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

TL;DR

This study develops quadratic Poisson polynomial regression models using reservoir temperature data to accurately predict oil and gas production in the THAI enhanced oil recovery process, emphasizing the importance of thermocouple interactions.

Contribution

It introduces a novel quadratic Poisson regression approach incorporating thermocouple interactions for improved production forecasting in THAI operations.

Findings

Quadratic models significantly improve prediction accuracy.

Interactions among thermocouples enhance model performance.

Poisson regression slightly improves oil production predictions.

Abstract

This research paper explores application of multivariable regression models using only reservoir temperatures for predicting oil and gas production in a Toe-Heal-Air-Injection (THAI) enhanced oil recovery process. This paper discusses effects of statistical interaction between thermocouples by using second degree quadratic polynomials, which showed significant production forecast accuracy. Interactions among thermocouples statistically include temperature of larger reservoir areas, hence improving the predictive models. The interaction of two thermocouples can be interpreted as temperature gradient of combustion zone as moving forward during THAI operations life cycle. Second degree polynomial regression including interactions showed major prediction improvement for both oil and natural gas productions compare to simple regression models. Application of Poisson regression slightly improved prediction accuracy for oil production and was less effective on improving natural gas production predictions. Quadratic Poisson regression models showed realistic production prediction method for both oil and gas production values, due to the nature of Poisson probability distribution which is non-negative for rates and count values.