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An automated quantitative multi-stage approach to invert velocity models for microseismic event locationsNormal access

Authors: Fernando Castellanos, Mike Preiksaitis, Ryan Nader, Vlad Shumila, Steve Falls, Dan Hook and Doug Angus
Journal name: First Break
Issue: Vol 37, No 12, December 2019 pp. 61 - 67
DOI: 10.3997/1365-2397.2019043
Special topic: Data Processing
Language: English
Info: Article, PDF ( 2.44Mb )
Price: € 30

Summary:
Complexity in hydraulic fracturing programmes has motivated microseismic service providers to innovate and propose creative methods to monitor drilling, completion and field development. Although microseismic analysis and interpretation have moved beyond the ‘dots-in-a-box’ solution, velocity model (VM) calibration using inversion plays a critical role in the initial phase of accurate microseismic event (event) locations to ensure the accuracy of subsequent higher-order microseismic attributes given data quality and monitoring geometry. Knowing that business decisions are, at times, required in real time, it is imperative to provide confident event locations efficiently through the construction of well-constrained VMs based on quantitative and objective methodologies. The most time-consuming aspects of microseismic data processing are optimal VM construction and inversion. In this paper, we demonstrate improved efficiency in microseismic data processing by developing and implementing an automated approach to perforation shot (perf) detection and VM inversion using Particle Swarm Optimization (PSO). These primary tasks (perf detection and VM inversion) are critical in the event location workflow and can benefit significantly from increased efficiency. Although more advanced Greens functions can provide more accurate solutions to the source location problem (e.g., Angus et al., 2014), we focus on ray-based approaches due to their high computational efficiency, especially for anisotropic media and hydraulic fracture monitoring where large volumes of microseismic data (commonly in excess of 100,000 events) must be processed.


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