Computer-based generation and validation of training images...

Details Computer-based generation and validation of training images... Computer-based generation and validation of training images...

2005-07-13

2009-12-08

Strege, John B (Department: 2624)

Image analysis

Applications

Seismic or geological sample measuring

C702S006000, C702S179000, C703S010000

Reexamination Certificate

active

07630517

ABSTRACT:
A computer-implemented method is provided that automatically characterizes and verifies stationarity of a training image for use in multipoint geostatistical analysis. The stationarity is preferably characterized by statistical measures of orientation stationarity, scale stationarity, and category distribution stationarity.

REFERENCES:
patent: 6035255 (2000-03-01), Murphy et al.
patent: 6070125 (2000-05-01), Murphy et al.
patent: 2004/0210547 (2004-10-01), Wentland et al.
patent: 2006/0041410 (2006-02-01), Strebelle
patent: 2006/0052938 (2006-03-01), Thorne et al.
patent: 2006/0241920 (2006-10-01), Le Ravalec-Dupin et al.
patent: 2 408 363 (2005-05-01), None
Bishop, C.M.Neural Networks for Pattern Recognition. Oxford: Oxford University Press (1995): 310-318.
Bradley et al. “Classification in Scale-Space: Applications to Texture Analysis.” International Conference on Information Processing in Medical Imaging (1995).
Caers, J. “Geostatistical History Matching Under Training-Image Based Geological Model Constraints.” SPE 77429, SPE Annual Technical Conference and Exhibition, San Antonio, TX, Sep. 29-Oct. 2.
Caers, J. “Efficient Gradual Deformation Using a Streamline-based Proxy Method”, Journal of Petroleum Science and Engineering, Elsevier, Amsterdam, NL, vol. 39, No. ½, Aug. 2003, pp. 57-83, XP001197320.
Clausi, D. Fast Co-occurrence Probability Texture Feature Extraction, Vision and Image Processing (VIP) Research Group, Systems Design Engineering, University of Waterloo, CRYSYS Workshop, Mar. 2002. From: http://www.ccin.ca/CRYSYS/meetings/2002—meeting/clausi—session7—2.ppt accessed Jun. 8, 2007.
Damsleth et al. “A Two-Stage Stochastic Model Applied to a North Sea Reservoir.” SPE 20605, 1992.
Deng et al. “Gaussian MRF Rotation-Invariant Features for Image Classification.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, No. 7 (Jul. 2004): 951-955.
Deutsch, C.V. and Libing Wang. “Hierarchical Object-Based Stochastic Modeling of Fluvial Reservoirs.” Mathematical Geology, vol. 28, No. 7 (1996): 857-880.
Feng, X. “Analysis and Approaches to Image Local Orientation Estimation.” Dess. University of California Santa Cruz, Mar. 2003.
Guardiano F. et al. “Multivariate Geostatistics: Beyond Bivariate Moments.”Geostatistics-Troia '92. Editor: A. Soares. Dordrecht, Netherlands, Kluwer Academic Publications. vol. 1: 133-144, 1992.
Hall et al. “Estimation of Critical Formation Evaluation Parameters Using Techniques of Neurocomputing.” SPWLA 36thAnnual Logging Symposium, Jun. 26-29, 1995: 1-12.
Haralick et al. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, No. 6 (Nov. 1973): 610-621.
Isaaks, E.H. And Srivastava, R.M. “An Introduction to Applied Geostatistics.”Applied Geostatistics. New York: Oxford University Press (1989): 369-399.
Latecki et al. “Tree-structured Partitioning Based on Splitting Histograms of Distances.” Proceedings of the Third IEEE International Conference on Data Mining (2003).
Latecki et al. “Shape Similarity Measure Based on Correspondence of Visual Parts.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No. 10 (Oct. 2000): 1185-1190.
Liu et al. “Multiple-point simulation integrating wells, three-dimensional seismic data, and geology.”AAPG Bulletin, v. 88, No. 7 (Jul. 2004): 905-921.
Ma et al. “Texture Features and Learning Similarity.”IEEE(1996): 425-430.
Manjunath et al. “Texture Features for Browsing and Retrieval of Image Data.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, No. 8 (Aug. 1996): 837-842.
Mardia, K.V.Statistics of Directional Data. New York: Academic Press Inc., 1972: 57-69, 132-137, 152-155, 160-163, 256-259, 276-281, 300-301, 200-203, 314-315.
Materka et al. Texture Analysis Methods—A Review, Technical Unversity of Lodz, Institute of Electronics, COST B11 Report, Brussels, 1998.
Metzler et al. “Texture Classification of Graylevel Images by Multiscale Cross-Cooccurrence Matrices.”IEEE(2002): 549-552.
Press et al.Numerical Receipes in C: The Art of Scientific Computing. 2ndEdition. Cambridge: Cambridge University Press (1992): 553-558.
Randen et al. “Filtering for Texture Classification: A Comparative Study.”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, No. 4 (Apr. 1999): 291-310.
Randen et al. “Three-Dimensional Texture Attributes for Seismic Data Analysis.”SEG 2000 Expanded Abstracts.
Rao et al. “Computing Oriented Texture Fields.” Computer Visualization Graphics and Image Processing: Graphical Models and Image Processing, vol. 53, No. 2 (Mar. 1991): 157-185.
Rock, N.M.S. Lecture Notes in Earth Sciences 18—Numerical Geology. New York: Springer Verlag (1988): 228-238.
Serra, J. Image Analysis and Mathematical Morphology. Orlando: Academic Press, Inc. (1982): 165-206.
Smith et al. “SUSAN—A New Approach to Low Level Image Processing.” International Journal of Computer Vision 23(1) (1997): 45-78.
Strebelle, Sebastien. “Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics.”Mathematical Geology, vol. 34, No. 1 (Jan. 2002): 1-21.
Strebelle et al. “Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics.”SPE Journal(Sep. 2003): 227-235.
Visa, A. “Identification of Stochastic Textures with Multiresoluation Features and Self-organizing Maps.”IEEE(1990): 518-522.
Wang, Libing. “Modeling Complex Reservoir Geometries with Multiple-Point Statistics.” Mathematical Geology, vol. 28, No. 7 (1996): 895-907.
Yamada et al. “A volcanic reservoir: Facies distribution model accounting for pressure communication” SPE, Apr. 5, 2005-Apr. 7, 2005, pp. 1-9, XP002404104, Jakarta, IN, SPE 93159.
Zhang et al. “Content-based Image Retrieval Using Gabor Texture Features.” Proceedings of the First IEEE Pacific-Rim Conference on Multimedia, Sydney, Dec. 2000.
Zhang et al. “Generic-Fourier Descriptor for Shape-based Image Retrieval.”IEEE(2002): 425-428.
Patent Cooperation Treaty International Search Report and Written Opinion for International Application No. PCT/US2006/026514 dated Nov. 8, 2006.
Caers et al., “Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models”, Stanford University, Stanford Center for Reservoir Forecasting, Jan. 2, 2002, 24 pages.
Caers, “History Matching Under Training-Image-Based Geological Model Constraints”, SPE Journal, Sep. 2003, pp. 218-226.

Affiliated with

Also associated with

Rating

Computer-based generation and validation of training images... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Computer-based generation and validation of training images..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computer-based generation and validation of training images... will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4092378