Methods and systems are disclosed for determining a material property transition within a wellbore. In some embodiments, a method includes determining a material transition profile for each of a plurality of test points within a linear measurement path including, for each test point, determining an acoustic response for a first location within the linear measurement path, and determining an acoustic response for a second location within the linear measurement path. The test point is between the first and second locations. The method further includes generating a material transition profile for the test point based, at least in part, on a difference between the acoustic response at the first location and the acoustic response at the second location.

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.

The present disclosure provides a method of processing data obtained from distributed optical fiber sensors to detect acoustic energy generated by a poroelastic effect of fractures in a structure, such as a rock formation. The sensing fiber of an optical fiber distributed sensing system may be deployed in the vicinity of the region where fracturing is occurring, for example, along a well that is offset from a treatment well undergoing hydraulic fracturing. The DAS data obtained from along the sensing fiber is processed to measure changes in the low-frequency strain caused by the poroelastic effects in the rock as the fractures open and close. This measured strain rate data is iteratively processed at each instant time to identify fracture opening features (characterised as compression-tension-compression) that are correlated with fracture closing features (characterised as tension-compression-tension) as a function of depth, to thereby identify and locate fracture hits in the vicinity of the sensing fiber.

A method is disclosed for radiaiiy profiling shear velocities of flexural wave modes in a formation. The method includes establishing sensitivity kernels with two non-dimensionalized parameters and using said sensitivity kernels to perform an inversion for radial shear wave velocity profiles. This method may be used for LWD, MWD, or wireline logging operations.

A method for improving a signal-to-noise ratio of distributed acoustic sensing data may comprise transmitting an acoustic wave from an acoustic source into a subterranean formation, recording a first acoustic noise at a first time interval with a distributed acoustic sensing system, recording at least one acoustic wave and a second acoustic noise at a second time interval with the distributed acoustic sensing system, calculating a noise spectrum from the first time interval, calculating the noise spectrum in the second time interval, and removing the noise spectrum from acoustic data measured during the second time interval to identify acoustic data of the subterranean formation. A system may comprise an acoustic source, a distributed acoustic sensing system disposed within a well, and an information handling system.

A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

A distributed acoustic system may comprise an interrogator which includes a single photon detector, an umbilical line comprising a first fiber optic cable and a second fiber optic cable attached at one end to the interrogator, and a downhole fiber attached to the umbilical line at the end opposite the interrogator. A method for optimizing a sampling frequency may comprise identifying a length of a fiber optic cable connected to an interrogator, identifying one or more regions on the fiber optic cable in which a backscatter is received, and optimizing a sampling frequency of a distributed acoustic system by identifying a minimum time interval that is between an emission of a light pulse such that at no point in time the backscatter arrives back at the interrogator that corresponds to more than one spatial location along a sensing portion of the fiber optic cable.

A distributed acoustic sensing (DAS) system for determining an acoustic event may include an interferometer and an acoustic event detection processing device. The interferometer may measure DAS data from sensed signals from a sensing fiber deployed in a wellbore. The acoustic event detection processing device may determine an acoustic event in the wellbore from an out-of-band signal using the DAS data by performing operations. The operations can include determining a first acoustic event and a second acoustic event from the DAS data. The operations can include determining a first set of aliased frequencies from the first acoustic event and a second set of aliased frequencies form the second acoustic event. The operations can include determining, using an intersection of the first set of aliased frequencies and the second set of aliased frequencies, a frequency or amplitude of out-of-band signals that are usable to determine the at least one acoustic event.

A method and system to be used in well inspection. An acoustic signal is transmitted from a well inspection tool into a well structure and one or more return signals is detected using at least one receiver. At least one processor is used to generate variable density log (VDL) data that includes multiple waveforms in a time domain from the one or more return signals. A number of independent components to be used based on variances in the VDL data is determined and the multiple waveforms are decomposed into multiple components associated with one or more local structure variances of the well structure using independent component analysis (ICA) and the number of independent components. Characteristics of the well structure is determined based in part on patterns or features associated with one or more independent components from the multiple components.

A seismic attenuation quality factor Q is determined for seismic signals at intervals of subsurface formations between a seismic source at a marine level surface and one or more receivers of a well. Hydrophone and geophone data are obtained. A reference trace is generated from the hydrophone and geophone data. Vertical seismic profile (VSP) traces are received. First break picking of the VSP traces is performed. VSP data representing particle motion measured by a receiver of the well are generated. The reference trace is injected into the VSP data. A ratio of spectral amplitudes of a direct arrival event of the VSP data and the reference trace is determined. From the ratio, a quality factor Q is generated representing a time and depth compensated attenuation value of seismic signals between the seismic source at the marine level surface and the first receiver.