Geobody Interpretation Through Multi-Attribute Surveys, Natural Clusters and Machine Learning

By Tom Smith | June 2017

Summary

Multi-attribute seismic samples (even as entire attribute surveys), Principal Component Analysis (PCA), attribute selection lists, and natural clusters in attribute space are candidate inputs to machine learning engines that can operate on these data to train neural network topologies and generate autopicked geobodies. This paper sets out a unified mathematical framework for the process from seismic samples to geobodies. SOM is discussed in the context of inversion as a dimensionality-reducing classifier to deliver a winning neuron set. PCA is a means to more clearly illuminate features of a particular class of geologic geobodies. These principles are demonstrated with geobody autopicking below conventional thin bed resolution on a standard wedge model.

Introduction

Seismic attributes are now an integral component of nearly every 3D seismic interpretation.  Early development in seismic attributes is traced to Taner and Sheriff (1977).  Attributes have a variety of purposes for both general exploration and reservoir characterization, as laid out clearly by Chopra and Marfurt (2007).  Taner (2003) summarizes attribute mathematics with a discussion of usage.

Self-Organizing Maps (SOM) are a type of unsupervised neural networks that self-train in the sense that they obtain information directly from the data.  The SOM neural network is completely self-taught, which is in contrast to the perceptron and its various cousins undergo supervised training.  The winning neuron set that results from training then classifies the training samples to test itself by finding the nearest neuron to each training sample (winning neuron).  In addition, other data may be classified as well.  First discovered by Kohonen (1984), then advanced and expanded by its success in a number of areas (Kohonen, 2001; Laaksonen, 2011), SOM has become a part of several established neural network textbooks, namely Haykin (2009) and Dutta, Hart and Stork (2001).  Although the style of SOM discussed here has been used commercially for several years, only recently have results on conventional DHI plays been published (Roden, Smith and Sacrey, 2015).

Three Spaces

The concept of framing seismic attributes as multi-attribute seismic samples for SOM training and classification was presented by Taner, Treitel, and Smith (2009) in an SEG Workshop.  In that presentation, survey data and their computed attributes reside in survey space.  The neural network resides in neuron topology space.  These two meet in attribute space where neurons hunt for natural clusters and learn their characteristics.

Results were shown for 3D surveys over the venerable Stratton Field and a Gulf of Mexico salt dome.  The Stratton Field SOM results clearly demonstrated that there are continuous geobody events in the weak reflectivity zone between C38 and F11 events, some of which are well below seismic tuning thickness, that could be tied to conventional reflections and which correlated with wireline logs at the wells.  Studies of SOM machine learning of seismic models were presented by Smith and Taner (2010).  They showed how winning neurons distribute themselves in attribute space in proportion to the density of multi-attribute samples.  Finally, interpretation of SOM salt dome results found a low probability zone where multi-attribute samples of poor fit correlated with an apparent salt seal and DHI down-dip conformance (Smith and Treitel, 2010).

 

You must log in to read the rest of this article. Please log in or register as a user.

 


Survey Space to Attribute Space: Equation 1

Ordinary seismic samples of amplitude traces in a 3D survey may be described as an ordered Ordinary seismic samples set .  A multi-attribute survey is a “Super 3D Survey” constructed by combining a number of attribute surveys with the amplitude survey.  This adds another dimension to the set and another subscript, so the new set of samples including the additional attributes is Survey Space to Attribute Space Additional set.  These data may be thought of as separate surveys or equivalently separate samples within one survey.  Within a single survey, each sample is a multi-attribute vector.  This reduces the subscript by one count so the set of multi-attribute vectors Equation Equation 4 .

Next, a two-way mapping function may be defined that references the location of any sample in the 3D survey by single and triplet indices 3D survey by single and triplet indices Now the three survey coordinates may be gathered into a single index so the multi-attribute vector samples are also an unordered set in attribute space  The index map is a way to find a sample a sample in attribute space from survey space and vice versa.

Multi-attribute sample and set in attribute space: Survey Space to Attribute Space

A multi-attribute seismic sample is a column vector in an ordered set of three subscripts c,d,e representing sample index, trace index, and line index. Survey bins refer to indices d and e.  These samples may also be organized into an unordered set with subscript i.  They are members of an -dimensional real space.  The attribute data are normalized so in fact multi-attribute samples reside in scaled attribute space.

Natural clusters in attribute space: Natural clusters in attribute space

Just as there are reflecting horizons in survey space, there must be clusters of coherent energy in attribute space.  Random samples, which carry no information, are uniformly distributed in attribute space just as in survey space.  The set  of natural clusters in attribute space is unordered and contains m  members.  Here, the brackets [1, M]  indicate an index range.  The natural clusters may reside anywhere in attribute space, but attribute space is filled with multi-attribute samples, only some of which are meaningful natural clusters.  Natural clusters may be big or small, tightly packed or diffuse.  The rest of the samples are scattered throughout F-space.  Natural clusters are discovered in attribute space with learning machines imbued with simple training rules and aided by properties of their neural networks.

A single natural cluster: A single natural cluster

A natural A natural cluster 4 cluster may have elements in it.  Every natural cluster is expected to have a different number of multi-attribute samples associated with it.  Each element is taken from the pool of the set of all multi-attribute samples set of all multi-attribute samples 2  Every natural cluster may have a different number of multi-attribute samples associated with it so for any natural cluster, A natural cluster 4 then N(m).  Every natural cluster has its own unique properties described by the subset of samples set of all multi-attribute samples that are associated with it.  Some sample subsets associated with a winning neuron are small (“not so popular”) and some subsets are large (“very popular”).  The distribution of Euclidean distances may be tight (“packed”) or loose (“diffuse”).

Geobody sample and geobody set in survey space: Geobody sample and geobody set in survey space

For this presentation, a geobody G_b is defined as a contiguous region in survey space composed of elements which are identified by members g.  The members of a geobody are an ordered set  which registers with those coordinates of members of the multi-attribute seismic survey 3D seismic survey “brick” with members.

A geobody member is just an identification number (id), an integer .  Although the 3D seismic survey is a fully populated “brick” with members 3D seismic survey “brick” with members,  the geobody members geobody members register at certain contiguous locations register at certain contiguous locations, but not all of them.  The geobody  is an amorphous, but contiguous, “blob” within the “brick” of the 3D survey.  The coordinates of the geobody blob in the earth are coordinates of geobody blob in earth 2 where coordinates of geobody blob in earth By this, all the multi-attribute samples in the geobody may be found, given the id and three survey coordinates of a seed point.

A single geobody in survey space: A single geobody in survey space
Each geobody geobody math 2 is a set of  N geobody geobody members with the same id 2 members with the same id.  That is, there are N members in geobody members with the same id 2, so N(b).  The geobody members for this geobody are taken from the pool of all geobody samples, the set pool of all geobody samples Some geobodies are small and others large.  Some are tabular, some lenticular, some channels, faults, columns, etc.  So how are geobodies and natural clusters related?

A geobody is not a natural cluster: geobody members id numbers

This expression is short but sweet.  It says a lot.  On the left is the set of all B geobodies.  On the right is the set of M natural clusters.  The expression says that these two sets aren’t the same.  On the left, the geobody members are id numbers geobody members id numbers These are in survey space.  On the right, the natural clusters natural clusters attribute space These are in attribute space.  What this means is that geobodies are not directly revealed by natural clusters.  So, what is missing?

Interpretation is conducted in survey space.  Machine learning is conducted in attribute space.  Someone has to pick the list of attributes.  The attributes must be tailored to the geological question at hand.  And a good geological question is always the best starting point for any interpretation.

A natural cluster is an imaged geobody: A natural cluster is an imaged geobody
Here, a natural cluster C_m is defined as an unorganized set of two kinds of objects: a function I of a set of geobodies G_i and random noise N.  The number of geobodies is I and unspecified.  The function illumination function is an illumination function which places the geobodies in imaged geobody The illumination function is defined by the choice of attributes.  This is the attribute selection list.  The number of geobodies in a natural cluster C_m is zero or more, 0<i<I.  The geobodies are distributed throughout the 3D survey.

The natural cluster concentrates geobodies of similar illumination properties.  If there are no geobodies or there is no illumination with a particular attribute selection list, attribute selection list , so the set is only noise.  The attribute selection list is a critically import part of multi-attribute seismic interpretation.  The wrong attribute list may not illuminate any geobodies at all.

Geobody inversion from a math perspective: Geobody inversion from a math perspective

Multi-attribute seismic interpretation proceeds from the preceding equation in three parts.  First, as part of an inversion process, a natural cluster natural cluster  is statistically estimated by a machine learning classifier such as SOM  with a neural network topology.  See Chopra, Castagna and Potniaguie (2006) for a contrasting inversion methodology.  Secondly, SOM employs a simple training rule that a neuron nearest a selected training sample is declared the winner and the winning neuron advances toward the sample a small amount.  Neurons are trained by attraction to samples.  One complete pass through the training samples is called an epoch.  Other machine learning algorithm have other training rules to adapt to data.  Finally, SOM has a dimensionality reducing feature because information contained in natural clusters is transferred (imperfectly) to the winning neuron set in the finalized neural network topology through cooperative learning.  Neurons in winning neuron neighborhood topology move along with the winning neuron in attribute space.  SOM training is also dynamic in that the size of the neighborhood decreases with each training time step so that eventually the neighborhood shrinks so that all subsequent training steps are competitive.

Because natural cluster statistical estimate is a statistical estimate, let it be called the statistical estimate of the “signal” part of natural cluster.  The true geobody is independent of an illumination function.  The dimensionality reduction  dimensionality reduction associated with multi-attribute interpretation has a purpose of geobody recognition through identification, dimensionality reduction and classification.  In fact, in the chain of steps there is a mapping and un-mapping process with no guarantee that the geobody will be recovered: Geobody inversion chain of steps

However, the image function Equation 42  may be inappropriate to illuminate the geobody in F-space because of a poor choice of attributes.  So at best, the geobodies is illuminated by an imperfect set of attributes and detected by a classifier that is primitive.  The results often must be combined, edited and packaged into useful, interpreted geobody units, ready to be incorporated into an evolving geomodel on which the interpretation will rest.

Attribute Space Illumination

One fundamental aspect of machine learning is dimensionality reduction from attribute space because its dimensions are usually beyond our grasp.  The approach taken here is from the perspective of manifolds which are defined as spaces with the property of “mapability” where Euclidean coordinates may be safely employed within any local neighborhood (Haykin, 2009, p.437-442).

The manifold assumption is important because SOM learning is routinely conducted on multi-attribute samples in attribute space using Euclidean distances to move neurons during training.  One of the first concerns of dimensionality reduction is the potential to lose details in natural clusters.  In practice, it has been found that halving the original amplitude sample interval is advantageous, but further downsampling has not proven to be beneficial.  Infilling a natural cluster allows neurons during competitive training to adapt to subtle details that might be missed in the original data.

Curse of Dimensionality

The Curse of Dimensionality (Haykin, 2009) is, in fact, many curses.  One problem is that uniformly sampled space increases dramatically with increasing dimensionality.  This has implications when gathering training samples for a neural network.  For example, cutting a unit length bar (1-D) with a sample interval of .01 results in 100 samples.  Dividing a unit length hypercube in 10-D with a similar sample interval results in 1020 samples (1010 x 102).  If the nature of attribute space requires uniform sampling across a broad numerical range, then a large number of attributes may not be practical.  However, uniform sampling is not an issue here because the objective is to locate and detail features of natural clusters.

Also, not all attributes are important.  In the hunt for natural clusters, PCA (Haykin, 2009) is often a valuable tool to assess the relative merits of each attribute in a SOM attribute selection list.  Depending on geologic objectives, several dominant attributes may be picked from the first, second or even third principal eigenvectors or may pick all attributes from one principle eigenvector.

Geobody inversion from an interpretation perspective: Geobody inversion from a math perspective

Multi-attribute seismic interpretation is finding geobodies in survey space aided by machine learning tools that hunt for natural clusters in attribute space.  The interpreter’s critical role in this process is the following:

  • Choose questions that carry exploration toward meaningful conclusions.
  • Be creative with seismic attributes so as to effectively address illumination of geologic geobodies.
  • Pick attribute selection lists with the assistance of PCA.
  • Review the results of machine learning which may identify interesting geobodies natural cluster statistical estimate in natural clusters autopicked by SOM.
  • Look through the noise to edit and build geobodies geobody math with a workbench of visualization displays and a variety of statistical decision-making tools.
  • Construct geomodels by combining autopicked geobodies which in turn allow predictions on where to make better drilling decisions.
The Geomodel

After classification, picking geobodies from their winning neurons starts by filling an empty geomodel empty geomodel.  Natural clusters are consolidators of geobodies with common properties in attribute space so M < B.  In fact, it is often found that M << B .  That is, geobodies “stack” in attribute space.  Seismic data is noisy.  Natural clusters are consequentially statistical.  Not every sample g classified by a winning neuron is important although SOM classifies every sample. Samples that are a poor fit are probably noise.  Construction of a sensible geomodel depends on answering well thought out geological questions and phrased by selection of appropriate attribute selection lists.

Working below classic seismic tuning thickness

Classical seismic tuning thickness is λ/4.  Combining vertical incidence layer thickness Combining vertical incidence layer thickness with  λ=V/f leads to a critical layer thickness classic seismic tuning thickness 2 Resolution below classical seismic tuning thickness has been demonstrated with multi-attribute seismic samples and a machine learning classifier operating on those samples in scaled attribute space (Roden, et. al., 2015). High-quality natural clusters in attribute space imply tight, dense balls (low entropy, high density).  SOM training and classification of a classical wedge model at three noise levels is shown in Figures 1 and 2 which show tracking well below tuning thickness.

Seismic Processing: Processing the survey at a fine sample interval is preferred over resampling the final survey to a fine sample interval. Highest S/N ratio is always preferred. Preprocessing: Fine sample interval of base survey is preferred to raising the density of natural clusters and then computing attributes, but do not compute attributes and then resample because some attributes are not continuous functions. Derive all attributes from a single base survey in order to avoid misties. Attribute Selection List: Prefer attributes that address the specific properties of an intended geologic geobody. Working below tuning, prefer instantaneous attributes over attributes requiring spatial sampling.  Thin bed results on 3D surveys in the Eagle Ford Shale Facies of South Texas and in the Alibel horizon of the Middle Frio Onshore Texas and Group corroborated with extensive well control to verify consistent results for more accurate mapping of facies below tuning without usual traditional frequency assumptions (Roden, Smith, Santogrossi and Sacrey, personal communication, 2017).

Conclusion

There is a firm mathematical basis for a unified treatment of multi-attribute seismic samples, natural clusters, geobodies and machine learning classifiers such as SOM.  Interpretation of multi-attribute seismic data is showing great promise, having demonstrated resolution well below conventional seismic thin bed resolution due to high-quality natural clusters in attribute space which have been detected by a robust classifier such as SOM.

Geobody Interpretation Figure 1
Figure 1: Wedge models for three noise levels trained and classified by SOM with attribute list of amplitude and Hilbert transform (not shown) on 8 x 8 hexagonal neuron topology. Upper displays are amplitude. Middle displays are SOM classifications with a smooth color map. Lower displays are SOM classifications with a random color map. The rightmost vertical column is an enlargement of wedge model tips at highest noise level. Multi-attribute classification samples are clearly tracking well below tuning thickness which is left of the center in the right column displays.

Geobody Interpretation Figure 2
Figure 2: Attribute space for three wedge models with horizontal axis of amplitude and vertical axis of Hilbert transform. Upper displays are multi-attribute samples before SOM training and lower displays after training and samples classified by winning neurons in lower left with smooth color map. Upper right is an enlargement of tip of third noise level wedge model from Figure 1 where below-tuning bed thickness is right of the thick vertical black line.

Acknowledgments

I am thankful to have worked with two great geoscientists, Tury Taner and Sven Treitel during the genesis of these ideas.  I am also grateful to work with an inspired and inspiring team of coworkers who are equally committed to excellence.  In particular, Rocky Roden and Deborah Sacrey are longstanding associates with a shared curiosity to understand things and colleagues of a hunter’s spirit.

References

Chopra, S. J. Castagna and O. Potniaguine, 2006, Thin-bed reflectivity inversion, Extended abstracts, SEG Annual Meeting, New Orleans.

Chopra, S. and K.J. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization, Geophysical Developments No. 11, SEG.

Dutta, R.O., P.E. Hart and D.G. Stork, 2001, Pattern Classification, 2nd ed.: Wiley.

Haykin, S., 2009, Neural networks and learning machines, 3rd ed.: Pearson.

Kohonen, T., 1984, Self-organization and associative memory, pp 125-245. Springer-Verlag. Berlin.

Kohonen, T., 2001, Self-organizing maps: Third extended addition, Springer, Series in Information Services.

Laaksonen, J. and T. Honkela, 2011, Advances in self-organizing maps, 8th International Workshop, WSOM 2011 Espoo, Finland, Springer.

Ma, Y. and Y. Fu, 2012, Manifold Learning Theory and Applications, CRC Press, Boca Raton.

Roden, R., T. Smith and D. Sacrey, 2015, Geologic pattern recognition from seismic attributes, principal component analysis and self-organizing maps, Interpretation, SEG, November 2015, SAE59-83.

Smith, T., and M.T. Taner, 2010, Natural clusters in multi-attribute seismics found with self-organizing maps: Source and signal processing section paper 5: Presented at Robinson-Treitel Spring Symposium by GSH/SEG, Extended Abstracts.

Smith, T. and S. Treitel, 2010, Self-organizing artificial neural nets for automatic anomaly identification, Expanded abstracts, SEG Annual Convention, Denver.

Taner, M.T., 2003, Attributes revisited, http://www.rocksolidimages.com/attributes-revisited/, accessed 22 March 2017.

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 hydrocarbon exploration: AAPG Memoir 26, 301–327.

Taner, M.T., S. Treitel, and T. Smith, 2009, Self-organizing maps of multi-attribute 3D seismic reflection surveys, Presented at the 79th International SEG Convention, SEG 2009 Workshop on “What’s New in Seismic Interpretation,” Paper no. 6.

 

You must log in to access the PDF file. Please log in or register as a user.

 

Welcome Back!

Download PDF here

OR

Request access by filling the form below to download full PDF.

Most Popular Papers
systematic workflow for reservoir thumbnail
Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification
A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal ...
Read More
identify reservoir rock
Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale
Published in the special Machine Learning edition of First Break, this paper lays out results from multi-attribute analysis using Paradise, ...
Read More
CNN_facies
Seismic Facies Classification Using Deep Convolutional Neural Networks
Using a new supervised learning technique, convolutional neural networks (CNN), interpreters are approaching seismic facies classification in a revolutionary way ...
Read More
  • Registration confirmation will be emailed to you.

  • We're committed to your privacy. Geophysical Insights uses the information you provide to us to contact you about our relevant content, events, and products. You may unsubscribe from these communications at any time. For more information, check out our Privacy Policy

    Bob A. Hardage

    Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine Learning

    Examination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric of down-going, illuminating, P and S wavefields with the fabric of up-going reflections and interbed multiples created by these wavefields. This concept is introduced in this paper by applying unsupervised learning to VSP data to better understand the physics of P and S reflection seismology. The zero-offset VSP data used in this investigation were acquired in a hard-rock, fast-velocity, environment that caused the shallowest 2 or 3 geophones to be inside the near-field radiation zone of a vertical-vibrator baseplate. This study shows how to use instantaneous attributes to backtrack down-going direct-P and direct-S illuminating wavelets to the vibrator baseplate inside the near-field zone. This backtracking confirms that the points-of-origin of direct-P and direct-S are identical. The investigation then applies principal component (PCA) analysis to VSP data and shows that direct-S and direct-P wavefields that are created simultaneously at a vertical-vibrator baseplate have the same dominant principal components. A self-organizing map (SOM) approach is then taken to illustrate how unsupervised machine learning describes the fabric of down-going and up-going events embedded in vertical-geophone VSP data. These SOM results show that a small number of specific neurons build the down-going direct-P illuminating wavefield, and another small group of neurons build up-going P primary reflections and early-arriving down-going P multiples. The internal attribute fabric of these key down-going and up-going neurons are then compared to expose their similarities and differences. This initial study indicates that unsupervised machine learning, when applied to VSP data, is a powerful tool for understanding the physics of seismic reflectivity at a prospect. This research strategy of analyzing VSP data with unsupervised machine learning will now expand to horizontal-geophone VSP data.

    Bob A. Hardage

    Bob A. Hardage received a PhD in physics from Oklahoma State University. His thesis work focused on high-velocity micro-meteoroid impact on space vehicles, which required trips to Goddard Space Flight Center to do finite-difference modeling on dedicated computers. Upon completing his university studies, he worked at Phillips Petroleum Company for 23 years and was Exploration Manager for Asia and Latin America when he left Phillips. He moved to WesternAtlas and worked 3 years as Vice President of Geophysical Development and Marketing. He then established a multicomponent seismic research laboratory at the Bureau of Economic Geology and served The University of Texas at Austin as a Senior Research Scientist for 28 years. He has published books on VSP, cross-well profiling, seismic stratigraphy, and multicomponent seismic technology. He was the first person to serve 6 years on the Board of Directors of the Society of Exploration Geophysicists (SEG). His Board service was as SEG Editor (2 years), followed by 1-year terms as First VP, President Elect, President, and Past President. SEG has awarded him a Special Commendation, Life Membership, and Honorary Membership. He wrote the AAPG Explorer column on geophysics for 6 years. AAPG honored him with a Distinguished Service award for promoting geophysics among the geological community.

    Carrie Laudon
    Senior Geophysical Consultant

    Calibrating SOM Results to Wells – Improving Stratigraphic Resolution in the Niobrara

    Over the last few years, because of the increase in low cost computer power, individuals and companies have stepped up investigations into the use of machine learning in many areas of E&P. For the geosciences, the emphasis has been in reservoir characterization, seismic data processing and most recently, interpretation.
    By using statistical tools such as Attribute Selection, which uses Principal Component Analysis (PCA), and Multi-Attribute Classification using Self Organizing Maps (SOM), a multi-attribute 3D seismic volume can be “classified.” PCA reduces a large set of seismic attributes to those that are the most meaningful. The output of the PCA serves as the input to the SOM, a form of unsupervised neural network, which when combined with a 2D color map facilitates the identification of clustering within the data volume.
    The application of SOM and PCA in Paradise will be highlighted through a case study of the Niobrara unconventional reservoir. 100 square miles from Phase 5 of Geophysical Pursuit, Inc. and Fairfield Geotechnologies’ multiclient library were analyzed for stratigraphic resolution of the Niobrara chalk reservoirs within a 60 millisecond two-way time window. Thirty wells from the COGCC public database were available to corroborate log data to the SOM results. Several SOM topologies were generated and extracted within Paradise at well locations. These were exported and run through a statistical analysis program to visualize the neuron to reservoir correlations via histograms. Chi2 squared independence tests also validated a relationship between SOM neuron numbers and the presence of reservoir for all chalk benches within the Niobrara.

    Dr. Carrie Laudon
    Senior Geophysical Consultant

    Carolan (Carrie) Laudon holds a PhD in geophysics from the University of Minnesota and a BS in geology from the University of Wisconsin Eau Claire. She has been Senior Geophysical Consultant with Geophysical Insights since 2017 working with Paradise®, their machine learning platform. Prior roles include Vice President of Consulting Services and Microseismic Technology for Global Geophysical Services and 17 years with Schlumberger in technical, management and sales, starting in Alaska and including Aberdeen, Scotland, Houston, TX, Denver, CO and Reading, England. She spent five years early in her career with ARCO Alaska as a seismic interpreter for the Central North Slope exploration team.

    Deborah Sacrey
    Owner, Auburn Energy

    Finding Hydrocarbons using SOM Classification

    In the past, the use of unsupervised neural analysis has been used only on one seismic attribute at a time and using a seismic wavelet to find the natural clusters in the data. A new approach, using multiple seismic attributes and looking at the statistical clustering in the data based on sample interval can significantly help in discerning thin beds and subtle stratigraphic changes in the subsurface.

    Advances in computing power and the creation of many new seismic attribute families, such as Geometric, AVO, Inversion and the use of Spectral Decomposition over the last 30 years has made multiple attribute analysis extremely powerful.

    The key to this presentation is showing examples of how the SOM classification process has led to hydrocarbon discoveries in different types of depositional environments. Examples of cases in which the decision was made not to drill a well, thus avoiding a potential dry hole, will also be shown.

    Deborah Sacrey
    Owner, Auburn Energy

    Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US. She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.

    She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past nine 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 public, guided by Dr. Tom Smith, founder of SMT. She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.

    Deborah has been 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 also Past President of the Gulf Coast Association of Geological Societies and just ended a term as one of the GCAGS representatives on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She belongs to AAPG, SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

    Dr. Tom Smith
    President & CEO

    Dr. Tom Smith received a BS and MS degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984 to develop PC software to support training workshops which subsequently led to development of the KINGDOM Software Suite for integrated geoscience interpretation with world-wide success.

    The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award. The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award.

    In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists, geologists and computer scientists in developing advanced technologies for fundamental geophysical problems. The company launched the Paradise® multi-attribute analysis software in 2013, which uses Machine Learning and pattern recognition to extract greater information from seismic data.

    Dr. Smith has been a member of the SEG since 1967 and is a professional member of SEG, GSH, HGS, EAGE, SIPES, AAPG, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013. On January 25, 2016, he was recognized by the Houston Geological Society (HGS) as a geophysicist who has made significant contributions to the field of geology. He currently serves on the SEG President-Elect’s Strategy and Planning Committee and the ISU Foundation Campaign Committee for Forever True, For Iowa State.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Services

    Statistical Calibration of SOM results with Well Log Data (Case Study)

    The first stage of the proposed statistical method has proven to be very useful in testing whether or not there is a relationship between two qualitative variables (nominal or ordinal) or categorical quantitative variables, in the fields of health and social sciences. Its application in the oil industry allows geoscientists not only to test dependence between discrete variables, but to measure their degree of correlation (weak, moderate or strong). This article shows its application to reveal the relationship between a SOM classification volume of a set of nine seismic attributes (whose vertical sampling interval is three meters) and different well data (sedimentary facies, Net Reservoir, and effective porosity grouped by ranges). The data were prepared to construct the contingency tables, where the dependent (response) variable and independent (explanatory) variable were defined, the observed frequencies were obtained, and the frequencies that would be expected if the variables were independent were calculated and then the difference between the two magnitudes was studied using the contrast statistic called Chi-Square. The second stage implies the calibration of the SOM volume extracted along the wellbore path through statistical analysis of the petrophysical properties VCL and PHIE, and SW for each neuron, which allowed to identify the neurons with the best petrophysical values in a carbonate reservoir.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Servicest

    Fabian Rada joined Petroleum Oil and Gas Services, Inc (POGS) in January 2015 as Business Development Manager and Consultant to PEMEX. In Mexico, he has participated in several integrated oil and gas reservoir studies. He has consulted with PEMEX Activos and the G&G Technology group to apply the Paradise AI workbench and other tools. Since January 2015, he has been working with Geophysical Insights staff to provide and implement the multi-attribute analysis software Paradise in Petróleos Mexicanos (PEMEX), running a successful pilot test in Litoral Tabasco Tsimin Xux Asset. Mr. Rada began his career in the Venezuelan National Foundation for Seismological Research, where he participated in several geophysical projects, including seismic and gravity data for micro zonation surveys. He then joined China National Petroleum Corporation (CNPC) as QC Geophysicist until he became the Chief Geophysicist in the QA/QC Department. Then, he transitioned to a subsidiary of Petróleos de Venezuela (PDVSA), as a member of the QA/QC and Chief of Potential Field Methods section. Mr. Rada has also participated in processing land seismic data and marine seismic/gravity acquisition surveys. Mr. Rada earned a B.S. in Geophysics from the Central University of Venezuela.

    Hal Green
    Director – Marketing & Business Development

    Introduction to the Paradise AI Workbench

    Companies worldwide are seeking solutions for their digital transformation initiatives and face a make-vs-buy decision when it comes to their E&P software tools. This talk will show how the commercial, off-the-shelf Paradise AI workbench can be a robust and cost-effective component of the new digital infrastructure. Using a combination of machine learning (ML) and deep learning applications, geoscientists apply Paradise to extract greater insights from seismic and well data for these and other objectives:

    • - Identify and calibrate detailed stratigraphy
    • - Distinguish thin beds below conventional tuning
    • - Classify seismic facies
    • - Detect faults automatically
    • - Interpret Direct Hydrocarbon Indicators
    • - Reveal fracture trends in shale plays
    • - Estimate reserves/resources

    The brief introduction includes single-slide use cases in different geologic settings to illustrate the general-purpose application of ‘AI’ technology. The summary also will provide some context to the other presentations available at the Geophysical Insights virtual booth.

    Hal Green
    Director of Marketing & Business Development

    Hal H. Green is a marketing executive and entrepreneur in the energy industry with more than 25 years of experience in starting and managing technology companies. He holds a B.S. in Electrical Engineering from Texas A&M University and an MBA from the University of Houston. He has invested his career at the intersection of marketing and technology, with a focus on business strategy, marketing, and effective selling practices. Mr. Green has a diverse portfolio of experience in marketing technology to the hydrocarbon supply chain – from upstream exploration through downstream refining & petrochemical. Throughout his career, Mr. Green has been a proven thought-leader and entrepreneur, while supporting several tech start-ups.

    He started his career as a process engineer in the semiconductor manufacturing industry in Dallas, Texas and later launched an engineering consulting and systems integration business. Following the sale of that business in the late 80’s, he joined Setpoint in Houston, Texas where he eventually led that company’s Manufacturing Systems business. Aspen Technology acquired Setpoint in January 1996 and Mr. Green continued as Director of Business Development for the Information Management and Polymer Business Units.

    In 2004, Mr. Green founded Advertas, a full-service marketing and public relations firm serving clients in energy and technology. In 2010, Geophysical Insights retained Advertas as their marketing firm. Dr. Tom Smith, President/CEO of Geophysical Insights, soon appointed Mr. Green as Director of Marketing and Business Development for Geophysical Insights, in which capacity he still serves today.

    Hana Kabazi
    Product Manager

    Hana Kabazi joined Geophysical Insights in October of 201, and is now one of our Product Managers for Paradise. Mrs. Kabazi has over 7 years of oil and gas experience, including 5 years and Halliburton – Landmark. During her time at Landmark she held positions as a consultant to many E&P companies, technical advisor to the QA organization, and as product manager of Subsurface Mapping in DecsionSpace. Mrs. Kabazi has a B.S. in Geology from the University of Texas Austin, and an M.S. in Geology from the University of Houston.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Gas Hydrates, Reefs, Channel Architecture, and Fizz Gas: SOM Applications in a Variety of Geologic Settings

    Students at the University of Oklahoma have been exploring the uses of SOM techniques for the last year. This presentation will review learnings and results from a few of these research projects. Two projects have investigated the ability of SOMs to aid in identification of pore space materials – both trying to qualitatively identify gas hydrates and under-saturated gas reservoirs. A third study investigated individual attributes and SOMs in recognizing various carbonate facies in a pinnacle reef in the Michigan Basin. The fourth study took a deep dive of various machine learning algorithms, of which SOMs will be discussed, to understand how much machine learning can aid in the identification of deepwater channel architectures.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Heather Bedle received a B.S. (1999) in physics from Wake Forest University, and then worked as a systems engineer in the defense industry. She later received a M.S. (2005) and a Ph. D. (2008) degree from Northwestern University. After graduate school, she joined Chevron and worked as both a development geologist and geophysicist in the Gulf of Mexico before joining Chevron’s Energy Technology Company Unit in Houston, TX. In this position, she worked with the Rock Physics from Seismic team analyzing global assets in Chevron’s portfolio. Dr. Bedle is currently an assistant professor of applied geophysics at the University of Oklahoma’s School of Geosciences. She joined OU in 2018, after instructing at the University of Houston for two years. Dr. Bedle and her student research team at OU primarily work with seismic reflection data, using advanced techniques such as machine learning, attribute analysis, and rock physics to reveal additional structural, stratigraphic and tectonic insights of the subsurface.

    Ivan Marroquin
    Senior Research Geophysicist

    Connecting Multi-attribute Classification to Reservoir Properties

    Interpreters rely on seismic pattern changes to identify and map geologic features of importance. The ability to recognize such features depends on the seismic resolution and characteristics of seismic waveforms. With the advancement of machine learning algorithms, new methods for interpreting seismic data are being developed. Among these algorithms, self-organizing maps (SOM) provides a different approach to extract geological information from a set of seismic attributes.

    SOM approximates the input patterns by a finite set of processing neurons arranged in a regular 2D grid of map nodes. Such that, it classifies multi-attribute seismic samples into natural clusters following an unsupervised approach. Since machine learning is unbiased, so the classifications can contain both geological information and coherent noise. Thus, seismic interpretation evolves into broader geologic perspectives. Additionally, SOM partitions multi-attribute samples without a priori information to guide the process (e.g., well data).

    The SOM output is a new seismic attribute volume, in which geologic information is captured from the classification into winning neurons. Implicit and useful geological information are uncovered through an interactive visual inspection of winning neuron classifications. By doing so, interpreters build a classification model that aids them to gain insight into complex relationships between attribute patterns and geological features.

    Despite all these benefits, there are interpretation challenges regarding whether there is an association between winning neurons and geological features. To address these issues, a bivariate statistical approach is proposed. To evaluate this analysis, three cases scenarios are presented. In each case, the association between winning neurons and net reservoir (determined from petrophysical or well log properties) at well locations is analyzed. The results show that the statistical analysis not only aid in the identification of classification patterns; but more importantly, reservoir/not reservoir classification by classical petrophysical analysis strongly correlates with selected SOM winning neurons. Confidence in interpreted classification features is gained at the borehole and interpretation is readily extended as geobodies away from the well.

    Ivan Marroquin
    Senior Research Geophysicist

    Iván Dimitri Marroquín is a 20-year veteran of data science research, consistently publishing in peer-reviewed journals and speaking at international conference meetings. Dr. Marroquín received a Ph.D. in geophysics from McGill University, where he conducted and participated in 3D seismic research projects. These projects focused on the development of interpretation techniques based on seismic attributes and seismic trace shape information to identify significant geological features or reservoir physical properties. Examples of his research work are attribute-based modeling to predict coalbed thickness and permeability zones, combining spectral analysis with coherency imagery technique to enhance interpretation of subtle geologic features, and implementing a visual-based data mining technique on clustering to match seismic trace shape variability to changes in reservoir properties.

    Dr. Marroquín has also conducted some ground-breaking research on seismic facies classification and volume visualization. This lead to his development of a visual-based framework that determines the optimal number of seismic facies to best reveal meaningful geologic trends in the seismic data. He proposed seismic facies classification as an alternative to data integration analysis to capture geologic information in the form of seismic facies groups. He has investigated the usefulness of mobile devices to locate, isolate, and understand the spatial relationships of important geologic features in a context-rich 3D environment. In this work, he demonstrated mobile devices are capable of performing seismic volume visualization, facilitating the interpretation of imaged geologic features.  He has definitively shown that mobile devices eventually will allow the visual examination of seismic data anywhere and at any time.

    In 2016, Dr. Marroquín joined Geophysical Insights as a senior researcher, where his efforts have been focused on developing machine learning solutions for the oil and gas industry. For his first project, he developed a novel procedure for lithofacies classification that combines a neural network with automated machine methods. In parallel, he implemented a machine learning pipeline to derive cluster centers from a trained neural network. The next step in the project is to correlate lithofacies classification to the outcome of seismic facies analysis.  Other research interests include the application of diverse machine learning technologies for analyzing and discerning trends and patterns in data related to oil and gas industry.

    Jie Qi
    Research Geophysicist

    Applications of Deep Learning-based Seismic Fault Detection

    The traditional fault detection method is based on geophysicists’ hand-picking, which is very time-consuming on large seismic datasets. Convolutional Neural Networks (CNN)-based fault detection method is an emerging technology that shows great promise for the seismic interpreter. One of the more successful deep learning CNN methods uses synthetic data to train a CNN model. Faults are labeled as a single classification and other background geologic features are another classification in CNN-based fault detection. The labeled faults with associated seismic amplitude data are used to train in a CNN model, then predict or classify the corresponding fault classification in a large seismic dataset by the trained CNN model. The outperformance of CNN-based methods is that the computation cost of applications of a pre-trained CNN model to seismic fault classification is extremely low. This study shows applications of CNN models to predict faults from 3D seismic data. Firstly, the CNN model is trained with multiple 3D synthetic seismic amplitude data and their associated fault label data. The training data has been considered with different data quality, frequency bandwidth, noise levels, and structural features. The well-trained CNN model is then applied to detect faults on datasets, which exhibit different noise level and geologic features. Then the results from CNN are compared to those obtained using traditional seismic attributes and manual interpretation. The comparison indicates that the CNN method can perform more accurately and has a high potential to do more on seismic fault detection.

    Jie Qi
    Research Geophysicist

    Jie Qi is a Research Geophysicist at Geophysical Insights, where he works closely with product development and geoscience consultants. His research interests include machine learning-based fault detection, seismic interpretation, pattern recognition, image processing, seismic attribute development and interpretation, and seismic facies analysis. Dr. Qi received a BS (2011) in Geoscience from the China University of Petroleum in Beijing, and an MS (2013) in Geophysics from the University of Houston. He earned a Ph.D. (2017) in Geophysics from the University of Oklahoma, Norman. His industry experience includes work as a Research Assistant (2011-2013) at the University of Houston and the University of Oklahoma (2013-2017). Dr. Qi was with Petroleum Geo-Services (PGS), Inc. in 2014 as a summer intern, where he worked on a semi-supervised seismic facies analysis. In 2017, he served as a postdoctoral Research Associate in the Attributed Assisted-Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma from 2017 to 2020.

    Jie Qi
    Research Geophysicist

    An Integrated Fault Detection Workflow

    Seismic fault detection is one of the top critical procedures in seismic interpretation. Identifying faults are significant for characterizing and finding the potential oil and gas reservoirs. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using seismic attributes or machine learning techniques, which in turn serve as input for automatic fault extraction. Deep learning Convolutional Neural Networks (CNN) performs well on fault detection without any human-computer interactive work. This study shows an integrated CNN-based fault detection workflow to construct fault images that are sufficiently smooth for subsequent fault automatic extraction. The objectives were to suppress noise or stratigraphic anomalies subparallel to reflector dip, and sharpen fault and other discontinuities that cut reflectors, preconditioning the fault images for subsequent automatic extraction. A 2D continuous wavelet transform-based acquisition footprint suppression method was applied time slice by time slice to suppress wavenumber components to avoid interpreting the acquisition footprint as artifacts by the CNN fault detection method. To further suppress cross-cutting noise as well as sharpen fault edges, a principal component edge-preserving structure-oriented filter is also applied. The conditioned amplitude volume is then fed to a pre-trained CNN model to compute fault probability. Finally, a Laplacian of Gaussian filter is applied to the original CNN fault probability to enhance fault images. The resulting fault probability volume is favorable with respect to traditional human-interpreter generated on vertical slices through the seismic amplitude volume.

    Laura Cuttill
    Practice Lead, Advertas

    Young Professionals – Managing Your Personal Brand to Level-up Your Career

    No matter where you are in your career, your online “personal brand” has a huge impact on providing opportunity for prospective jobs and garnering the respect and visibility needed for advancement. While geoscientists tackle ambitious projects, publish in technical papers, and work hard to advance their careers, often, the value of these isn’t realized beyond their immediate professional circle. Learn how to…

    • - Communicate who you are to high-level executives in exploration and development
    • - Avoid common social media pitfalls
    • - Optimize your online presence to best garner attention from recruiters
    • - Stay relevant
    • - Create content of interest
    • - Establish yourself as a thought leader in your given area of specialization
    Laura Cuttill
    Practice Lead, Advertas

    As a 20-year marketing veteran marketing in oil and gas and serial entrepreneur, Laura has deep experience in bringing technology products to market and growing sales pipeline. Armed with a marketing degree from Texas A&M, she began her career doing technical writing for Schlumberger and ExxonMobil in 2001. She started Advertas as a co-founder in 2004 and began to leverage her upstream experience in marketing. In 2006, she co-founded the cyber-security software company, 2FA Technology. After growing 2FA from a startup to 75% market share in target industries, and the subsequent sale of the company, she returned to Advertas to continue working toward the success of her clients, such as Geophysical Insights. Today, she guides strategy for large-scale marketing programs, manages project execution, cultivates relationships with industry media, and advocates for data-driven, account-based marketing practices.

    Mike Dunn
    Sr. Vice President of Business Development

    New Capabilities of 3.4

    Paradise has given interpreters the ability detect more detail within the seismic data. Therefore, a natural extension of the current software is the ability to easily compare the SOM and Geobody results to borehole logs and lithofacies. As a result of this exciting capability, Paradise is now able to display digital well logs, TD charts, formation tops, and cross-sections in simple and straightforward manner. In this What’s New in Paradise 3.4 presentation we will be discussing the new Well Log Cross Section functionality, GPU support for 3 AASPI algorithms, demonstrating significant speedup, and the latest Petrel 2020 connector. Examples of the new well functionality will use the offshore New Zealand Maui Field data set. In addition, a live demonstration will walk users through a well cross section workflow.

    Mike Dunn
    Senior Vice President Business Development

    Michael A. Dunn is an exploration executive with extensive global experience including the Gulf of Mexico, Central America, Australia, China and North Africa. Mr. Dunn has a proven a track record of successfully executing exploration strategies built on a foundation of new and innovative technologies. Currently, Michael serves as Senior Vice President of Business Development for Geophysical Insights.

    He joined Shell in 1979 as an exploration geophysicist and party chief and held increasing levels or responsibility including Manager of Interpretation Research. In 1997, he participated in the launch of Geokinetics, which completed an IPO on the AMEX in 2007. His extensive experience with oil companies (Shell and Woodside) and the service sector (Geokinetics and Halliburton) has provided him with a unique perspective on technology and applications in oil and gas. Michael received a B.S. in Geology from Rutgers University and an M.S. in Geophysics from the University of Chicago.

    Rocky R. Roden
    Senior Consulting Geophysicist

    What Interpreters Should Know about Machine Learning

    Our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning. Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how do these applications relate to geoscience interpretation and finding oil and gas. More importantly, do these Machine Learning methods produce better results than conventional interpretation approaches? This webinar will initially wade through the vernacular of Machine Learning and Data Science as it relates to the geoscientist. The presentation will review how these methods are employed, along with interpretation case studies of different machine learning applications. An overview of computer power and machine learning will be described. Machine Learning is a disruptive technology that holds great promise, and this webinar is an interpreter’s perspective, not a data scientist. This course will provide an understanding of how Machine Learning for interpretation is being utilized today and provide insights on future directions and trends.

    Rocky R. Roden
    Senior Consulting Geophysicist

    Over 45 years in industry as a Geophysicist, Exploration/Development Manager, Director of Applied Technology, and Chief Geophysicist. Previously with Texaco, Pogo Producing, Maxus Energy, YPF Maxus, and Repsol (retired as Chief Geophysicist 2001). Mr. Roden has authored or co-authored over 30 technical publications on various aspects of seismic interpretation, AVO analysis, amplitude risk assessment, and geoscience machine learning. Ex-Chairman of The Leading Edge editorial board. Currently a consultant with Geophysical Insights developing machine learning advances for oil and gas exploration and development and is a principal in the Rose and Associates DHI Risk Analysis Consortium, which has involved 85 oil companies since 2001, developing a seismic amplitude risk analysis program and worldwide prospect database. He holds a B.S. in Oceanographic Technology-Geology from Lamar University and an M.S. in Geological and Geophysical Oceanography from Texas A&M University.

    Sarah Stanley
    Senior Geoscientist

    New Capabilities of 3.4

    Paradise has given interpreters the ability detect more detail within the seismic data. Therefore, a natural extension of the current software is the ability to easily compare the SOM and Geobody results to borehole logs and lithofacies. As a result of this exciting capability, Paradise is now able to display digital well logs, TD charts, formation tops, and cross-sections in simple and straightforward manner. In this What’s New in Paradise 3.4 presentation we will be discussing the new Well Log Cross Section functionality, GPU support for 3 AASPI algorithms, demonstrating significant speedup, and the latest Petrel 2020 connector. Examples of the new well functionality will use the offshore New Zealand Maui Field data set. In addition, a live demonstration will walk users through a well cross section workflow.

    Sarah Stanley
    Senior Geoscientist and Lead Trainer

    Sarah Stanley joined Geophysical Insights in October, 2017 as a geoscience consultant, and became a full-time employee July 2018. Prior to Geophysical Insights, Sarah was employed by IHS Markit in various leadership positions from 2011 to her retirement in August 2017, including Director US Operations Training and Certification, the Operational Governance Team, and, prior to February 2013, Director of IHS Kingdom Training. Sarah joined SMT in May, 2002, and was the Director of Training for SMT until IHS Markit’s acquisition in 2011.

    Prior to joining SMT Sarah was employed by GeoQuest, a subdivision of Schlumberger, from 1998 to 2002. Sarah was also Director of the Geoscience Technology Training Center, North Harris College from 1995 to 1998, and served as a voluntary advisor on geoscience training centers to various geological societies. Sarah has over 37 years of industry experience and has worked as a petroleum geoscientist in various domestic and international plays since August of 1981. Her interpretation experience includes tight gas sands, coalbed methane, international exploration, and unconventional resources.

    Sarah holds a Bachelor’s of Science degree with majors in Biology and General Science and minor in Earth Science, a Master’s of Arts in Education and Master’s of Science in Geology from Ball State University, Muncie, Indiana. Sarah is both a Certified Petroleum Geologist, and a Registered Geologist with the State of Texas. Sarah holds teaching credentials in both Indiana and Texas.

    Sarah is a member of the Houston Geological Society and the American Association of Petroleum Geologists, where she currently serves in the AAPG House of Delegates. Sarah is a recipient of the AAPG Special Award, the AAPG House of Delegates Long Service Award, and the HGS President’s award for her work in advancing training for petroleum geoscientists. She has served on the AAPG Continuing Education Committee and was Chairman of the AAPG Technical Training Center Committee. Sarah has also served as Secretary of the HGS, and Served two years as Editor for the AAPG Division of Professional Affairs Correlator.

    Tom Smith
    President and CEO, Geophysical Insights

    Machine Learning for Incomplete Geoscientists

    This presentation covers big-picture machine learning buzz words with humor and unassailable frankness. The goal of the material is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, multi-attribute samples, and several interpretation examples. After the presentation, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and avoid some of their limitations.

    Scroll to Top