FORCE ML 2019: project round-up

The FORCE Machine Learning Hackathon and Symposium were a great success again this year (read all about last year). Kudos to Peter Bormann of ConocoPhillips Norge, who put the programme together — held over 3 days at the NPD in Stavanger, Norway, together. Here’s a round-up of the projects.

A visualization of how human-generated rock descriptions were distributed with respect to porosity measured from the core plug.

from.cr.dscrptn.to.clssfctn

The team took up Peter’s challenge of translating abbreviated core descriptions (hence the strange team name) into something useful. Overall, the pipeline was clean > translate > classify. Cleaning was required to deal with a lot of ‘as above’ and other expediencies. As a first pass for translation, they tried simply substituting complete words for abbreviations: sandstone for ss, limestone for ls, and so on, but had more success with a bidirectional LSTM.

Find it clean it analyse it

Given a pile of undifferentiated well files containing over 40,000 curves including LAS and DLIS, the team wanted to find and analyse image log data, especially FMIs. They successfully read the data they wanted with the new dlisio library from Equinor, then threw some texture analysis at it after interpolating across the data gaps and resampling to 360 bins. They then applied a k-means clustering with 6 clusters, to find some key textures in the data. GitHub repo.

Just Surf

Using a synthetic dataset, the team (mostly coders from Emerson) set out to use convolutional deep neural networks to check if the structural model seems sensible, quantify the uncertainty, and validate the gridding algorithm used. The team brought 100 realizations for each map, and tried various combinations of single realizations and statistics from the cohort. They found that transfer learning on ResNet-50 did better than training from scratch. They said they looked forward to building on the work to produce tools for quality assurance, and they hope to use seismic data next time.

Siamese seismic

The team applied a Siamese network, normally used on human faces, to the problem of classifying 3D seismic facies. The method is semi-supervised: the network is trained on the entire dataset, with some labeled subimages. This establises a latent space (a 3D latent space of the F3 seismic data is shown to the right) with semantically meaningful norms (i.e. distance between points means something useful), in which clusters can be found. Classification on unseen subimages is done in the latent space. The team almost had an app working, and also produced the start of a new open dataset of labels for the F3 seismic volume. The team was rewarded with a prize for innovation. GitHub repo.

Lost Frequencies

This team formed spontaneously at the Tuesday meetup when it looked like there might not be any seismic projects! They set out to estimate attenuation using neural networks. This involved learning to pick maximum frequency from the peak frequency plus the seismic trace. They found that a 1D CNN did best out of all the methods they tried, and that including well logs somehow would likely improve the result quite a bit.

Rock Pandas

A creenshot from the app the team built. Each circle is a collection of documents that can be filtered dynamically.

Geolocalizing documents is a much-needed task in any pile of PDF files. This team got lots of documents from Peter, with the goal to put them on a map. The characteristically diverse team extracted keywords from an NPD corpus, with preprocessing and regular expressions for well names and so on. They built a nice-looking slippy map app allowing a user to click on a well or field entity, and see the documents associated with the location. Documents hitting multiple keywords were tagged on many entities. The Rock Pandas team won the coveted People's Choice Award, for making a great start on a hard problem, and producing a working app in limited time. GitHub repo.

Core team

In a reprise of a project last year, the team set out to get grain size from core photos. But then they thought: why not cut out the middle man and go straight for reservoir parameters? So they tried to get permeability from core photos. Using simple models, they got an accuracy of 60% with linear regression, and 69% with a neural network. Although they had some glitches in their approach (using porosity and not using depth, for example), they built a first pipeline for an interesting problem.

Some Unsupervised team members clustering around a problem.

Somehow Unsupervised

Unsupervised learning has been a theme in a coupe of previous hackathons (Copenhagen and FORCE 2018), and it was good to see another iteration of these exciting ideas. The team used the very nice Geolink dataset. After filtering out poor quality data (based on caliper and local statistics), the team applied dimensionality reduction methods like UMAP and t-SNE (these are conceptually like PCA, but much more effective) to reduce the dataset to just 2 dimensions — allowing them to make lots of crossplots. Coloring points by lithology, sand type, GR, or fluid type allowed them to look at all sorts of trends and patterns. The team won a prize for the amount of ground they covered and the attractive plots. GitHub repo.

Rock Stars

The Rock Stars took on Peter’s Make me that rock project. He wants an app which provides plausible rock properties and uncertainty for any location, depth, and formation on the Norwegian shelf. This gigantic team (12 of them!) decided to cluster the data first, then build a model for each cluster. They built an app which could indeed provide porosity and permeability given a location and depth. That such a huge team managed to converge on anything was an achievement, and they won a prize for taking on a tough project and getting a good way into it.

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That’s it for this year! Thanks to all the participants for a fun week, and thank you to the sponsors (below) for supporting the event. Hope to see you in 2020.

More pictures from the event. Thanks to Alex Schaaf and the others that took photos.