scholarly journals Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method

2011 ◽  
Vol 27 (14) ◽  
pp. 1964-1970 ◽  
Author(s):  
Gengjie Jia ◽  
Gregory N. Stephanopoulos ◽  
Rudiyanto Gunawan
Keyword(s):  
Parameter Estimation ◽  
Kinetic Models ◽  
Metabolic Profiles ◽  
Two Phase ◽  
Decoupling Method ◽  
Phase Dynamic ◽  
Dynamic Decoupling

History Matching in Two-Phase Petroleum Reservoirs

1980 ◽  
Vol 20 (06) ◽  
pp. 521-532 ◽  
Author(s):  
A.T. Watson ◽  
J.H. Seinfeld ◽  
G.R. Gavalas ◽  
P.T. Woo
Keyword(s):  
Optimal Control ◽  
Parameter Estimation ◽  
Objective Function ◽  
History Matching ◽  
Computing Time ◽  
Absolute Permeability ◽  
Two Phase ◽  
Automatic History Matching ◽  
Control Approach ◽  
First Order

Abstract An automatic history-matching algorithm based onan optimal control approach has been formulated forjoint estimation of spatially varying permeability andporosity and coefficients of relative permeabilityfunctions in two-phase reservoirs. The algorithm usespressure and production rate data simultaneously. The performance of the algorithm for thewaterflooding of one- and two-dimensional hypotheticalreservoirs is examined, and properties associatedwith the parameter estimation problem are discussed. Introduction There has been considerable interest in thedevelopment of automatic history-matchingalgorithms. Most of the published work to date onautomatic history matching has been devoted tosingle-phase reservoirs in which the unknownparameters to be estimated are often the reservoirporosity (or storage) and absolute permeability (ortransmissibility). In the single-phase problem, theobjective function usually consists of the deviationsbetween the predicted and measured reservoirpressures at the wells. Parameter estimation, orhistory matching, in multiphase reservoirs isfundamentally more difficult than in single-phasereservoirs. The multiphase equations are nonlinear, and in addition to the porosity and absolutepermeability, the relative permeabilities of each phasemay be unknown and subject to estimation. Measurements of the relative rates of flow of oil, water, and gas at the wells also may be available forthe objective function. The aspect of the reservoir history-matchingproblem that distinguishes it from other parameterestimation problems in science and engineering is thelarge dimensionality of both the system state and theunknown parameters. As a result of this largedimensionality, computational efficiency becomes aprime consideration in the implementation of anautomatic history-matching method. In all parameterestimation methods, a trade-off exists between theamount of computation performed per iteration andthe speed of convergence of the method. Animportant saving in computing time was realized insingle-phase automatic history matching through theintroduction of optimal control theory as a methodfor calculating the gradient of the objective functionwith respect to the unknown parameters. Thistechnique currently is limited to first-order gradientmethods. First-order gradient methods generallyconverge more slowly than those of higher order.Nevertheless, the amount of computation requiredper iteration is significantly less than that requiredfor higher-order optimization methods; thus, first-order methods are attractive for automatic historymatching. The optimal control algorithm forautomatic history matching has been shown toproduce excellent results when applied to field problems. Therefore, the first approach to thedevelopment of a general automatic history-matchingalgorithm for multiphase reservoirs wouldseem to proceed through the development of anoptimal control approach for calculating the gradientof the objective function with respect to theparameters for use in a first-order method. SPEJ P. 521＾

scholarly journals Benchmarking optimization methods for parameter estimation in large kinetic models

2018 ◽  
Vol 35 (5) ◽  
pp. 830-838 ◽  
Author(s):  
Alejandro F Villaverde ◽  
Fabian Fröhlich ◽  
Daniel Weindl ◽  
Jan Hasenauer ◽  
Julio R Banga
Keyword(s):  
Parameter Estimation ◽  
Kinetic Models ◽  
Optimization Methods

Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques

2014 ◽  
Vol 83 ◽  
pp. 104-115 ◽  
Author(s):  
Jimena Di Maggio ◽  
Cecilia Paulo ◽  
Vanina Estrada ◽  
Nora Perotti ◽  
Juan C. Diaz Ricci ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Mathematical Programming ◽  
Kinetic Models ◽  
Large Scale ◽  
Programming Techniques

Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems

2019 ◽  
Vol 116 ◽  
pp. 131-146 ◽  
Author(s):  
Muhammad Akmal Remli ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Azurah A Samah ◽  
Sigeru Omatu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kinetic Models ◽  
Biological Systems ◽  
Scatter Search ◽  
High Dimensional ◽  
Opposition Based Learning ◽  
Learning Schemes

scholarly journals Parameter estimation and enzyme kinetic models

FEBS Letters ◽  
1970 ◽  
Vol 9 (5) ◽  
pp. 245-251 ◽  
Author(s):  
Jens G. Reich
Keyword(s):  
Parameter Estimation ◽  
Kinetic Models ◽  
Enzyme Kinetic
Sign in / Sign up

Export Citation Format

Share Document