All Blog posts by date

Using application microprocessors for seismic

First practical application that I know of using the next thing after TPUs (Tensor Processing Units): ASICs or Application Specific Integrated Circuit. Ideal dedicated hardware for the massive seismic data and processing. Machine Learning (ML) algorithms build a mathematical model based upon representative sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task. I limit my discussion here to supervised learning in the context of a potential application to seismic data image processing of a real marine seismic dataset, and then discuss how the computational scale of such exercises reinforce the need to develop computing technology that is customized for large ML problems.

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Well Failure Analysis by Kevin Ward

Article in LinkedIn.

How do you analyse well failure? Just to be clear upfront, I’m referring to geological failure, as opposed to engineering failure – I’ll leave that one for the engineers!

Well Failure Analysis by Kevin Ward.

Keywords: geology, well, Petrosys, dbMap


On Data Science

At request of colleagues, what I have to say on this is: I believe that data science doesn’t have the word “science” to make it look sexy. It really means it. Data Science as a discipline is not new. It has been living among us for 50 years. It was invented by scientists with deeply ingrained love for statistics. Statisticians have been the inventors and guardians of data science. They still are.

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Python, 3D seismic using segyio by Matteo Nicoli

Found this interesting article in LinkedIn:

WORKING WITH 3D SEISMIC DATA IN PYTHON USING SEGYIO AND NUMPY (MOSTLY) by Matteo Nicoli. It comes with code, Python notebook and repository.

Keywords: segyio, seismic, python, notebook


The whole point of data science

Article Predict Core Properties with Machine Learning by Amrita Sen. _“The OAG platform allows subject matter experts to then quickly build and assess machine learning models without having to learn Python or R.” _ <- ARR. But that is the whole point of the data science revolution: getting rid of black boxes and bring more open science to increase the discoveries and untap hidden oil. Petroleum engineers should strive to make reproducible examples.

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Calculate economic risk with regression using Python by Matteo Niccoli

Another reproducible example of regression using Python to calculate economic risk. By Matteo Niccoli (2017).



Reproducible notebook to generate cross sections from well logs by Jesse Pisel

Reproducible Python notebook to generate cross sections from well logs (Denver Basin). Includes Github repo. By Jesse Pisel (2019).

Keywords: LAS files, glob, cross section, Niobrara Formation, Denver Basin, Wyoming, gamma-ray curves


Python and PVT by Mark Burgoyne

PVT coded in Python!

Keywords: PVT, Python, phase behavior, EOS


Multiple Regression in Engineering Applications by Marco Rizk

Great article. And also an eye opener, specially, for those interested in correlations. I hope you publish your scripts, data and manuscript, soon for reproducibility purposes, and the benefit of the petroleum engineering community.

Keywords: linear regression, engineering, Alternating Conditional Expectations, algorithms, transformations


Not travelling but half way through the multiwell stats tutorial

No, I am not travelling or anything like that. I am actually half done writing the tutorial for the multiwell-stats application in Python. I will be writing it using the magnificent tools of data science, so we have a fully reproducible document. My pick for writing tutorials, booklets and books is #bookdown. It is an #rstats package that lets you combine math, code and text in the same document. Remember, data science is about reproducibility, as in reproducible research.

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