana.
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.
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.
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 thedisplayed 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.
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.
Figure 4: SOM analysis interpretation of channel feature with 2D color map
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.
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.
Figure 6: SOM results showing thin beds in a strandline setting
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 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 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.