Over 80% of the vast reserves of Alberta's Oil Sands can be produced only by using in-situ recovery methods. Among them, one which is likely the most efficient and important is the steamassisted gravity drainage (SAGD) process. Numerical simulation allows the ideal way of predicting reservoir performance under SAGD process during the whole field development cycle. However, in the earlier stages of development studies when it is necessary to make preliminary engineering design, estimate reserves, screen among other SAGD prospects, as well as consider the uncertainty of some reservoir parameters, it may not be feasible to do a detailed simulation study, due to high computational time involved in a SAGD process simulation.
Under these circumstances, a method of predicting reservoir performance using a simple statistical model, that can approximate the reservoir simulator over a given range of some important input parameters, is a good approach to provide means of comparison and preliminary predictions without resorting to numerical simulation.
The purpose of this work is to use Experimental Design Techniques to develop a response surface that can predict SAGD performance without the expense of doing simulation. A preliminary screening study was done in order to select the most influential variables on the SAGD performance The variables used for that purpose include reservoir rock/fluid properties such as reservoir thickness, porosity, vertical permeability, vertical-horizontal permeability ratio, methane content, rock thermal conductivity, initial oil saturation and bitumen viscosity; along with SAGD design and operating variables including: spacing between injector/producer, operating pressure, preheating period, maximum steam injection rate and SAGD well pattern spacing.
In a second stage the influential variables were used to create a statistically significant correlation, by using the experimental design method and response surface techniques. This simple model allows the prediction of the SAGD performance in terms of maximum Net Present Value over 15 years of project life, for a given range of the most influential parameters.
Numerical simulation of complex systems such as SAGD processes require high computational times due to the compositional nature and transient temperature behavior of the models used in the solution. Bigger simulation times lead either to delay the making decision process or to make decisions without a complete screening of all possible scenarios in which the field can be developed. This is particularly important at the earliest field development stages. In other words, although the ideal way to predict reservoir performance under SAGD, in any stage of the field development cycle, is through numerical simulation, in early stages when the lack of knowledge of some reservoir or operational parameters is a constant, a detailed simulation study where all possible scenarios should be considered leads to prohibitive simulation times, making it a very difficult and highly expensive task.
To overcome that situation, engineers need simple models to predict SAGD performance. A first step to make it possible is by selecting among a given set of input parameters those ones which have the most influential effect on the SAGD performance. To achieve this purpose efficiently, it is necessary a methodology to choose the proper simulation runs.
