Steam-assisted Gravity Drainage Optimization for Extra Heavy Oil

Exploitation of extra heavy oil assets involves complex and costly production processes, one of which is steam-assisted gravity drainage (SAGD). This method requires the generation of substantial quantities of steam, which is injected into a horizontal injection well parallel to and above a paired horizontal producer. We focus on maximization of the net present value (NPV) of a SAGD production process by combining optimization and simulation. The use of a neural network algorithm improves on the numerous limitations of manual sensitivities while needing a limited number of iterations. We demonstrate this optimization process using an example reservoir containing an extra heavy Canadian crude. A 2D proxy with rapid solution times is used to address the practical issues of the very long run-times associated with thermal simulation. This proxy is used to identify only those control parameters that impact the objective function (NPV), thereby reducing the solution search space, and also to suggest better starting points for the optimizer. Both of these facets may accelerate finding the optimum for full 3D optimizations. Tangible benefits forthcoming from this investigation are new operational strategies for maximizing NPV, recognition of the impact and optimal duration of preheating, and efficient comparisons of different well patterns.