Spectral analysis, machine learning, and frac score assignment to acoustic signatures of fracking events

A method of classifying fracture quantity and quality during hydraulic fracturing operations, the method comprising:

• providing a sensor coupled to a wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fluid in the well into an electrical signal in a time domain;
• recording the electrical signal to a memory;
• converting the electrical signal for a window of time to a current frequency domain spectrum comprising one or more amplitude spikes, the current frequency domain spectrum constituting a measured frequency signature;
• analyzing the current frequency domain spectrum via a machine-learning system trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine-learning system;
• and
• classifying the current frequency domain spectrum as associated with initiation of a fracture in the well;
• and
• returning a classification of the current frequency domain spectrum to a well operator.

A method of identifying downhole hydraulic fracturing operations in real time, the method comprising:

• providing a sensor coupled to a wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fluid in the well into an electrical signal in a time domain;
• recording the electrical signal to a memory;
• converting the electrical signal for a window of time to a current frequency domain spectrum comprising one or more amplitude spikes, the current frequency domain spectrum constituting a measured frequency signature;
• analyzing the current frequency domain spectrum via a machine learning system trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine-learning system;
• classifying the current frequency domain spectrum as associated with a downhole hydraulic fracturing operation;
• and
• returning a classification of the current frequency domain spectrum to a well operator.

A method of training a machine-learning model for hydraulic fracturing operations, the method comprising:

• providing a sensor coupled to a wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fluid in the well into an electrical signal in a time domain;
• recording the electrical signal to a memory;
• converting the electrical signal for a window of time to a current frequency domain spectrum comprising one or more amplitude spikes, the current frequency domain spectrum constituting a measured frequency signature;
• analyzing the current frequency domain spectrum via a machine learning system trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine learning system;
• classifying the current frequency domain spectrum as associated with subterranean drilling or fracking activity;
• and
• wherein the machine-learning system is trained by grouping the current frequency domain spectrum with similar ones of the previous frequency domain spectra that are also associated with the subterranean drilling or fracking activity, and classifying each grouping.