Multivariate Stochastic Seismic Inversion with Adaptive Sampling

  • Author / Creator
    Hadavand-Siri, Maryam
  • Reservoir management tools use geostatistical models to make better decisions and improve hydrocarbon recovery. Developing improved numerical modeling techniques that lead to more accurate and precise geostatistical models will improve flow forecasting, hydrocarbon recovery, reservoir management and contribute to more responsible and sustainable management of our natural resources. The conventional stochastic seismic inversion techniques integrates different source of data to obtain high quality geostatistical models. These techniques initially provide acoustic impedance models that match well and seismic data. Such acoustic impedance models are related to reservoir physical properties via rock physics models. These techniques aim to reproduce the data within the quality of data. However, there is no guarantee that the final petrophysical models will reproduce original seismic data. Fidelity with the original seismic data will be reduced due to an element of randomness at each step. To overcome this issue a new approach is proposed and developed. The new approach proposes to simulate multiple reservoir physical properties simultaneously. This research study presents a research toward a fully coupled categorical - multivariate continuous reservoir modeling in stochastic inversion context with Petro-Elastic Model and convolution. The application of multivariate geostatistical techniques would improve conventional stochastic inversion approaches. The flexibility of using variogram based techniques and multi point statistic methods to model complex geological features further improves the stochastic inversion approach. The multivariate stochastic inversion approach combines a trace by trace (column wise) adaptive sampling algorithm with multivariate geostatistical techniques to pick the best physical properties of reservoir that match the actual seismic data. The adaptive sampling method uses an acceptance-rejection approach to condition geostatistical models to well and seismic data. This technique samples the realizations inside the space of uncertainty. The number of realizations attempted is changed based on the size of space of uncertainty. In this study, a general framework is presented to calculate the size of the space of uncertainty. This becomes practically relevant when rejection sampling approaches are being used to condition geostatistical models as in the case of stochastic inversion. The size of the space of uncertainty is shown to be the product of exponential entropy values. This is corroborated from information theory, but the application of this in presence of spatial correlation and conditioning data is new. Modeling multiple reservoir properties simultaneously through the close integration of seismic inversion and multivariate geostatistical techniques leads to high resolution reservoir property models that are suitable for improved reservoir management. A case study with realistic data set is developed to compare the results of multivariate stochastic inversion approach with conventional stochastic method.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Specialization
    • Mining Engineering
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Boisvert, Jeff (Civil and Environmental Engineering)
    • Bentley, Larry (Geoscience, University of Calgary)
    • Leung, Juliana (Civil and Environmental Engineering)
    • Sacchi, Mauricio ( Physics, Geophysics)
    • Askari-Nasab, Hooman (Civil and Environmental Engineering)