FORCE ML Hackathon: project round-up
The FORCE Machine Learning Hackathon last week generated hundreds of new relationships and nine new projects, including seven new open source tools. Here’s the full run-down, in no particular order…
Predicting well rates in real time
Team Virtual Flow Metering: Nils Barlaug, Trygve Karper, Stian Laagstad, Erlend Vollset (all from Cognite) and Emil Hansen (AkerBP).
Tech: Cognite Data Platform, scikit-learn. GitHub repo.
Project: An engineer from AkerBP brought a problem: testing the rate from a well reduces the pressure and therefore reduces the production rate for a short time, costing about $10k per day. His team investigated whether they could instead predict the rate from other known variables, thereby reducing the number of expensive tests.
This project won the Most Commercial Potential award.
The predicted flow rate (blue) compared to the true flow rate (orange). The team used various models, from multilinear regression to boosted trees.
Reinforcement learning tackles interpretation
Team Gully Attack: Steve Purves, Eirik Larsen, JB Bonas (all Earth Analytics), Aina Bugge (Kalkulo), Thormod Myrvang (NTNU), Peder Aursand (AkerBP).
Tech: keras-rl. GitHub repo.
Project: Deep reinforcement learning has proven adept at learning, and winning, games, and at other tasks including image segmentation. The team tried training an agent to pick these channels in the Parihaka 3D, as well as some other automatic interpretation approaches.
The agent learned something, but in the end it did not prevail. The team learned lots, and did prevail!
This project won the Most Creative Idea award.
Early in training, the learning agent wanders around the image (top left). After an hour of training, the agent tends to stick to the gullies (right).
A new kind of AVO crossplot?
Team ASAP: Per Avseth (Dig), Lucy MacGregor (Rock Solid Images), Lukas Mosser (Imperial), Sandeep Shelke (Emerson), Anders Draege (Equinor), Jostein Heredsvela (DEA), Alessandro Amato del Monte (ENI).
Tech: t-SNE, UMAP, VAE. GitHub repo.
Project: If you were trying to come up with a new approach to AVO analysis, these are the scientists you’d look for. The idea was to reduce the dimensionality of the input traces — using first t-SNE and UMAP then a VAE. This resulted in a new 2-space in which interesting clusters could be probed, chiefly by processing synthetics with known variations (e.g. in thickness or porosity).
This project won the Best In Show award. Look out for the developments that come from this work!
Top: Illustration of the variational autoencoder, which reduces the input data (top left) into some abstract representation — a crossplot, essentially (top middle) — and can also reconstruct the data, but without the features that did not discriminate between the datasets, effectively reducing noise (top right).
The lower image shows the interpreted crossplot (left) and the implied distribution of rock properties (right).
Acquiring seismic with crayons
Team: Jesper Dramsch (Technical University of Denmark), Thilo Wrona (University of Bergen), Victor Aare (Schlumberger), Arno Lettman (DEA), Alf Veland (NPD).
Tech: pix2pix GAN (TensorFlow). GitHub repo.
Project: Not everything tht looks like a toy is a toy. The team spent a few hours drawing cartoons of small seismic sections, then re-trained the pix2pix GAN on them. The result — an app (try it!) that turns sketches into seismic!
This project won the People’s Choice award.
A sketch of a salt diapir penetrating geological layers (left) and the inferred seismic expression, generated by the neural network. In principal, the model could also be trained to work in the other direction.
Extracting show depths and confidence from PDFs
Team: Florian Basier (Emerson), Jesse Lord (Kadme), Chris Olsen (ConocoPhillips), Anne Estoppey (student), Kaouther Hadji (Accenture).
Tech: sklearn, PyPDF2, NLTK, JavaScript. GitHub repo.
Project: A couple of decades ago, the last great digital revolution gave us PDFs. Lots of PDFs. But these pseudodigital documents still need to be wrangled into Proper Data. This team took on that project, trying in particular to extract both the depth of a show, and the confidence in its identification, from well reports.
This project won the Best Presentation award.
Kaouther Hadji (left), Florian Basier, Jesse Lord, and Anne Estoppey (right).
Grain size and structure from core images
Team: Eirik Time, Xiaopeng Liao, Fahad Dilib (all Equinor), Nathan Jones (California Resource Corp), Steve Braun (ExxonMobil), Silje Moeller (Cegal).
Tech: sklearn, skimage, fast.ai. GitHub repo.
Project: One of the many teams composed of professionals from all over the industry — it’s amazing to see this kind of collaboration. The team did a great job of breaking the problem down, going after what they could and getting some decent results. An epic task, but so many interesting avenues — we need more teams on these problems!
The pipeline was as ambitious as it looks. But this is a hard problem that will take some time to get good at. Kudos to this team for starting to dig into it and for making amazing progress in just 2 days.
Learning geological age from bugs
Team: David Wade (Equinor), Per Olav Svendsen (Equinor), Bjoern Harald Fotland (Schlumberger), Tore Aadland (University of Bergen), Christopher Rege (Cegal).
Tech: scikit-learn (random forest). GitHub repo.
Project: The team used DEX files from five wells from the recently released Volve dataset from Equinor. The goal was to learn to predict geological age from biostratigraphic species counts. They made substantial progress — and highlighted what a great resource Volve will be as the community explores it and publishes results like these.
David Wade and Per Olav Svendsen of Equinor (top), and some results (bottom)
Lost in 4D space!
Team: Andres Hatloey, Doug Hakkarinen, Mike Brhlik (all ConocoPhillips), Espen Knudsen, Raul Kist, Robin Chalmers (all Cegal), Einar Kjos (AkerBP).
Tech: scikit-learn (random forest regressor). GitHub repo.
Project: Another cross-industry collaboration. In their own words, the team set out to “identify trends between 4D seismic and well measurements in order to calculate reservoir pressures and/or thickness between well control”. They were motivated by real data from Valhall, and did a great job making sense of a lot of real-world data. One nice innovation: using the seismic quality as a weighting factor to try to understand the role of uncertainty. See the team’s presentation.
Clustering reveals patterns in 4D maps
Team: Tetyana Kholodna, Simon Stavland, Nithya Mohan, Saktipada Maity, Jone Kristoffersen Bakkevig (all CapGemini), Reidar Devold Midtun (ConocoPhillips).
Project: The team worked on real 4D data from an operating field. Reidar provided a lot of maps computed with multiple seismic attributes. Groups of maps represent different reservoir layers, and thirteen different time-lapse acquisitions. So… a lot of maps. The team attempted to correlate 4D effects across all of these dimensions — attributes, layers, and production time. Reidar, the only geoscientist on a team of data scientists, also provided one of the quotes of the hackathon: “I’m the geophysicist, and I represent the problem”.
That’s it for the FORCE Hackathon for 2018. I daresay there may be more in the coming months and years. If they can build on what we started last week, I think more remarkable things are on the way!
One more thing…
I mentioned the UK hackathons last time, but I went and forgot to include the links to the events. So here they are again, in case you couldn’t find them online…
Aberdeen, 16 to 18 November, at RGU — sign up on Eventbrite.
London, 23 to 25 November at Olympia (right before PETEX) — sign up on Eventbrite.
What are you waiting for? Get signed up and tell your friends!