Steam-assisted gravity drainage (SAGD) is the preferred thermal recovery method used to produce bitumen from Athabasca deposits in Alberta, Canada. SAGD operation is experiencing five stages: ramp-up, initial plateau, full-length plateau, wind-down and coalescence. The physics controlling the production mechanisms in each stage is different. Ramp-up is controlled by sweeping and injection pressure and water mobility are the most important factors. In initial plateau and full-length plateau, the chamber growth and bitumen viscosity-temperature dependency are the main controllers. Wind-down initiates as heat-loss overcomes the input enthalpy, and production controls by reduction of heated front close to steam front. Finally, coalescence is a result of reduction of oil availability at the edge of steam chamber. Such reduction is modeled with linearly reducing oil pathway as chambers are coalescing.
Butler's model commonly used for history matching and prediction of SAGD oil rate is mainly meant to model the full-length plateau stage and that is why it is over-predicting the ramp-up stage and not estimating the oil rate trend in wind-down and coalescence. This work is a continuation of a previous part discussing the predicve model for SAGD process (Irani, 2019). The purpose of this work is to create an end-life stage: winddown and coalescence; and then use the decision tree to optimize the solution. The model that includes ramp-up, early plateau, plateau, wind-down and coalescence is called 5-LINE model is a mechanistic model that controls main physic on each stage. Although the 5-LINE model is mainly derived based on physics controlling each stage, it has enough flexibility to match different geological characteristics. The 5-LINE model is structured in regression tree to minimize the error and then a decision-tree (DT) learning that branches from it to honour dynamics that cannot be honoured by 5-LINE model. This model is tested vs. oil production results of Suncor/MacKay River and Devon/Jackfish, as a result the final predictive model can predict oil rate reasonably good enough that can compete with results of dynamic reservoir numerical simulation.