This disclosure presents a system, method, and apparatus for preventing fracture communication between wells, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fracking fluid in the well into an electrical signal; a memory configured to store the electrical signal; a machine-learning system configured to analyze current frequency components of the electrical signal in a window of time and to identify impending fracture communication between the well and an offset well, the machine-learning system having been trained on previous frequency components of electrical signals measured during previous instances of fracture communication between wells; and a user interface configured to return a notification of the impending fracture communication to an operator of the well.

Treatment fluid may be injected into a wellbore of a geological formation to stimulate fracturing of the geological formation. A plurality of measurements may be received from one or more sensors on a surface of the geological formation or downhole which measures an indication of change of the geological formation based on the stimulation. A fracture configuration of a fracture in the geological formation may be determined based on an Kalman gain and the plurality of measurement data. Injection of the treatment fluid into the wellbore may be adjusted based on the fracture configuration.

Methods are disclosed for monitoring operation integrity during hydrocarbon production or fluid injection operations. According to the methods, received microseismic data is processed to obtain a plurality of data panels corresponding to microseismic data measured over a predetermined time interval. For each data panel, trigger values are calculated for data traces corresponding to sensor receivers of the microseismic monitoring system. At least one data panel is selected as a triggered data panel that satisfies predetermined triggering criteria. A value is calculated for each of at least two event attributes of a plurality of event attributes of the event. An event is classified into at least one event category of a plurality of event categories based on the event score. Related non-transitory computer usable mediums are also disclosed.

An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

An apparatus includes a mechanical wave source; a set of mechanical wave sensors in a borehole to provide subsurface wave measurements based on formation waves from the mechanical wave source, and a processor. The apparatus also includes a machine-readable medium having program code to acquire the subsurface wave measurements, select a first set of tool wave measurements based on the subsurface wave measurements, and generate a set of filtered subsurface wave measurements by filtering the subsurface wave measurements based on the first set of tool wave measurements. The program code also includes instructions to generate a time-domain semblance map based on the set of filtered subsurface wave measurements, wherein the time-domain semblance map includes an initial set of compression wave peaks, determine a selected qualified compression wave peak based on a semblance value in the time-domain semblance map, and determine a compression wave slowness based on the selected qualified compression wave peak.

A timing alignment method for data acquired by monitoring units of a borehole-surface micro-seismic monitoring system includes acquiring two rock-burst waveform data segments with GPS timestamps; calculating a time difference and a number of sampling points between each pair of adjacent GPS timestamps; adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp between each pair of adjacent GPS timestamps; calculating average sampling frequencies of the two rock-burst waveform data segments, adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp except first and last GPS timestamps in each of the two data segments; obtaining sampling times of all sampling points, resampling the sampling times according to a uniform sampling frequency; calculating a rock-burst waveform data segment at a new sampling time with a linear interpolation formula, and aligning the sampling times of the two rock-burst waveform data segments.

Provided are methods and systems for monitoring and modifying stimulation operations in a reservoir. In particular, the methods and systems utilize a downhole telemetry system, such as a network of sensors and downhole wireless communication nodes, to monitor various stimulation operations.

Various aspects described herein relate to a machine learning based detecting of fracture hits in offset monitoring wells when designing hydraulic fracturing processes for a particular well. In one example, a computer-implemented method includes receiving 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; inputting the set of features into a trained neural network; and providing, as output of the trained neural network, a probability of a fracture hit at a 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.

System, method, and apparatus for classifying fracture quantity and quality of fracturing operation activities during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fracking fluid in the fracking wellhead into an electrical signal; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the electrical signal in a window of time into a current frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum, the machine-learning system having been trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine-learning system; and a user interface configured to return a classification of the current frequency domain spectrum to an operator of the fracking wellhead.

Disclosed herein are earthquake detection devices capable of initiating a safety response in the event of an earthquake. The earthquake detection devices comprise a plurality of three-component accelerometers for measuring acceleration in three directions; and a processing unit for: receiving acceleration measurements from each of the plurality of accelerometers, determining if the acceleration measurements meet or exceed a predetermined threshold value and sending a signal to one or more transducers.