Early Recognition of Liquid Loading in Gas Wells Using Neural Networks

Mohamed Amine Ouarda

Early Recognition of Liquid Loading in Gas Wells Using Neural Networks

Mohamed Amine Ouarda

Chair of Petroleum and Geothermal Energy Recovery (550)

Research output: Thesis › Master's Thesis

Abstract

During the mature stage in the lifetime of a gas well, production volumes decrease along with its bottom-hole flowing pressure which may lead to the “loading of liquids” inside the wellbore. The continuous buildup of these liquids can cause significant problems to the gas production and can also lead to the killing of the well. Artificial intelligence is increasingly implemented to deal with the challenges faced in the oil and gas industry. Neural networks are suitable to identify the trends in the production data and to recognize the patterns that describe this phenomenon early on and the problem easier to resolve.

Translated title of the contribution	Früherkennung der Flüssigkeitsbeladung in Gasquellen mittels neuronaler Netze
Original language	English
Qualification	Dipl.-Ing.
Awarding Institution	Montanuniversität
Supervisors/Advisors	Zamani, Abbas, Supervisor (internal) Hofstätter, Herbert, Co-Supervisor (internal) Burgstaller, Christian, Supervisor (external), External person Fruhwirth, Rudolf, Co-Supervisor (internal)
Award date	15 Dec 2017
Publication status	Published - 2017

Bibliographical note

embargoed until 23-08-2022

Keywords

Neural Networks
Liquid Loading
Gas

Cite this

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title = "Early Recognition of Liquid Loading in Gas Wells Using Neural Networks",

abstract = "During the mature stage in the lifetime of a gas well, production volumes decrease along with its bottom-hole flowing pressure which may lead to the “loading of liquids” inside the wellbore. The continuous buildup of these liquids can cause significant problems to the gas production and can also lead to the killing of the well. Artificial intelligence is increasingly implemented to deal with the challenges faced in the oil and gas industry. Neural networks are suitable to identify the trends in the production data and to recognize the patterns that describe this phenomenon early on and the problem easier to resolve.",

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AU - Ouarda, Mohamed Amine

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N2 - During the mature stage in the lifetime of a gas well, production volumes decrease along with its bottom-hole flowing pressure which may lead to the “loading of liquids” inside the wellbore. The continuous buildup of these liquids can cause significant problems to the gas production and can also lead to the killing of the well. Artificial intelligence is increasingly implemented to deal with the challenges faced in the oil and gas industry. Neural networks are suitable to identify the trends in the production data and to recognize the patterns that describe this phenomenon early on and the problem easier to resolve.

AB - During the mature stage in the lifetime of a gas well, production volumes decrease along with its bottom-hole flowing pressure which may lead to the “loading of liquids” inside the wellbore. The continuous buildup of these liquids can cause significant problems to the gas production and can also lead to the killing of the well. Artificial intelligence is increasingly implemented to deal with the challenges faced in the oil and gas industry. Neural networks are suitable to identify the trends in the production data and to recognize the patterns that describe this phenomenon early on and the problem easier to resolve.

KW - Neural Networks

KW - Liquid Loading

KW - Gas

KW - Liquid Loading

KW - Neural Networks

KW - Gas

M3 - Master's Thesis

ER -