Estimation With P-field Calibrated From a Training Image

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Abstract/Contents

Abstract: We propose to build a non-linear estimator that corrects for the smoothing effect of kriging. The proposed estimator integrates multi-point statistics through probability fields, borrowed from a training image B deemed 'similar' to the actual field A under study. The probability field retained for the estimation at any unsampled location u E A is calibrated using the known probability values at those locations u' E B that share with u a similar data event. An criterion of similarity between data events in the actual field A and in the training image B is defined and a calibrated p-field estimation algorithm is implemented. The performance of the proposed estimation algorithm is then analyzed on a set of 2D training images, practical issues are discussed and improvements are proposed as future research.

Type of resource	text
Date created	December 1998

Author	Eberle, Nicolas
Primary advisor	Journel, Andre G.
Degree granting institution	Stanford University, Department of Petroleum Engineering

Subject	School of Earth Energy & Environmental Sciences
Genre	Thesis

Location	https://purl.stanford.edu/ny999bq5621

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.

Preferred Citation: Eberle, Nicolas. (1998). Estimation With P-field Calibrated From a Training Image. Stanford Digital Repository. Available at: https://purl.stanford.edu/ny999bq5621

Master's Theses, Doerr School of Sustainability

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