Inversion of Muon Tomography and Gravity Gradiometry for Reservoir Monitoring

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Authors
Pieczonka, Sara
Keyword
inversion , inverse modelling , muon tomography , gravity , gradiometry , SAGD , geophysics
Abstract
Steam-assisted gravity drainage (SAGD) reservoirs utilize enhanced oil recovery methods, namely steam injection, to mobilize bitumen in-situ. High-resolution continuous monitoring is required to make informed decisions on steam injection, which affect the economic and environmental impact of the operation. Muon tomography uses 2D transmission radiographs of muon intensity to produce 3D volume imaging of the subsurface. Muon sensors placed below the target record the deviation of particle intensity from the expected value at a certain density and depth from all directions within the field-of-view, to reconstruct the relative density in the subsurface. Muon sensors are simulated below a realistic steam-assisted gravity drainage reservoir and muon intensity data is forward modeled using various sensor arrays. Synthesized intensity data is used to calculate the inferred opacity (density-length) along discrete directions from sensor to surface which is subsequently inverted to yield 3D density distributions across the reservoir. Results are presented to assess the feasibility of using muon tomography to monitor SAGD reservoirs during production, and demonstrate possible solutions to mitigate limitations, including lack of vertical resolution and inter-well noise. Muon tomography is demonstrated to be a feasible approach for passive monitoring which can be achieved in high spatial resolution on short observation times of weeks to months. By placing muon sensors below the well pairs, resolution on the order of meters can be achieved, but resolution is limited in the vertical direction when all sensors are placed at a single depth. Vertical gravity and vertical gravity gradient data are simulated at the surface and are jointly inverted with muon data to reduce the vertical smearing in the inverse models by ~12% and 6% for reservoir depths of 100m and 200m, respectively. The addition of gravity data reduces the model uncertainty for shallow reservoirs and demonstrates the advantages of joining multiple data types for improved modeling solutions.
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