Data driven models to predict pore pressure using drilling and petrophysical data

The mud weight window (MW) determination is one of the most important parameters in drilling oil and gas wells, where accurate design can secure the drilled well and deliver a stable borehole. In this paper, novel algorithms based on the most influential set of input features are developed to predic...

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Bibliographic Details
Main Authors:	Farshad Jafarizadeh, Meysam Rajabi, Somayeh Tabasi, Reza Seyedkamali, Shadfar Davoodi, Hamzeh Ghorbani, Mehdi Ahmadi Alvar, Ahmed E. Radwan, Mako Csaba
Format:	Article
Language:	English
Published:	Elsevier 2022-11-01
Series:	Energy Reports
Subjects:	K-nearest neighbor distance weighted (DWKNN) Pore pressure prediction Hybrid machine learning Feature selection Root mean squared error
Online Access:	http://www.sciencedirect.com/science/article/pii/S2352484722008381

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http://www.sciencedirect.com/science/article/pii/S2352484722008381

Data driven models to predict pore pressure using drilling and petrophysical data

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