Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field

    Research output: Contribution to conferencePaper

    Abstract

    In seismic history matching we use production data from wells and time-lapse (4D) seismic to constrain simulation models so that they better represent reservoir properties and behaviour. Together, these data types reduce the non-uniqueness of the problem, and therefore reduce the uncertainty of both the reservoir description and also the estimation of future behaviour. The more constraints we have, however, the harder it is to find the best models and more simulations may be required to search the parameter space. This leads to increasing computing costs, which must be balanced against the reduction in model uncertainty.

    We have developed a method of performing a cost:benefit analysis of including extra data and simulations in the history matching process. We use a Neighbourhood Algorithm to sample the parameter space and work in a Bayesian framework to determine model probabilities. After history matching, we then resample the posterior probability density to estimate parameter uncertainties. In addition, the parameter sampling has a density roughly in proportion to the probability distribution of the models. With this property of our method and with sufficient models, we then determine the most likely model outcome and its uncertainty. This enables calculation of expected saturation and pressure distributions at the time the data was measured and into the future. This is beneficial for reservoir management, particularly for identifying unswept areas.

    We apply our method to a UKCS field and analyse how the uncertainty changes in response to adding the seismic data to the history match. We also analyse the change in uncertainty as a function of the number of simulations carried out. We identify an optimum number of models that are required before we enter the domain of diminishing returns. We confirm that seismic is important if we wish to describe the reservoir some distance from production wells. We also find that some parameters may be determined more quickly than others, depending on their location relative to the data being used.
    Original languageEnglish
    Pages1-9
    Number of pages9
    Publication statusPublished - Sep 2006
    Event10th European Conference on the Mathematics of Oil Recovery 2006 - Amsterdam, Netherlands
    Duration: 4 Sep 20067 Sep 2006

    Conference

    Conference10th European Conference on the Mathematics of Oil Recovery 2006
    Abbreviated titleECMOR X
    CountryNetherlands
    CityAmsterdam
    Period4/09/067/09/06

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    history
    simulation
    well
    measuring
    cost-benefit analysis
    seismic data
    parameter
    saturation
    sampling
    cost
    method
    distribution

    Cite this

    Stephen, K. D. (2006). Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field. 1-9. Paper presented at 10th European Conference on the Mathematics of Oil Recovery 2006, Amsterdam, Netherlands.
    Stephen, Karl Dunbar. / Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field. Paper presented at 10th European Conference on the Mathematics of Oil Recovery 2006, Amsterdam, Netherlands.9 p.
    @conference{7cde9c47379c4d5484ad5624305812f9,
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    abstract = "In seismic history matching we use production data from wells and time-lapse (4D) seismic to constrain simulation models so that they better represent reservoir properties and behaviour. Together, these data types reduce the non-uniqueness of the problem, and therefore reduce the uncertainty of both the reservoir description and also the estimation of future behaviour. The more constraints we have, however, the harder it is to find the best models and more simulations may be required to search the parameter space. This leads to increasing computing costs, which must be balanced against the reduction in model uncertainty.We have developed a method of performing a cost:benefit analysis of including extra data and simulations in the history matching process. We use a Neighbourhood Algorithm to sample the parameter space and work in a Bayesian framework to determine model probabilities. After history matching, we then resample the posterior probability density to estimate parameter uncertainties. In addition, the parameter sampling has a density roughly in proportion to the probability distribution of the models. With this property of our method and with sufficient models, we then determine the most likely model outcome and its uncertainty. This enables calculation of expected saturation and pressure distributions at the time the data was measured and into the future. This is beneficial for reservoir management, particularly for identifying unswept areas.We apply our method to a UKCS field and analyse how the uncertainty changes in response to adding the seismic data to the history match. We also analyse the change in uncertainty as a function of the number of simulations carried out. We identify an optimum number of models that are required before we enter the domain of diminishing returns. We confirm that seismic is important if we wish to describe the reservoir some distance from production wells. We also find that some parameters may be determined more quickly than others, depending on their location relative to the data being used.",
    author = "Stephen, {Karl Dunbar}",
    year = "2006",
    month = "9",
    language = "English",
    pages = "1--9",
    note = "10th European Conference on the Mathematics of Oil Recovery 2006, ECMOR X ; Conference date: 04-09-2006 Through 07-09-2006",

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    Stephen, KD 2006, 'Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field' Paper presented at 10th European Conference on the Mathematics of Oil Recovery 2006, Amsterdam, Netherlands, 4/09/06 - 7/09/06, pp. 1-9.

    Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field. / Stephen, Karl Dunbar.

    2006. 1-9 Paper presented at 10th European Conference on the Mathematics of Oil Recovery 2006, Amsterdam, Netherlands.

    Research output: Contribution to conferencePaper

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    T1 - Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field

    AU - Stephen, Karl Dunbar

    PY - 2006/9

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    N2 - In seismic history matching we use production data from wells and time-lapse (4D) seismic to constrain simulation models so that they better represent reservoir properties and behaviour. Together, these data types reduce the non-uniqueness of the problem, and therefore reduce the uncertainty of both the reservoir description and also the estimation of future behaviour. The more constraints we have, however, the harder it is to find the best models and more simulations may be required to search the parameter space. This leads to increasing computing costs, which must be balanced against the reduction in model uncertainty.We have developed a method of performing a cost:benefit analysis of including extra data and simulations in the history matching process. We use a Neighbourhood Algorithm to sample the parameter space and work in a Bayesian framework to determine model probabilities. After history matching, we then resample the posterior probability density to estimate parameter uncertainties. In addition, the parameter sampling has a density roughly in proportion to the probability distribution of the models. With this property of our method and with sufficient models, we then determine the most likely model outcome and its uncertainty. This enables calculation of expected saturation and pressure distributions at the time the data was measured and into the future. This is beneficial for reservoir management, particularly for identifying unswept areas.We apply our method to a UKCS field and analyse how the uncertainty changes in response to adding the seismic data to the history match. We also analyse the change in uncertainty as a function of the number of simulations carried out. We identify an optimum number of models that are required before we enter the domain of diminishing returns. We confirm that seismic is important if we wish to describe the reservoir some distance from production wells. We also find that some parameters may be determined more quickly than others, depending on their location relative to the data being used.

    AB - In seismic history matching we use production data from wells and time-lapse (4D) seismic to constrain simulation models so that they better represent reservoir properties and behaviour. Together, these data types reduce the non-uniqueness of the problem, and therefore reduce the uncertainty of both the reservoir description and also the estimation of future behaviour. The more constraints we have, however, the harder it is to find the best models and more simulations may be required to search the parameter space. This leads to increasing computing costs, which must be balanced against the reduction in model uncertainty.We have developed a method of performing a cost:benefit analysis of including extra data and simulations in the history matching process. We use a Neighbourhood Algorithm to sample the parameter space and work in a Bayesian framework to determine model probabilities. After history matching, we then resample the posterior probability density to estimate parameter uncertainties. In addition, the parameter sampling has a density roughly in proportion to the probability distribution of the models. With this property of our method and with sufficient models, we then determine the most likely model outcome and its uncertainty. This enables calculation of expected saturation and pressure distributions at the time the data was measured and into the future. This is beneficial for reservoir management, particularly for identifying unswept areas.We apply our method to a UKCS field and analyse how the uncertainty changes in response to adding the seismic data to the history match. We also analyse the change in uncertainty as a function of the number of simulations carried out. We identify an optimum number of models that are required before we enter the domain of diminishing returns. We confirm that seismic is important if we wish to describe the reservoir some distance from production wells. We also find that some parameters may be determined more quickly than others, depending on their location relative to the data being used.

    M3 - Paper

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    EP - 9

    ER -

    Stephen KD. Measuring the value of time-lapse (4D) seismic as part of history matching in the Schiehallion UKCS field. 2006. Paper presented at 10th European Conference on the Mathematics of Oil Recovery 2006, Amsterdam, Netherlands.