• Study of Stress Concentration Factors at Pipe Welds in Relation to Hydrogen Induced Stress Cracking using Finite Element Method and Response Surface Modeling 

  Donthi, Nikhil (Master thesis, 2022)

  Duplex stainless steels, which are extensively used in the energy industry for subsea piping, have seen some significant failures, especially at the weld locations of cathodic protected piping components due to hydrogen ...

• 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 ...

• Implementation of Adaptive Localization for Enhancing Ensemble-Based History Matching in Hydrocarbon Reservoir Management 

  Pernalete Marta (Master thesis, 2022)

  In reservoir management, the ensemble-based history matching is applied to quantify and update uncertainty in reservoir characterization with the main objective to support high quality decisions. However, the ensemble-based ...

• Structural evolution of basement fault bounded fold structures using forward modelling methods: Application to the Beta structure in the Smeaheia area. 

  Gilje, Hanne (Master thesis, 2022)

  In rift systems, extensional fault-related folds play an important part on the deformation pattern, and they are key to understand fault and sediment growth. In this thesis, the Beta structure in the Smeaheia area, Horda ...

• IMPACT OF DATA PRE-PROCESSING TECHNIQUES ON MACHINE LEARNING MODELS 

  Tahir, Ali (Master thesis, 2022)

  The Volve dataset, which contains the time series values of different sensors that have been used at the Volve drilling site contains many flaws which make it hard for machine learning models to learn from the dataset and ...

• Joint History Matching of Multiple Types of Field Data in a 3D Field-Scale Case Study 

  Chalub Cruz, William; Luo, Xiaodong; Petvipusit, Kurt Rachares (Peer reviewed; Journal article, 2022-08)

  This work presents an ensemble-based workflow to simultaneously assimilate multiple types of field data in a proper and consistent manner. The aim of using multiple field datasets is to improve the reliability of estimated ...

• Seismic and log characterization of the Rogaland Group in the Norwegian Central Graben to the Åsta Graben 

  Yndestad Kirkhus, Adrian (Master thesis, 2022)

  With the rapid transformation in the energy sector, near field exploration is becoming a large priority. Although, the Paleocene Rogaland Group has been a successful target in different areas of the Norwegian Continental ...

• APPLICATION OF REINFORCEMENT LEARNING IN MANAGED PRESSURE DRILLING 

  Usama, Muhammad (Master thesis, 2022)

  Automation in any industry has a control system as its base, and control systems are composed of a controller. In recent years an area of machine learning known as reinforcement learning (RL) has been focused on solving ...

• Path design and optimization with obstacle avoidance via reinforcement learning 

  Haque, Md Fazlul (Master thesis, 2022)

  For the last couple of decades, finding an optimized drilling path has been one of the key concerns for drilling engineers. It takes a couple of months to plan a well for a large number of people. The motive of this thesis ...

• Ml-based porosity modeling tested on synthetic and subsurface data 

  Salomonsen, Einar (Master thesis, 2022)

  This thesis investigates if synthetic porosity models are useful as a basis for comparison between machine learning (ML) approaches to porosity prediction. In addition to the ML methods, the sequential gaussian simulation ...

• The Generation of Synthetic Healthcare Data Using Deep Neural Networks 

  Hossein Zadeh Nik, Alireza (Master thesis, 2022)

  High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict ...

• Porosity prediction from seismic data of the F3 block, offshore Netherlands, through seismic inversion and machine learning 

  Umar, Muhammad (Master thesis, 2022)

  Porosity is a key rock property that influences reservoir quality and plays an important role in petroleum exploration and production. The most common sources of information for reservoir characterization are well and ...

• Sensitivity Analysis for Drilling data using Machine Learning models 

  Tariq,Sofia (Master thesis, 2022)

  Over the last decade, machine learning models have become highly popular. Without requiring significant human participation, Machine Learning models improve the efficiency and dependability of any system. Machine Learning ...

• Experimental Reproduction of Wettability for Core Analysis 

  Aslanidis, Panagiotis (PhD thesis UiS;, Doctoral thesis, 2022-09)

  The ideal scenario when core samples are retrieved from the field to the lab is to start the experiment immediately without any treatment. Due to a decrease in pressure and temperature from reservoir to standard conditions, ...

• Relative Geologic Time By Dynamic Time Warping 

  Strøm, Aleksander S. (Master thesis, 2022)

  This thesis considers an approach to tackle a core problem within seismic interpretation, which is bringing an autonomously generated interpretation of the seismic data, which is now known as a Relative Geologic Time. The ...

• Testing Geological Reliability of Assisted History Matching Tools 

  Coffey, Martin (Master thesis, 2022)

  Assisted History Matching (AHM) has become a common practice in reservoir simulations. The algorithms used try to obtain through a function the models that best approximates the production data recorded during the reservoir ...

• Large Time Step schemes for Hyperbolic Conservation Laws in 2 Space Dimensions 

  Hoydal, Rúnar (Master thesis, 2022)

  Numerical solutions to partial differential equations (PDEs) will create varying amounts of error depending on different factors such as the numerical scheme and how fine the grid size is. In this thesis, we explored two ...

• Influence of Hyperparameters of Neural Ordinary Differential Equations in Their Ability to Model Dynamic Systems Governed by ODEs 

  Perilla Jonathan (Master thesis, 2022)

  In this thesis the Neural Ordinary Differential Equations (NODEs) are studied in their ability to model dynamic systems governed by ODEs. NODEs are a new type of artificial neural network that uses a feed-forward artificial ...

• Machine learning for pay zone identification in the Smørbukk field using well logs and XRF data 

  Dura Daniel (Master thesis, 2022)

  As geosciences enter the age of big data, a faster and more sophisticated tool is needed to automate manual interpretation workflows, limiting industry professionals' ability to harness all available well-log data to reduce ...

• Trajectory Control via Reinforcement Learning with RSS Model 

  Gocmen, Emre Baris (Master thesis, 2022)

  Various researchers proposed several types of methods, algorithms, and simulator to control bottom hole assembly (BHA) while drilling a deviated well. These works consist of drilling, control, mechanical and electrical ...

View more