The disclosure is directed to a method of utilizing an acoustic sensing cable, such as a fiber optic distributed acoustic sensing (DAS) cable, in a borehole to detect microseismic events and to generate three dimensional fracture plane parameters utilizing the detected events. Alternatively, the method can use various categorizations of microseismic data subsets to generate one or more potential fracture planes. Also disclosed is an apparatus utilizing a single acoustic sensing cable capable of detecting microseismic events and subsequently calculating fracture geometry parameters. Additionally disclosed is a system utilizing a processor to analyze collected microseismic data to generate one or more sets of fracture geometry parameters.

Providing real-time prediction and mitigation of seismically-induced effects comprises receiving measured seismic data; pre-processing to transform to a uniform format; inputting the preprocessed data into a predictive model; training the predictive model to learn hidden patterns in recorded seismic data, and extract underlying relations between the received measured seismic data and a predicted response at a location of interest at further time instance, as described by the equation: u.sub.I.sup.pred(t+τ)=model(u.sub.I(t), u.sub.M.sub.1(t), u.sub.M.sub.2(t), . . . , u.sub.M.sub.N (t)), where t is current time instance, τ is lead time, u.sub.M.sub.i(t) represents digital signal recorded by the i.sup.th seismic sensor, and N is overall number of sensors in a sensor array that provided the measured seismic data; receiving new real-time seismic data and pre-processing the received new real-time seismic data into the uniform format; inputting the data into the predictive model; and predicting future response at the location of interest in lead time of τ ahead: u.sub.I.sup.pred(t+τ).

Using microseismic analysis to identify and quantify the hydraulic fracture interaction in the Earth formation. Identification of the interaction is based on the magnitude of the events and therefore independent of the location uncertainty. Quantification of the interaction is location based.

In order to monitor the subsoil of the earth under a target zone, seismic waves coming from an identified mobile noise source are recorded by means of at least one pair of sensors disposed on either side of the target zone, time periods are selected corresponding to the alignments of the pairs of sensors with the noise source, a seismogram of the target zone is reconstructed by interferometry based on the recorded seismic waves and on the selected time periods and an image of the subsoil of the target zone is generated using the seismogram.

The embodiments of the present application include acquiring a monitoring region and each observation point therein; partitioning the monitoring region into N layers of grids according to a seismic source positioning accuracy, wherein a side length of a grid cell of an i-th layer of grid is D/2.sup.i-1, i=1, . . . N, and D is an initial side length of the grid cell and not more than a double of a distance between the respective observation points; searching all nodes in a first layer of grid to acquire a node satisfying a preset condition therefrom; from i=2, determining and searching nodes satisfying a first preset requirement in the i-th layer of grid, to acquire a node satisfying the preset condition therefrom, until a search in an N-th layer of grid is completed, wherein a node satisfying the preset condition acquired in the N-th layer of grid is a seismic source point location.

A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model. The method may further include generating an image of the subterranean formation of interest using the updated velocity model.

A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.

A method for performing a borehole and/or subsurface formation-related action for a subsurface formation of interest includes: receiving a plurality of sets of fracture data for a subsurface rock; generating a discrete fracture network (DFN) for each set of fracture data; and determining a property of each DFN that corresponds to each set of fracture data. The method also includes: mapping the plurality of sets of fracture data to the corresponding property using artificial intelligence (AI) to provide an AI model; inputting a set of fracture data for the subsurface formation of interest into the AI model; outputting a property of the subsurface formation of interest from the AI model; and performing the borehole and/or subsurface formation-related action for the subsurface formation of interest using the property and equipment configured to perform the borehole and/or subsurface formation-related action.

A system for determining a fingerprint of a structure is provided. The system includes a plurality of granules inserted in a structure having a plurality of fissures, fractures, and cracks (collectively apertures), each granule comprising a membrane, and at least one bubble of compressed gas formed in the membrane, the membrane selectively dissolving in presence of a predetermined fluid and thereby selectively bursting the at least one bubble, thereby generating a concussing vibration, at least at least i) three detection devices for two-dimensional mapping or ii) four detection devices for three-dimensional mapping placed proximate to the structure according to a predetermined placement schedule, and a computing device comprising a processor configured to receive data from the at least three or four detection devices and to determine location of the at least one bubble of each of the plurality of the granules at the time of bursting by triangulating the concussive vibration in order to determine location of the at least one bubble.

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.