IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v118y2015icp224-238.html
   My bibliography  Save this article

Multi-physics Markov chain Monte Carlo methods for subsurface flows

Author

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

Abstract

In CO2 sequestration in deep saline aquifers, contaminant transport in subsurface, or oil or gas recovery, we often need to forecast flow patterns. In the flow forecasting, subsurface characterization is an important step. To characterize subsurface properties we establish a statistical description of the subsurface properties that are conditioned to existing dynamic (and static) data. We use a Markov chain Monte Carlo (MCMC) algorithm in a Bayesian statistical description to reconstruct the spatial distribution of two important subsurface properties: rock permeability and porosity. The MCMC algorithm requires repeatedly solving a set of nonlinear partial differential equations describing displacement of fluids in porous media for different values of permeability and porosity. The time needed for the generation of a reliable MCMC chain using the algorithm can be too long to be practical for flow forecasting. In this paper we develop computationally fast and effective methods of generating MCMC chains in the Bayesian framework for the subsurface characterization. Our strategy consists of constructing a family of computationally inexpensive preconditioners based on simpler physics as well as on surrogate models such that the number of fine-grid simulations is drastically reduced in the generation MCMC chains. We assess the quality of the proposed multi-physics MCMC methods by considering Monte Carlo simulations for forecasting oil production in an oil reservoir.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:118:y:2015:i:c:p:224-238
    DOI: 10.1016/j.matcom.2014.11.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475414003231
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2014.11.023?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. 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.
    2. 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. Akbari, Hani & Engsig-Karup, Allan P., 2018. "Screening wells by multi-scale grids for multi-stage Markov Chain Monte Carlo simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 151(C), pages 15-28.
    2. 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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.

    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:118:y:2015:i:c:p:224-238. 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.