The London hackathon

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At the end of November I reported on the projects at the Oil & Gas Authority’s machine learning hackathon in Aberdeen. This post is about the follow-up event at London Olympia.

Like the Aberdeen hackathon the previous weekend, the theme was ‘machine learning’. The event unfolded in the Apex Room at Olympia, during the weekend before the PETEX conference. The venue was excellent, with attentive staff and top-notch catering. Thank you to the PESGB for organizing that side of things.

Thirty-eight digital geoscientists spent the weekend with us, and most of them also took advantage of the bootcamp on Friday; at least a dozen of those had not coded at all before the event. It’s such a privilege to work with people on their skills at these events, and to see them writing their own code over the weekend.

Here’s the full list of projects from the event…

Sweet spot hunting

Sweet Spot Sweat Shop: Alan Wilson, Geoff Chambers, Marco van der Linden, Maxim Kotenev, Rowan Haddad.

Project: We’ve seen a few people tackling the issue of making decisions from large numbers of realizations recently. The approach here was to generate maps of various outputs from dynamic modeling and present these to the user in an interactive way. The team also had maps of sweet spots, as determined by simulation, and they attempted to train models to predict these sweetspots directly from the property maps. The result was a unique and interesting exploration of the potential for machine learning to augment standard workflows in reservoir modeling and simulation. Project page. GitHub repo.

sweetspot_prediction.png

An intelligent dashboard

Dash AI: Vincent Penasse, Pierre Guilpain.

Project: Vincent and Pierre believed so strongly in their project that they ran with it as a pair. They started with labelled production history from 8 wells in a Pandas dataframe. They trained some models, including decision trees and KNN classifiers, to recognizedata issues and recommend required actions. Using skills they gained in the bootcamp, they put a flask web app in front of these to allow some interaction. The result was the start of an intelligent dashboard that not only flagged issues, but also recommended a response. Project page.

This project won recognition for impact.

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Predicting logs ahead of the bit

Team Mystic Bit: Connor Tann, Lawrie Cowliff, Justin Boylan-Toomey, Patrick Davies, Alessandro Christofori, Dan Austin, Jeremy Fortun.

Project: Thinking of this awesome demo, I threw down the gauntlet of real-time look-ahead prediction on the Friday evening, and Connor and the Mystic Bit team picked it up. They did a great job, training a series of models to predict a most likely log (see right) as well as upper and lower bounds. In the figure, the bit is currently at 1770 m. The model is shown the points above this. The orange crosses are the P90, P50 and P10 predictions up to 40 m ahead of the bit. The blue points below 1770 m have not yet been encountered. Project page. GitHub repo.

This project won recognition for best execution.

MysticBit_log-pred.png

The seals make a comeback

Selkie Se7en: Georgina Malas, Matthew Gelsthorpe, Caroline White, Karen Guldbaek Schmidt, Jalil Nasseri, Joshua Fernandes, Max Coussens, Samuel Eckford.

Project: At the Aberdeen hackathon, Julien Moreau brought along a couple of satellite image with the locations of thousands of seals on the images. They succeeded in training a model to correctly identify seal locations 80% of the time. In London, another team of almost all geologists picked up the project. They applied various models to the task, and eventually achieved a binary prediction accuracy of over 97%. In addition, the team trained a multiclass convolutional neural network to distinguish between whitecoats (pups), moulted seals (yearlings and adults), double seals, and dead seals.

Impressive stuff; it’s always inspiring to see people operating way outside their comfort zone. Project page.

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Interpreting the language of stratigraphy

The Lithographers: Gijs Straathof, Michael Steventon, Rodolfo Oliveira, Fabio Contreras, Simon Franchini, Malgorzata Drwila.

Project: At the project bazaar on Friday (the kick-off event at which we get people into teams), there was some chat about the recent paper on lithology prediction using recurrent neural networks (Jiang & James, 2018). This team picked up the idea and set out to reproduce the results from the paper. In the process, they digitized lithologies from one of the Posiedon wells. Project page. GitHub repo.

This project won recognition for teamwork.

Lithographers_team_logs.png

Know What You Know

Team KWYK: Malcolm Gall, Thomas Stell, Sebastian Grebe, Marco Conticini, Daniel Brown.

Project: There’s always at least one team willing to take on the billions of pseudodigital documents lying around the industry. The team applied latent semantic analysis (a standard approach in natural language processing) to some of the gnarlier documents in the OGA’s repository. Since the documents don’t have labels, this is essentially an unsupervised task, and therefore difficult to QC, but the method seemed to be returning useful things. They put it all in a nice web app too. Project page. GitHub repo.

This project won recognition for Most Value.

A new approach to source separation

Cocktail Party Problem: Song Hou, Fai Leung, Matthew Haarhoff, Ivan Antonov, Julia Sysoeva.

Project: Song, who works at CGG, has a history of showing up to hackathons with very cool projects, and this was no exception. He has been working on solving the seismic source separation problem, more generally known as the cocktail party problem, using deep learning… and seems to have some remarkable results. This is cool because the current deblending methods are expensive. At the hackathon he and his team looked for ways to express the uncertainty in the deblending result, and even to teach a model to predict which parts of the records were not being resolved with acceptable signal:noise. Highly original work and worth keeping an eye on.

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A big Thank You to the judges: Gillian White of the OGTC joined us a second time, along with the OGA’s own Jo Bagguley and Tom Sandison from Shell Exploration. Jo and Tom both participated in the Subsurface Hackathon in Copenhagen earlier this year, so were able to identify closely with the teams.

Thank you as well to the sponsors of these events, who all deserve the admiration of the community for stepping up so generously to support skill development in our industry:

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That’s it for hackathons this year! If you feel inspired by all this digital science, do get involved. There are computery geoscience conversations every day over at the Software Underground Slack workspace. We’re hosting a digital subsurface conference in France in May. And there are lots of ways to get started with scientific computing… why not give the tutorials at Learn Python a shot over the holidays?

To inspire you a bit more, check out some more pictures from the event…

The Scottish hackathon

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On 16−18 November the UK Oil & Gas Authority (OGA) hosted its first hackathon, with Agile providing the format and technical support. This followed a week of training the OGA provided — again, through Agile — back in September. The theme for the hackathon was ‘machine learning’, and I’m pretty sure it was the first ever geoscience hackathon in the UK.

Thirty-seven digital geoscientists participated in the event at Robert Gordon University; most of them appear below. Many of them had not coded at all before the bootcamp on Friday, so a lot of people were well outside their comfort zones when we sat down on Saturday. Kudos to everyone!

The projects included the usual mix of seismic-based tasks, automated well log picking, a bit of natural language processing, some geospatial processing, and seals (of the mammalian variety). Here’s a rundown of what people got up to:

Counting seals on Scottish islands

Seal Team 6: Julien Moreau, James Mullins, Alex Schaaf, Balazs Kertesz, Hassan Tolba, Tom Buckley.

Project: Julien arrived with a cool dataset: over 6000 seals located on two large TIFFs images of Linga Holm, an island off Stronsay in the Orkneys. The challenge: locate the seals automatically. The team came up with a pipeline to generate HOG descriptors, train a support vector machine on about 20,000 labelled image tiles, then scan the large TIFFs to try to identify seals. Shown here is the output of one such scan, with a few false positive and false negatives. GitHub repo.

This project won the Most Impact award.

seals_test_image.png

Automatic classification of seismic sections

Team Seis Class: Jo Bagguley, Laura Bardsley, Chio Martinez, Peter Rowbotham, Mike Atkins, Niall Rowantree, James Beckwith.

Project: Can you tell if a section has been spectrally whitened? Or AGC’d? This team set out to attempt to teach a neural network the difference. As a first step, they reduced it to a binary classification problem, and showed 110 ‘final’ and 110 ‘raw’ lines from the OGA ESP 2D 2016 dataset to a convolutional neural net. The AI achieved an accuracy of 98% on this task. GitHub repro.

This project won recognition for a Job Well Done.

Why do get blocks relinquished?

Team Relinquishment Surprise: Tanya Knowles, Obiamaka Agbaneje, Kachalla Aliyuda, Daniel Camacho, David Wilkinson (not pictured).

Project: Recognizing the vast trove of latent information locked up in the several thousand reports submitted to the OGA. Despite focusing on relinquishment, they quickly discovered that most of the task is to cope with the heterogeneity of the dataset, but they did manage to extract term frequencies from the various Conclusions sections, and made an ArcGIS web app to map them.

relinquishment_team.jpg

Recognizing reflection styles on seismic

Team What’s My Seismic? Quentin Corlay, Tony Hallam, Ramy Abdallah, Zhihua Cui, Elia Gubbala, Amechi Halim.

Project: The team wanted to detect the presence of various seismic facies in a small segment of seismic data (with a view to later interpreting entire datasets). They quickly generated a training dataset, then explored three classifiers: XGBoost, Google’s AutoML, and a CNN. All of the methods gave reasonable results and were promising enough that the team vowed to continue investigating the problem. Project website. GitHub repo.

This project won the Best Execution award.

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Stretchy-squeezey well log correlation

Team Dynamic Depth Warping: Jacqueline Booth, Sarah Weihmann, Khaled Muhammad, Sadiq Sani, Rahman Mukras, Trent Piaralall, Julio Rodriguez.

Project: Making picks and correlations in wireline data is hard, partly because the stratigraphic signal changes spatially — thinning and thickening, and with missing or extra sections. To try to cope with this, the team applied a dynamic time (well, depth) warping algorithm to the logs, then looking for similar sections in adjacent wells. The image shows a target GR log (left) with the 5 most similar sections. Two, maybe four, of them seem reasonable. Next the team planned to incorporate more logs, and attach probabilities to the correlations. Early results looked promising. GitHub repo.

Making lithostrat picks

Team Marker Maker: Nick Hayward, Frédéric Ramon, Can Yang, Peter Crafts, Malcolm Gall

Project: The team took on the task of sorting out lithostratigraphic well tops in a mature basin. But there are speedbumps on the road to glory, e.g. recognizing which picks are lithological (as opposed to chronological), and which pick names are equivalent. The team spent time on various subproblems, but there’s a long road ahead.

This project won recognition for a Job Well Done.

marker-maker.jpg

Alongside these projects, Rob and I floated around trying to help, and James Beckwith hacked on a cool project of his own for a while — Paint By Seismic, a look at unsupervised classification on seismic sections. In between generating attributes and clustering, he somehow managed to help and mentor most of the other teams — thanks James!

Thank you!

Thank you to The OGA for these events, and in particular to Jo Bagguley, whose organizational skills I much appreciated over the last few weeks (as my own skills gradually fell apart). The OGA’s own Nick Richardson, the OGTC’s Gillian White, and Robert Gordon Universty’s Eyad Elyan acted as judges.

These organizations contributed to the success of these events — please say Thank You to them when you can!

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I’ll leave you with some more photos from the event. Enjoy!

Machine learning goes mainstream

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At our first machine-learning-themed hackathon, in New Orleans in 2015, we had fifteen hackers. TImes were hard in the industry. Few were willing or able to compe out and play. Well, it’s now clear that times have changed! After two epic ML hacks last year (in Paris and Houston), at which we hosted about 115 scientists, it’s clear this year is continuing the trend. Indeed, by the end of 2018 we expect to have welcomed at least 240 more digital scientists to hackathons in the US and Europe.

Conclusion: something remarkable is happening in our field.

The FORCE hackathon

Last Tuesday and Wednesday, Agile co-organized the FORCE Machine Learning Hackathon in Stavanger, Norway. FORCE is a cross-industry geoscience organization, coordinating meetings and research in subsurface. The event preceeded a 1-day symposium on the same theme: machine learning in geoscience. And it was spectacular.

Get a flavour of the spectacularness in Alessandro Amato’s beautiful photographs:

Fifty geoscientists and engineers spent two days at the Norwegian Petroleum Directorate (NPD) in Stavanger. Our hosts were welcoming, accommodating, and generous with the waffles. As usual, we gently nudged the participants into teams, and encouraged them to define projects and find data to work on. It always amazes me how smoothly this potentially daunting task goes; I think this says something about the purposefulness and resourcefulness of our community.

Here’s a quick run-down of the projects:

  • Biostrat! Geological ages from species counts.

  • Lost in 4D Space. Pressure drawdown prediction.

  • Virtual Metering. Predicting wellhead pressure in real time.

  • 300 Wells. Extracting shows and uncertainty from well reports.

  • AVO ML. Unsupervised machine learning for more geological AVO.

  • Core Images. Grain size and lithology from core photos.

  • 4D Layers. Classification engine for 4D seismic data.

  • Gully Attack. Strat trap picking with deep reinforcement learning.

  • sketch2seis. Turning geological cartoons into seismic with pix2pix.

I will do a complete review of the projects in the coming few days, but notice the diversity here. Five of the projects straddle geological topics, and five are geophysical. Two or three involve petroleum engineering issues, while two or three move into sed/strat. We saw natural language processing. We saw random forests. We saw GANs, VAEs, and deep reinforcement learning. In terms of input data, we saw core photos, PDF reports, synthetic seismograms, real-time production data, and hastily assembled label sets. In short — we saw everything.

Takk skal du ha

Many thanks to everyone that helped the event come together:

  • Peter Bormann, the mastermind behind the symposium, was instrumental in making the hackathon happen.

  • Grete Block Vargle (AkerBP) and Pernille Hammernes (Equinor) kept everyone organized and inspired.

  • Tone Helene Mydland (NPD) and Soelvi Amundrud (NPD) made sure everything was logistically honed.

  • Eva Halland (NPD) supported the event throughout and helped with the judging.

  • Alessandro Amato del Monte (Eni) took some fantastic photos — as seen in this post.

  • Diego Castaneda and Rob Leckenby helped me on the Agile side of things, and helped several teams.

And a huge thank you to the sponsors of the event — too many to name, but here they all are:

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There’s more to come!

If you’re reading this thinking, “I’d love to go to a geoscience hackathon”, and you happen to live in or near the UK, you’re in luck! There are two machine learning geoscience hackathons coming up this fall:

Don’t miss out! Get signed up and we’ll see you there.

New open data and a competition

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First, a quick announcement. EMC, the data storage and cloud computing company, has stepped up to sponsor the Subsurface Hackathon in Vienna in a few weeks. Their generous help will ensure a fun event with some awesome prizes — so get signed up and start planning your project!

New open data

A correspondent got in touch last week about an exciting new open seismic dataset. In the late summer and early autumn of 2015, the WesternGeco-acquired a large new 2D seismic survey in the Rockall Basin and the Mid North Sea High for the UK Government. The survey cost about £20 million and consists of 20,000 km of new broadband PreSTM data. At the end of March, the dataset will be released to the public for free download, along with about 20,000 km of legacy 2D data, 40,000 km of new gravity and magnetic data, and wells.

© Crown Copyright — Used under fair use provision.

If you are interested in downloading the data, the government is asking that you fill out this form — it will help them figure out what to make available, and how much infrastructure to provision. Excitingly, they are asking about angle stacks, PSTM gathers, not just the full stack. It sounds like being an important resource for our community.

They are even asking about interest in the field data — all 60TB of it. There will almost certainly be a fee associated with the larger datasets, by the way. I asked about this and it sounds like it will likely be on the order of several thousand pounds to handle the full SEGD data, because of course it will be on physical media. But the government is open to suggestions if the geophysical community would like to find another way to distribute the data — do let me know if you'd like to talk about this.

New seed funding

Along with the data package, the government has announced an exciting new competition for 'seed funding':

The £500,000 competition has been designed to encourage geoscientists and engineers to develop innovative interpretations and products potentially using [this new open data]...

The motivation for the competition is clear:

It is hoped the competition will not only significantly increase the understanding of these frontier areas in respect of the 29th Seaward Licensing Round later in the year, but also retain talent in the oil and gas community which has been affected by the oil and gas industry downturn.

The parameters of the competition are spelled out in the Word document on this tender notice. It sounds like almost anything goes: data analysis, product development, even exploration activity. So get creative — and pitch the coolest thing you can think of!

You'll have to get cracking though, because applications to take part must be in by 1 April. If selected, the project must be delivered on 11 November.