Affiliation:
1. Advanced Energy Consortium, Bureau of Economic Geology, The University of Texas at Austin
2. Multi-Phase Technologies, LLC
3. Duke University
4. The University of North Carolina
5. The University of Texas at Austin
Abstract
Abstract
Characterizing hydraulically induced fractures—height, length, orientation, and shape—is key to understanding reservoir performance. Our previous work has focused on the comparison of the state-of-the-art geophysical techniques currently used in hydraulic fracture imaging (microseismicity, tracer, tiltmeter, and distributed acoustic and temperature sensors) to perform a comprehensive set of electromagnetically active proppant (EAP)–assisted tomography methods (LaBrecque et al., 2016; Ahmadian et al., 2018). In our latest study, we conducted a field pilot at The University of Texas at Austin Bureau of Economic Geology's Devine Test Site, located approximately 50 miles southwest of San Antonio, Texas. Following hydraulic fracturing with EAP, we detected a measurable electromagnetic (EM) fracture anomaly at a depth of 175 ft (~53 m) by use of a set of four PVC-cased wells equipped with electrode arrays for single hole, hole-to-surface, and cross-hole electrical resistivity tomography. Because of relatively low overburden pressure, and as designed, fractures grew horizontally and appear nonaxisymmetric about the center injection well (fracture image looks like a human foot). This design allowed us to verify our results with drilling and logging of eight vertical wells. In addition, we cored two wells, and these samples further corroborated the presence of EAP proppants at the predicted depth. Together, these results conclusively corroborate the accuracy of our EM inversion models to within 5 ft of the physical edge of the EAP-filled fracture anomaly. We are currently using results from our ongoing geophysical surveys to refine and verify the efficiency of forward and inverse EM modeling codes for open-borehole and through steel casing scenarios. This paper describes the ground-truth validation of our model predictions, as well as the future direction of our research.
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