A system includes a first instrumented bridge plug positionable in a downhole wellbore environment. The first instrumented bridge plug includes an acoustic source for transmitting an acoustic signal. The system also includes a second instrumented bridge plug positionable in the downhole wellbore environment. The second instrumented bridge plug includes an acoustic sensor for receiving a reflected acoustic signal originating from the acoustic signal. The reflected acoustic signal being usable to interpret wellbore formation characteristics of the downhole wellbore environment.

Monitoring and diagnosing completion during hydraulic fracturing operations provides insights into the fracture geometry, inter-well frac hits and connectivity. Conventional monitoring methods (microseismic, borehole gauges, tracers, etc.) can provide a range of information about the stimulated rock volume but may often be limited in detail or clouded by uncertainty. Utilization of DAS as a fracture monitoring tool is growing, however most of the applications have been limited to acoustic frequency bands of the DAS recorded signal. In this paper, we demonstrate some examples of using the low-frequency band of Distributed Acoustic Sensing (DAS) signal to constrain hydraulic fracture geometry. DAS data were acquired in both offset horizontal and vertical monitor wells. In horizontal wells, DAS data records formation strain perturbation due to fracture propagation. Events like fracture opening and closing, stress shadow creation and relaxation, ball seat and plug isolation can be clearly identified. In vertical wells, DAS response agrees well with co-located pressure and temperature gauges, and illuminates the vertical extent of hydraulic fractures. DAS data in the low-frequency band is a powerful attribute to monitor small strain and temperature perturbation in or near the monitor wells. With different fibered monitor well design, the far-field fracture length, height, width, and density can be accurately measured using cross-well DAS observations.

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.

Systems and methods are disclosed for hydrocarbon exploration using seismic imaging and, more specifically, measuring randomness in seismic data utilizing a vectorized disorder algorithm. The vectorized disorder algorithm is configured to measure the randomness level (e.g., noise) in seismic data to improve seismic data processing/imaging and the ability to expose subsurface geology. The vectorized disorder algorithm includes performing convolution of seismic data with a vectorized disorder operator having an extra dimension than the seismic data. A nonlinear reduction operation is performed on the vectorized output to generate a randomness distribution dataset having the same dimension as the input data. The randomness distribution dataset comprises data points representing the level of randomness for respective seismic data points. A more accurate seismic image is generated from the seismic data as a function of the measured randomness distribution.

The present invention provides an intelligent geophysical data acquisition system and acquisition method for shale oil and gas optical fiber. A pipe string is arranged in a metal casing, and an external armored optical cable is fixed outside the metal casing; an, internal armored optical cable is fixed outside the pipe string; the external armored optical cable comprises a downhole acoustic sensing optical cable, two multi-mode optical fibers, a strain optical cable and a pressure sensor array, and further comprises horizontal ground acoustic sensing optical cables arranged in the shallow part of the ground according to an orthogonal grid, and artificial seismic source excitation points arranged on the ground according to the orthogonal grid.

A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.

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 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.

Methods and systems for characterizing fractures in a subterranean formation are provided. The method includes introducing an encapsulated explosive unit into a casing located in a wellbore within the subterranean formation and maintaining the encapsulated explosive unit in a stage of the casing. The method also includes detonating the encapsulated explosive unit within the stage to generate a pressure wave that passes through a group of perforations and into the fractures and measuring a reflected pressure wave using a pressure sensor coupled to the bridge plug to produce a pressure measurement. The method further includes converting the pressure measurement into an acoustic signal correlated with the pressure measurement by an acoustic signal generator contained in the bridge plug and transmitting the acoustic signal to apply acoustic pressure on a fiber optic cable coupled to an exterior surface of the casing.