Integration of machine learning and data analysis for the SAGD production performance with infill wells

There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data-driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio ($R_{\text{increase}}$), a total steam–oil ratio (SOR_total), and a stolen ratio ($R_{\text{Stolen}}$) for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network ANN] algorithm, three gradient boosting decision tree GBDT] algorithms, and a support vector machine SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.