Data processing: artificial intelligence – Machine learning – Genetic algorithm and genetic programming system
Reexamination Certificate
2006-09-20
2010-02-02
Vincent, David R (Department: 2129)
Data processing: artificial intelligence
Machine learning
Genetic algorithm and genetic programming system
C702S013000, C703S010000
Reexamination Certificate
active
07657494
ABSTRACT:
A method utilizing genetic programming to construct history matching and forecasting proxies for reservoir simulators. Acting as surrogates for computer simulators, the genetic programming proxies evaluate a large number of reservoir models and predict future production forecasts for petroleum reservoirs.
REFERENCES:
patent: 6101447 (2000-08-01), Poe, Jr.
patent: 2003/0036890 (2003-02-01), Billet et al.
patent: 2006/0173559 (2006-08-01), Kirshenbaum et al.
patent: 2007/0288214 (2007-12-01), DeMartini et al.
Bissell et al., History Matching Using the Method of Gradients: Two Case Studies, Paper SPE 28590 presented at the SPE 69thAnnual Technical Conference and Exhibition held in New Orleans, Louisiana, U.S.A., Sep. 25-28, 1994, Society of Petroleum Engineers, Inc., pp. 275-290.
Castellini et al., Practical Methods for Uncertainty Assessment of Flow Predictions for Reservoirs with Significant History—A Field Case Study, Paper A-33, 9thEuropean Conference on the Mathematics of Oil Recovery (ECMOR IX), Cannes, France, Aug. 30-Sep. 2, 2004, pp. 1-8.
Eide et al., Automatic History Matching by use of Response Surfaces and Experimental Design, presented at the 4thEuropean Conference on the Mathematics of Oil Recovery, Roros, Norway, Jun. 7-10, 1994, pp. 1-14.
Fang et al., Uniform Design: Theory and Application, Technometrics, Aug. 2000, pp. 237-248, vol. 42, No. 3, American Statistical Association and the American Society for Quality.
Koza, Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Computer Science Department, Stanford University, Margaret Jacks Hall, Stanford, California 94305, Jun. 1990, pp. 1-127.
Landa et al., A Methodology for History Matching and the Assessment of Uncertainties Associated with Flow rediction, Paper SPE 84465 presented at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., Oct. 5-8, 2003, Society of Petroleum Engineers Inc., pp. 1-14.
Wen et al., Coupling Sequential-Self Calibration and Genetic Algorithms to Integrate Production Data in Geostatiscal Reservoir Modeling, In Proceedings of the Seventh Geostatistics Congress, (2004), pp. 1-10.
Yeten et al., A Comparison Study on Experimental Design and Response Surface Methodologies, Paper SPE 93347 presented at the 2005 SPE Reservoir Simulation Symposium held in Houston, Texas U.S.A., Jan. 31-Feb. 2, 2005, Society of Petroleum Engineers Inc., pp. 1-15.
Yu et al., A Hybrid of Sequential-Self Calibration and Genetic Algorithm Inversion Technique for Geostatistical Reservoir Modeling, In Proceedings of the IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, Jul. 16-21, 2006.
Castellini Alexandre
Wilkinson David A.
Yu Tina
Bharadwaj Kalpana
Chevron U.S.A. Inc.
Teixeira Maurice
Vincent David R
LandOfFree
Method for forecasting the production of a petroleum... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method for forecasting the production of a petroleum..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for forecasting the production of a petroleum... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4212368