Abstract
Modern monitoring and surveillance software are very powerful in analyzing all types of reservoirs including Heavy Oil and Extra Heavy Oil. In addition to the software, easy to use statistical tools can add enormous analytical value in understanding reservoir performance. The engineering focus is to analyze reservoirs to predict the locations of underperforming wells based on the integrated approach discussed in this paper. Wells in the reservoir are evaluated using spatial statistics (i.e. kriging or nearest neighbour) to locate underperforming wells or regions within the reservoir. This goal is achieved by using known (measured) petro-physical information from the reservoir and coupling it with their production history (production/injection flow and pressures). Spatial statistics including other statistical tools, such as multivariate regression analysis techniques is then used to evaluate the reservoir performance.
This paper presents a unique approach developed for reservoir monitoring and diagnostics. It shows that the combination of statistical techniques coupled with monitoring and surveillance software can be used to identify regions, as well as individual distressed wells, within Oil, Heavy Oil and Extra Heavy Oil reservoirs. The focus is to identify underperforming wells relative to their theoretical average well in a specific reservoir analyzed. The reservoirs that can be analyzed include any enhanced oil recovery (EOR) process: waterfloods, polymer floods, cyclic steam stimulation (CSS) and steam assisted gravity drainage (SAGD).
The process to achieve this analysis begins with collecting and statistically filtering data for erroneous data entries. The data collected includes but not limited to:
Production/Injection history;
Pressure-volume-temperature (PVT) data;
Petro-physical and seismic data;
Well completion and work-over history data.
Production, PVT, Petro-physical and Geo-Statistical data are analyzed and mapped. The reservoirs total moving average production data is calculated and compared to its geological data to calculate a theoretical moving average well that is representative over the life of the reservoir. Once the theoretical average well is determined, it can be used as the basis for comparing each well individually to identify distressed wells or regions of the reservoir for the individual well and total reservoir optimization.
