A simulation-free approach for well placement in tight gas reservoirs
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Well placement in an infill drilling program is a routine procedure for reservoir management. In tight gas reservoirs specifically, well locations depend upon the internal reservoir heterogeneity, the complex dynamic reservoir response associated with depletion history, and also the uncertainties associated with the reservoir parameters. This paper introduces a rapid simulation-free procedure to determine infill well locations, which also takes reservoir uncertainty into consideration. The general practice of well placement usually requires reservoir simulation to predict the dynamic reservoir response. Numerous well placement scenarios require many reservoir simulation runs, which may have significant CPU demands, and this is only for one specific realization. Furthermore, to account for the geologic uncertainty, people tend to consider multiple realizations, thus make it more expensive, even impractical, for large scale simulation for each realization. This paper proposes a novel simulation-free screening approach based on a combination of static and dynamic reservoir properties. The areas with higher values (ranks) are poorly drained, and thus become the sweet spots for drilling the infill wells. The dynamic property discussed in this paper is pressure, since primary depletion is the recovery mechanism for tight gas reservoirs. We introduce and utilize a geometric pressure approximation, which is a simulation-free approach, to access the pressure changes during depletion history. The geometric pressure approximation may not give the exact pressure solution, but the pressure ranking from the approximation is quite close to the flow simulation, thus good enough for the model screening. The screening process produces a quality map, based on a deterministic model. This paper also generates the corresponding risk map to capture the geologic uncertainty. We have the most confidence about the reservoir data at the existing well locations. As the distance from the existing wells increases, our confidence about the data at this new location decreases, and the risk correspondingly increases. This risk map is generated through a spatial connectivity analysis. Combining the quality map and risk map, good infill well locations and a drilling sequence can be determined for improved reservoir management. We demonstrate this approach based on a U.S. on-shore tight gas reservoir. We study a section of this tight gas reservoir first to verify this approach by the comparison with flow simulation results, and then apply this method to the the large scale model to determine the infill well locations at each stage and propose an appropriate drilling sequence for the decision maker. Current work only examines vertical wells. We expect to further extend this workflow to study more complex well trajectories and fracture design in the future.
Society of Petroleum Engineers - International Petroleum Technology Conference 2013, IPTC 2013: Challenging Technology and Economic Limits to Meet the Global Energy Demand
