Monitoring fluid movement is important for selecting infill locations and completion intervals and optimizing production operations in reservoirs producing under waterflood and gravity drainage mechanisms. Estimation of the fluid contacts in the interwell regions can be difficult due to complex interactions between fluid movement and reservoir heterogeneities. Previously, we developed a systematic geostatistical fluid mapping methodology that integrates multiple sources of data and accounts for the spatial correlation and uncertainty due to the sparcity of the data. We found that this methodology is more efficient and accurate than the conventional mapping approach. This paper describes several enhancements incorporated into the geostatistical methodology and evaluates their advantages.
The major sources of data used in the geostatistical methodology are the surveillance and shale databases. The surveillance data provide the locations of fluids (oil, gas and water) intervals at the wells based on logs. The shale data provide the locations of shales intervals at the wells based on cores and logs. The first step in the methodology is to transform the surveillance and shale data into indicators. Then, the methodology uses indicator variograms to evaluate the spatial correlation of the data. The last step generates multiple equi-probable three-dimensional fluid and shale descriptions using a conditional simulation technique that honors the well data and variograms.
The enhancements introduced into the geostatistical methodology account for more information about the data and quantify the quality of the surveillance data.
The stratigraphic coordinates and vertical proportion curves account for variations in the reservoir structure and major trends in the data, respectively. The indicator variables for fluid movement at different times and shales in different zones account for the different correlations. The quality variables account for the degree of confidence engineers assign to the log interpretations. Cross-validation of the enhanced methodology consisted of the estimation of fluid column thicknesses at infill locations and visualization of three-dimensional distributions of oil and gas in a gravity drainage area of Prudhoe Bay. The results of the methodology are in excellent agreement with actual data.
Geostatistical techniques provide a framework to integrate and model several sources of reservoir data at different scales. With the recent development of high-speed and large-memory computer workstations, geostatistics has become a powerful tool for detailed reservoir analysis, description and evaluation. These technologies make it possible to integrate geological, geophysical and petrophysical data for building more realistic reservoir models.