Lean optimization using supersaturated experimental design

Siomina, Iana; Ahlinder, Sven

doi:10.1016/j.apnum.2006.10.007

Cited by 10 publications

(11 citation statements)

References 29 publications

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“…At Volvo, supersaturated design (SSD) optimizations have been tested on mathematical functions [11]. Some theoretical functions and a genuine engine model were tested.…”

Section: Sven Ahlinder and Ivar Gustafssonmentioning

confidence: 99%

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On supersaturated experimental design

Ahlinder

Gustafsson

2012

ACUTM

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“…At Volvo, supersaturated design (SSD) optimizations have been tested on mathematical functions [11]. Some theoretical functions and a genuine engine model were tested.…”

Section: Sven Ahlinder and Ivar Gustafssonmentioning

confidence: 99%

“…Except for using pseudo inverse solution, optimization with SSD does not differ from ordinary (overdetermined) DoE. The solution x, the correct gradient or steepest descent, is estimated byx and a step along this direction is taken with a reasonable step length [11].…”

Section: Optimization With Supersaturated Designsmentioning

confidence: 99%

On supersaturated experimental design

Ahlinder

Gustafsson

2012

ACUTM

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“…The (statistical) design of experiment (DOE) is defined as an efficient procedure for planning the experiments so that the data obtained from experiment can be analyzed to yield valid and objective conclusion [45].…”

Section: Design Of Experimentsmentioning

confidence: 99%

Development of a Predictive Model for Carbon Dioxide Sequestration in Deep Saline Carbonate Aquifers

Anbar

2010

SPE Annual Technical Conference and Exhibition

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, 184 pages Although deep saline aquifers are found in all sedimentary basins and provide very large storage capacities, a little is known about them because they are rarely a target for the exploration. Furthermore, nearly all the experiments and simulations made for CO 2 sequestration in deep saline aquifers are related to the sandstone formations. The aim of this study is to create a predictive model to estimate the CO 2 storage capacity of the deep saline carbonate aquifers since a little is known about them. To create a predictive model, the variables which affect the CO 2 storage capacity and their ranges are determined from published literature data. They are rock properties (porosity, permeability, vertical to horizontal permeability ratio), fluid properties (irreducible water saturation, gas permeability end point, Corey water and gas coefficients), reaction properties (forward and backward reaction rates) and reservoir properties (depth, pressure gradient, v temperature gradient, formation dip angle, salinity), diffusion coefficient and Kozeny-Carman Coefficient. Other parameters such as pore volume compressibility and density of brine are calculated from correlations found in literature. To cover all possibilities, Latin Hypercube Space Filling Design is used to construct 100 simulation cases and CMG STARS is used for simulation runs. By using least squares method, a linear correlation is found to calculate CO 2 storage capacity of the deep saline carbonate aquifers with a correlation coefficient 0.81 by using variables found from literature and simulation results. Numerical dispersion effects have been considered by increasing the grid dimensions. It has been found that correlation coefficient decreased to 0.77 when the grid size was increased from 250 ft to 750 ft. The sensitivity analysis shows that the most important parameter that affects CO 2 storage capacity is depth since the pressure difference between formation pressure and fracture pressure increases with depth. Also, CO 2 storage mechanisms are investigated at the end of 300 years of simulation. Most of the gas (up to 90%) injected into formation dissolves into the formation water and negligible amount of CO 2 reacts with carbonate. This result is consistent with sensitivity analysis results since the variables affecting the solubility of CO 2 in brine have greater affect on storage capacity of aquifers. Dimensionless linear and nonlinear predictive models are constructed to estimate the CO 2 storage capacity of all deep saline carbonate aquifers and it is found that the best dimensionless predictive model is linear one independent of bulk volume of the aquifer.

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“…These restrictions of the conventional methodology of experimentation can be avoided by applying the design of experiments (DoE). The experimental design is connected to the need of minimizing the number of experiments in order to reduce the experimental cost [28]. This is very important for the experiments that include the set-up with elaborate modules or pilot scale-up.…”

Section: Introductionmentioning

confidence: 99%