Microseismic monitoring is a crucial element to understanding hydraulic fracturing operations prior to oil and gas production. One of the more tedious quality control (QC) measures that must often be performed following a microseismic processing workflow is a visual inspection of seismic data to determine whether the data contain microseismic events or only noise. The manual nature of these inspections can take many weeks, sometimes over a month, to perform for one geophysicist. Automated approaches usually use a short-term-average long-termaverage (STA/LTA) ratio, but end up picking false positives on noisy data. We propose using a supervised deep learning algorithm, a convolutional neural network (CNN), to automatically classify microseismic events from noise. Using our deep learning approach, we show that the time for QC can be reduced from weeks to hours with high accuracy.
Presentation Date: Monday, October 12, 2020
Session Start Time: 1:50 PM
Presentation Time: 3:30 PM
Location: Poster Station 1
Presentation Type: Poster
