Predictive modeling of oil rate for wells under gas lift using machine learning

Optimizing oil production in wells employing gas lift systems is a critical challenge due to the complex interplay of operational and reservoir parameters. This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. The dataset, comprised of 169 rigorously validated samples, includes key features such as basic sediment and water content, choke size, pressures, gas injection characteristics, gas lift valve depth, oil density, and temperature. Input and output variables were normalized and split into training and test sets to ensure fairness and reliability. Multiple machine learning models (Decision Tree, AdaBoost, Random Forest, Ensemble Learning, CNN, SVR, MLP-ANN, and Lasso Regression) were trained and evaluated using 5-fold cross-validation and key statistical metrics (R², MSE, AARE%). The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. Sensitivity analysis and SHAP interpretability methods revealed that basic sediment and water content, choke size, and upstream pressure had the greatest influence on oil output. These findings underscore the importance of both statistical rigor and model interpretability in oil production forecasting and provide actionable insights for optimizing gas lift operations in oil wells.