Unsupervised Machine Learning Techniques for Subtle Fault Detection

By Marwa Hussein, Robert R. Stewart and Jonny Wu | Published with permission: EAGE | December 2020

Summary

Subtle fault detection plays a vital role in reservoir development analysis because faults may form baffles or conduits that significantly control how a petroleum reservoir is swept. Small throw faults may be overlooked in seismic amplitude data. Seismic attributes aid in mapping small faults, however, over the years, dozens of seismic attributes have been developed to offer additional tempting but challenging features for interpreters. Using the Maui 3D seismic data acquired in the Offshore Taranaki Basin, New Zealand, we generated typical seismic attributes useful for fault detection. We found multiple attribute analyses provided greater geological information than analyzing individual attribute volumes. We extract geological content of multiple simultaneous attributes with principal component analysis (PCA) and an unsupervised machine learning algorithm, Self-Organizing Maps (SOM). From our investigation, incorporating appropriate seismic attributes that exhibit anomalous features at the same seismic voxel with PCA and SOM analyses aided in integrating the geological context, in this case four seismic attributes combined in one classification volume. This classification volume enables us to image subtle faults affecting the C Sand reservoir of Maui Field which are difficult to image by using conventional seismic interpretation techniques.

Introduction

Imaging subtle faults plays a crucial role in reservoir modelling and reservoir characterization analysis. Mapping faults with small throws, which we will call small faults in this paper, aids in understanding the hydrocarbon migration pathways and identifying bypassed oil accumulation within a petroleum reservoir. The conventional fault interpretation techniques are highly demanding (i.e. require more time and experience) and sometimes, challenging for mapping small faults. Over the last few decades, geoscientists have devoted great effort to developing computer-aided fault detection techniques and seismic attributes to help detecting faults. Dozens of geometric attributes such as dip, coherence (Chopra and Marfurt, 2007), curvature (Al-Dossary and Marfurt, 2006) and aberrancy attributes (Qi and Marfurt, 2018) have been developed. Although these attributes accelerated the interpretation of faults, for large projects, analyzing every individual attribute volume becomes a tedious and time-consuming process.

To ease the interpretation process of small faults, we integrate the geological information from multiple seismic attribute volumes by using machine learning clustering techniques such as self- organizing maps. In this paper, we will investigate the different geometric attributes that are sensitive to small faults by using a dataset from Maui field in Offshore Taranaki Basin, New Zealand. We suggest a workflow that enables interpreters to apply principal component analysis (PCA) and self- organizing maps (SOM) on the most appropriate mathematically independent seismic attributes to get one classification volume. Our aim is to obtain one clustered volume that best shows all small faults affecting the area.

Field and Dataset

The Maui field is one of the largest New Zealand gas fields, covers around 1000 km2 and is located 40 km off the West coast of the North Island, New Zealand (Figure 1a). The field consists of two gentle anticlinal features; Maui A region in the northeast and Maui B region in the southwest (Figure 1b). The field produces gas from three main sand reservoirs called Mangahewa/C Sand, Kaimiro/D Sand and Farewell/F Sand formations of the Kapuni group (Figure 1c). The C Sand reservoir, which is the focus of this research, contains a shallower and larger gas column compared to the D Sand, however, it has a very thin oil-rim in the Maui B region (Stephen et al., 2004). Structurally, the field is highly complicated; the Maui A region is bounded to the east by the normal Cape Egmont fault (CEF) and Pungawerewere normal fault (PF) while the Maui B region is bounded to the south-west by the reverse Whitiki fault (WF) (Figure 1a). A seismic line extracted from the seismic volume over the Maui A region shows that the reservoir is mainly affected by normal faults (Figure 2a). Another seismic line crossing the Maui B region shows slightly different structural features (Figure 2b) where this part of the field is highly affected by regional reverse fault WF. This indicates that the northern part of the field is more faulted compared to the southern part of the field.

The dataset used in this research is shown in Figure 1 consists of a 1000 km2 3D prestack depth migrated (PSDM) seismic volume, which is a merged volume of two 3D seismic volumes acquired in 1991 and 2002 with eight vertical wells sets of conventional wells logs. The data were made available for research purposes by the Ministry of Business and Innovation & Employment (MBIE), New Zealand.

Methodology

The seismic data was cropped laterally to cover 410 km2 and vertically to focus on the area of interest between 1.8 and 2.8 s. The data was then resampled from 4 ms to 2 ms. We followed Marfurt’s (2006a) approach and applied a well established principal component structure-oriented filter along structure dip to simultaneously enhance the signal aligned with the estimated dip and also cancel random noise that crosses in other directions. Following this, we calculated most of the geometric attributes available for a seismic interpreter such as volumetric dip, coherence (multispectral and broadband energy ratio similarity), curvature, curvedness and aberrancy attributes. We analyzed individual seismic attributes to select attributes that would work best for PCA and SOM analyses.

Unsupervised Machine Learning Techniques for Subtle Fault Detection Figure 1
Figure 1 a) Location map of Maui Field (modified from Google Earth and Haque et al., 2016). b) Time structure map of the top of the C Sand reservoir. c) Paleogene stratigraphic column of Maui field (modified from Stephen et al., 2004). CEF – Cape Egmont Fault; WF – Whitiki Fault; PF – Pungawerewere Fault; IF – Ihi Fault; MPA & MPB – Maui A and Maui B platforms.

Unsupervised Machine Learning Techniques for Subtle Fault Detection Figure 2
Figure 2. a) A-A’ seismic line (the red line in Figure 1b) crosses the Maui A region. b) B-B’ seismic line (the blue line in Figure 1b) crosses the Maui B region. IF – Ihi fault; WF – Whitiki fault.

Seismic attribute analysis

We investigated most of geometric seismic attributes that aid in detecting faults and will discuss the most useful ones by using horizon extractions along the top of the C Sand reservoir comparing seismic amplitude and various geometric attribute volumes. A seismic amplitude slice (Figure 3a) shows faults with clear offsets such as WF (green arrow) affecting the west side of Maui B and parts of the faults affecting the northern part of the field. However, the result seem very sensitive to noise (purple arrow).

A dip magnitude slice (Figure 3b) shows clear edges that indicate faults within the reservoir (white, orange, black and cyan arrows). The multispectral energy ratio attribute (Figure 3c) shows small faults affecting the northern part of field (white arrow) and Ihi fault (IF; black arrow). The most positive (K1) and most negative (K2) principal curvature attributes are useful for mapping faults, however, K2 attribute shows better lineaments compared to those imaged by K1 (Figures 3d and 3e). The curvedness attribute describes the total deformation of a seismic event and shows lineaments that indicate faults and fractures affecting the reservoir. We observe that the curvature attributes illuminate faults with small offsets below seismic resolution that can’t be imaged by using energy ratio similarity attribute. For instance, curvedness slice shows the conjugate faults of fault IF nicely (Figure 3f) whereas the energy ratio similarity shows only parts of these faults that exhibit clear offset (Figure 3c). However, energy ratio similarity shows the en-echelon set of small faults affecting the northern part of the field nicely while dip and curvature attributes show two continuous interesting faults (Figures 3b and 3e). These faults show clear offset but don’t cause a large deformation (i.e. changes in dip or bending) within the faulted seismic events. The total aberrancy magnitude attribute shows faults and microfracture that aren’t well imaged by other geometric attributes (Figures 3g). Thus, there is no one attribute that can show all the faults and fractures affecting a reservoir because every attribute shows anomalous features based on the deformation occurred within the faulted layers. Therefore, integrating the geological information from multiple seismic attributes by using computer-aided machine learning techniques (e.g. SOM and PCA) into one classification volume would be useful.

 

Unsupervised Machine Learning Techniques for Subtle Fault Detection Figure 3
Figure 3. Horizon slices extracted along the top of the C Sand reservoir from a) Seismic amplitude, b) Dip magnitude, c) Multispectral energy ratio similarity, d) Most positive principal curvature (K1), e) Most negative principal curvature (K2), f ) Curvedness, g) Total aberrancy magnitude (Tot_Ab_mag) volumes. Colored arrows show the different faults mentioned in the text.
Unsupervised machine learning techniques for small fault detection

SOM trains a non-linear neural network to numerous seismic attribute volumes simultaneously to better understand how these attributes relate. It groups similar data and uses a 2D topology map to plot these groups or clusters, thus reduces the dimension of the data (Roden et al., 2015). Selecting the right attributes to use for SOM specifically controls the classification results (Barnes and Laughlin, 2002). PCA is a linear mathematical technique that aids in distilling a big library of seismic attributes into a small set of mathematically independent attributes based on the relative variance of the attributes within the area of interest (Roden et al., 2015). However, PCA doesn’t provide any information about the spatial positions of the anomalous zones within the different seismic attributes. Thus, the appropriate input attributes to PCA and SOM analyses should show anomalous features at the same seismic voxel.

In the proposed workflow, we used four attributes consisting of multispectral energy ratio similarity, dip magnitude, curvedness and total aberrancy magnitude for PCA analysis. The aforementioned weighted attributes based on the relative contribution of each attribute within the interval of interest from the PCA analysis were then fed to SOM analysis to combine the geological context from these attributes into a classification volume of several distinct classifications (Figure 4a). The PCA and SOM analyses were performed on the seismic samples (voxels) within the interval of interest from the C Shale horizon to D Sand horizon. The SOM analysis employed an 8×8 topology.

A horizon slice extracted along top of the C Sand reservoir from the SOM classification volume clearly shows small faults affecting the reservoir (Figure 4b). It shows sharp edge of a NW-SE fault that seemed to be cut by a set of en-echelon NE-SW normal faults (white arrow) that are affecting the northern part of the field. It provides nice and sharp edges for faults affecting Maui east and west (orange arrows) and Maui south (blue arrow). The SOM shows the sharpest image of the conjugate Ihi faults (black arrow), which consist of a small segmented faults that seem to touch each other at the tips of these small faults. Although being small conjugate faults with drags, the sealing of this fault is questionable. After 29 years of production from the C Sand reservoir in the east of IF, the MA-02A well (white circle) penetrated the same reservoir in the west side of IF encountered bypassed gas with the original depth of the gas-water contact, revealing that reservoir was unswept (unpubl. report). We also found SOM useful in mapping small faults that can be easily overlooked by using conventional fault interpretation techniques. For instance, we were able to image small faults affecting the southern part of the field (blue arrow). A recent study published about the structural modeling of Maui field (Haque et al., 2016) using 3D seismic amplitude and variance volumes didn’t clearly reveal these faults.

Unsupervised Machine Learning Techniques for Subtle Fault Detection Figure 4
Figure 4. a) Proposed workflow to cluster and combine the data from four mathematically independent geometric seismic attributes by using PCA and SOM. b) Horizon slice extracted along the top of the C Sand reservoir from SOM classification volume. PSDM – prestack depth migrated; PCA – principal component analysis; SOM – self-organizing map. Colored arrows show the different faults mentioned in the text.
Conclusions

Subtle faults can be overlooked in seismic amplitude data but in appropriate geologic situations, they can form important seals or conduits that dramatically control how hydrocarbon migrates and trap within a petroleum reservoir. An effective approach to image small faults is to integrate the geological context of different seismic attributes using machine learning algorithms. Using computer-aided PCA and SOM techniques, we were able to image subtle faults affecting the C Sand reservoir of Maui Field, New Zealand that were not easily revealed from conventional seismic interpretation techniques.

Acknowledgments

The authors would like to thank the PL 537 licence partners for permission to publish this paper. We would also like to thank to our colleagues in IPN: Toshiro Tamura, Tone Merete Øksenvåg and Marius Lunde for their contributions. Thanks also are extended to Geophysical Insights for the research and development of the Paradise AI workbench and the machine learning applications used in this paper. Finally, we would like to thank Hal Green for review of the manuscript and Leah Mann for editing the graphics.

References

Al-Dossary, S. and Marfurt, K. J. [2006] 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics, 5, p. 41-51.

Barnes, A. E. and Laughlin, K. J. [2002] Investigation of methods for unsupervised classification of seismic data. 72nd Annual International Meeting, SEG, Expanded Abstracts, 2221–2224.

Chopra, S. and Marfurt, K. J. [2007a] Seismic attributes for prospect identification and reservoir characterization. SEG Geophysical Developments Series No. 11, 481 pp.

Haque, A. E., Islam, MD. A. and Shalaby M. R. [2016] Structural modeling of the Maui Gas Field, Taranaki basin, New Zealand. ScienceDirect, Petroleum Exploration and Development, 6, 965 – 975. Qi, X. and Marfurt, K. J. [2018] Volumetric aberrancy to map subtle faults and flextures. Interpretation, 2, 349-365.

Roden, R., Smith, T. and Sacrey, D. [2015] Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps. Interpretation, 4, 59-83.

Stephen, P., Slger, S. and Dekker, S. [2004] Constraining complex gas-water dynamic color model using 4D seismic. Annual Technical Conference & Exhibition, SPE, Houston, Texas, USA.

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    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.

    Dr. Jie Qi
    Research Geophysicist

    An integrated machine learning-based fault classification workflow

    We introduce an integrated machine learning-based fault classification workflow that creates fault component classification volumes that greatly reduces the burden on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network (CNN). However, the resulting “fault probability” volume delineates other non-fault edges such as angular unconformities, the base of mass transport complexes, and noise such as acquisition footprint. We find that image processing-based fault discontinuity enhancement and skeletonization methods can enhance the fault discontinuities and suppress many of the non-fault discontinuities. Although each fault is characterized by its dip and azimuth, these two properties are discontinuous at azimuths of φ=±180° and for near vertical faults for azimuths φ and φ+180° requiring them to be parameterized as four continuous geodetic fault components. These four fault components as well as the fault probability can then be fed into a self-organizing map (SOM) to generate fault component classification. We find that the final classification result can segment fault sets trending in interpreter-defined orientations and minimize the impact of stratigraphy and noise by selecting different neurons from the SOM 2D neuron color map.

    Dr. Jie Qi
    Research Geophysicist

    Dr. 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.

    Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Automatic Fault Detection and Applying Machine Learning to Detect Thin Beds

    Rapid advances in Machine Learning (ML) are transforming seismic analysis. Using these new tools, geoscientists can accomplish the following quickly and effectively:

    • Run fault detection analysis in a few hours, not weeks
    • Identify thin beds down to a single seismic sample
    • Generate seismic volumes that capture structural and stratigraphic details

    Join us for a ‘Lunch & Learn’ sessions daily at 11:00 where Dr. Carolan (“Carrie”) Laudon will review the theory and results of applying a combination of machine learning tools to obtain the above results.  A detailed agenda follows.

    Agenda

    Automated Fault Detection using 3D CNN Deep Learning

    • Deep learning fault detection
    • Synthetic models
    • Fault image enhancement
    • Semi-supervised learning for visualization
    • Application results
      • Normal faults
      • Fault/fracture trends in complex reservoirs

    Demo of Paradise Fault Detection Thoughtflow®

    Stratigraphic analysis using machine learning with fault detection

    • Attribute Selection using Principal Component Analysis (PCA)
    • Multi-Attribute Classification using Self-Organizing Maps (SOM)
    • Case studies – stratigraphic analysis and fault detection
      • Fault-karst and fracture examples, China
      • Niobrara – Stratigraphic analysis and thin beds, faults
    Mike DunnSr. Vice President of Business Development

    Machine Learning in the Cloud

    Machine Learning in the Cloud will address the capabilities of the Paradise AI Workbench, featuring on-demand access enabled by the flexible hardware and storage facilities available on Amazon Web Services (AWS) and other commercial cloud services. Like the on-premise instance, Paradise On-Demand provides guided workflows to address many geologic challenges and investigations. The presentation will show how geoscientists can accomplish the following workflows quickly and effectively using guided ThoughtFlows® in Paradise:
    • Identify and calibrate detailed stratigraphy using seismic and well logs
    • Classify seismic facies
    • Detect faults automatically
    • Distinguish thin beds below conventional tuning
    • Interpret Direct Hydrocarbon Indicators
    • Estimate reserves/resources
    Attend the talk to see how ML applications are combined through a process called "Machine Learning Orchestration," proven to extract more from seismic and well data than traditional means.
    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

    The Relationship of Self-Organization, Geology, and Machine Learning

    Self-organization is the nonlinear formation of spatial and temporal structures, patterns or functions in complex systems (Aschwanden et al., 2018). Simple examples of self-organization include flocks of birds, schools of fish, crystal development, formation of snowflakes, and fractals. What these examples have in common is the appearance of structure or patterns without centralized control. Self-organizing systems are typically governed by power laws, such as the Gutenberg-Richter law of earthquake frequency and magnitude. In addition, the time frames of such systems display a characteristic self-similar (fractal) response, where earthquakes or avalanches for example, occur over all possible time scales (Baas, 2002).

    The existence of nonlinear dynamic systems and ordered structures in the earth are well known and have been studied for centuries and can appear as sedimentary features, layered and folded structures, stratigraphic formations, diapirs, eolian dune systems, channelized fluvial and deltaic systems, and many more (Budd, et al., 2014; Dietrich and Jacob, 2018). Each of these geologic processes and features exhibit patterns through the action of undirected local dynamics and is generally termed “self-organization” (Paola, 2014).

    Artificial intelligence and specifically neural networks exhibit and reveal self-organization characteristics. The reason for the interest in applying neural networks stems from the fact that they are universal approximators for various kinds of nonlinear dynamical systems of arbitrary complexity (Pessa, 2008). A special class of artificial neural networks is aptly named self-organizing map (SOM) (Kohonen, 1982). It has been found that SOM can identify significant organizational structure in the form of clusters from seismic attributes that relate to geologic features (Strecker and Uden, 2002; Coleou et al., 2003; de Matos, 2006; Roy et al., 2013; Roden et al., 2015; Zhao et al., 2016; Roden et al., 2017; Zhao et al., 2017; Roden and Chen, 2017; Sacrey and Roden, 2018; Leal et al, 2019; Hussein et al., 2020; Hardage et al., 2020; Manauchehri et al., 2020). As a consequence, SOM is an excellent machine learning neural network approach utilizing seismic attributes to help identify self-organization features and define natural geologic patterns not easily seen or seen at all in the data.

    Rocky R. Roden
    Senior Consulting Geophysicist

    Rocky R. Roden started his own consulting company, Rocky Ridge Resources Inc. in 2003 and works with several oil companies on technical and prospect evaluation issues. He is also a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. Rocky is a proven oil finder with 37 years in the industry, gaining extensive knowledge of modern geoscience technical approaches.

    Rocky holds a BS in Oceanographic Technology-Geology from Lamar University and a MS in Geological and Geophysical Oceanography from Texas A&M University. As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised of 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. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES; he is also a past Chairman of The Leading Edge Editorial Board.

    Sarah Stanley
    Senior Geoscientist

    Stratton Field Case Study – New Solutions to Old Problems

    The Oligocene Frio gas-producing Stratton Field in south Texas is a well-known field. Like many onshore fields, the productive sand channels are difficult to identify using conventional seismic data. However, the productive channels can be easily defined by employing several Paradise modules, including unsupervised machine learning, Principal Component Analysis, Self-Organizing Maps, 3D visualization, and the new Well Log Cross Section and Well Log Crossplot tools. The Well Log Cross Section tool generates extracted seismic data, including SOMs, along the Cross Section boreholes and logs. This extraction process enables the interpreter to accurately identify the SOM neurons associated with pay versus neurons associated with non-pay intervals. The reservoir neurons can be visualized throughout the field in the Paradise 3D Viewer, with Geobodies generated from the neurons. With this ThoughtFlow®, pay intervals previously difficult to see in conventional seismic can finally be visualized and tied back to the well data.

    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.

    Thomas ChaparroSenior Geophysicist - Geophysical Insights

    Paradise: A Day in The Life of the Geoscientist

    Over the last several years, the industry has invested heavily in Machine Learning (ML) for better predictions and automation. Dramatic results have been realized in exploration, field development, and production optimization. However, many of these applications have been single use ‘point’ solutions. There is a growing body of evidence that seismic analysis is best served using a combination of ML tools for a specific objective, referred to as ML Orchestration. This talk demonstrates how the Paradise AI workbench applications are used in an integrated workflow to achieve superior results than traditional interpretation methods or single-purpose ML products. Using examples from combining ML-based Fault Detection and Stratigraphic Analysis, the talk will show how ML orchestration produces value for exploration and field development by the interpreter leveraging ML orchestration.

    Thomas ChaparroSenior Geophysicist - Geophysical Insights

    Thomas Chaparro is a Senior Geophysicist who specializes in training and preparing AI-based workflows. Thomas also has experience as a processing geophysicist and 2D and 3D seismic data processing. He has participated in projects in the Gulf of Mexico, offshore Africa, the North Sea, Australia, Alaska, and Brazil.

    Thomas holds a bachelor’s degree in Geology from Northern Arizona University and a Master’s in Geophysics from the University of California, San Diego. His research focus was computational geophysics and seismic anisotropy.

    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.