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OilVoice Magazine | FEBRUARY 2014
For this neoBASIN™ program, NEOS acquired airborne multi-physics data – magnetic, electromagnetic (EM), radiometric, gravity, and hyperspectral – over 1,000 square miles of Tioga County. These data were integrated with existing geophysical, geochemical, and seismic measurements from various public domain and third-party sources and interpreted by NEOS and operator geoscientists. The acquired data delivered new insights to the program underwriters, even when interpreted individually.
Using hyperspectral analysis, interpreters located numerous oil seeps and gas plumes. The seeps and plumes were then traced back to surfacepenetrating faults that were mapped used an analysis of magnetic data. The result provided insights into the relative liquids generating potential of the target shale intervals in the subsurface. Airborne EM resistivity measurements provided insights into both lateral and vertical resistivity variations throughout the geologic column, down to roughly 10,000 feet. When the EM voxel was depth-sliced at the Marcellus interval, geoscientists noted that resistive hot spots in the Marcellus corresponded to many of the county’s ‘best well’ locations. Geoscientists on the project also incorporated more traditional geophysical measurements into the interpretation. Well logs were analyzed to enhance structural control and to calibrate the airborne EM data. Seismic data were incorporated into the regional structural model and, in combination with the magnetic and EM data, provided insights into how faults were creating pathways for hydrocarbons to migrate toward the surface.
Using Predictive Analytics to find the Sweet Spots Well productivity can vary widely in an unconventional shale play, even within the same county. While well design plays a part in this variance, so too does the geology. NEOS’s multi-measurement methodology is helping explorationists understand the geologic drivers of well productivity. On the typical survey, nearly 100 G&G measurements, attributes, and derivatives are acquired and analyzed to identify the 10-20 that correlate with the ‘best’ (or worst) wells in an area. Using advanced geostatistical and predictive analytics methods, proprietary NEOS software than undertakes a pattern search to identify other parts of the play in which the ‘correlative attributes’ appear. The resulting analysis allows NEOS to develop ‘sweet spot’ maps. In the case of the Tioga predictive analytics exercise, twenty G&G measurements were identified as correlating with the most productive wells in the county. The measurements aren’t without geologic significance, as they relate to four categories extremely relevant to well productivity:
Structural context The size and composition of the ‘tank’ Reservoir plumbing and Halo effects (above the reservoirs in question)