Integration of big data analysis and engineering advanced analytics to intelligently manage and control completion and artifical lift for selected well in Croatia

master's thesis

• Title: Integration of big data analysis and engineering advanced analytics to intelligently manage and control completion and artifical lift for selected well in Croatia : master's thesis
• Title (croatian): Integracija "big data" analize i inženjerskog načina razmišljanja s ciljem upravljanja i kontroliranja inteligentnog opremanja i umjetnog načina podizanja nafte i plina za odabranu bušotinu s područja Hrvatske : diplomski rad
• Author: Monika Miličević
• Mentor: Borivoje Pašić (mentor)
• Mentor: Mišo Soleša (mentor); VIAF: 305474609
• Committee member: Borivoje Pašić (predsjednik povjerenstva)
• Committee member: Vladislav Brkić (član povjerenstva)
• Committee member: Sonja Koščak Kolin (član povjerenstva)
• Granter: University of Zagreb; Faculty of Mining, Geology and Petroleum Engineering; (Department of Petroleum and Gas Engineering and energy); Zagreb
• Defense date and country: 2019-09-23, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mining, Petroleum and Geology Engineering; Petroleum Mining

Abstract

In order to reach more complex reservoir and increase ultimate recovery, engineers are searching for new technologies. One of these is intelligent completion which provides system monitoring, fluid production or injection control, and optimization. Operator can make decisions about managing completion based on real-time data coming from the downhole sensors. In addition, machine learning is becoming more popular in the oil industry. It finds application in automatization of processes and reducing time and error in decision making process. The aim of the thesis is to couple intelligent completion with machine learning (neural network) on the real example-gas well. The goal is to see if neural network can predict optimal interval control valve sizes for specific scenarios.

Abstract (croatian)

Inženjeri kontinuirano istražuju nove tehnologije kako bi razradili kompleksnija ležišta i povećali njihov ukupni iscrpak ležišta. Jedan od načina je i inteligentno opremanje koje pruža mogućnost daljinskog nadgledanja i kontrole cjelokupnog procesa pridobivanja ugljikovodika, te optimizaciju cijelog procesa. U takvom sustavu operator donosi odluke na temelju podataka koji dolaze u stvarnom vremenu sa senzora postavljenih u bušotinu. Nadalje, strojno učenje (engl. machine learning) postaje sve popularnije i u naftnoj industriji. Primjenjuje se u automatizaciji procesa kako bi se smanjilo vrijeme i greške prilikom donošenja odluka. Cilj ovoga rada je spojiti inteligentno opremanje sa neuronskom mrežom na stvarnom primjeru plinske bušotine. Uz to, cilj je vidjeti može li neuronska mreža predvidjeti optimalne veličine intervalnog kontrolnog ventila za različite slučajeve.

• Keywords: intelligent completion; ICV; neural network; classification problem; prediction
• Keywords (croatian): inteligentno opremanje; intervalni kontrolni ventil; neuronska mreža; problem klasifikacije; predikcija
• Language: english
• URN:NBN: urn:nbn:hr:169:056127
• Study programme: Title: Petroleum engineering; specializations in: Petroleum engineering, Energy production and supply, Environmental protection in petroleum engineering; Course: Petroleum engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka naftnog rudarstva (magistar/magistra inženjer/inženjerka naftnog rudarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-11-21 12:33:18