Machine-learning based fracture-hit detection using low-frequency DAS signal

A device comprising:

• one or more memories having computer-readable instructions stored therein;
• and
• one or more processors configured to execute the computer-readable instructions to:
• receive a set of features for a first well proximate to a second well, the second well undergoing a hydraulic fracturing process for extraction of natural resources from underground formations, and the set of features including:
• a summation strain rate, at a location associated with the set of features, during a set period;
• and
• filtered data corresponding to the summation strain rate;
• input the set of features into a trained neural network;
• and
• provide, as output of the trained neural network, a probability of a fracture hit at the location associated with the set of features in the first well during a given completion stage of the hydraulic fracturing process in the second well.

One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors, cause the one or more processors to:

• receive a set of features for a first well proximate to a second well, the second well undergoing a hydraulic fracturing process for extraction of natural resources from underground formations, and the set of features including:
• a summation strain rate, at a location associated with the set of features, during a set period;
• and
• filtered data corresponding to the summation strain rate;
• input the set of features into a trained neural network;
• and
• provide, as output of the trained neural network, a probability of a fracture hit at the location associated with the set of features in the first well during a given completion stage of the hydraulic fracturing process in the second well.