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Machine Learning Models for Generating the Drilled Porosity Log for Composite Formations by Hany Gamal; Salaheldin Elkatatny; Ahmed Abdulhamid Mahmoud is a Earth and Planetary Sciences article available to read on EtoBox.

What is Machine Learning Models for Generating the Drilled Porosity Log for Composite Formations about?

Determining the porosity of the drilled formation is a significant task for formation evaluation purposes for further implementation in petroleum reservoir simulation and estimating the hydrocarbon reserves. The cost and time are common limitations for the rock porosity determination by the conventional logging way. To overcome these restrictions, this research presents a new approach for porosity estimation from the drilling data by employing the vision of the fourth industrial revolution. The study utilized three machine learning methods (support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and functional network (FN)) for developing rock porosity prediction models from the drilling parameters for composite types of formation lithology that include sandstone and carbonate formations in addition to shale rock. The drilling data covered the penetration rate, rotating speed, torque, weight on bit, mud pumping rate, and pressure of standpipe with 3817 measurements for developing the models. The three models were optimized for enhancing better accuracy level, and the models were evaluated by calculating the coefficient of correlation (R), variance accou

Who reads Machine Learning Models for Generating the Drilled Porosity Log for Composite Formations?

Author

Hany Gamal; Salaheldin Elkatatny; Ahmed Abdulhamid Mahmoud

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)