2019 Oil & Gas Machine Learning Symposium

2019 Oil & Gas Machine Learning Symposium

30 October 2019

Call for abstracts open now!

We are delighted to announce that Geophysical Insights will be the hosting sponsor for the second annual Oil & Gas Machine Learning Symposium, which is now open for abstracts. 

The 2018 Oil & Gas Machine Learning Symposium was a huge success as it hosted thought leaders from E&P companies, consulting firms, and large technology companies. This year’s Symposium will be even larger and more impactful. With a focus on geoscience, reservoir characterization, and technology, the Symposium will highlight developments in Machine Learning, Data Analytics, Cloud Computing, and the Industrial Internet of Things (IIoT). Together, technologies are enabling the Digital Transformation of the energy industry, starting upstream where hydrocarbons are found and monetized.

Join industry innovators on 30 October 2019 in Houston, Texas to share ideas, best practices, and key learnings in the development and application of machine learning technologies.

For more information on the 2019 Oil & Gas Machine Learning Symposium, Click Here.

 

URTeC 2019 – Denver

URTeC 2019 – Denver

22-24 July 2019

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

Join us at URTeC 2019 hosted by SPE, SEG, and AAPG from 22-24 July 2019 where Carrie Laudon will be presenting “Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara” with Paradise software.

For more information, please click here.

Webinar: Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Webinar: Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

20 March 2019

We are delighted to kick off our quarterly machine learning webinar series this year with a presentation on applications of Convolutional Neural Networks (CNN) to seismic interpretation by Dr. Tao Zhao, Sr. Research Geophysicists with Geophysical Insights in Houston, Texas. Enabled by the development of GPU computing CNN, a form of deep learning, is proving to be an effective tool for many pattern recognition problems. Dr. Zhao will focus on three important applications of CNN that can have an immediate impact on interpretation workflows:

  • Seismic facies classification
  • Channel extraction
  • Fault detection

The webinar is free and runs an hour, including time for questions and answers at the end of the presentation.

Register today to attend the free webinar on Applications of Convolutional Neural Networks to Seismic Interpretation by Dr. Tao Zhao via the link below. You will receive a confirmation of your registration and details about how to log into the event upon registration. See below for the time of the webinar in your region.

Tao Zhao

Research Geophysicist | Geophysical Insights

TAO ZHAO joined Geophysical Insights in 2017. As a Research Geophysicist, Dr. Zhao develops and applies shallow and deep machine learning techniques on seismic and well log data, and advances multiattribute seismic interpretation workflows. He received a B.S. in Exploration Geophysics from the China University of Petroleum in 2011, an M.S. in Geophysics from the University of Tulsa in 2013, and a Ph.D. in geophysics from the University of Oklahoma in 2017. During his Ph.D. work at the University of Oklahoma, Dr. Zhao was an active member of the Attribute-Assisted Seismic Processing and Interpretation (AASPI) Consortium developing pattern recognition and seismic attribute algorithms.

Abstract:

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest.

In this webinar, using several seismic surveys acquired from different regions, I will discuss three CNN applications in seismic interpretation: seismic facies classification, fault detection, and channel extraction. Seismic facies classification aims at classifying seismic data into several user-defined, distinct facies of interest. Conventional machine learning methods often produce a highly fragmented facies classification result, which requires a considerable amount of post-editing before it can be used as geobodies. In the first application, I will demonstrate that a properly built CNN model can generate seismic facies with higher purity and continuity. In the second application, compared with traditional seismic attributes, I deploy a CNN model built for fault detection which provides smooth fault images and robust noise degradation. The third application demonstrates the effectiveness of extracting large scale channels using CNN. These examples demonstrate that CNN models are capable of capturing the complex reflection patterns in seismic data, providing clean images of geologic features of interest, while also carrying a low computational cost.

Boosting Reserves and Recovery Using Machine Learning and Analytics – AAPG Workshop

Boosting Reserves and Recovery Using Machine Learning and Analytics – AAPG Workshop

16-17 January 2019

Join us at the Boosting Reserves and Recovery Using Machine Learning and Analytics workshop hosted by the American Association of Petroleum Geologists on 16 and 17 January 2019 where Deborah Sacrey will be presenting “Finding Hydrocarbons and estimating reserves using Neural Net Classification and Geobodies” with Paradise software.

For more information, please click here.

Deborah Sacrey

Sr. Geoscientist | Geophysical Insights

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).

EAGE 2019 – London

EAGE 2019 – London

EAGE 2019, 3-6 June
Booth #740

Speakers & Presentations

Solving interpretation problems with deep learning and machine learning

Visit booth #740 to hear industry thought leaders present findings from applying machine learning and deep learning technologies to seismic interpretation in different geologic settings.  Learn how technologies like GPU processing will change and enable geoscience workflows.  Here are a few of the topics:

  • Seismic facies classification using deep learning
  • Applications of machine learning on North Sea data
  • Comparing machine learning methods
  • Multi-spectral fault enhancement using deep learning
  • GPU processing for attribute generation
  • Case studies in different depositional environments

For a printable version of the booth schedule, click here.

TimeTuesday, 4 June  Wednesday, 5 June  Thursday, 6 June  
9:30 – 10:30

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

Tim Gibbons

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

Tim Gibbons

Multiple Seismic Attributes and Machine Learning: North Sea Examples

Tim Gibbons

10:30 – 11:30

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

13:30 – 14:30

Generating Attributes on GPU

Paul Holzhauer and Mike Dunn

Finding the Best Attribute Combination for Seismic Facies Classification

Kurt Marfurt

Machine Learning on the Geoscience Technology Adoption Cycle

Rocky Roden

14:30 – 15:30

Accelerate Seismic Interpretation with Deep Learning

Dustin Dewett

Accelerate Seismic Interpretation with Deep Learning

Dustin Dewett

Introduction to Multi-Spectral Fault Enhancement with Case Studies

Dustin Dewett

15:30 – 16:30

Comparing Machine Learning Methods and the Black Box Perception

Rocky Roden

Machine Learning Applied to 3D Seismic Data from the Denver Julesburg Basin –  Improved Stratigraphic Resolution in the Niobrara

Mike Dunn

 

Access to 2019 EAGE Booth Presentations

Dr. Kurt Marfurt

Guest Speaker – The University of Oklahoma
Principal Investigator, AASPI Consortium

Finding the Best Attribute Combination for Seismic Facies Classification

Rocky Roden

Senior Geophysicist

Comparing Machine Learning Methods and the Black Box Perception

Machine Learning on the Geoscience Technology Adoption Cycle

Deborah Sacrey

Senior Geoscientist

A Journey through Paradise – Case Histories in Different Depositional Environments

Hal Green

Director – Marketing & Business Development

For more information, contact Hal at (M) +1 (713) 480-2260

Paul Holzhauer

Guest Speaker – NVIDIA
Director of Oil and Gas

Generating Attributes on GPU

Dustin Dewett

Product Manager

Accelerate Seismic Interpretation with Deep Learning

Introduction to Multi-Spectral Fault Enhancement with Case Studies

Mike Dunn

Senior Vice President, Business Development

Machine Learning Applied to 3D Seismic Data from the Denver Julesburg Basin –  Improved Stratigraphic Resolution in the Niobrara

Tim Gibbons

Geoscience Consultant

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

EAGE Workshop – Kuala Lumpur

EAGE Workshop – Kuala Lumpur

25-27 February 2019

For more information, please click here.

Dr. Iván Marroquín

Sr. Research Geophysicist | Geophysical Insights

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.