Artificially Intelligent Earth Exploration: Teaching the Machine How to Characterize the Subsurface workshop

19-21 April 2020 | Muscat, Oman

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About

19-21 April 2020
Muscat, Oman

Artificial intelligence, and specifically machine learning, has become a powerful tool to address many of the challenges we face as we try to illuminate the Earth and make the proper prediction of its content. From fault detection, to salt boundary picking, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks efficiently and accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in computer power, machine learning algorithms, and the availability of the modules to apply such algorithms, have allowed the geoscientist to focus on the potential applications of such tools.

The objective of this workshop is to bring together interested individuals to share their experiences in applying machine learning algorithms in geoscience applications, especially applications related to the Oil and Gas sector. In addition, we’ll examine the various schools of thought in Machine Learning applications in the geoscience field to share advances and breakthroughs, highlight advantages and limitations, compare results, and debate emerging approaches in the world of artificial intelligence. What can we teach the machine, what can’t we teach it, and more importantly, what can it teach us?

The workshop invites contributions in all related subjects that utilize machine learning to solve geophysical data analysis challenges in the Oil and Gas sector. This includes the application of machine learning methods in supervised, unsupervised, reinforcement or any other method. Implementations may include numerous types of neural networks, like deep, feed-forward, convolutional, recurrent, GAN to the tasks solved by linear regression, SVM, and many other applications.

Organizing Committee

Committee Co-Chair: Tariq Alkhalifah, King Abdullah University of Science and Technology
Committee Co-Chair: Ali Almomin, Saudi Aramco
Committee Co-Chair: Ali Al-Naamani, Petroleum Development Oman

  • Ahmed Riza Ghazali, Petronas
  • Dimitri Bevc, Chevron
  • Elita Li, Singapore National Unviersity
  • Hussein Mustapha, Schlumberger
  • Robello Samuel, Halliburton
  • Saud Zakwani, Petroleum Development Oman
  • Song Hou, CGG
  • Tianyue Hu, Peking University
  • Tahar Ben Youssef, CGG
  • Thekriat Hussain, Kuwait Oil Company
  • Umair bin Waheed, King Fahd University of Petroleum and Minerals
  • Xiangliang Zhang, King Abdullah University of Science and Technology
  • Yang Liu, China University of Petroleum
  • Yang Ping, BGP
  • Yike Liu, Chinese Academy of Science

Contact

Anneke de Klerk
SEG Middle East
Telephone: +97143712710
Email: [email protected]

Attend

Who should attend?

Geoscientists, data scientists and engineers from industry and academia who have an interest in all related subjects that utilize machine learning to solve geophysical data analysis challenges in the Oil and Gas sector.

Workshop Format

The format of the workshop will feature presentations by invited key experts who will share their experiences. The workshop will serve as a unique opportunity to have open and extended discussions among the participants.

6 sessions (3 days). We will aim for one keynote speaker per session with a closing session at the end of the workshop to serve as the outlook going forward. The oral presentations are comprised of 20-minute talks followed by 5 minutes of Q&A. After each session, a 45 minute discussion session is planned. The objective of this session is to allow participants to discuss in detail and learn from each other what has worked for them and debate the issues and challenges facing them during their daily workflows. Session chairs will also serve as discussion leaders during the 45 minutes.

Call for Abstracts

Call for Abstracts submission deadline: 20 January 2020

Submit your abstract to [email protected]

Selection Framework

We would like you to share your machine learning experience, encompassing the following:

  • Data acquisition, pre-processing and classification?
  • Training data construction and labeling?
  • Reservoir characterization and seismic inversion
  • Choice of algorithm and architecture (the parameters)?
  • Constraints from physics based and classical methods?
  • Evaluation, validation procedures and uncertainty analysis?
  • Added value from ML: efficiency, accuracy etc.?
  • What is next? The future?

Abstract Technical Topics

We invite abstracts for the following technical topics, including but not limited to:

  • Interpretation and picking
  • 4D seismics and data matching
  • Data and wavefield interpolation and extrapolation
  • Image enhancements and improved resolution
  • Definition of Salt bodies and other subsurface features
  • Velocity analysis and inversion
  • Data processing automation and acceleration
  • Geophysical data integration
  • Reservoir geomechanics
  • Well logging and formation evaluation

Abstract Format

Max 2 page abstract + 1 figure, single column

Abstracts should include sufficient details for the committee to judge the quality of the submitted work. Abstracts should be a minimum of 1 page, text plus 1 figure, with a maximum of 2 pages. Abstracts should be on 8.5 x 11 inch paper size, text in Roman font, and include both text and figures.

Title should be one or two lines, at the top of the page, in bold font, and size 12 point. Authors should be listed in Roman italic font, size 10 point, and located just below the title. All text must stay 1 inch clear of the margins of the page. Submissions should be in Adobe Acrobat PDF format.

Sponsorship Opportunities

To maximize exposure and visibility for our partners, we offer an array of unique sponsorship opportunities designed to suit a range of budgets with specific target audiences for optimum return on investment. For a list of sponsorship opportunities and an application form for sponsorship, see the Sponsorship Packet in the Important Documents section of this page, or contact us at [email protected] for additional information.

Important Dates

Call for Abstracts deadline:
20 January 2020

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