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Data-Driven Signal Recognition- A Machine Learning Application For The Real-Time Microseismic MonitoringNormal access

Authors: A. Shamsa and M. Paydayesh
Event name: First EAGE/PESGB Workshop Machine Learning
Session: Types of Machine Learning and Deep Learning
Publication date: 30 November 2018
DOI: 10.3997/2214-4609.201803007
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 767.68Kb )
Price: € 20

A simple and robust machine learning technique is applied to automate signal detection and analyse recorded microseismic data. The method’s performance is tested and evaluated on real data. The fracture signals were well-detected using the proposed workflow and techniques when more data were introduced. In contrast to conventional methods, the techniques implemented herein described work on training the model prediction with additional data without restarting from the beginning, making them viable for continuous online learning. This method attempts to remove the burden of labour-intensive processing of microseismic data and replace it with a faster, cheaper, and more accurate way of achieving signal detection.

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