Improving the economic recovery of oil and gas through data analysis and optimal flow measurement

    Student thesis: Doctoral ThesisDoctor of Philosophy

    Abstract

    Although our energy supply is not longed purely wedded to fossil fuels but produced from a wider range of sources such as solar or wind these days, there remains a considerable challenge in providing affordable and reliable energy to all households around the world. The oil and gas industry as the biggest supplier to address this demand for energy still plays the major role in the energy market and has an extensive influence on the energy price. Increasing the economic efficiency of the processes and the energy-producing systems in this industry can therefore significantly contribute to securing energy affordability. With the ever-increasing application of data in the oil and gas industry, its availability and accuracy are of vital importance in hydrocarbon field management and increasing the economic recovery of oil and gas. Perhaps the most important type of data in the oil and gas industry are production flow rates which is a basis of decisions in hydrocarbon field management. In many cases, however, the production data of wells contain large flow measurement uncertainties or are not available continuously due to the shortcomings of the traditional methods of flow measurement or estimation that are still used in the industry. This research has investigated the effects of these uncertainties on the economic recovery of oil and gas reservoirs and tried to propose solutions for mitigating them. In order to do that, the uncertainties in the production data have been statistically analysed and the effects of the frequency of flow tests on the accuracy of allocation calculations and hydrocarbon accounting have been investigated (Chapter 3). The case studies in the analysis showed up to 80 million dollars reduction in the annual cost of allocation uncertainties when flow tests were undertaken weekly instead of monthly in an oil field with 36 production wells. Based on the statistical analysis, a method that includes the application of an artificial neural network has been proposed to find the minimum frequency of flow tests required to achieve a desired allocation error (Chapter 4). The effects of the uncertainties of flow data on history matching and well testing (Chapter 5), which are two main exercises contributing to reservoir management, have been investigated subsequently. The results show the significance of the negative effect of systematic errors and therefore the importance of regular calibration and maintenance of flow meters, installing multi-phase flow meters on individual wells, and recording the data downhole instead of on the surface.
    Date of AwardSep 2020
    Original languageEnglish
    Awarding Institution
    • Coventry University
    SupervisorSeyed Shariatipour (Supervisor), Andrew Hunt (Supervisor), Elena Gaura (Supervisor) & Manus Henry (Supervisor)

    Keywords

    • oil and gas
    • data analysis
    • flow measurement
    • reservoir management
    • allocation
    • machine learning
    • artificial neural network
    • reservoir simulation
    • modelling
    • uncertainty
    • hydrocarbon accounting
    • history matching
    • well testing

    Cite this

    '