Data processing: structural design – modeling – simulation – and em – Simulating nonelectrical device or system – Fluid
Reexamination Certificate
2011-03-01
2011-03-01
Jones, Hugh (Department: 2128)
Data processing: structural design, modeling, simulation, and em
Simulating nonelectrical device or system
Fluid
Reexamination Certificate
active
07899657
ABSTRACT:
System and method for parameterizing one or more steady-state models each having a plurality of model parameters for mapping model input to model output through a stored representation of an in-situ hydrocarbon reservoir. For each model, training data representing operation of the reservoir is provided including input values and target output values. A next input value(s) and next target output value are received from the training data. The model is parameterized with the input value(s) and target output value, and derivative constraints imposed to constrain relationships between the input value(s) and a resulting model output value, using an optimizer to perform constrained optimization on the parameters to satisfy an objective function subject to the derivative constraints. The receiving and parameterizing are performed iteratively, generating a parameterized model. Multiple models form an aggregate model of the system/process, which may be optimized to satisfy a second objective function subject to operational constraints.
REFERENCES:
patent: 5444619 (1995-08-01), Hoskins et al.
patent: 6002985 (1999-12-01), Stephenson
patent: 6047221 (2000-04-01), Piche et al.
patent: 6101447 (2000-08-01), Poe, Jr.
patent: 6236894 (2001-05-01), Stoisits et al.
patent: 6256603 (2001-07-01), Celniker
patent: 6278899 (2001-08-01), Piche et al.
patent: 6434435 (2002-08-01), Tubel et al.
patent: 6662109 (2003-12-01), Roggero et al.
patent: 6901391 (2005-05-01), Storm et al.
patent: 7058617 (2006-06-01), Hartman et al.
patent: 2002/0072828 (2002-06-01), Turner et al.
patent: 2003/0014131 (2003-01-01), Havener et al.
patent: 2003/0018399 (2003-01-01), Havener et al.
patent: WO/0048022 (2000-08-01), None
patent: WO/02099464 (2002-12-01), None
James K. Dietrich, “Three-Phase Oil Relative Permeability Models,” SPE 6044, Oct. 3, 1976 (pp. 1-12).
McCormack, M. D. and R. Day, “How Artificial Intelligence Impacts E&P Productivity”,World Oil, Oct. 1993, pp. 81-87.
E. Hartman, “Training Feedforward Neural Networks with Gain Constraints,”Neural Computation, vol. 12, pp. 811-829, Apr. 2000.
B. J. Kuipers, C. Chiu, D. T. Dalle Molle & D. R. Throop. 1991. “Higher-order derivative constraints in qualitative simulation”,Artificial Intelligencevol. 51, pp. 343-379.
David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, “An Introduction to Management Science: Quantitative Approaches to Decision Making”, West Publishing Co., 1991.
Efraim Turban and Jack R. Meredith, “Fundamentals of Management Science,” Third Edition, Business Publications, Inc., 1985.
Terje Loken and Jan Komorowski; “Rough Modeling—A Bottom-Up Approach to Model Construction”; International Journal of Applied Mathematics and Computer Science; 2001; pp. 675-690; vol. 11, No. 3.
Simon Hayking; “Neural Networks: A Comprehensive Foundation”; 1999; pp. 77, 171, 172, 234, 235, 277, 278; Prentice Hall.
H. Schwetlick and V. Kunert; “Spline smoothing under constraints on derivatives”; 1993; vol. 33, No. 33; pp. 512-528; BIT Numerical Mathematics.
A. Centilmen, T. Ertekin and A. S. Grader; “Applications of Neural Networks in Multiwell Field Development”; 1999; 1999 SPE Annual Technical Conference; pp. 1-11.
Virginia M. Johnson and Leah L. Rogers; “Applying soft computing methods to improve the computational tractability of a subsurface simulation-optimization problem”; 2001; Journal of Petroleum Science and Engineering; vol. 29; pp. 153-175.
Fletcher Yoder LLP
Jones Hugh
Miller John M.
Rockwell Automoation Technologies, Inc.
Walbrun William R.
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