Browsing NTNU Open by Author "Kristoffersen, Brage Strand"

Now showing items 1-5 of 5

    • An open-source approach to Integrated Operations 

      Kristoffersen, Brage Strand (Master thesis, 2017)
      This thesis is based on exploring the topic of Integrated Operations (IO) and software development in the oil and gas industry. The philosophy of IO and how it impacted the oil and gas industry in the last decade is ...

    • Effect of CO2 tax on energy use in oil production: waterflooding optimization under different emission costs 

      Angga, I Gusti Agung Gede; Bellout, Mathias; Kristoffersen, Brage Strand; Bergmo, Per Eirik Strand; Slotte, Per Arne; Berg, Carl Fredrik (Peer reviewed; Journal article, 2022)
      Tackling emissions from hydrocarbon production is a necessity because hydrocarbon production will last for a prolonged time. As a popular hydrocarbon production method, waterflooding operation is energy-intensive and ...

    • Efficient well placement optimization under uncertainty using a virtual drilling procedure 

      Kristoffersen, Brage Strand; Silva, Thiago Lima; Bellout, Mathias; Berg, Carl Fredrik (Peer reviewed; Journal article, 2021)
      An Automatic Well Planner (AWP) is used to efficiently adjust pre-determined well paths to honor near-well properties and increase overall production. AWP replicates modern geosteering decision-making where adjustments to ...

    • Machine learning procedures for automatic well planning in reservoir simulation models 

      Kristoffersen, Brage Strand (Doctoral theses at NTNU;2021:404, Doctoral thesis, 2021)
      Simulation of reservoir models is a tool to optimize the development of an oil and gas reservoir. Part of the development is placement of wells in the reservoir, and this well placement optimization process is performed ...

    • Reduced well path parameterization for optimization problems through machine learning 

      Kristoffersen, Brage Strand; Bellout, Mathias; Silva, Thiago Lima; Berg, Carl Fredrik (Peer reviewed; Journal article, 2021)
      In this work we apply a recently developed machine learning routine for automatic well planning to simplify well parameterization in reservoir simulation models. This reduced-order parameterization is shown to be beneficial ...