Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Petroleum and Natural Gas Engineering

Committee Chair

Shahab Mohaghegh

Committee Co-Chair

Sam Ameri

Committee Member

Sam Ameri

Committee Member

Kashy Aminian


There has been an increase in the need for energy in the recent past. Oil and gas stand as the source of energy that are widely used. The oil and gas reservoirs are targeted for the purposes of field development. The conventional methods of reservoir characteristics require computing techniques that are unique and complex, some of which are labor and time intensive. Mohaghegh argues that all efforts must be tried and made possible to apply Petroleum Data analytics in production and management of reservoir so as to earn a maximum return (Mohaghegh, Shale Analytics, 2017). Different methodologies have been applied to derive synthetic well logs from existing logs using technologies such as the artificial neural networks which can be used when well logs are absent due to several factors such as difficulty in the logging operation and the tool timing. The aim of this research is to explore the form and the nature of artificial intelligence and machine learning (neural network systems) to develop synthetic well logs and to explore this technology’s capabilities of building new strategies seeking development of oil and gas fields. By obtaining the data and feed it to the neural network the results demonstrate that developing synthetic well logs using artificial intelligence and machine learning is a feasible approach for the enhancement of formation evaluation and reservoir characterization. Artificial intelligence is a reliable and promising technology that can significantly contribute to solving petroleum engineering related problems especially when it comes to the importance of fast decision-making processes.