THREE-DIMENSIONAL MODELING OF THE DENVER BASIN: PROCESS IMPROVEMENTS AND LESSONS LEARNED

The Denver Basin is a sedimentary basin spanning over 150,000 km² and covering portions of CO, WY, NE, and KS. The basin contains hydrocarbon reservoirs and aquifers of importance to the many residential and industrial users of the region. Basin-scale three-dimensional (3-D) models will help understand and manage these resources.

Here, a digital 3-D model of the Denver Basin is presented. The model was generated by the U.S. Geological Survey in Leapfrog software using a large database of inputs including subsurface formation tops (n=350,000), surface geologic map contacts, and structural elevation contours. Initial 3-D modeling was implicit with controlled boundary conditions such as contact type and sequential layering. Manual modification of 3-D surfaces was undertaken to improve the depiction of major structures.

The 3-D model includes 24 subsurface volumes representing the Precambrian basement through the Quaternary. These units match the source data favorably, presenting a depiction of the geology of the Denver Basin in a manipulable 3-D volume. The resulting basin geometry is consistent with basin-bounding uplifts on the north, east, and south sides of the basin; the western edge of the model is a necessarily simplified depiction of the Rocky Mountain Front (RMF), a reverse faulted zone active during multiple orogenies. Special effort was made to align the subsurface volumes at this RMF boundary to scenic and prominent geological features, such as the Dakota Hogback, Red Rocks, Garden of the Gods, and the Boulder Flatirons. A basement-involved fault south of the Hartville Uplift, a basement high separating the Denver Basin from the Powder River Basin to the north, was included to match well top and structural data. The surface trace of this fault was guided by recently published mapping produced by the Wyoming State Geological Survey.

Model construction required development of new processes in data-cleaning, linking surface contacts to subsurface volumes, and integration of existing structural elevation contour datasets. Data-cleaning involved removal of directional wells, calculation of reference elevations, and removal of outlier data points. Outliers were visually identified in a novel technique using 3-D Local Scenes in ArcGIS Pro software. These processes in ‘data hygiene’ will be implemented in future models, likely significantly shortening cycle time.