Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis

Abstract

Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for steam-assisted gravity drainage (SAGD), the simulation process is usually deterministic and computationally demanding, and it is not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain.In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly available sources is analyzed. Exploratory data analysis (EDA) is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. An extensive dataset consisting of over 70 records is assembled. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural network (ANN) are employed to facilitate SAGD production performance prediction. The principal components analysis (PCA) technique is implemented to reduce the dimensionality of the input vector, alleviate the effects of over-fitting, and improve forecast quality. Statistical analysis is performed to analyze the uncertainties related to ANN model parameters and dataset. Predictions from the proposed approaches are both successful and reliable. It is demonstrated that model predictability is highly influenced by model parameter uncertainty. This work illustrates that data-driven models are capable of predicting SAGD recovery performance from log-derived and operational variables. The modeling approach can be updated when new information becomes available. The analysis presents an important potential to be integrated directly into existing reservoir management and decision-making routines.