Fast Elastic Full Waveform Inversion And Search Engine For Microseismic Location And Focal Mechanism

Passive microseismic monitoring has been widely used in many fields such as mining, geothermal, and gas/oil industries. For example, it is a valuable tool for mapping fractures in shale gas development. The microseismic event locations and focal mechanisms provide important information for the site engineers to assess the effectiveness of the hydraulic fracturing operations. In this study, we aim at inferring both the microseismic event locations and focal mechanisms simultaneously by matching the microseismic full waveform data through inversion methods or search engine method. We firstly introduce a gradient based method to simultaneously invert the event location and focal mechanism. To deal with the local minimum problem, we present a fast elastic full waveform inversion method based on a prepared Green’s function database. We also borrow the search engine concept from the computer science industry to solve the location and focal mechanism problem in one second. The main methods presented in this study can be summarized as following:1. Fast elastic full waveform inversion method for microseismic location and focal mechanism.Given a velocity model, we first calculate synthetic Green’s functions in the possible location grids and create a database. We then convolve the source with the Green’s function to generate synthetic waveform for calculating an approximate objective function based on the discrete and precomputed Green’s function database. Fast computation of synthetic seismograms with the Green’s function database allows using Neighborhood Algorithm to determine the global minimum in a computationally efficient manner. The method applies an envelope crosscorrelation approach to match high-frequency waveforms, and it requires event detection but no accurate time picking needed. We use both waveform residual and crosscorrelation concept in the objective function. The method is efficient to find the best matched waveform to the input with the Neighborhood Algorithm applied although there are noises in the input waveform or error in the velocity model. We also apply the method to 452 microseismic events obtained from two stages during hydraulic fracturing operations in the Barnett shale.2. Microseismic search engine for the estimation of the source location and focal mechanism.Similar to a web search engine, we develop a microseismic search engine that can estimate both an event location and the focal mechanism in less than a second to monitor the hydraulic fracturing process. The method is extended from a real-time earthquake monitoring approach for seismological applications. We first calculate the full waveforms of all possible microseismic events over a 3D grid with a known velocity model for a given acquisition geometry to create a database. We then index and rank all of the seismic waveforms in the database by following the characteristics of the phase and amplitude of the waveform through a computer fast search technology, specifically, the Multiple Randomized K-Dimensional (MRKD) tree method. When a microseismic event occurs, the approximate best matches to the entry waveform are found immediately by comparing the characteristic features between the input data and the database. The method returns not just one but a series of solutions, similar to a web search engine. Thus, we can obtain a solution space that delineates the resolution and confidence level of the results. Also similar to a web search engine, the microseismic search engine does not require any input parameter or processing experience; thus, the solutions are the same for any user. We demonstrate the method with both synthetic and real data, and the method shows great potential for the routine real-time monitoring of microseismic events during hydraulic fracturing.