Watch videos of Presentations from SEG 2017
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016

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Interpretation Below Seismic Tuning Using Multi-attribute Analysis

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Interpretation Below Seismic Tuning Using Multi-attribute Analysis

Earlier this week, Rocky Roden's presentation on "Interpreting Below Seismic Tuning Using Multi-Attribute Analysis" garnered quite the buzz.  Geosicentists across the globe learned of new processes in visualizing thin beds and facies with machine learning technology.  In the presentation, Mr. Roden covered topics such as: 

  • Rayleigh's Criterion and the classical basis of seismic tuning

  • Work by Brown et al. (1984,1986) and Connolly (2007) on thin bed calculations

  • Phenomena at or below tuning

  • Applications of attributes to the wedge model

  • How multi-attribute classification techniques that use machine learning enable visualization below tuning

  • Case studies in the application of this new technique in conventional and unconventional geologic settings


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.

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Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

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Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Inversion versus 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, Senior Geoscience Consultant, 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.

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The Value of Instantaneous Attributes

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The Value of Instantaneous Attributes

Wheeler Diagram - Instantaneous Attribute for Multi-attribute analysis

Our industry may have heard of instantaneous attributes for the first time in 1979 with the publication of Taner et al’s paper on “Complex Trace Analysis”1 in Geophysics.  This author first became aware of them when in 1982 a little blue booklet, published by the same authors in 1981 in association with Seiscom Delta, came to my attention. It seemed that the suggested use for these attributes was for structural analysis. As an early seismic stratigrapher specializing in lateral prediction at Shell’s Bellaire Research Center, I acquired the programs to run as PAL jobs on our mainframe computer. Color plots of instantaneous phase, envelope, and instantaneous frequency with my own proprietary colorbars helped to look at the Atlantic Margin in a whole new way.
Often called instantaneous attributes, their fundamental distinction is of having a value at every 4 or 2ms sample which enhances continuity, makes discontinuities more apparent, and need not include the “cloaking” and distortion of amplitude.
The culmination at Shell of my work came with it very effective and most confidential application, in the late 80s earliest 90s, at Mars field to solve a drilling problem, reclassify a regional structure, and, most importantly to rewrite the book on the architecture and extents of multiple key reservoirs. Earlier work (’86-’89) on key applications in Brazil had literally caused a rewrite of the stratigraphic lexicon there and brought attention to the importance of their application.
I continued to use the technology ever since on all sorts of platforms and ,until 1999, when Clemenceau and Colbert of Amoco published one instantaneous phase section over Ram Powell field, had never seen another stratigraphic application. 
A second go round with multiple attributes in the period from ’98 -2002, was unsupervised but not neural network. It rather was based on technology that was used to map the human genome and as such was limited to the use of only 4 attributes at a time.

Current Case studies
Up to 16 Instantaneous attributes have been generated in a convenient interpretation software for which it is important to know that the Real Part amplitude data is used as the parent volume. A set of “instantaneous attributes” can also be created in the Paradise software (approx. 16) but these are calculated on the conventional RFC amplitude data.  This produces an inherent difference in some of the results, notably the Instantaneous Phase.
The intent of either approach is to deliver inherently smoother and higher frequency simultaneous multiple-attribute data to PCA and subsequently to and from the SOM classifier, a learning machine that organizes the samples into natural clusters that reveal geologic features and anomalies in the data. The use of machine learning tools represent a fundamental and dramatic step change in the science of Seismic interpretation (Tom Smith, pers. comm. April 2016). Using these tools, a relatively small group of instantaneous attributes, regardless of depositional environment,  seem to do an excellent job of resolving identifiable system and facies tracts in seismic data here with a sample based resolution of 10-12’ for 2ms data.
From the PCA analysis for the Eagle Ford for example, nine attributes were run in the SOM along with using the base survey data to “prune,” that is, remove null values from the data volume so as to not assign a neuron wastefully. A brief description of the five most commonly occurring and most independent instantaneous attributes in neuron clusters in the Eagle Ford is as follows: 

  • Instantaneous Phase (10.1%) which is useful for stratigraphic and structural continuity and discontinuity enhancement;
  • Normalized Amplitude (13.9%) aka Cosine of Instantaneous Phase which returns the energy distinctly from peaks versus troughs; 
  • Relative Acoustic Impedance (14.8%) helps to resolve geobodies;
  • Envelope or Total energy of the entire reflected waveform includes the Real Part (15.9%) or that which is equivalent to the base survey data that is measurable and the Imaginary Part (12.2%) which is not. 
  • Trace Envelope (5%) comprises a much smaller part of the data
  • In addition to the above, Thin Bed Indicator (3.5%), Instantaneous Frequency (2.5%), and Envelope 2nd Derivative (?), rounded out the nine suggested by the PCA. However, these three were less evident in the area investigated or were possibly in the background.

A check of the total relative and total independence of these attributes in the neuron clusters that characterize the facies of the Eagle Ford Group clastics and carbonates showed the following:

Only 3 neuron clusters (facies) out of 26 have just 3 prominent attributes (IP, NA, Real Part). Two of these are the uppermost facies of a carbonate stack #62 for the carbonate regressive margin and #6 at the top of the EF Marl. Both are high resistivity facies. The lowermost part of Geobody 2, N51, has the same order as the upper marl and is not calibrated. 

Only 1 neuron cluster or facies, N57, has 6 prominent attributes and its samples comprise only .3 of 1% of the total samples in the 110ms model.  Its 2nd, 4th, and 6th attributes are those that each comprise 5% or less of the data.  Of these, Trace Envelope, is nearly completely restricted to three neuron clusters (N57, 58, 59) that form the mid to downdip central core of Geobody 1 and N52 in Geobody 2.  The only other occurrence is in N24 at the base of the marl section updip which appears to be 95% carbonate in one XRD sample. All calibrated instances are High resistivity.

1 Taner, M. T., F. Koehler, and R. E. Sheriff, Complex seismic trace analysis, 1979, Geophysics, v.44, no. 6, p. 1041-1063, 16 Figs., 1 Table, June.

Seismic Attributes
Patricia Santogrossi is a geoscientist who has enjoyed 40 years in the oil business. She is currently 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. A longtime member of SEPM, AAPG, GCSSEPM, HGS and SEG, Ms. Santogrossi has held various elected and appointed positions in these industry organizations. She has recently begun her fourth three-year term as a representative to the AAPG House of Delegates from the Houston Geological Society (HGS). In addition, she has been invited to continue her role this fall on the University of Illinois’ Department of Geology Alumni Board. Ms. Santogrossi was born, raised, and educated in Illinois before she headed to Texas to work for Shell after she received her MS in Geology from the University of Illinois, Champaign-Urbana. Her other ‘foreign assignments’ have included New Orleans and London. She resides in Houston with her husband of twenty-four years, Joe Delasko.

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Machine Learning - The Next Generation Seismic Interpretation

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Machine Learning - The Next Generation Seismic Interpretation

Most people associate neural networks, big data and big number crunching as parts of a single paradigm for access to web information.  Articulate a query and wait for an answer.  But in this particular field and at this particular time, we “must” place neural networks and big data tools in the hands of seismic interpreters.  They are accustomed to working interactively with their data.  We do this not because they are a narrow-minded bunch who are unwilling to work with new tools unless it’s interactive, but because none of us know enough about the multi-attribute properties of seismic data – we don’t know the semantics of the words in these data – to let neural networks fly through seismic data unattended.  In other words, it ain’t English.  We don’t know the language yet.  

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Machine Learning and Truck Driving

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Machine Learning and Truck Driving

Einstein stated eloquently, “We can't solve problems by using the same kind of thinking we used when we created them.” Unlike us, machines don’t have cognitive biases and “experience baggage” when reading through data. They make their assessments based on pattern recognition and algorithms....

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