Forward and Inverse Modeling of Fluid Migration Reservoirs using Muon Tomography
As the occurrences of near-surface and easily accessible natural resources decline, it becomes increasingly important to develop new exploration and monitoring methods to locate unconventional resources. It is also imperative to exploit current producing wells completely before abandonment, to minimize costs and environmental harm. Muon tomography is a technique of imaging the subsurface using detectors located in the subsurface below the target. Muons attenuate when passing through matter, and this property can be exploited to model density anomalies similar to gravity, but muons only sense density along their path making the data more localized. This project will model the depletion of SAGD (steam-assisted gravity drainage) reservoirs over time, using a real-world reservoir as the input model. The reservoir is forward modelled to obtain muon intensity values, which are proportional to the underground density anomalies above the sensors. The data will then be inverted to show the density depletion over two time steps, to assess the resolvability of muon data in modeling bitumen depletion. The models consider various parameters such as well-pair separation, type/number/placement of detectors, and the magnitude and geometry of the depletion. Finally, a joint inversion between muon and gravity data is conducted, to show the advantages of joining multiple data sets by utilizing their respective advantages. This research will help to constrain the survey array and model parameters for borehole detectors to be tested in the field. Ultimately, muon tomography data could be used to complement seismic data in monitoring of hydrocarbon reservoirs, which will lead to lower production cost while assessing the environmental impact of leaving bitumen behind or detecting out-of-zone flow. Considering the high cost of steam production in SAGD operations, even small changes in efficiency arising from improved monitoring could turn into significant savings in both energy consumption and recovery rates.
AAPG Datapages/Search and Discovery Article #90351 © 2019 AAPG Foundation 2019 Grants-in-Aid Projects