Robust Hydraulic Fracture Monitoring (HFM) of Multiple Time Overlapping Events Using a Generalized Discrete Radon Transform

TL;DR

This paper introduces a new sparse signal processing algorithm for accurately localizing multiple overlapping hydraulic fracture events from seismic data, improving resolution and robustness in monitoring efforts.

Contribution

It develops a generalized Radon transform-based sparse recovery method for super-resolution localization of multiple seismic events in hydraulic fracture monitoring.

Findings

Effective localization of overlapping seismic events demonstrated

Improved resolution over traditional methods shown

Algorithm applicable to real well data

Abstract

In this work we propose a novel algorithm for multiple-event localization for Hydraulic Fracture Monitoring (HFM) through the exploitation of the sparsity of the observed seismic signal when represented in a basis consisting of space time propagators. We provide explicit construction of these propagators using a forward model for wave propagation which depends non-linearly on the problem parameters - the unknown source location and mechanism of fracture, time and extent of event, and the locations of the receivers. Under fairly general assumptions and an appropriate discretization of these parameters we first build an over-complete dictionary of generalized Radon propagators and assume that the data is well represented as a linear superposition of these propagators. Exploiting this structure we propose sparsity penalized algorithms and workflow for super-resolution extraction of time overlapping multiple seismic events from single well data.