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Estimation of Permeability of a Reservoir using Deep Learning Algorithms on Well Logs

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Document pages: 5 pages

Abstract: Over the recent years, many of the researchers have made wide use of artificial intelligence in petroleum engineering. In this study of an oil reservoir, the determination of permeability is the primary key. Here, permeability(k) has been predicted through a dataset which has only four well logs present in it that form a direct or indirect relationship with permeability. Those four logs are Depth, Neutron Porosity (NPHI), Density (RHOB) and Gamma Ray (GR) using the approach of Deep Learning algorithms. The key algorithm of Deep Learning is Artificial Neural Networks. They are computing systems that are analogous to the neural networks in a human body. It is basically not a model but a framework for several different algorithms pertaining to machine learning working together and evaluate through a composite set of inputs inn the dataset. Feature scaling has been used to calibrate the parameters in the given dataset while the usage of Principle Component Analysis (PCA) has been done for feature extraction. This study has proved to pave the way for powerful and a concrete model for the determination of permeability in the projects of natural gas.

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