Blar i UiS Brage på forfatter "Hong, Aojie"
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A Decision Analysis Framework for Offshore Green Ammonia Project Investments
Valberg, Vegard (Master thesis, 2021)Ammonia has great importance to our daily lives – it is used in household cleaning products and to make agricultural fertilizers. Recently it has attracted attention from the energy sector, since it can be used as a ... -
A Decision Analysis Framework for Optimizing Electrification of Offshore Hydrocarbon Production Facilities for CO2 Emission Reduction
Heinze Moreno, Gabriel Esteban (Master thesis, 2021)The oil and gas industry has been increasingly focusing on sustainability and emissions in their offshore production facilities, where 80% of emissions come from the generation of electricity for powering their platforms ... -
A decision support system for multi-target geosteering
Alyaev, Sergey; Suter, Erich; Bratvold, Reidar Brumer; Hong, Aojie; Luo, Xiaodong; Fossum, Kristian (Journal article; Peer reviewed, 2019-12)Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation. In this paper ... -
Accounting for Model Error in Probabilistic History Matching to Improve Uncertainty Quantification
Heydari, Siavash (Master thesis, 2022)Model Error in probabilistic history matching is an important topic to study, but calculating the model error is a challenge since the truth is uncertain. In this thesis, sources of model error will be discussed briefly; ... -
An analysis of gas production forecasts on the NCS
Yogachandran, Ajhanth (Master thesis, 2021)Thesis assess operators ability to deliver accumulated gas forecasts for 40 fields on the Norwegian continental shelf (NCS) that received Plan for Plan for Development and Operations (PDO) approval in the years 2000 to ... -
Application of machine learning to assess the value of information in polymer flooding
Tadjer, Mohamed Amine Amazigh; Bratvold, Reidar Brumer; Hong, Aojie; Hanea, Remus Gabriel (Peer reviewed; Journal article, 2021-12)In this work, we provide a more consistent alternative for performing value of information (VOI) analyses to address sequential decision problems in reservoir management and generate insights on the process of reservoir ... -
Bayesian interactive decision support for multi-attribute problems with even swaps
Sandbakk, Trym Seim (Master thesis, 2022)Even swaps (ES) is a multi-criteria decision-making method introduced by Hammond et al. (1998) that makes it easier for decision makers (DMs) to make trade-offs between the decision criteria. The ES method can be further ... -
Decision-Driven Data Analytics for Well Placement Optimization in Field Development Scenario - Powered by Machine Learning
Kor, Peyman (Masteroppgave/UIS-TN-IER/2019;, Master thesis, 2019-06-15)Application of Data Analytics and Machine Learning (ML) in petroleum reservoir management have received a lot of attention in recent years, mainly due to the availability of sheer computational resources and recorded big ... -
Forecast of investment in renewable energy with consideration of dependency of investment in renewable energy and oil and gas prices
Waqar, Muhammad (Master thesis, 2021)Renewables are taking up the role of increasing importance in the energy industry, especially with the recent wave of the drop in prices of oil and gas. Therefore, oil and gas companies are gradually positioning themselves ... -
Impact of risk attitude on optimal IOR initiation time: A case study solved in a sequential decision-making framework powered by machine learning-based non-linear regression
Yanez Sanchez, Marianne Soledad (Master thesis, 2023)The least-squares Monte Carlo algorithm (LSM) is an efficient approximate dynamic programming algorithm for solving sequential decision-making problems, leveraging regression. Previous studies have showcased the LSM workflow ... -
Machine learning based decline curve analysis for short-term oil production forecast
Tadjer, Mohamed Amine Amazigh; Hong, Aojie; Bratvold, Reidar Brumer (Peer reviewed; Journal article, 2021-05)Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional ... -
Machine learning methods for assessing value-of-information
Shahali, Reihaneh (Master thesis, 2022)One of the most useful features of decision analysis is its ability to distinguish between constructive and wasteful information gathering. Value-of-information (VOI) and sequential information gathering (Value-of-Flexibility, ... -
Probabilistic decline curve analysis with multiple models
Bin Alim, Maaz (Master thesis, 2022)Numerical models have been established to help understand the longevity of projects when exploring and drilling for hydrocarbons. They aid in understanding and optimizing decisions on the long-term feasibility of a project ... -
Probabilistic Machine Learning for Production Optimization Under Uncertainty
Mirmohammadhosseini, Seyedehmina (Master thesis, 2023)This thesis investigates the application of Bayesian Optimization (BO) in the optimization of the Expected Net Present Value (ENPV) for oil field development. The objective is to maximize the ENPV while reducing the ... -
Project Portfolio Optimization in a Changing Energy Landscape
Moubarak, Racha (Master thesis, 2021)Portfolio analysis and optimization has for the past several decades been applied in the oil & gas industry for asset allocation with the goal of maximizing corporate value creation. More recently, the traditional task of ... -
Project Portfolio Optimization in a Changing Energy Landscape
Moubarak, Racha (Studentoppgave/UIS-TN-IEP/2020;, Master thesis, 2020-06-15)Portfolio analysis and optimization has for the past several decades been applied in the oil & gas industry for asset allocation with the goal of maximizing corporate value creation. More recently, the traditional task of ... -
Reinforcement Learning for Automated Power Grid Operation: Can a machine be trained to operate a power grid?
Bueno, Rebecca Santana (Master thesis, 2021)The increasingly high demand and amount of renewable energy sources in the power grid have added more complexity to power grid operations. The electrical power system must ensure that the generated power and the consumed ... -
A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring
Tadjer, Mohamed Amine Amazigh; Hong, Aojie; Bratvold, Reidar Brumer (Peer reviewed; Journal article, 2021-12)Carbon capture and sequestration (carbon capture and storage or CCS) represents a unique potential strategy that can minimize CO2 emissions in the atmosphere, and it creates a pathway toward a neutral carbon balance, which ... -
Utilizing machine learning algorithms in the ensemble-based optimization (EnOpt) method for enhancing gradient estimation
Raji, Nidaa (Master thesis, 2022)High or even prohibitive computational cost is one of the key limitations of robust optimization using the Ensemble-based Optimization (EnOpt) approach, especially when a computationally demanding forward model is involved ... -
Value of Information Analysis in CO2 Sequestration Projects
Elvaretta, Stacia (Master thesis, 2021)As one of the main risks related to CO2 storage is leakage, it is important to consider the impact of the reservoir uncertainties on the long-term CO2 migration and leakage which affecting the decisions in CO2 storage ...