Porosity Permeability (Poro-Perm) Log-Linear Regression in Python - Petrophysics

Permeability is one of the key reservoir properties we as petrophysicists attempt to derive as part of our workflow. As well logging tools do not provide a direct measurement for permeability, we have to infer it through relationships with core data from the same field or well, from empirically derived equations or NMR data.

One common method of deriving permeability is to plot core porosity (on a linear scale) against core permeability (on a logarithmic scale) and observe the trend.

From this, a regression can be applied to the porosity permeability (poro-perm) crossplot to derive an equation. This can subsequently be used to predict a continuous permeability from a computed porosity in any well.

In this week's video, I demonstrate the application of linear regression within Python to derive a poro-perm relationship. However, when doing this we need to account for permeability being logarithmically scaled and porosity being linearly scaled.

▼ ---- SUPPORT THE CHANNEL ---- ▼

▼ ---- GET THE CODE --- ▼

▼ ---- RECOMMENDED BOOKS ---- ▼
As an Amazon Associate, I earn from qualifying purchases. By buying through any of the links below I will earn a commission at no extra cost to you.

PYTHON FOR DATA ANALYSIS: Data Wrangling with Pandas, NumPy, and IPython

FUNDAMENTALS OF PETROPHYSICS

PETROPHYSICS: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties

WELL LOGGING FOR EARTH SCIENTISTS

GEOLOGICAL INTERPRETATION OF WELL LOGS

▼ ---- SOCIAL CHANNELS ---- ▼
Thanks for watching, if you want to connect you can find me at the links below:

#petrophysics #regression #python #pythonprogramming

[joaovictorfernandes]

Congratulations on the channel. Here is shown a high quality content of Petrophysics.

Andy, I would like to propose a mental exercise:

Imagine a situation where you have wells situated in a mature field where common logging data such as:

GR, ILD, NPhi, RHOb, SP and DTco. Nothing else, no core data or the ability to get them.

Apart from the basic conventional petrophysical analysis, with the calculation of VSh, Porosity and Water saturation by the Archie equation (or other models), what else you could do to improve and work with this data, generating results that improve and facilitate evaluation or even generate interesting results? i.e, what to apply (Machine Learning, Neural Networks, Fuzzy Logic, Rock Typing, or any other methodology...) - and in order to acquire which new properties - only with these conventional logs, without the need for core data, that can generate interesting results? It's possible? What would you do with this data and in this specific case?

[kushwantsingh]

Hi Andy. What's the difference between the CKHG and CKHL perm values? Can you shed some light? Cheers