Detection and characterization of microseismic events from fiber-optic DAS data using deep learning

TL;DR

This paper presents a deep learning approach for automatic detection and characterization of microseismic events from fiber-optic DAS data, significantly improving accuracy and efficiency over traditional methods.

Contribution

It introduces a novel deep learning model trained on a large curated dataset for microseismic detection in DAS data, achieving high accuracy and uncovering low-amplitude events.

Findings

Achieved 98.6% accuracy on benchmark dataset

Detected over 100,000 microseismic events

Improved fracture characterization efficiency

Abstract

Microseismic analysis is a valuable tool for fracture characterization in the earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time-consuming. We design, train, and deploy a deep learning model to detect microseismic events in DAS data automatically. We create a curated dataset of nearly 7,000 manually-selected events and an equal number of background noise examples. We optimize the deep learning model's network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieves an accuracy of 98.6% on our benchmark dataset and even detects low-amplitude events missed during manual labeling. Our methodology detects more than 100,000 events allowing the reconstruction of spatio-temporal fracture development far more accurately and efficiently than would have been feasible by traditional methods.