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A data science blog for Petroleum Engineering.

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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 ▸ VLP

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).

Multiple VLP correlations on well segments

12 July 2017

VLP

I just finished the design of a new R package for petroleum engineering that will run vertical lift performance (VLP) using the most used correlations in the industry; maybe I would be able to add on top a couple of mechanistic models. The design contemplates the ability of applying different VLP correlations to multiple segments of the well. Picture this: applying Duns-Ros in the first 1/3 of the well, Hagedorn-Brown in the next 1/3, and mechanistic Azis in the last 1/3.