Microseismic event detection on fiber-optic data using machine learning

• Author: Fantine Huot

Microseismic analysis is the primary tool available for fracture characterization in unconventional reservoirs. As distributed acoustic sensing (DAS) fibers are installed in the target reservoir and are thus close to the microseismic events, they hold vast potential for their high-resolution analysis.

However, accurately detecting microseismic signals in continuous data is challenging and time-consuming. DAS acquisitions generate substantial data volumes, and microseismic events have a low signal-to-noise ratio in individual DAS channels.

In this project, we design, train, and deploy a machine learning model to automatically detect thousands of microseismic events in DAS data acquired inside a shale reservoir. The stimulation of two offset wells generates the microseismic activity.

The deep learning model achieves an accuracy of over 98% on our benchmark dataset of manually-picked events and even detects low-amplitude events missed during manual picking.

Getting started

Update the submodules

After cloning the repository, run the following commands to initialize and update the submodules.

git submodule init
git submodule update

Requirements

You can run the project from an interactive bash session within the provided Docker container:

docker run --gpus all -it fantine/ml_framework:latest bash

If you do not have root permissions to run Docker, Singularity might be a good alternative for you. Refer to containers/README.md for more details.

Folder structure

• bin: Scripts to run machine learning jobs.
• config: Configuration files.
• containers: Details on how to use containers for this project.
• docs: Documentation.
• log: Directory for log files.
• ml_framework: Machine learning framework.
• tfrecords: Utility functions for converting files to TFRecords.

Set the datapath for the project

Set the DATAPATH variable inside config/datapath.sh to the data or scratch directory to which you want write data files.

Create and run a machine learning model

This repository provides a parameterized, modular framework for creating and running ML models.

• Convert input data to TensorFlow records
• Machine learning training and inference
• Hyperparameter tuning