Microseismic guided wave eigenfunction theory with application to event depth classification in the Eagle Ford shale

Abstract

The properties of guided waves that propagate in deep low-velocity zones are investigated using eigenfunction theory, and a novel application for microseismic event depth constraint to an unconventional reservoir is developed. Eigenfunctions, historically used in the analysis of Rayleigh and Love surface waves, allow rapid calculation of response functions that are linearly-correlated with guided wave displacements as a function of source depth, with only a 1-D velocity model required for their input. These response functions are used to investigate the impact of velocity contrast, reservoir thickness, and receiver depth on guided wave amplitudes for discrete frequencies. It is found that receivers located within the low-velocity zone record larger guided wave amplitudes, which may be used to infer the location of a recording array in relation to a low-velocity reservoir. The energy distribution of guided waves between layers and dispersion modes in a model representing the Eagle Ford Shale is then studied. High frequency guided wave energy is found to be largely confined to the Eagle Ford, and the modal energy distribution shows a nodal point in the first higher mode for events originating in the middle of the Eagle Ford. These observations are corroborated with field microseismic data recorded in the Eagle Ford during hydraulic fracturing, and provide possible constraints for microseismic event depth. To this end, a depth classification algorithm is developed based on the confinement of guided wave energy. This algorithm is applied to microseismic DAS data from the Eagle Ford, and quantifies the amplitude of guided wave energy relative to background noise in order to classify events with strong guided waves as occurring within or close to the Eagle Ford, circumventing the large depth uncertainties associated with measurements from surface arrays. Analysis of the horizontal locations of the in-reservoir events shows them to be closer to the stimulation well than the out-of-reservoir events, providing a constraint on stimulated reservoir volume that may improve well-spacing decisions.