Patricia Santogrossi

Patricia Santogrossi

Sr. Geoscientist

Patricia Santogrossi is a geoscientist who has enjoyed 40 years in the oil business. She is currently a Consultant to Geophysical Insights, producer of the Paradise multi-attribute analysis software platform. Formerly, she was a Leading Reservoir Geoscientist and Non-operated Projects Manager with Statoil USA E & P. In this role Ms. Santogrossi was engaged for nearly nine years in Gulf of Mexico business development, corporate integration, prospect maturation, and multiple appraisal projects in the deep and ultra-deepwater Gulf of Mexico. Ms. Santogrossi has previously worked with domestic and international Shell Companies, Marathon Oil Company, and Arco/Vastar Resources in research, exploration, leasehold and field appraisal as well as staff development. She has also been Chief Geologist for Chroma Energy, who possessed proprietary 3D voxel multi-attribute visualization technology, and for Knowledge Reservoir, a reservoir characterization and simulation firm that specialized in Deepwater project evaluations. A longtime member of SEPM, AAPG, GCSSEPM, HGS and SEG, Ms. Santogrossi has held various elected and appointed positions in these industry organizations. She has recently begun her fourth three-year term as a representative to the AAPG House of Delegates from the Houston Geological Society (HGS). In addition, she has been invited to continue her role this fall on the University of Illinois’ Department of Geology Alumni Board. Ms. Santogrossi was born, raised, and educated in Illinois before she headed to Texas to work for Shell after she received her MS in Geology from the University of Illinois, Champaign-Urbana. Her other ‘foreign assignments’ have included New Orleans and London. She resides in Houston with her husband of twenty-four years, Joe Delasko.

Published Work by Patricia Santogrossi:

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The Value of Instantaneous Attributes

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with ...
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Eagle Ford Case Study

Latest Technology for Seismic Interpretation: Direct detection & delineation of facies architecture in the Eagle Ford Group or How did ...
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First Steps in the Sub-Seismic Resolution of the Eagle Ford, Part I

Using machine learning to analyze 5 instantaneous attributes helped reveal patterns across 5 instantaneous attributes and unique Eagle Ford facies ...
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Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

Results of a Self-Organizing Map (SOM) of many instantaneous attributes to reveal different types of facies and shale that apply ...
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Resolution of Faults in the Eagle Ford, Part 3

Resolution of Faults in the Eagle Ford, Part 3

Applying Principal Component Analysis (PCA) and Self-Organizing Map (SOM) process to show faults on the base amplitude seismic survey and ...
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Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Concurrent analysis of multiple attributes through machine learning to spectral decomposition sub-bands and other geology that apply attributes for stratigraphic ...
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Dr. ChingWen Chen

Dr. ChingWen Chen

Sr. Geophysicist

ChingWen Chen received an M.S. (2007) and a Ph.D. (2011) in Geophysics from the University of Houston, studying global seismology. After graduation, she joined the industry as a geophysicist with Noble Energy where she supported both exploration and development projects. Dr. Chen has a great passion for quantitative seismic interpretation, and more specifically rock physics, seismic imaging and multi-seismic attribute analysis. She later joined Geophysical Insights as a Senior Geophysicist, where the application of machine learning techniques became a focus of her work. Since 2015, her primary interest has been in increasing the efficiency of seismic interpretation.

Published Work by Dr. ChingWen Chen

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CAPA 2017 Technical Symposium – Dr. ChingWen Chen

3 November 2017 Dr. ChingWen Chen speaks at the 2017 Chinese American Petroleum Association (CAPA) Technical Symposium. During Section B: ...
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Deborah Sacrey

Deborah Sacrey

Sr. Geoscientist

Deborah Sacrey is a geologist/geophysicist with 41 years of oil and gas exploration experience in the Texas, Louisiana Gulf Coast, and Mid-Continent areas of the US. Deborah specializes in 2D and 3D interpretation for clients in the US and internationally.

She received her degree in Geology from the University of Oklahoma in 1976 and began her career with Gulf Oil in Oklahoma City. She started Auburn Energy in 1990 and built her first geophysical workstation using the Kingdom software in 1996. Deborah then worked closely with SMT (now part of IHS) for 18 years developing and testing Kingdom. For the past eight years, she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience community, guided by Dr. Tom Smith, founder of SMT. Deborah has become an expert in the use of the Paradise® software and has over five discoveries for clients using the technology.

Deborah is very active in the geological community. She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is currently the incoming President of the Gulf Coast Association of Geological Societies (GCAGS) and is a member of the GCAGS representation on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She is active in the Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

Published Work by Deborah Sacrey

Chinese American Petroleum Association

Applications of Machine Learning and Multi-Attribute Analysis to Conventional Reservoirs

SPEAKER: Deborah Sacrey - Senior Geoscience Consultant DATE: Friday, November 4, 2016 VENUE: Marathon Oil Tower, 5555 San Felipe, Houston TX 77056 REGISTRATION: ...
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Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Deborah Sacrey of Auburn Energy hosts a webinar addressing challenges like interpretation of thin bedded reservoirs far below conventional seismic ...
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Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Deborah Sacrey, Owner and Geophysicist of Auburn Energy, provides a review of the various attribute categories and their possible machine ...
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Using Self-Organizing Maps to Define Seismic Facies

Using Self-Organizing Maps to Define Seismic Facies

Using multiple attributes to evaluate a 3D volume in offshore South America containing unexpected high pressure zone and the application ...
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Visualization and Characterization of Paleozoic (Ordovician-Devonian) Tight Carbonate Reservoirs, Oklahoma, Part 1

Visualization and Characterization of Paleozoic (Ordovician-Devonian) Tight Carbonate Reservoirs, Oklahoma, Part 1

Part 1 of a 2-part Paradise Application Brief series demonstrating better well planning, identifying more productive perforation intervals and aiding ...
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Visualization and Characterization of Paleozoic (Ordovician-Devonian) Tight Carbonate Reservoirs, Oklahoma, Part 2

Part 2 of a 2-part Paradise Application Brief series applying multiple seismic attributes to enable easy high-grading of leaseholds, asses ...
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Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast

Exploring shallow Yegua formation as an independent method to accurately identify anomalies and exposing direct hydrocarbon indicators using Self-Organizing Map ...
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Rocky Roden

Rocky Roden

Sr. Consulting Geophysicist

Rocky R. Roden owns his own consulting company, Rocky Ridge Resources Inc., and works with several oil companies on technical and prospect evaluation issues. He also is a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. He is a proven oil finder (36 years in the industry) with extensive knowledge of modern geoscience technical approaches (past Chairman – The Leading Edge Editorial Board). As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in U.S.A., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. He holds a BS in Oceanographic Technology-Geology from Lamar University and a M.S. in Geological and Geophysical Oceanography from Texas A&M University. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES.

Published Work by Rocky Roden:

Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

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 ...
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Making Sense of Machine Learning

Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning

The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir ...
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Seismic Interpretation of DHI Characteristics with Machine Learning

The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this ...
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Interpretation of DHI Characteristics with Machine Learning

Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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Seismic Interpretation Below Tuning with Multiattribute Analysis

Seismic Interpretation Below Tuning with Multiattribute Analysis

Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of ...
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Seismic Interpretation Below Tuning with Multi-Attribute Analysis

This international webinar describes how multi-attribute seismic analysis is applied using the Paradise software to visualize thin beds and facies ...
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Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Comparison of Seismic Inversion and SOM Seismic Multi-Attribute Analysis

Self-Organizing Maps (SOM) is a relatively new approach for seismic interpretation in our industry and should not be confused with ...
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Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with ...
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Dr. Tom Smith

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.

Published Work by Dr. Tom Smith:

NEW e-Course by Dr. Tom Smith: Machine Learning Essentials for Seismic Interpretation

NEW e-Course by Dr. Tom Smith: Machine Learning Essentials for Seismic Interpretation

Machine learning is foundational to the digital transformation of the oil & gas industry and will have a dramatic impact ...
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The Holy Grail of Machine Learning in Seismic Interpretation

Dr. Tom Smith shares the "Holy Grail" of Machine Learning in Seismic Interpretation with the Geophysical Society of Houston ...
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Geobodies in Paradise: a Machine Learning Application

Dr. Tom Smith presents "Geobodies in Paradise: a Machine Learning Application" at the 2018 SEG Convention in Anaheim, California. Dr ...
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Machine Learning Revolutionizing Seismic Interpretation

Machine Learning for Seismic Interpretation driven by the nature of the technical and business demands facing geoscientists as oil and ...
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Geobody Interpretation Through Multi-Attribute Surveys, Natural Clusters and Machine Learning

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

This paper sets out a unified mathematical framework for the process from seismic samples to geobodies ...
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Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition ...
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Delighting in Geophysics

Dr. Thomas A. Smith founded Seismic Micro-Technology (SMT) in 1984 and led the development of the widely adopted Kingdom Suite ...
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Seismic Attribute Analysis Can Benefit From Unsupervised Neural Network

Process identifies anomalies from original data without bias using Unsupervised Neural Networks in Greenfield Exploration ...
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Introduction to Self-Organizing Maps in Multi-Attribute Seismic Data

Introduction to Self-Organizing Maps in Multi-Attribute Seismic Data

Unsupervised neural network searches multi-dimensional data for natural clusters. Neurons are attracted to areas of higher information density. The SOM ...
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Unsupervised Neural Networks - Disruptive Technology for Seismic Interpretation

Unsupervised Neural Networks – Disruptive Technology for Seismic Interpretation

Reducing the risk and time in finding oil and gas using machine learning techniques via unsupervised neural networks ...
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