A Graph Theoretic Approach for Hydraulic Fracturing and Wellbore Leakage Risk Modeling

Recent large scale development of unconventional formations for fossil energy has raised concerns over the potential for fluid leakage between subsurface systems and wellbores. This is particularly true in regions with extensive drilling history, where spatial densities of wellbores are higher, and where significant uncertainties in the location and mechanical integrity of such wellbores exist. The generation of induced fracture networks during hydraulic fracturing may increase subsurface connectivity, and create the potential for unwanted fluid migration between operational and legacy wellbores and subsurface fracture networks. We present a graph theoretic approach for identifying geospatial regions and wellbores at increased risk for subsurface connectivity based on wellbore proximity and local geologic characteristics. The algorithm transforms user inputted geospatial data (geologic and wellbore x,y,z) to graph structure, where wellbores are represented as nodes, and where potential overlapping fracture network zones are represented as edges. The algorithm can be used to complement existing fracture models to better account for the reach of induced fractures, and to identify spatial extents at increased risk for unwanted subsurface connectivity. Additionally, the model can be used to identify regions in need of geophysical detection methods for locating undocumented wells. As a result, the method can be part of a cumulative strategy to reduce uncertainty inherent to combined geologic and engineered systems. The algorithm has been successfully tested against a known leakage scenario in Pennsylvania. In addition to identifying wells associated with the leakage event, the algorithm identified two other higher risk networks in the region. The algorithm output provides valuable information for industry to develop environmentally safe drilling and injection plans; and for regulators to identify specific wellbores at greater risk for leakage, and to develop targeted, science-based monitoring policies for higher risk regions.