Subsurface Hackathon project round-up, part 2

Following on from Part 1 yesterday, here are the other seven team projects from the hackathon:

Interactive visualization of Water Table heights over many years.

Interactive visualization of Water Table heights over many years.

Water, water everywhere

Water Underground: Martin Bentley (NMMU), Joseph Barraud (Rolls Royce), Rabah Cheknoun (UPPA)

The team built readers for the groundwater data available from, both the groundwater levels and the hydrochemistry. They clustered the data by aggregating by month and then looking for similarities in levels in the boreholes and built an open Jupyter notebook.




Seismic from noise

OBSNoise: Fernando Villanueva-Robles (IPGP), Yann Huet (Setec-Lerm), Ngoc Huyen Luu (Ecole Polytechnique), Dorian Bagur (Telecom ParisTech), Jonathan Grandjean (Independent)

The OBSNoise project investigated the application of machine learning to coherently stack ambient noise records collected from ocean bottom seismic (OBS) arrays in order to extract reservoir information. The team's results from synthetic data showed promise. If fully developed, this technology could be a virtually real-time monitoring system of dynamic reservoir properties.

The Killers. Killing It. 

The Killers. Killing It. 

Global geochemical data analytics

The Killers: Alexandre Sache, Violaine Delahaye, Karl Sache (all from Institute Polytechnique UniLaSalle), Côme Arvis, Guillaume Ligner (Ecole Polytechnique)

Two geoscience undergrads and one automotive design student (I know right?) from UniLaSalle hooked up with two data science students from Ecole Polytechnique to interogate the massive GeoRoc database using some clever data analytics tricks and did some novel many-dimensional geochemical classifications.

Team LogFix.

Team LogFix.

Fixing broken well data

LogFix: Guillaume Coffin (Telecom Evolution), Florian Napierala (EISTI), Camille Gimenez (Université Paris-Saclay), Tristan Siméon (Université de Montpellier), Robert Leckenby (Independent)

A truly pristine, calibrated, and corrected petrophysical data is so rare it has a sort of mythical status. Team LogFix used machine learning to identify bad-data zones, repair, QC, and fill-in missing sections. They got an impressive way with the problem, using a dataset from the Athabasca of Canada.

Between the hand-drawn lines

Automagical: Louis Poirier (Independent), Maggie Baber (Independent), Georg Semmler (GiGa infosystems), Björn Wieczoreck (GiGa infosystems), Jonas Kopcsek (GiGa infosystems)


You don't need to believe in magic. Team Automagical used machine learning to create 3D geological models from 2D cross-sections sections. They trained a predictive model using a collection of standardized hand-drawn cross-sections from human geoscientists. The model learns how to propagate rocks throughout a 3D scene. Their goal is to be able to generate cross-sections along any direction through the model. The AI learned how to do geologically realistic interpolation on simple structures. What kind of geologic complexity is possible with more input from more cross-sections?

The document on the left contains a log display with a lithology column. It's a 'hit'. The one on the right has no lithlogies and is a 'miss'.   

The document on the left contains a log display with a lithology column. It's a 'hit'. The one on the right has no lithlogies and is a 'miss'.


There's rocks in them hills! Hills of paper, that is

Logs on the Rocks: Daniel Stanton (Leeds University), Jack Woolam (Leeds University), Adam Goddard (Leeds University), Henri Blondelle (AgileDD)

If the oil and gas industry is to get more efficient, we better get really good at finding lithology and fluid information in the mountains of paper we've collectively built. Team Logs on the Rocks used CNNs to identify graphical depictions of rock types in a sea of unstructured PDFs and TIFFs. They introduced themselves as a team of non-coders, but these guys were were doing cloud computing on AWS and using NVIDIA's GPUs before the end of the weekend. 

Robot vision for seismic interpretation

It's not our FAULT! Claire Birnie (Leeds University), Carlos Alberto da Costa Filho (Edinburgh University), Matteo Ravasi (Statoil), Filippo Broggini (ETHZ), Gijs Straathof (SGS)

Geologic feature recognition using machine learning. The goal was to assist seismic interpreters in detecting geologic features – faults, folds, traps, etc. – in seismic data . They used Haar cascade classifiers, which are routinely used for identifying faces or kittens or beer bottles in photographs and video streams, specially trained to work on seismic data. They used the awesome OpenCV library to build this technology. At the time of writing, their website appears to be maxed out for the month, so if you're dying to see it, leave them a comment on LinkedIn asking them increase their capacity. And in the meantime, you can check out their project's repo on GitHub.

Kudos for the open source repo, team!

It was thrilling to see such a large range of data and applications. Digital thin-sections, ground water maps, seismic data, well logs, cross-sections, information in unstructured documents, and so on. Thanks to each and every individual that showed up with their expertise and enthusiasm. We're all better off because of it.

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