A multi-system, multi-parameter, integrated, comprehensive early warning method for a coal and rock dynamic disaster includes: obtaining monitoring data of a plurality of monitoring systems for the coal rock dynamic disaster, and extracting multivariate characteristic parameters capable of reflecting precursor information of the coal and rock dynamic disaster in each monitoring system based on the monitoring data; screening out a combination of characteristic parameters with a highest early warning effectiveness in each monitoring system and an optimal critical value of each characteristic parameter based on the multivariate characteristic parameters; calculating a comprehensive early warning index and an early warning effectiveness of each monitoring system; and calculating a multi-system comprehensive early warning result of the coal and rock dynamic disaster based on the comprehensive early warning index and the early warning effectiveness of each monitoring system.

A system for real-time sinkhole detection comprises a plurality of measuring devices, a network system, and an analysis system. The plurality of measuring devices include a plurality of sensors, wherein each of the plurality sensors is configured to record, process and compile spatial data into a data set. The network system is configured to electronically collect a plurality of the data sets from each of the plurality of sensors. The analysis system comprises an electronic database system and a server. The server is configured to electronically transmit the plurality of the data sets to the electronic database system; query the data set from the electronic database system; process the data set by applying a machine learning algorithm to generate a real-time result about sinkhole detection; transmit the real-time result to an interface system; and update the electronic database system by transmitting the real-time result back to the electronic database system.

A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.

An underground tunneling detection system and method includes a tunneling detection control unit that receives one or more sound detection signals including a sound signature output by a component that is underground and at or proximate to a location. The tunneling detection control unit compares the sound signature to sound signature data. The tunneling detection control unit determines that the sound signature is non-tunneling activity in response to the sound signature matching a non-tunneling portion of the sound signature data. The tunneling detection control unit determines that the sound signature is tunneling activity in response to the sound signature matching a tunneling portion of the sound signature data. The tunneling detection control unit determines a similarity metric between the sound signature and one or both of the non-tunneling portion of the sound signature data or the tunneling portion of the sound signature data in response to the sound signature differing from the sound signature data.

A well system includes a fiber optic cable positionable downhole along a length of a wellbore and a reflectometer communicatively coupleable to the fiber optic cable. The reflectometer detects and locates a microseismic event using strain detected in reflected optical signals received from the fiber optic cable. Further, the reflectometer computes a set of spectra for waveforms of the microseismic event. Additionally, the reflectometer aggregates each spectrum from the set of spectra that meet an acceptance threshold to generate an aggregate spectrum. Furthermore, the reflectometer applies a fault source model to the aggregate spectrum to determine a magnitude of the microseismic event.

A well system includes a fiber optic cable positionable downhole along a length of a wellbore. The well system also includes a reflectometer communicatively coupleable to the fiber optic cable. The reflectometer injects optical signals into the fiber optic cable and receives reflected optical signals from the fiber optic cable. Further, the reflectometer identifies strain detected in the reflected optical signals generated from seismic waves of a microseismic event. Additionally, the reflectometer identifies a focal mechanism of the microseismic event and velocities of the seismic waves. The reflectometer also determines a position of the microseismic event using the strain detected in the reflected optical signals, the focal mechanism of the microseismic event, and the velocities of the seismic waves.

A method for mapping fiber optic distributed acoustic sensing (DAS) measurements to particle motion involves obtaining, from a fiber optic DAS system in a wellbore, a first set of DAS data associated with a first seismic wave; obtaining, from a discrete seismic receiver in the wellbore, measured particle motion data associated with the first seismic wave; generating training data from the first set of DAS data and the measured particle motion data; training a machine learning model using the training data; obtaining a second set of DAS data associated with a second seismic wave; and determining a predicted particle motion in response to the second seismic wave using the machine learning model applied to the second set of DAS data.

An illustrative monitoring system for a hydraulic fracturing operation includes: a data acquisition module collecting microseismic signals from a subterranean formation undergoing a hydraulic fracturing operation; a processing module implementing a monitoring method; and a visualization module that displays an estimate or prediction of fracture extent. The monitoring method implemented by the processing module includes: deriving microseismic event locations and times from the microseismic signals; fitting at least one fracture plane to the microseismic event locations; projecting each microseismic event location onto at least one fracture plane; determining a time-dependent distribution of the projected microseismic event locations; calculating one or more envelope parameters from the time-dependent distribution; and generating an estimate or prediction of fracture extent using the or more envelope parameters. The envelope parameters may include an exponent of a time-power law, and said generating may include re-fitting the fracture plane if the exponent isn't approximately one-half.

A method of performing oilfield operations at a wellsite is disclosed. The wellsite is positioned about a subterranean formation having a wellbore therethrough and a fracture network therein. The fracture network includes natural fractures. The method involves generating fracture parameters including a hydraulic fracture network based on wellsite data including a mechanical earth model, generating reservoir parameters including a reservoir grid based on the wellsite data and the generated fracture wellsite parameters, forming a finite element grid from the fracture and reservoir parameters by coupling the hydraulic fracture network to the reservoir grid, generating integrated geomechanical parameters including estimated microseismic events based on the finite element grid, and performing fracture operations and production operations based on the integrated geomechanical parameters.

A method of performing oilfield operations at a wellsite is disclosed. The wellsite is positioned about a subterranean formation having a wellbore therethrough and a fracture network therein. The fracture network includes natural fractures. The method involves generating fracture parameters including a hydraulic fracture network based on wellsite data including a mechanical earth model, generating reservoir parameters including a reservoir grid based on the wellsite data and the generated fracture wellsite parameters, generating production parameters comprising production rate over time based on the wellsite data and the hydraulic fracture network, forming a finite element grid from the fracture parameters, the production parameters, and the reservoir parameters by coupling the hydraulic fracture network to the reservoir grid, generating integrated geomechanical parameters including estimated microseismic events based on the finite element grid, and performing fracture operations and production operations based on the integrated geomechanical parameters.