Evolving Smart Model to Predict the Combustion Front Velocity for In Situ Combustion

Abstract

Abstract To determine the breakthrough time of the combustion front in the in situ combustion process for heavy oil recovery processes, no records have been reported in previous literature to date. In this work, the developed model was inspired by a new intelligent method called the “least‐squares support vector machine” (LSSVM) to specify the combustion front velocity in heavy oil recovery process. The proposed approach is applied to the experimental data from Iranian oil fields and reported data from the literature has been incorporated to develop and test this model. The estimated outcomes from the LSSVM approach are compared to the aforementioned actual in situ combustion data. By comparing the results obtained from suggested method with the relevant experimental ones it is clear that the LSSVM approach predicts the combustion front velocity with reasonable degree of precision. It worth mentioning that the LSSVM contains no conceptual errors, such as over‐fitting, which is an issue for artificial neural networks. The results of this study could couple with the industrial reservoir simulation software for heavy oil reservoirs to select the proper production method or achieve related goals.