Blog

A data science blog for Petroleum Engineering.

Topics covered

artificial-intelligence artificial-lift batch-automation business-case cloud computational-physics computer-science conference courses data-driven-vs-physics data-engineering data-science data-scientists data-structures datasets deep-learning engineering-library fluid-properties gas-lift geology geophysics geoscience latex linux literate-programming logs loose-questions machine-learning modeling open-source-software paper-research petroleum-engineering petrophysics production-technology prosper pvt python r r-package reproducibility reservoir-engineering reservoir-simulation seismic shiny spe statistics text-mining tikz to-do transcript virtualization visualization-of-data vlp volve webapp well well-data well-logs well-modeling

Subject ▸ batch automation

Descending into the bottomhole. A Marching Algorithm for Vertical Lift Performance in Petroleum Engineering

5 February 2019

VLP, well modeling, batch automation, engineering library

Introduction I have always been captivated by calculations performed at depth in wells. The numerous correlations and curves that were built in the golden years of production engineering are just fascinating. Thank you Mr. Brown. Thank you Mr. Beggs. From all the various algorithms, I particularly liked one calculating the pressure losses in the tubing as the hydrocarbon fluids ascend to the surface, also called tubing performance, or vertical lift performance (VLP).

Scripts for Well Modeling and Batch Automation

29 November 2018

well modeling, batch automation

Motivation I am moving my collection of slides and previous data science work in production engineering to a static type website using Hugo and Rmarkdown. I came to the realization that is too much work to perform the calculations in one environment (R or Python), and then having to copy-paste code and plots. I think it will be more efficient to have R and Rmarkdown do that for me instead. I think that reproducibility is one of the most important aspects of data science that petroleum engineers have to strive for work in machine learning and artificial intelligence.