Methods and systems for determining an image of a subterranean region of interest are disclosed. The method includes obtaining a seismic dataset and a geological dip model for the subterranean region of interest and determining a set of input seismic gathers from the seismic dataset. The method further includes determining a central seismic gather and a set of neighboring seismic gathers in a vicinity of the central seismic gather from the set of seismic gathers, determining a set of dip-corrected neighboring seismic gathers based, at least in part, on the set of neighboring seismic gathers and a geological dip from the geological dip model, and determining a noise-attenuated central seismic gather by combining the dip-corrected neighboring seismic gathers and the central seismic gather. The method still further includes forming the image of the subterranean region of interest based, at least in part, on the noise-attenuated central seismic gather.

This disclosure describes systems, methods, and apparatuses for preventing wireline sticking 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 measured in fracking fluid in the wellhead, fluid line, or standpipe into an electrical signal in a time domain; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the time domain electrical signal into a frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum as associated with increasing wireline friction, the machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system; and a user interface configured to return an indication of the increasing wireline friction to an operator of the hydraulic fracturing operations.

An early earthquake detection method may comprise acquiring a frame image from a camera; acquiring a vibration signal from the frame image; removing a noise signal due to vibration of the camera from the vibration signal; acquiring a motion signal obtained by magnifying subtle motions from the noise signal-removed vibration signal; extracting vibration characteristics from the motion signal; estimating an occurrence of an earthquake by extracting a peak signal from the vibration characteristics; and determining whether an earthquake occurs by receiving earthquake estimation information from at least one other camera located within a certain range.

An ensemble of machine learning models is trained to evaluate seismic and risk-related data in order to evaluate, value, or otherwise rank various prospective hydrocarbon reservoir (“prospects”) of a field. A classification machine learning model is trained to classify a prospect or region of a prospect based on the exploration risk level. From the seismic data, a frequency-filtered volume (FFV) for each prospect is calculated, where the FFV is a measure of reservoir volume which takes into account seismic resolution limits. Based on the risk classification and FFV, prospects of the field are ranked based on their economic value which is a combination of the risk associated with drilling and their potential reservoir volume.

A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

This disclosure presents processes and systems for generating an image of a subterranean formation from seismic data recorded in a seismic survey of the subterranean formation. The seismic data is contaminated with low frequency noise in a low frequency band. Processes and systems reconstruct seismic data in the low frequency band of the seismic data to obtain low frequency reconstructed seismic data that is free of the low frequency noise. The low frequency reconstructed seismic data is used to construct a velocity model of the subterranean formation. The velocity model and the low frequency reconstructed seismic data are used to generate an image of the subterranean formation that reveals structures of the subterranean formation without contamination from the low frequency noise.

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.

A valve controller device for controlling a set of one or more solenoid valves is provided. The valve controller comprises an accelerometer for making acceleration measurements in three directions comprising acceleration measurements in a vertical direction. The valve controller comprises a processing unit that determines the arrival of seismic P-waves when the ratio of vibrations' power in the vertical direction with respect to a sum of the vibrations' power in the three directions exceeds a first threshold. The processing unit then determines the arrival of seismic S-waves when the vector sum of the vibrations' power in the three directions exceeds a second threshold. The processing unit then determines the arrival of seismic surface waves when the vector sum of the vibrations' power in the three directions exceeds a third threshold. The processing unit then sends one or more signals to close the set of solenoid valves.

An automatic microseismic monitoring-intelligent rockburst early warning integrated method is further provided.

A system is provided. The system includes a plurality of seismic sensors and a computer device. The computer device is programmed to a) store a plurality of distances between each of the plurality of seismic sensors; b) store one or more fingerprints of a signal to be detected; c) receive a first signal transmitted from a first seismic sensor of the plurality of seismic sensors; d) receive the first signal transmitted from a second seismic sensor of the plurality of seismic sensors; e) compare the first signal to the one or more fingerprints of the signal to be detected; and f) determine a direction of travel of the first signal based on the distance between the first seismic sensor and the second seismic sensor, the first time, and the second time.