Moridis, Nefeli George (2020-04). Reserves and Resources Tracking. Doctoral Dissertation. Thesis uri icon

abstract

  • In this work, we develop a robust methodology for hydrocarbon inventory management by creating visual representations describing how volumes move from Prospective Resources to Reserves. This helps engineers visualize how volumes move for a given project, and also provides a visual description of the definitions in the Petroleum Resources Management System (PRMS) document, which is dense and can be difficult to understand. We propose methods to understand and quantify expected Reserves and Resources other than Reserves (ROTR) assets at any future time, incorporating the uncertainties that cause a change between the different Reserves and ROTR categories. We also develop a methodology to simulate the progression of hydrocarbons through the value chain based on actual events or specific planning strategies. The model will work in resources volumes, but we will incorporate conversions allowing us to quantify these volumes in units of energy or mass. The results from the proposed model are acceptable for decision making, can reduce analysis time, and may reduce the need for traditional evaluation methods. Furthermore, we incorporate the chance of commerciality (COC) to show the impact through the development of a project. This is a novel approach that shows the mathematical impact of the COC on Reserves and ROTR volumes. We then propose a methodology that aims to help engineers understand the spatial and time relationship of hydrocarbons. The results from this work show the impact of well spacing on Reserves, and discuss the time to move through different sub-classes which can be used to determine the return on investment. Finally, we discuss model accuracy through time by comparing a truncated dataset to a full dataset estimation results. Ideally, we want our initial estimates with the truncated dataset to be accurate. By comparing the amount of hydrocarbon booked as Reserves from the truncated dataset to the amount booked from the full dataset, we see the accuracy of the model through time. We aim to increase the accuracy of earlytime estimates to reduce the need to re-run the model, and to have a better understanding of the actual Reserves for the future of the project.

publication date

  • May 2020
  • May 2020
  • April 2020
  • May 2020
  • May 2020
  • May 2020