IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v99y2014icp139-152.html
   My bibliography  Save this article

Rapid quantification of uncertainty in permeability and porosity of oil reservoirs for enabling predictive simulation

Author

Listed:
  • Ginting, V.
  • Pereira, F.
  • Rahunanthan, A.

Abstract

One of the most difficult tasks in subsurface flow simulations is the reliable characterization of properties of the subsurface. A typical situation employs dynamic data integration such as sparse (in space and time) measurements to be matched with simulated responses associated with a set of permeability and porosity fields. Among the challenges found in practice are proper mathematical modeling of the flow, persisting heterogeneity in the porosity and permeability, and the uncertainties inherent in them. In this paper we propose a Bayesian framework Monte Carlo Markov Chain (MCMC) simulation to sample a set of characteristics of the subsurface from the posterior distribution that are conditioned to the production data. This process requires obtaining the simulated responses over many realizations. In reality, this can be a prohibitively expensive endeavor with possibly many proposals rejection, and thus wasting the computational resources. To alleviate it, we employ a two-stage MCMC that includes a screening step of a proposal whose simulated response is obtained via an inexpensive coarse-scale model. A set of numerical examples using a two-phase flow problem in an oil reservoir as a benchmark application is given to illustrate the procedure and its use in predictive simulation.

Suggested Citation

  • Ginting, V. & Pereira, F. & Rahunanthan, A., 2014. "Rapid quantification of uncertainty in permeability and porosity of oil reservoirs for enabling predictive simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 99(C), pages 139-152.
    • Handle: RePEc:eee:matcom:v:99:y:2014:i:c:p:139-152
    DOI: 10.1016/j.matcom.2013.04.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475413000827
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2013.04.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pereira, F. & Rahunanthan, A., 2011. "A semi-discrete central scheme for the approximation of two-phase flows in three space dimensions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(10), pages 2296-2306.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hashan, Mahamudul & Jahan, Labiba Nusrat & Tareq-Uz-Zaman, & Imtiaz, Syed & Hossain, M. Enamul, 2020. "Modelling of fluid flow through porous media using memory approach: A review," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 643-673.
    2. Ginting, V. & Pereira, F. & Rahunanthan, A., 2015. "Multi-physics Markov chain Monte Carlo methods for subsurface flows," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 224-238.
    3. Al-Mamun, A. & Barber, J. & Ginting, V. & Pereira, F. & Rahunanthan, A., 2020. "Contaminant transport forecasting in the subsurface using a Bayesian framework," Applied Mathematics and Computation, Elsevier, vol. 387(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Correa, Maicon R., 2017. "A semi-discrete central scheme for incompressible multiphase flow in porous media in several space dimensions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 140(C), pages 24-52.
    2. Francisco, A. & Ginting, V. & Pereira, F. & Rigelo, J., 2014. "Design and implementation of a multiscale mixed method based on a nonoverlapping domain decomposition procedure," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 99(C), pages 125-138.
    3. Al-Mamun, A. & Barber, J. & Ginting, V. & Pereira, F. & Rahunanthan, A., 2020. "Contaminant transport forecasting in the subsurface using a Bayesian framework," Applied Mathematics and Computation, Elsevier, vol. 387(C).
    4. Ginting, V. & Pereira, F. & Rahunanthan, A., 2015. "Multi-physics Markov chain Monte Carlo methods for subsurface flows," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 224-238.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:99:y:2014:i:c:p:139-152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.