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This research paper explores application of multivariable regression modelsusing only reservoir temperatures for predicting oil and gas production in aToe-Heal-Air-Injection (THAI) enhanced oil recovery process. This paperdiscusses effects of statistical interaction between thermocouples by usingsecond degree quadratic polynomials, which showed significant productionforecast accuracy. Interactions among thermocouples statistically includetemperature of larger reservoir areas, hence improving the predictive models.The interaction of two thermocouples can be interpreted as temperature gradientof combustion zone as moving forward during THAI operations life cycle.  Second degree polynomial regression including interactions showed majorprediction improvement for both oil and natural gas productions compare tosimple regression models. Application of Poisson regression slightly improvedprediction accuracy for oil production and was less effective on improvingnatural gas production predictions. Quadratic Poisson regression models showedrealistic production prediction method for both oil and gas production values,due to the nature of Poisson probability distribution which is non-negative forrates and count values.

Toe-Heal-Air-Injection Thermal Recovery Production Prediction and Modelling Using Quadratic Poisson Polynomial Regression