Welcome to our Digital Corner! A lot is happening in the EAGE digital world: on this page you can find some highlights from the latest initiatives on machine learning, A.I. and digitalization involving EAGE members worldwide, in addition to new contributions on the topic of Artificial Intelligence by the EAGE A.I. special interest community every week.
On 29 October 2020 you are invited to take part in the Applied Data Science ML Workshop on O&G, which complements the programme of the EAGE First Online Conference on Machine Learning in Latin America.
On 2 July 2020 EAGE Local Chapter Netherlands organized an online event on "Artificial Intelligence in Oil & Gas" featuring the following talks:
- Machine learning in seismic data processing by Dr Paul Zwartjes (Senior Research Geophysicist, Aramco)
- From super human performance in games to augmented decision making in the real world by Mr. Norbert Dolle (Managing Partner, WhiteSpaceEnergy)
The EAGE Interactive Online Short Courses (IOSC) are a new format. They bring carefully selected programme by experienced instructors from industry and academia online to give participants the possibility to follow the latest education in geoscience and engineering remotely. The courses are designed to be easily digested over the course of 2 or 3 days. Participants can interact live with the instructors and ask questions.
In EAGE's education offering, Machine Learning is a red thread. Keep an eye on upcoming IOSC courses and other learning opportunities to match your digitalization goals.
News from the EAGE A.I. Community
The A.I. Committee aims to share tips, techniques, learning experiences and whatever else is of interest to stay informed and up to speed with this emerging field. We also want geoscientists to maintain their employability throughout the many reorganization cycles when the world requires different skills than those taught to many of us technical experts and generalists while in university.
This week's focus is on staying up-to-date with the rapidly moving field of AI.
"Two-minute papers" is a video podcast series that aims to distill the hottest and most fascinating research in the field of computer vision and machine learning into a format accessible for everyone. The artificial intelligence podcast by Lex Friedman is an excellent resource for interviews with researchers around the field of AI and machine learning. Arxiv-sanity preserver is your one-stop-shop to the world of AI and machine learning preprints, where the most recent publications from the ArXiv can be found in one place.
|The Artificial Intelligence Podcast||Intermediate|
|ArXiv Sanity Preserver||Advanced|
The coronavirus outbreak put us in unprecedented times. This week we take a special look at the role A.I. can play in battling the pandemic as well as transforming the healthcare practice. Check out the latest issue of Nature Machine Intelligence for a general read about the potential advantages and challenges of deploying A.I. in the pandemic. With no prior medical expertise required "A.I. for Medicine Specialization" teaches how to apply A.I. tools to medical diagnosis, prognosis and treatment, including working with 2D and 3D medical image data. You can obtain open dataset, share code and models, and enter competitions on the largest machine learning community Kaggle to join the battle against Covid-19 as an A.I. practitioner.
|A path for A.I. in the pandemic||Beginner|
|AI for Medicine Specialization||Intermediate|
|Kaggle ML Community||Advanced|
If you haven’t had a go before – try it, get your hands (digitally) dirty. You can play without breaking anything (or in some cases even without installing anything). It can help you understand what’s possible. It can help at work, in your AI studies or across the rest of your life. Thankfully these days you don’t have to be a coding supremo to take those first steps. Much of the AI world is moving towards ‘low-code’ or even ‘no-code’, so you can do some pretty impressive AI stuff without leaving the comfort of a friendly app, whether that be on your phone or laptop. Below are a few cool places where you can start exploring the art-of-the-possible – hopefully it will inspire you and be fun, enjoy!
|AI Experiments with Google||Beginner|
|Machine Learning Experiments with GitHub||Beginner to Intermediate|
|Anaconda, incl. Orange (no code),
Jupyter, Spyder (Python)
& RStudio (low to high code)
|Beginner to Advanced|
A hurdle for many wanting to gain hands-on experience with AI is setting up a development environment - hours of frustration trying to install Python on Windows, we have all been there! Google's CoLab provides an online Jupyter-like environment with FREE GPU resources where you can experiment to your heart's content. Whilst Google has already provided a number of data science tutorials, one of the great benefits of CoLab is it is possible to open any .ipynb file. Whether you are looking at csv files, images or jumping right into manipulating segy data, there are hundreds of geoscience-specific examples sitting in open GitHub repositories. Here are three notebooks to get you started:
|Analysing thin section compositions||Beginner|
|An image segmentation example from the
TGS salt detection Kaggle competition
|Seismic inversion on the Volve dataset||Advanced|
Drastic improvements in hardware performance (GPU) enabled wide spread use of Deep Neural Networks (DNN). Combined with the Convolutional Neural Network (CNN) approach, they complement seismic workflows very well; fault detection, time lapse, inversion seismic-log integration, etc.
In application, careful consideration is advised: non transparency (black box), dependence on training data, outcomes being approximations, sometimes artefacts.
However, because of the multilayered architecture, Deep Learning has proven ‘unreasonably effective’, and improved understanding through research (MIT) will enable novel breakthroughs.
|TensorFlow Neural Network||Beginner|
|Deep Learning Specialization||Intermediate|
|Seismic Deep Learning libraries||Advanced|
Understanding what happens in images in crucial in the field of machine vision. This problem is broken up into separate but similar topics, such as classification, localization, object detection, semantic segmentation and image segmentation. Without realizing it, geoscientists face similar challenges. Think first break picking or salt interpretation. One of the workhorses for image segmentation problems is the U-net and to get ahead in the field, or simply grasp what your colleagues have developed for you now, one should really
have a basic understanding of this algorithm. Here are three useful links:
|Convolutional Networks for Biomedical Image Segmentation (video)||Beginner|
|Convolutional Networks for Biomedical Image Segmentation (paper)||Intermediate|
|U-net application fo TGS challenge||Advanced|
There is plenty of online training material on Deep Learning. This week we recommend three sources that are very useful for illustrating the practicalities of Deep Learning. They are real fun to use!
TensorFlow playground (already discussed in a different context) provides simple two-dimensional examples of feed-forward neural networks, mostly for classification, and displays the results in a very useful way for somebody who is new to neural networks.
3D Visualization of a Convolutional Neural Network shows the details of the structure and performance of a simple convolutional neural network applied to the classical MNIST dataset.
GAN Lab explains Generative Adversarial Networks, and it really helps understand the interaction between the Generator and the Discriminator.
|3D Visualization of a Convolutional Neural Network||Intermediate|
|Generative Adversarial Networks||Advanced|
In geoscience, efforts such as the SEG contest on facies prediction have inspired geoscientists to engage in the field of AI and serve as an excellent entry point for machine learning in geoscience.
Currently ongoing, the FORCE machine learning contest on wells and seismic provides a labeled dataset for facies prediction from wireline logs and a seismic dataset for fault detection.
These and other collaborative challenges will help to inspire future geoscientists and breakthrough technologies in applied machine learning for geoscience.
Google – Image classification
Microsoft – Image classification
When you want to step into run your own more domain specific data (e.g. timeseries or multiple attribute data) then many of the ‘AI Platforms’, like Dataiku and DataRobot allow you to register and run free versions. These systems can run ‘code free’, so if you can use excel then should be able to run those.
These are great ways to explore quickly what A.I. can do and see if it might be relevant for you and your data challenges.
The underlying principles of PINNs are detailed in this page.
An example of such a network being to solve the wave equation is illustrated by this paper.
And, for those ready to get your hands dirty, checkout the DeepXDE python library.
In short, interpretability means to determine a representation in terms of human understanding of the results; with few parameters (i.e. linear regression) this is straightforward. At the other end, Deep Neural Networks (DNN) are effective in finding subtle relationships among many features but are hard to interpret.
Recently developed methods to analyze DNN include LIME (Local Interpretable Model-Agnostic Explanations) and DeepLIFT (Deep Learning Important Features)
With using alternatives to DNN, the common belief is that interpretability goes at the expense of accuracy, on which assertion some disagree with.
For more inspiration, make sure to check out First Break's Special Topic "Machine Learning" 2020.
First Break is temporarily offered open access, including technical content and industry news.
More opportunities and news are shared within the A.I. community in LinkedIn: join to hear first-hand about upcoming initiatives and get involved!
Questions? Ideas? You can always reach us at email@example.com