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.
A primary difference between conventional oil and unconventional heavy oil reservoirs is the added economic value to recovery from heavy oil reserves due to the sweep efficiency. To determine the added value, one needs to obtain the recovery factor of in situ combustion; however, this requires special experimental and laboratory combustion study and field tests. In the absence of experimental studies during the early period of field exploration, techniques that correlate such a parameter are of interest for engineers. In this work, a new method called “least‐squares support vector machine” was developed to monitor the recovery factor of the in situ combustion employment through heavy oil reservoirs. The proposed approach is applied to the experimental data from extensive works reported in the literature and the model has been implemented, developed, and tested. The predicted results from the least‐squares support vector machine model were compared to the addressed real in situ combustion data. A comparison between the generated outcomes of our model and the alternatives proves that the least‐squares support vector machine model estimates the efficiency of the in situ combustion with high degree of accuracy. The least‐squares support vector machine does not contain any conceptual errors such as over‐fitting, which can be an issue for artificial neural networks. The outcomes of this research could be coupled with commercial production software for heavy oil reservoirs to enhance production optimization and facilitate design.
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