A short guide on applying a linear regression in Python to semi-log data .Porosity-Permeability Relationships Using Linear Regression in Python
Core data analysis is a key component in the evaluation of a field or discovery, as it provides direct samples of the geological formations in the subsurface over the interval of interest. It is often considered the ‘ground truth’ by many and is used as a reference for calibrating well log measurements and petrophysical analysis. Core data is expensive to obtain and not acquired on every well at every depth. Instead, it may be acquired at discrete intervals on a small number of wells within a field and then used as a reference for other wells.
Once the core data has been extracted from the well it is taken to a lab to be analysed. Along the length of the retrieved core sample a number of measurements are made. Two of which are porosity and permeability, both key components of a petrophysical analysis.
Porosity is a key control on permeability, with larger pores resulting in wider pathways for the reservoir fluids to flow through.
Well logging tools do not provide a direct measurement for permeability and therefore it has to be inferred through relationships with core data from the same field or well, or from empirically derived equations.
In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...
Lets begin our machine learning journey. A Deep Dive into Linear Regression. Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.
What is regression analysis in simple words? How is it applied in practice for real-world problems?
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics.