Factorial experimental designs can be used when investigating the effects of more than one factor on the in-situ combustion process. The factorial design readily permits investigation of interactions between the factors, and allows some compensation for the effect of experimental error. This is illustrated inter asia by an analysis of twelve combustion tube experiments.

A factorial design on oxygen partial pressure, mole fraction and flowrate, and the subsequent statistical analysis, demonstrated that the oxygen partial pressure had no statistically significant effect on any reaction parameter for the limited range and modest pressures employed. The combustion time was dominated by the oxygen flowrate, as were the global reaction rates. Fuel and oxygen consumption depended mainly on the oxygen mole fraction. Increasing the oxygen mole fraction reduced the consumption figures. These observations are consistent with surface reaction models.

The reaction stoichiometry was substantially independent of the three chosen factors and more surprisingly the same was found to be so for oil recovery. The latter appeared to depend upon the initial oil saturation.


In-situ combustion experiments are often done without any replication of the experimental conditions. The result is that some of the effects of the factors may be due to experimental error - to the small variations of conditions from one experiment to the next1.

Some workers have also varied the factors being studied without apparently realizing that they can affect some of the other parameter supposedly being held constant. A common example of this is in the study of the reaction kinetics of wet combustion, The presence of the steam formed by wet combustion significantly lowers the oxygen partial pressure in the reaction zone, yet the analyses for reaction equations proceed using pressures for dry air 2, 3. Another example occurs when studying the effect of oxygen mole fraction on the process. Some workers have ascribed all the resulting effects on the process to the mole fraction changes, ignoring the effect of these changes on the oxygen flux and partial pressure 4,5.

Factorial tests can overcome some of these problems 6,7. To begin with, they force the experimental design to be planned beforehand, and thinking about the design can identify some of the factors that interact. The subsequent analysis can allow for some of these interactions, and determine the significance of the interactions. Replicated experiments permit an estimate of the experimental error, and must be made if all interactions are to be studied. In addition, factorial designs can reduce the total number of experiments to be performed when studying the effects of the various factors 6,7.

Factorial Tests

A simple example is a three-factor, two-level design. This will require 2x2x2 or 8 experiments without replication. The eight experiments are organized so that each factor-level combination occurs once. Representing one of the two levels by "0 " and the second level by "1 ", the eight experiments cover:

List of Experiment (Available In Full Paper)

The analysis of the results is done using an F-test 6,7. The statistical computer package.

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