Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

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" Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques "
Safari-Zanjani, Masoud Abdus Salam, Mohammad

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1105842
Doc. No	:	TLpq2330627608
Main Entry	:	Abdus Salam, Mohammad
	:	Safari-Zanjani, Masoud
Title & Author	:	Data-Driven Hydrocarbon Production Forecasting Using Machine Learning Techniques\ Safari-Zanjani, MasoudAbdus Salam, Mohammad
College	:	Southern University and Agricultural and Mechanical College
Date	:	2019
student score	:	2019
Degree	:	M.S.
Page No	:	100
Abstract	:	Data analytics utilizes advanced statistical and machine learning methods to find the concealed information and trends present in different types of datasets. These methods have recently shown a great potential to solve the problems in oil and gas industry. The ability to find insights from large datasets can make an oil company more profitable and successful. Innovation of sophisticated artificial intelligence methods as well as new developments in powerful high-speed computing resources have made the machine learning techniques more powerful than ever. The oil and gas industry has been benefited from these algorithms and machine learning techniques have been applied to many petroleum engineering challenges. Artificial Neural Network (ANN), Linear Regression (LR), and Support Vector Regression (SVR) were employed in this thesis to forecast the daily oil production using Volve oil field production dataset. All three methods show a great potential for hydrocarbon production forecasting. Results for well NO159-F-1C, however, indicate that ANN had the best performance compare to other two methods. This doesn’t mean that ANN is the superior method compare to LR and SVR in every situation. The performance of an algorithm must be examined for each specific case in order to select the best technique.
Subject	:	Computer science