There is provided systems and methods for providing enhanced high definition images of subterranean activities, and structures using migrated data from two independent sources. There are provided systems and methods for imaging hydraulic fracturing and hydraulic fractures and the resultant images of hydraulic fracturing and hydraulic fractures, including the image of the shape of the fracture.

A method for evaluating and optimizing complex fractures, in one non-limiting example far-field complex fractures, in subterranean shale reservoirs significantly simplifies how to generate far-field fractures and their treatment designs to increase or optimize complexity. The process gives information on how much complexity is generated for a given reservoir versus distance from the wellbore under known fracturing parameters, such as rate, volume and viscosity. The method allows the evaluation of the performance of diversion materials and processes by determining the amount of fracture volume generated off of primary fractures, including far-field secondary fracture volumes. The methodology utilizes fracture hit times, volumes, pressures and similar parameters from injecting fracturing fluid from a first primary lateral wellbore to create fractures and record fracture hit times, pressures and volumes from a diagnostic lateral wellbore in the same interval.

A rock sample is nano-indented from a surface of the rock sample to a specified depth less than a thickness of the rock sample. While nano-indenting, multiple depths from the surface to the specified depth and multiple loads applied to the sample are measured. From the multiple loads and the multiple depths, a change in load over a specified depth is determined, using which an energy associated with nano-indenting rock sample is determined. From a Scanning Electron Microscope (SEM) image of the nano-indented rock sample, an indentation volume is determined responsive to nano-indenting, and, using the volume, an energy density is determined. It is determined that the energy density associated with the rock sample is substantially equal to energy density of a portion of a subterranean zone in a hydrocarbon reservoir. In response, the physical properties of the rock sample are assigned to the portion of the subterranean zone.

Seismic data processing using one or more non-linear stacking enabling detection of weak signals relative to noise levels. The non-linear stacking includes a double phase, a double phase-weighted, a real phasor, a squared real phasor, a phase and an N-th root stack. Microseismic signals as recorded by one or more seismic detectors and transformed by transforming the signal to enhance detection of arrivals. The transforms enable the generation of an image, or map, representative of the likelihood that there was a source of seismic energy occurring at a given point in time at a particular point in space, which may be used, for example, in monitoring operations such as hydraulic fracturing, fluid production, water flooding, steam flooding, gas flooding, and formation compaction.

A method for using microseismic data during an injection or perforation event includes injecting fluid or perforating a well to create cracks in the formation. Microseismic data is obtained from the formation and forward modelling source parameter estimations are performed using a full moment tensor space source model and a double-couple source model. Likelihoods of the microseismic data are calculated for each model type by forward modelling synthetic data from a sampled source parameter probability distribution derived from each estimation, and by comparing the synthetic data with the microseismic data. The likelihoods are marginalized over prior probabilities for the source models, and Bayesian inference converts the likelihoods and prior probabilities to posterior probabilities. The posterior probabilities for the full tensor space and double-couple source models are compared to reveal whether an event is a fracture opening, fracture closing, or a slip on a fault plane.

A method for automatically locating microseismic events based on a deep belief neural network and coherence scanning includes the following steps: randomly selecting data of one three-component geophone; performing arrival time picking and phase identification of microseismic events on the data thereof using a deep belief neural network; and then, on the basis of the obtained arrival time and phases, performing coherence scanning and positioning imaging using the microseismic data received by all three-component geophones. In the image, the space position representing the highest stacking energy may be considered as a real space position where the microseismic events occur, implementing the automatic and accurate locating of the microseismic events.

A method for identifying a location of a distributed acoustic system channel in a distributed acoustic system. The method may comprise generating a two or three dimensional layer model interface with an information handling system, preparing a P-wave first arrival pick time table, estimating an initial model layer properties, estimating a location of the distributed acoustic system channels, preparing an overburden file of layer properties, running an anisotropic ray tracing, defining an upper and a lower limits for model parameters, specifying parameters for the inversion, running an inversion, selecting a solution based at least in part on stored error predictions, and calculating a mean and a standard deviation of an inverted model parameter.

A distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network, which belongs to infrastructure safety monitoring field, which is aimed to improve the intelligent ability of detection and identification of the existing distributed optical fiber sound/vibration sensing system under complex application conditions. The present invention utilizes the distributed optical fiber sound/vibration sensing system to pick up the sound or vibration signal of the whole line along the detection cable; and the customized short time feature and long time feature are respectively extracted from the relative quantity of the sound or the vibration signal at each spatial point in the whole monitoring range. The Bayesian identification and classification network at each spatial point is constructed and trained based on the prior knowledge of the collected signal features and their different background noises.

A structure safety detection system includes a first sensor, a second sensor, and a server. The first sensor detects a wobble and slant state of a structure and generates a corresponding first detection result. The second sensor detects a characteristic width of the structure and generates a corresponding second detection result. The server analyzes the first detection result and the second detection result and generates a corresponding first analysis result.

Some implementations relate to a computer-implemented method for creating digital acoustic sensing (DAS) related training data for a learning machine. The method may include moving a first optimal microseismic event location to a first perturbed microseismic event location in each of a plurality of first images. The method also may include modifying first shear waves and first compressional waves in each of the first images based on one or more signal travel times between the first perturbed microseismic event location and a fiber optic cable to form a plurality of training images configured to train a learning machine.