Use of solution error models in history matching

Michael Andrew Christie, Gillian Elizabeth Pickup, Alannah Eileen O'Sullivan, Vasily Demyanov

    Research output: Contribution to conferencePaper

    Abstract

    Uncertainty in reservoir models can be quantified by generating large numbers of history-matched models, and using those models to forecast ranges of hydrocarbons produced. The need to run large numbers of simulations inevitably drives the engineer to compromises in either the physics represented in the reservoir model, or in the resolution of the simulations run. These compromises will often introduce biases in the simulations, and the unknown reservoir parameters are estimated using the biased simulations, which can lead to biases in the parameter estimates. Solution error models can be used to correct for the effects of the biases. Solution error models work by building a statistical model for the differences between fine and coarse simulations (or between full physics and reduced physics simulations) using data from simulations at a limited number of locations in parameter space. The statistical model then produces estimates of the error elsewhere in parameter space; these estimates are used to correct the effects of the coarse model biases. In this work, we apply a solution error model to material balance calculations. Material balance is frequently used in reservoir engineering to estimate the initial oil in place. However such models are very simple, treating the reservoir as a tank and allowing instantaneous equilibration of fluids within the tank. The results of material balance simulations will therefore not be consistent with multi-cell reservoir simulations. We use a model based on Teal South Reservoir in the Gulf of Mexico to demonstrate how an error model can correct a material balance model to the accuracy of a reservoir simulation.
    Original languageEnglish
    Pages1-8
    Number of pages8
    Publication statusPublished - Sep 2008
    Event11th European Conference on the Mathematics of Oil Recovery 2008 - Bergen, Norway
    Duration: 8 Sep 200811 Sep 2008

    Conference

    Conference11th European Conference on the Mathematics of Oil Recovery 2008
    Abbreviated titleECMOR XI
    CountryNorway
    CityBergen
    Period8/09/0811/09/08

    Fingerprint

    history
    simulation
    physics
    hydrocarbon
    engineering
    fluid
    parameter
    material
    oil

    Cite this

    Christie, M. A., Pickup, G. E., O'Sullivan, A. E., & Demyanov, V. (2008). Use of solution error models in history matching. 1-8. Paper presented at 11th European Conference on the Mathematics of Oil Recovery 2008, Bergen, Norway.
    Christie, Michael Andrew ; Pickup, Gillian Elizabeth ; O'Sullivan, Alannah Eileen ; Demyanov, Vasily. / Use of solution error models in history matching. Paper presented at 11th European Conference on the Mathematics of Oil Recovery 2008, Bergen, Norway.8 p.
    @conference{5e1b397d841d4d2a999efc16af0c7185,
    title = "Use of solution error models in history matching",
    abstract = "Uncertainty in reservoir models can be quantified by generating large numbers of history-matched models, and using those models to forecast ranges of hydrocarbons produced. The need to run large numbers of simulations inevitably drives the engineer to compromises in either the physics represented in the reservoir model, or in the resolution of the simulations run. These compromises will often introduce biases in the simulations, and the unknown reservoir parameters are estimated using the biased simulations, which can lead to biases in the parameter estimates. Solution error models can be used to correct for the effects of the biases. Solution error models work by building a statistical model for the differences between fine and coarse simulations (or between full physics and reduced physics simulations) using data from simulations at a limited number of locations in parameter space. The statistical model then produces estimates of the error elsewhere in parameter space; these estimates are used to correct the effects of the coarse model biases. In this work, we apply a solution error model to material balance calculations. Material balance is frequently used in reservoir engineering to estimate the initial oil in place. However such models are very simple, treating the reservoir as a tank and allowing instantaneous equilibration of fluids within the tank. The results of material balance simulations will therefore not be consistent with multi-cell reservoir simulations. We use a model based on Teal South Reservoir in the Gulf of Mexico to demonstrate how an error model can correct a material balance model to the accuracy of a reservoir simulation.",
    author = "Christie, {Michael Andrew} and Pickup, {Gillian Elizabeth} and O'Sullivan, {Alannah Eileen} and Vasily Demyanov",
    year = "2008",
    month = "9",
    language = "English",
    pages = "1--8",
    note = "11th European Conference on the Mathematics of Oil Recovery 2008, ECMOR XI ; Conference date: 08-09-2008 Through 11-09-2008",

    }

    Christie, MA, Pickup, GE, O'Sullivan, AE & Demyanov, V 2008, 'Use of solution error models in history matching', Paper presented at 11th European Conference on the Mathematics of Oil Recovery 2008, Bergen, Norway, 8/09/08 - 11/09/08 pp. 1-8.

    Use of solution error models in history matching. / Christie, Michael Andrew; Pickup, Gillian Elizabeth; O'Sullivan, Alannah Eileen; Demyanov, Vasily.

    2008. 1-8 Paper presented at 11th European Conference on the Mathematics of Oil Recovery 2008, Bergen, Norway.

    Research output: Contribution to conferencePaper

    TY - CONF

    T1 - Use of solution error models in history matching

    AU - Christie, Michael Andrew

    AU - Pickup, Gillian Elizabeth

    AU - O'Sullivan, Alannah Eileen

    AU - Demyanov, Vasily

    PY - 2008/9

    Y1 - 2008/9

    N2 - Uncertainty in reservoir models can be quantified by generating large numbers of history-matched models, and using those models to forecast ranges of hydrocarbons produced. The need to run large numbers of simulations inevitably drives the engineer to compromises in either the physics represented in the reservoir model, or in the resolution of the simulations run. These compromises will often introduce biases in the simulations, and the unknown reservoir parameters are estimated using the biased simulations, which can lead to biases in the parameter estimates. Solution error models can be used to correct for the effects of the biases. Solution error models work by building a statistical model for the differences between fine and coarse simulations (or between full physics and reduced physics simulations) using data from simulations at a limited number of locations in parameter space. The statistical model then produces estimates of the error elsewhere in parameter space; these estimates are used to correct the effects of the coarse model biases. In this work, we apply a solution error model to material balance calculations. Material balance is frequently used in reservoir engineering to estimate the initial oil in place. However such models are very simple, treating the reservoir as a tank and allowing instantaneous equilibration of fluids within the tank. The results of material balance simulations will therefore not be consistent with multi-cell reservoir simulations. We use a model based on Teal South Reservoir in the Gulf of Mexico to demonstrate how an error model can correct a material balance model to the accuracy of a reservoir simulation.

    AB - Uncertainty in reservoir models can be quantified by generating large numbers of history-matched models, and using those models to forecast ranges of hydrocarbons produced. The need to run large numbers of simulations inevitably drives the engineer to compromises in either the physics represented in the reservoir model, or in the resolution of the simulations run. These compromises will often introduce biases in the simulations, and the unknown reservoir parameters are estimated using the biased simulations, which can lead to biases in the parameter estimates. Solution error models can be used to correct for the effects of the biases. Solution error models work by building a statistical model for the differences between fine and coarse simulations (or between full physics and reduced physics simulations) using data from simulations at a limited number of locations in parameter space. The statistical model then produces estimates of the error elsewhere in parameter space; these estimates are used to correct the effects of the coarse model biases. In this work, we apply a solution error model to material balance calculations. Material balance is frequently used in reservoir engineering to estimate the initial oil in place. However such models are very simple, treating the reservoir as a tank and allowing instantaneous equilibration of fluids within the tank. The results of material balance simulations will therefore not be consistent with multi-cell reservoir simulations. We use a model based on Teal South Reservoir in the Gulf of Mexico to demonstrate how an error model can correct a material balance model to the accuracy of a reservoir simulation.

    M3 - Paper

    SP - 1

    EP - 8

    ER -

    Christie MA, Pickup GE, O'Sullivan AE, Demyanov V. Use of solution error models in history matching. 2008. Paper presented at 11th European Conference on the Mathematics of Oil Recovery 2008, Bergen, Norway.