Summer of HPC: Machine-Learning in Python for Well Log Correlation

I am currently in Edinburgh, home of the biggest celebration of arts and culture on the planet, and of course I am referring to the famous Fringe Festival. But we have plenty of time until the festival begins and my blog starts documenting all the exciting cultural events that happen in this cool city. So it is just the right moment for me to introduce you to my internship project for PRACE’s Summer of HPC.

I have spent the past two weeks working at EPCC on exciting topics that concern state-of-the-art geological applications and the use of Python libraries – such as Sklearn – that support machine-learning and scientific computing. It seems that there has been a change of plan, as far as the theme of my summer project is concerned. So, instead of earthquake hazard modelling, I will be working on the development of an application that geologists will be using to discover the geological time in which sediments with similar properties were deposited over large areas of geophysical interest. These properties have been measured up to this day in well logging. But to further explore areas for oil and gas, or minerals, requires many man-hours of well log correlation.

So now, you will be wondering how my work might help geologists and engineers decide which regions are most suited for specific geo-technical exploration. A very useful tool for the experts in this area of study and analysis would be simultaneous and automatic well log correlation based on measurements over large areas of interest. Since methodologies that can be implemented in the well log correlation problem have been documented, it sounds reasonable to combine these scientific breakthroughs with machine-learning methods for a more efficient overall documentation of an area's lithology in terms of facies classification based on limited well log measurements.

The project as presented above requires a lot of work, but I am more than content to work on the preliminary design and concept of this idea during my internship.

My main goal for this summer will be not only to work with a large amount of available measurements well logs data, but also to use existing models of well log correlation in the general concept of machine-learning applications using Python!!!

Just to give you a glimpse of the data available, I provide you with a visualization (see right) of the most common parameters as functions of depth that characterize a well log, along with a colobar with information concerning the lithology of the well. Wouldn’t it be awesome if we trained a neural network to identify the lithology with only these 5 parameters as shown here?

Image: Parameters as Functions of Depth[m] => Gamma Ray(GR), Induction Resistivity(ILD_log10), Photoelectric effect(PE), neutron-density porosity difference(DeltaPHI), average neutron-density porosity(PHIND).

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This post first appeared on the Summer of HPC blog.