A new approach to seismic data integration during reservoir characterization using optimal non‐parametric transformations

Xue, Guoping; Datta‐Gupta, Akhil

doi:10.1190/1.1868154

Cited by 4 publications

(2 citation statements)

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“…An overview of this inverse problem can be found in Yeh (1986). Important applications include contaminant cleanup (James et al, 1997;Jin et al, 1995) and oil production (Xue and Datta-Gupta, 1996).…”

Section: Application In Subsurface Flowmentioning

confidence: 99%

Efficient models for correlated data via convolutions of intrinsic processes

Lee

Higdon

Calder

et al. 2005

Statistical Modelling

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Gaussian processes (GP) have proven to be useful and versatile stochastic models in a wide variety of applications including computer experiments, environmental monitoring, hydrology and climate modeling. A GP model is determined by its mean and covariance functions. In most cases, the mean is specified to be a constant, or some other simple linear function, whereas the covariance function is governed by a few parameters. A Bayesian formulation is attractive as it allows for formal incorporation of uncertainty regarding the parameters governing the GP. However, estimation of these parameters can be problematic. Large datasets, posterior correlation and inverse problems can all lead to difficulties in exploring the posterior distribution. Here, we propose an alternative model which is quite tractable computationally - even with large datasets or indirectly observed data - while still maintaining the flexibility and adaptiveness of traditional GP models. This model is based on convolving simple Markov random fields with a smoothing kernel. We consider applications in hydrology and aircraft prototype testing.

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Section: Application In Subsurface Flowmentioning

confidence: 99%

Efficient models for correlated data via convolutions of intrinsic processes

Lee

Higdon

Calder

et al. 2005

Statistical Modelling

View full text Add to dashboard Cite

show abstract

“…When limited geological and reservoir data were integrated in a stochastic model, studies that reduce their uncertainty had to be performed. Namely, reservoir characterization studies have been achieved by integrating static data, such as cores, logs, seismic, experiment, etc., and dynamic data, such as production, 4D seismic data (Huang et al, 1997;Xue and Datta-Gupta, 1996).…”

mentioning

confidence: 99%