Technical Comparison of Downhole Methods through Fiber Optic VSP, in the Eagle Ford Formation
The utilization of fiber optic technology is a relatively new technique however is considered a robust method for determining cluster efficiency for engineering purposes. Applications using fiber optic for seismic purposes have been limited, primarily due to directional detection and noise floor issues (e.g. Rassenfoss, 2014). This paper presents a methodology for comparing conventional, primarily mechanically driven, detection sondes to the use of fiber optic cable via seismically generated sources through vertical seismic profiling (VSP). Ultimately we will demonstrate the two comparative methods, through the utilization of a hydraulic stimulation detection project.
Devon Energy conducted a multi-well stimulation monitoring program residing in Lavaca County Texas with a target of the Eagle Ford Formation. Two horizontal wells were drilled 500’ apart laterally and 150’ differential in depth, Well B relative to Well A. The goal of the project was to determine if stimulation of the upper-landed well was detectable through fiber optic and conventional tool downhole geophysical and engineering methods. If detectability was determined feasible, a proxy of fracture description and stimulation would be useful for well placement aid in fracture models. In addition to the above an experimental Walk Above or Vertical Incident VSP (ZIVSP) was attempted inside of casing (e.g. Jones and Pereira, 2010).
Fiber optic recording is a relatively new technique, but has been considered a robust method for determining cluster efficiency. A few case scenarios have been identified utilizing the same cable for vertical seismic profiling, however require a need for an increased source effort. Here we show an example of applying the technique with minimal source effort and relatively high fidelity compared to conventional tools deployed in a nearby lateral. Additional to the downhole mechanically driven tools and fiber optic cable, surface microseismic, image log data and near-by petrophysical data were utilized for integration.
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