Event detection and classification using distributed acoustic sensing (das) in hydrocarbon wells using deep-learning algorithms

A system for detecting an event within a wellbore, the system comprising:

• a processor;
• and
• a memory storing a plurality of models, that when executed on the processor, configures the processor to:
• obtain a sample data set from a fiber optic sensor, wherein the sample data set is a sample of fiber optic-based signal originating within a wellbore, and wherein the sample data set is representative of the fiber optic-based signal across a depth section of the wellbore over a time period;
• form an image using the fiber optic-based signal;
• use the image as an input to an anomaly detection model;
• detect an anomaly within the image with the anomaly detection model;
• and
• output an indication of the anomaly to a display device.

A method of detecting an event within a wellbore, the method comprising:

• obtaining a sample data set from a fiber optic sensor disposed in a wellbore, wherein the sample data set is a sample of fiber optic-based signal, and wherein the sample data set is representative of the fiber optic-based signal across a depth section of the wellbore over a time period;
• forming an image using the fiber optic-based signal;
• using the image as an input to an anomaly detection model, wherein the anomaly detection model comprises a deep-learning algorithm;
• detecting an anomaly within the image with the anomaly detection model;
• forming a second image from the sample data set;
• applying the second image to an event detection model, wherein the event detection model comprises a convolutional neural network (CNN);
• detecting an event in the second image using the event detection model;
• and
• outputting an indication of the event.