НИЦ СИНАПС принимает участие в работе международной конференции EAGE 2012 4-7 июня в Копенгагене с двумя докладами:

Statistical analysis of microseismic noise during hydraulic fracturing

и

Evaluation of statistical characteristics of seismic noise during hydraulic fracturing.


Statistical analysis of microseismic noise during hydraulic fracturing : New aspects covered

Presence of correlated noise can be a factor increasing number of false alarms in microseismic bulletins which are created by semblance-based SET techniques.

Summary

Spatial and temporal spectral analysis of the background noise before and during hydraulic fracturing shows that surface noise is generally uncorrelated before and after well injections. Noise becomes sufficiently coherent (correlated) during fluid injection and even during a break between fracturing stages. Stacking of seismic array records with proper move-out corrections of Seismic Emission Tomography (SET) helps to suppress non-correlated noise component, but enhances both correlated technogenic noise and signals from microseismic events.  Presence of correlated noise can be a factor increasing number of false alarms in microseismic bulletins which are created by semblance-based SET techniques.

Evaluation of statistical characteristics of seismic noise during hydraulic fracturing : Main objectives

The presence of correlated noise is a factor that increases the number of false detections in microseismic bulletins created by semblance-based techniques.

New aspects covered

Significant suppression of correlated noise achieved by Statistically Optimal Algorithms leads to improvement of microseismic events location.

Summary

The benefits of data processing approach based on the Statistically Optimal (SO) Algorithms for processing of microseismic data observed during hydraulic fracturing jobs is presented in the paper. The presence of correlated noise is a factor that increases the number of false detections in microseismic bulletins created by semblance-based SET techniques. Significant suppression of correlated noise can be achieved by application of the SO algorithms for surface array data processing if statistical characteristics of noise are taken into account; specifically, if spatial and/or temporal correlations of noise are strong enough. The two SO-methods outlined in the paper: Adaptive Microseismic Location Algorithm and Frequency-Phase Microseismic Location Algorithm. Each SO-algorithm has its advantages and limitations, so the best results of detection/location are reachable by a combination of SO-algorithms. Results of SO-algorithms benchmarking for microseismic synthetic events using a double-couple source mixed with the recorded noise during hydraulic fracturing demonstrate that for 141-sensor surface array SO-algorithms allow to accurately locate microseismic events with SNR ~ 0.05, while traditional SET processing fails to even detect events with SNR lower than 0.3.



Please, sign up or sign in to leave a comment.